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Robin Girod

Christian Appel

Dmitry Karpov

Michel Mermoux

Frédéric Maillard

Viktoriia A Saveleva

Vasiliki Tileli

Thomas J Schmidt

Juan Herranz
email: juan.herranz@psi.ch

Decoupling the Contributions of Different Instability Mechanisms to the PEMFC Performance Decay of Non-noble Metal O2-Reduction Catalysts

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The strong reliance on the use of fossil fuels for transportation applications renders this sector one of the major contributors to the greenhouse gas emissions (accounting for ≈ 24 % of the global anthropogenic CO2 inventory). [START_REF]International Energy Agency's CO2 emissions from fuel combustion highlights[END_REF] As a result, the extended adaptation of environmentally sustainable transportation technologies is crucial to achieve the stringent societal decarbonization targets required to mitigate climate change. Among these emerging technologies, proton exchange membrane fuel cells (PEMFCs) fueled with green H2 (i.e., produced using renewable energy sources 2 ) are particularly appealing due to their fast refueling times, extended driving ranges and suitability for heavy-duty applications. [START_REF] Staffell | The role of hydrogen and fuel cells in the global energy system[END_REF] However, the market penetration of PEMFCs is still limited by their excessive cost, which is largely ascribed to the high Pt loadings (currently ≥ 0.2 -0.3 mgPt•cm -2 ) required to catalyze the oxygen reduction reaction (ORR) at their cathodes, and that is expected to account for ≈ 40 % of the overall PEMFC stack cost once fuel cell electric vehicle (FCEV) manufacture volumes ≥ 500,000 FCEV per year shall be reached. [START_REF] Thompson | Platinum group metal-free catalysts boost cost competitiveness of fuel cell vehicles[END_REF] In order to tackle this issue, tremendous efforts are devoted to substitute these Pt-based materials with inexpensive non-noble metal catalysts (NNMCs). This has resulted in NNMC layers with initial low-current density performances similar to those of Pt-based catalyst layers (CLs), but featuring a PEMFC-durability that generally remains largely insufficient for their implementation in FCEVs.

Decades of research have led to a wide consensus linking this instability to operativelyinduced changes in the species responsible for NNMC's ORR-activity, and that are in turn acknowledged to entail the involvement of one or more metal-based phases in the catalytic process (since metal-free catalysts generally feature a very low ORR-activity in acidic media). [START_REF] Sahraie | Quantifying the density and utilization of active sites in nonprecious metal oxygen electroreduction catalysts[END_REF] In this regard, many experimental and computational studies have demonstrated that atomically-isolated metal (Me-) ions coordinated by 4 or more N-functionalities hosted in the catalyst's carbonaceous matrix (often referred to as MeNx sites) possess an ORR-activity whose intrinsic value (or turn-over frequency, TOF) is determined by fine differences in their coordination environment (e.g., number and nature of the direct coordination species, proximity of other surface groups and/or sites' location along the carbon matrix). [START_REF] Ferrandon | Stability of iron species in heat-treated polyaniline-iron-carbon polymer electrolyte fuel cell cathode catalysts[END_REF][START_REF] Li | Identification of durable and non-durable FeNx sites in Fe-N-C materials for proton exchange membrane fuel cells[END_REF] Most NNMCs contain a multiplicity of these slightly different MeNx sites with disparate TOFs and intrinsic stabilities, along with Me-based particles (consisting of the reduced metal phase, oxides and/or carbides) [START_REF] Ebner | Effect of Cobalt Speciation and the Graphitization of the Carbon Matrix on the CO2 Electroreduction Activity of Co/N-Doped Carbon Materials[END_REF][START_REF] Ebner | Fe-Enrichment effect on the composition and performance of Fe-based O2-reduction electrocatalysts[END_REF] that are often encapsulated in a carbon shell and whose contributions to the catalyst's overall ORR-activity remains under debate. [START_REF] Varnell | Identification of carbon-encapsulated iron nanoparticles as active species in non-precious metal oxygen reduction catalysts[END_REF] Beyond this uncertainty, and in the specific context of the Fe-based NNMCs that generally feature the highest beginning-oflife device performance, the highly acidic reaction environment (pH ≈ 0) and relatively high potentials (typically > 0.65 V vs the standard hydrogen electrode (SHE)) [START_REF] Kongkanand | The Priority and Challenge of High-Power Performance of Low-Platinum Proton-Exchange Membrane Fuel Cells[END_REF] encountered by the catalysts in PEMFC cathodes are expected to cause the leaching of the iron in these particulate phases unless they are fully encapsulated in a protective carbon shell. For example, at room temperature, FeO(OH) is only foreseen to be thermodynamically stable at pHs ≥ 1.2 and potentials ≥ 0.77 V vs SHE. [START_REF] Banham | A review of the stability and durability of non-precious metal catalysts for the oxygen reduction reaction in proton exchange membrane fuel cells[END_REF][START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Choi | Minimizing Operando Demetallation of Fe-N-C Electrocatalysts in Acidic Medium[END_REF] By comparison, the thermodynamic stability of FeNx sites under PEMFC-cathode conditions remains under debate, with some density functional theory (DFT) studies endorsing that these sites are completely insensitive to dissolution [START_REF] Glibin | Thermodynamic Stability in Acid Media of FeN4-Based Catalytic Sites Used for the Reaction of Oxygen Reduction in PEM Fuel Cells[END_REF][START_REF] Sun | Spin State as a Participator for Demetalation Durability and Activity of Fe-N-C Electrocatalysts[END_REF] and other computational [START_REF] Sun | Spin State as a Participator for Demetalation Durability and Activity of Fe-N-C Electrocatalysts[END_REF][START_REF] Holby | Acid Stability and Demetalation of PGM-Free ORR Electrocatalyst Structures from Density Functional Theory: A Model for "Single-Atom Catalyst" Dissolution[END_REF] and experimental [START_REF] Li | Identification of durable and non-durable FeNx sites in Fe-N-C materials for proton exchange membrane fuel cells[END_REF][START_REF] Chenitz | A specific demetalation of Fe-N4 catalytic sites in the micropores of NC_Ar + NH3 is at the origin of the initial activity loss of the highly active Fe/N/C catalyst used for the reduction of oxygen in PEM fuel cells[END_REF] works advocating for a much more restricted stability window. For instance, according to Holby et al., at pH ≈ 0, FeNx sites would only be thermodynamically stable between ≈ 0.7 and ≈ 0.95 V vs. SHE. [START_REF] Holby | Acid Stability and Demetalation of PGM-Free ORR Electrocatalyst Structures from Density Functional Theory: A Model for "Single-Atom Catalyst" Dissolution[END_REF] In either case, the Fe cations leached from the FeNx sites and/or Fe-based particles could precipitate in the CL as Fe oxides seemingly stable under these condition [START_REF] Li | Identification of durable and non-durable FeNx sites in Fe-N-C materials for proton exchange membrane fuel cells[END_REF][START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Chen | Insight into the Rapid Degradation Behavior of Nonprecious Metal Fe-N-C Electrocatalyst-Based Proton Exchange Membrane Fuel Cells[END_REF] and/or attach to the sulfonic acid groups in the membrane and CL-ionomer, causing performance losses due to a poorer H + -conduction along these components.

The electro-oxidation of carbon is also known to play a crucial role in the stability of NNMC layers, particularly at high operative potentials. In this regard, carbon electro-oxidation in Pt-based, carbon supported (Pt/C) catalyst is generally observed at potentials > 1.2 V, [START_REF] Kangasniemi | Characterization of Vulcan Electrochemically Oxidized under Simulated PEM Fuel Cell Conditions[END_REF] but NNMCs are often constituted by more disordered carbons on which this corrosion can take place at significantly lower potentials. As an example of this, Choi et al. observed CO2-release from an Fe-based NNMC already at potentials ≥ 0.8 V vs. the reversible hydrogen electrode (RHE) in 0.1 M HClO4 electrolyte at 50 °C, [START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF] while other works reported significant carbon oxidation currents at cell voltages as low as 0.5 V. [START_REF] Wang | Surface Fluorination to Boost the Stability of the Fe/N/C Cathode in Proton Exchange Membrane Fuel Cells[END_REF][START_REF] Zhang | Is iron involved in the lack of stability of Fe/N/C electrocatalysts used to reduce oxygen at the cathode of PEM fuel cells?[END_REF] This electrochemical oxidation of the catalyst can be highly detrimental to its performance, since the subsequent destruction of the carbonaceous matrix hosting the FeNx sites and/or protecting the Fe-particles can trigger the demetallation issues discussed above. [START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Martinaiou | Effect of metal species on the stability of Me-N-C catalysts during accelerated stress tests mimicking the start-up and shut-down conditions[END_REF] Moreover, the concomitant enrichment of the catalyst surface with oxidized carbon functional groups (e.g., phenols, carboxylic acids [START_REF] Avasarala | Carbon Corrosion in Proton-Exchange Membrane Fuel Cells: From Model Experiments to Real-Life Operation in Membrane Electrode Assemblies[END_REF] ) of hydrophilic character may be detrimental to the CL's water management / mass transport properties and worsen its high current density performance. [START_REF] Zhang | Is iron involved in the lack of stability of Fe/N/C electrocatalysts used to reduce oxygen at the cathode of PEM fuel cells?[END_REF] Finally, the ORR on NNMCs reportedly leads to a significant amount of H2O2 that is in turn known to decompose via Fenton reactions possibly triggered by the Fe-cations leaching from the catalyst upon PEMFC operation (vide supra), yielding highly aggressive radicals (e.g.,

•OH, HOO•) that decompose the membrane and ionomer in the CL [START_REF] Laconti | Handbook of Fuel Cells: Fundamentals, Technology and Applications[END_REF] as well as active sites, inducing again severe performance losses. [START_REF] Banham | A review of the stability and durability of non-precious metal catalysts for the oxygen reduction reaction in proton exchange membrane fuel cells[END_REF][START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF] As a result of this (and unlike in the case of Ptbased ORR-catalysts), the use of N2 vs. O2 (or air) as the cathode feed in NNMC-degradation protocols is known to lead to highly different results, since only the latter tests entailing cathodic O2-reduction also involve this H2O2-induced stability decay. More precisely, Choi et al. observed that exposing an NNMC to H2O2 leads to an oxidation of the catalysts' carbonaceous matrix that lowers the TOF of its FeNx sites and consequently reduces its ORRactivity. [START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF] Complementarily, Kumar et al. recently reported that an electrochemical accelerated stress test (AST) in O2-containing electrolyte causes the loss of a significant fraction of the iron in the NNMC's FeNx sites due to the corrosion of the catalyst's carbon by H2O2 and/or H2O2derived radical species. [START_REF] Kumar | On the Influence of Oxygen on the Degradation of Fe-N-C Catalysts[END_REF] The above-discussed multiplicity of NNMC-degradation pathways and their relation to the device's operative conditions (e.g., potential-dependent carbon electro-oxidation, ORRrelated H2O2-production) implies that more than one of these mechanisms is likely to be at play upon PEMFC-operation. Despite this relevant aspect and the specificity of these mechanisms to NNMCs, these materials' durability is often assessed through ASTs that were originally designed to study the degradation of Pt-based catalysts, [START_REF] Ferreira | Instability of Supported Platinum Nanoparticles in Low-Temperature Fuel Cells[END_REF] which is preponderantly driven by the operative potential and independent of the presence / absence of O2 in the cathode gas feed. [START_REF] Castanheira | Carbon Corrosion in Proton-Exchange Membrane Fuel Cells: Effect of the Carbon Structure, the Degradation Protocol, and the Gas Atmosphere[END_REF] Consequently, such ASTs generally entail extensive potential cycling using an inert gas (as opposed to air/O2) at the cathode, which for NNMCs would imply overlooking the plausible effects of ORR-byproducts on these catalyst's stability. While this important aspect has recently been addressed through revised, NNMC-specific protocols that implement O2/air as the cathode reactant, [START_REF] Zhang | Standardized protocols for evaluating platinum group metal-free oxygen reduction reaction electrocatalysts in polymer electrolyte fuel cells[END_REF] even such novel ASTs do not allow to differentiate the relative contributions of the above mechanisms to the overall PEMFC-performance losses. With this motivation, in this study we propose a novel approach for benchmarking NNMCs' durability through four degradation protocols in which the combination of different cathode gas feeds (i.e., air vs. N2), potential hold values and durations allow decoupling the relative contributions of the three deactivation paths discussed above to these materials' overall performance decay.

We accomplished this by assessing the changes in the kinetic, ohmic and mass transport overpotentials inferred to an in-house synthesized NNMC by each protocol through a careful Tafel analysis and impedance spectroscopy measurements. Combining this with state-of-theart imaging and analyses of the pristine and end-of-life CLs, our results reveal that carbon electro-oxidation is more detrimental to the catalyst's ORR-activity than Fe-demetallation, and suggest that H2O2-related instability only depends on the ORR charge transferred during the protocol, and not on the applied potential. As such, we believe that this approach provides an excellent platform for the comparative benchmarking of NNMCs' operational durability.

EXPERIMENTAL METHODS

Catalyst Synthesis:

The catalyst used in this work was synthesized following the procedure provided for the 'wet BM-3mm' NNMC in our recent publications. [START_REF] Ünsal | Effect of Aggregate Size and Film Quality on the Electrochemical Properties of Non-noble Metal Catalysts in Rotating Ring Disk Electrode Measurements[END_REF][START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] In summary, polyacrylonitrile (PAN, Sigma-Aldrich), sodium carbonate (Na2CO3, Sigma-Aldrich, anhydrous, 99.999 % trace metal basis) and Fe 2+ acetate (Sigma-Aldrich, > 99.99 % trace metal basis) pre-complexed with phenanthroline (Sigma-Aldrich, > 99 %) were used as the C-/Nprecursor, pore inducing agent and Fe-precursor, respectively. Firstly, PAN and Na2CO3 were stirred separately overnight in dimethylformamide (DMF, Sigma-Aldrich, anhydrous 99.8 % -in a Na2CO3/PAN:DMF weight ratio of 1:95) at 80 °C with a PAN to Na2CO3 mass ratio of 1:2. After mixing and stirring these two solutions for an additional hour at 80 °C, the volume of an Fe 2+ :phenantroline complex solution (pre-prepared in DMF with a molar ratio of 1:5) needed for an Fe-content of 1 wt. % on the basis of all precursors' masses was added into the PAN and Na2CO3 mixture. Afterwards, the solvent was evaporated until a viscous slurry was formed, and the latter was left overnight in a furnace at 80 °C to attain complete dryness. 1.5 g of the resulting dry precursor mixture underwent 16 h of wet-milling (planetary ball mill, Fritsch, Pulverisette, 45 mL ZrO2 crucible) in 10 mL of ethanol (VWR, ≥ 99.8 %, AnalaR NORMAPUR) with 3 mm diameter ZrO2 balls using a balls-to-precursor-mixture mass ratio of 27. The slurry obtained after wet-milling was passed through a 100 μm sieve and dried. The resulting ground powder was then submitted to a first heat treatment (HT) in N2 (Messer AG, 5.0 -200 mL•min -1 ) at 700 °C for 1 h. Afterwards, an acid treatment in 1 M sulfuric acid (H2SO4, Merck, 95-97 %) at 80 °C was performed for 4 h to remove soluble side-phases. After water-rinsing and drying, this acid-washed powder underwent a second HT in 5 % H2 (balanced with Ar, Pangas (5.0) -200 mL•min -1 ) at 950 °C for 40 min.

MEA preparation:

The membrane electrode assemblies (MEAs) were fabricated using a procedure similar to the one detailed in our previous publication. [START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] Briefly, a commercial gas diffusion electrode (GDE -Johnson Matthey, 0.4 mgPt•cm -2 HISPEC 9100 Pt/HAS on a 1 × 1 cm 2 piece of Sigracet 25 BC gas diffusion layer (GDL)) used as the anode was hot pressed on a piece of Nafion ® HP membrane (Ion Power) in order to obtain a half-MEA, as previously described in Ref. [START_REF] Yin | Effects of MEA Fabrication and Ionomer Composition on Fuel Cell Performance of PGM-free ORR Catalyst[END_REF] . To prepare the cathode catalyst ink, a weighed amount of the above NNMC was dispersed in a mixture of isopropanol (Sigma-Aldrich, HPLC gradient grade, 99.9 %) and ultrapure water (18.2 MΩ•cm, ELGA Purelab Ultra) in a volume ratio of 3:7, yielding an ink with an NNMC-to-solvents ratio of 9 mgNNMC•mLH2O+IPA -1 , and with the volume of Nafion ® ionomer solution (5 %, Sigma-Aldrich) required for an ionomer-to-catalyst mass ratio (ICR) of 0.6. After this ink was sonicated for 30 min, it was automatically sprayed (SONOTEK ® ExactaCoat Automated Spray Coater) on the upper (i.e., cathode) side of the above half-MEA placed on a plate heated at 60 °C at a flow rate of 50 μL•s -1 until achieving and NNMC cathode loading of ≈ 1 mgNNMC•cm -2 .The precise loading of each cathode CL was determined by weighing the sample before and after spraying, and was systematically within the range of 1.2 -1.4 mgNNMC •cm -2 (leading to a CL thicknesses of 25-30 μm, as determined using a Mitutoyo MCD-25PX dial gauge with a nominal accuracy of ± 1 μm by measuring 5 different points on the MEA). Lastly, a carbon paper GDL (Sigracet SG29BC) was placed onto the NNMC layer prior to cell assembly. Freshly prepared MEAs were used for each degradation experiment.

PEMFC tests:

The resulting assembly was placed inside a differential fuel cell with an active area of 1 cm 2 (of which the design was detailed in Ref. [START_REF] Oberholzer | Local Characterization of PEFCs by Differential Cells: Systematic Variations of Current and Asymmetric Relative Humidity[END_REF] ) and compressed by selecting the total thickness of the gaskets resulting in a ≈ 25 % compression of the diffusion media in both anode and cathode sides (assuming that catalyst-and microporous-layers are uncompressible).

All experiments were performed with a Bio-Logic VSP-300 potentiostat equipped with a 10 A / 5 V current booster. Prior to each degradation protocol, the initial ORR-activity of the cathode CL was assessed by recording a polarization curve galvanostatically (i.e., holding the current at selected values for 90 s) starting from the open circuit voltage (OCV) and down to a current value corresponding to a voltage slightly above ≈ 0.7 V; this relatively high cutoff potential was selected in order to minimize the deactivation of the CL in this verification of the initial performance and prior to the actual degradation experiment. After each degradation protocol, however, a full polarization curve between the OCV and 0.8 A•cm -2 was recorded in a similar way as described above. Note that the potentials reported on the polarization curves correspond to the average values during the last 30 seconds of each current hold. Following each measurement at a given current, the corresponding high frequency resistance (HFR) was obtained by galvanostatic electrochemical impedance spectroscopy (GEIS) with a 10 % current perturbation in a frequency range of 1 MHz to 500 mHz and with 6 points per decade. All polarization curves were recorded at 80 °C, 1.5 barabs and 100 % relative humidity (RH) using 600 mL•min -1 of H2 (Pangas 5.0) and 1000 mL•min -1 of air (Messer AG, 5.5) at anode vs. cathode, respectively. Following the recording of this end-of-life, full polarization curves, potentiostatic electrochemical impedance spectroscopy (PEIS) tests at 0.2 V (with a 5 mV perturbation) were performed with the PEMFC still at 80 °C and 1.5 barabs but with fully humidified H2 and N2 feeds at the anode and cathode, respectively, in order to determine the H + conduction resistance in the NNMC layers (𝑅 𝐻+,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 ). [START_REF] Liu | Proton Conduction and Oxygen Reduction Kinetics in PEM Fuel Cell Cathodes: Effects of Ionomerto-Carbon Ratio and Relative Humidity[END_REF] The resulting spectra were fitted to the transmission line model (TLM, with X 2 < 0.005) using the Z-fit package included in the EC-Lab ® V11.36 software. Lastly, the cell was cooled down to room temperature and cyclic voltammograms (CVs) were recorded between 0.05 and 1.00 V with a scan rate of 50 mV•s -1 at ambient pressure, with a H2 flow rate of 100 mL•min -1 at the anode and the N2 flow at the cathode stopped. As a control sample, one full polarization curve of an additional MEA prepared from the pristine NNMC was also recorded without applying any degradation protocol, but including the PEIS and CV measurements in the similar conditions applied for the degraded samples described above. The ORR-activities (ikins) before and after each degradation protocol were obtained from the linear regime of the corresponding Tafel plots at 0.8 V. Carbon electro-oxidation experiments were performed by repeating the protocol followed for the recording of the polarization curves (i.e., holding the potentials at selected values for 90 s) between OCV and 0.45 V, using the same conditions in which the H2/Air polarization curves were recorded but replacing the cathode feed with N2. As above, the reported currents correspond the average values during the last 30 seconds of each potential hold.

Determination of the mass transport overpotential (ηtx):

In order to quantify the mass transport overpotential (ηtx) at the beginning-and end-of-life (i.e., before and after each degradation protocol), the overall cell potential (Ecell) was decoupled based on Equation 1, [START_REF] Neyerlin | Cathode Catalyst Utilization for the ORR in a PEMFC[END_REF] which relies on the assumption that the kinetic and H + -conduction overpotentials for the hydrogen oxidation reaction at the PEMFC's anode CL are negligible. 37

𝐸 𝑐𝑒𝑙𝑙 = 𝐸 𝑟𝑒𝑣 -𝑖 • 𝑅 𝐻𝐹𝑅 -𝑖 • 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff -𝜂 𝑂𝑅𝑅 -𝜂 𝑡𝑥 ( 1
)
where Erev is the thermodynamic equilibrium potential (in turn a function of the gases' partial pressures and PEMFC temperature), [START_REF] Bernardi | A Mathematical Model of the Solid-Polymer-Electrolyte Fuel Cell[END_REF] RHFR corresponds to the sum of membrane H +conduction resistance and contact and bulk electronic resistance that can be determined by the high frequency resistance (HFR) in impedance measurements, i is the applied current density,

𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff
is the effective H + -conduction resistance of the cathode CL, ηORR is the cathodic ORR-overpotential, and ηtx is the mass transport overpotential caused by O2 concentration gradients in the electrode due to gas transport resistances.

After determining 𝑅 𝐻+,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 in the CLs from the fits of the impedance spectra recorded in H2/N2 by applying the TLM, the effective H + -conduction resistance ( 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff ) in the CL was calculated using the Equation:

𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff = 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 3+𝜁 (2)
where ζ is a dimensionless correction factor that depends on the product of current density and the H + -conduction resistance divided by the intrinsic Tafel slope if the ORR (b), (i.e., on

(𝑖 • 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 ) 𝑏 ⁄ )
, and that can be read out in Figure 2 of Ref. [START_REF] Neyerlin | Cathode Catalyst Utilization for the ORR in a PEMFC[END_REF] For the ORR on NNMCs used in this study, a Tafel slope of 70 mV•dec -1 (at 80 °C) was considered for this analysis. The spatially resolved XRF and STEM measurements were performed on pristine and post mortem MEAs that were embedded in EPON TM epoxy resin in a two-step process, first in a 1:1 acetone:EPON mixture and subsequently in pure EPON. For 2D mapping, 150 nm thick slices were prepared using a microtome setup confirmed via optical inspection. Sections for STEM analysis were transferred on ultrathin lacey carbon TEM copper grids and for XRF on 500 nm thick graphene coated silicon nitrate membranes. For 3D XRF computed tomography (-CT), mm-sized pieces of the embedded CL were glued onto individual custom-built tomography pins using a pre-cured epoxy to avoid sample contamination. [START_REF] Holler | OMNY PIN-A versatile sample holder for tomographic measurements at room and cryogenic temperatures[END_REF] These pins were mounted on a custom-built micro-lath and a cylinder was milled out with a diameter of ≈ 70 µm and a height of 100 µm. [START_REF] Holler | A lathe system for micrometre-sized cylindrical sample preparation at room and cryogenic temperatures[END_REF] The diameter of the pre-milled samples was further reduced to 36 µm for the pristine sample and to 42 µm for the 'Air-0.5 V' sample using a Xenon plasma Focussed Ion Beam (FIB). In all preparation steps, the samples were handled with utmost care to avoid any type of contamination and to allow the quantitative analysis of the XRF measurements.

STEM analysis: energy dispersive X-ray spectroscopy (EDS) mapping and high-angle annular dark field (HAADF) imaging were performed on a ThermoFischer Scientific Orisis TEM equipped with four windowless X-ray detectors with a 0.9 srad solid angle and operated in scanning mode at 200 kV and at room temperature. A probe current of 1 nA was used and EDS acquisitions were performed with a dwell time of 100 μs, integrating 512x512 px frames over 10 cycles. The pixel size of these acquisitions was 27.7 nm for the microtomed samples and 0.65 nm for the dispersed ones. The Velox software was used for EDS analysis. All lines and spectra were fitted and deconvoluted and weight fractions were computed using the standardless k-factor method. To cover the entire CL thickness, image stitching was performed with an in-house code written in python and using the sci-kit image library. To study the Ferich secondary phases, > 10 HAADF images with a 1.2 nm pixel size were acquired from the cross-sections of each investigated sample. The bright Fe precipitates were segmented with a pixel classifier implemented in the Ilastik software. [START_REF] Berg | ilastik: interactive machine learning for (bio)image analysis[END_REF] All intensity, edge and texture features were selected with sigma 1, 3, and 10. A morphological open operation was performed on the resulting binary maps to separate particles in contact. Each particle was then identified with a labeling operation and measurements of their maximum Feret diameter, aspect ratio, projected area and number of particles per image were performed using FIJI's MorpholibJ plugin [START_REF] Legland | MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ[END_REF] and the python sci-kit image library. mapping and a 200 nm step size for the CT measurements. The X-ray fluorescence signal was captured by two detectors mounted at 90 º with respect to the incoming beam on two opposing sides of the sample. Both detectors and the sample were mounted in a vacuum chamber to minimize air absorption and scattering. Furthermore, the detectors were calibrated using an AXO X-ray fluorescence calibration target. The weighted signal from the two calibrated detectors allows a quantitative measurement of iron (Fe Kα, at 6.4 keV) and sulfur (S Kα, 2.31 keV). The acquired spectra were fitted with PyMca software. For the XRF-CT, 100 projections were recorded for both the pristine and 'Air-0.5V' samples (vide infra). After the alignment of the projections, tomographic reconstructions of the Fe Kα line were performed with an iterative maximum likelihood estimation method (MLEM) [START_REF] Dempster | Maximum Likelihood from Incomplete Data via the EM Algorithm[END_REF] algorithm using 100 iterations. Image analysis of tomographic slices was performed with the Matlab Image Processing Toolbox and 3D renderings were created using the Avizo software package 2021.

X-ray diffraction (XRD):

The XRD measurement was performed on a SmartLab Rigaku system. The measurement was carried out for 2θ angles between 5 and 90° using a copper rotating anode (Cu Kα = 1.5406 Å), a Cu Kβ filter, and a Rigaku HyPix3000 detector. All measurements were performed in the Bragg-Brentano mode with the Cu-source set to 160 mA and 40 kV.

X-ray absorption spectroscopy (XAS):

Ex situ XAS measurements were carried out at the SuperXAS (X10DA) beamline of the Swiss Light Source (SLS). The spectra were recorded for 4 minutes using quickXAS [START_REF] Clark | Fluorescence-detected quick-scanning X-ray absorption spectroscopy[END_REF] at the Fe K-edge (7.112 keV) in transmission mode. Each sample was prepared by filling the catalyst powder into a Kapton capillary that was placed between two ionization chambers (15 cm long) filled with 1 bar of N2 to measure both the incident and transmitted beam intensity. The polychromatic beam from the 2.9 T bending magnet (the SLS operated at 400 mA and 2.4 GeV) was collimated by a Si-coated collimating mirror at 2.9 mrad, subsequently monochromatized by a liquid nitrogen-cooled Si(111) channel-cut monochromator oscillating at 1 Hz, and focused using a Rh-coated toroidal doublefocusing mirror giving a spot size of 1 mm × 0.2 mm. An Fe-foil was placed before a third ionization chamber measured in parallel and its spectrum was subsequently used as a reference for energy calibration by aligning the maximum of the first derivative of the reference spectrum to 7.112 keV (E0) for all sample spectra. The data treatment and analysis including calibration, background correction, normalization and linear combination fitting (LCF) was carried out using both the ProQEXAFS [START_REF] Clark | ProQEXAFS: a highly optimized parallelized rapid processing software for QEXAFS data[END_REF] and the Demeter 46 software packages, and the LCFs were carried out between E0-50 eV (7.062 keV) and E0+110 eV (7.222 keV) with the fit forced to sum up to unity.

Raman Spectroscopy:

The Raman spectra were recorded on a Renishaw inVia Raman system, with an excitation wavelength at 532 nm. Measurements were performed in a backscattering configuration, using a 50x objective (NA = 0.85), and a 2400 grooves mm -1 grating. This configuration ensured a spectral resolution much better than 1 cm -1 . To decrease the power density at the sample, the line mode of the spectrometer was used. This configuration allowed to record 'averaged' spectra over an area of ca. 50 µm 2 in a single shot, with a power density at the sample much less than ~ 50 µW µm -2 . These conditions effectively ensured that the sample was not degraded/modified during the analysis. For visualization purposes, the spectra were simply normalized to the amplitude of the so-called 'G' mode that peaks at ~ 1600 cm -1 , after subtracting the background signal.

RESULTS and DISCUSSIONS

The NNMC used in this work was prepared using the same approach presented in our previous publication (abbreviated as 'wet-BM-3mm' in Ref. [START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] ), and the results of its detailed characterization are available in the same work. [START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] In brief, this Fe-based catalyst possesses a surface area of ≈ 1000 m 2 •g -1 , and its visual inspection unveils that it contains Fe-based nanoparticles (NPs) preponderantly encapsulated in carbon shells (see Figure S1). Our previous characterization by Mössbauer spectroscopy of a similarly-prepared set of catalysts unveiled that these NPs consist of a small fraction of Fe 0 and a majority of Fe3C, [START_REF] Ebner | Fe-Enrichment effect on the composition and performance of Fe-based O2-reduction electrocatalysts[END_REF] of which the presence in the current NNMC is corroborated by the X-ray diffractogram featured in Figure S2.

Moreover, in the same study [START_REF] Ebner | Fe-Enrichment effect on the composition and performance of Fe-based O2-reduction electrocatalysts[END_REF] the multivariate curve resolution analysis of the NNMCs' X-ray absorption near edge structure (XANES) spectra allowed to isolate the spectral components of their FeNx sites and NP-based side phases. When those same component spectra were used to fit the XANES of the NNMC featured herein, they yielded an excellent fit (cf. Figure S3) from which we inferred that ≈ 25 vs. ≈ 75 % of the sample's total atomic metal inventory (1.8 wt. % Fe) is featured in the form of FeNx sites vs. Fe-based NPs, respectively. Based on our experience with similarly-prepared NNMCs, their ORR-activity linearly increases with their content of FeNx-sites, but catalysts featuring ≈ 90 % of their metal inventory in the form of Fenanophases still display a non-negligible catalytic performance. [START_REF] Ebner | Fe-Enrichment effect on the composition and performance of Fe-based O2-reduction electrocatalysts[END_REF][START_REF] Ebner | N/C-type Oxygen Reduction Reaction Catalysts From Synthesis to Advanced In Situ Spectroscopic Characterization[END_REF] Thus, the ORR-activity of the NNMC in this work may not exclusively stem from its FeNx-sites, but also from Cencapsulated Fe-nanoparticles and/or surface N-functionalities hosted in their carbon shells that have been shown to possess a non-negligible ORR-activity. [START_REF] Varnell | Identification of carbon-encapsulated iron nanoparticles as active species in non-precious metal oxygen reduction catalysts[END_REF][START_REF] Strickland | Highly active oxygen reduction non-platinum group metal electrocatalyst without direct metal-nitrogen coordination[END_REF] In order to decouple the contribution of the different degradation mechanisms to this catalyst' PEMFC performance decay, we designed four protocols at two different potentials of 0.5 or 0.8 V and using either air or N2 as the cathode gas feed. The detailed conditions of these protocols and the corresponding degradation pathways expected to be at play in each of them are visually summarized in Figure 1. Note that each protocol was performed using a freshly prepared NNMC layer of which the initial ORR-activity was verified at the beginning of each protocol by recording a polarization curve from the OCV down to a maximum potential of ≈ 0.7 V.

Looking at the details behind each protocol, the one denoted as 'Air-0.5V' consists of a 1 h potential hold at 0.5 V with air fed to the cathode, and thus involves operative conditions under which Fe-demetallation as well as H2O2-induced effects can be expected to affect the performance of the NNMC layer. In order to isolate the effect of the former mechanism (i.e., Fe-demetallation) under equivalent condition, the 'N2-0.5V' protocol entails the same potential hold value and duration, but eliminates the occurrence of H2O2-related effects by using N2 instead of air as the cathode feed. Next, the 'Air-0.8V' protocol was designed to study the performance decay at a higher potential of 0.8 V in which a significantly lesser extent of Fedemetallation is expected, [START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Choi | Minimizing Operando Demetallation of Fe-N-C Electrocatalysts in Acidic Medium[END_REF] but carbon electro-oxidation may appear along with the H2O2effects intrinsic to the use of air in the cathode feed. Chiefly, the duration of the 'Air-0.8V' test was adjusted to match the ORR-charge that had been previously observed in the one-hour-long, 'Air-0.5V test'; due to the significantly lower current recorded at this higher potential, this translated in a hold duration of ≈ 250 h (see Figure S4 for the time dependent currents recorded in each protocol). Finally, the 'N2-0.8V' protocol is identical to the previous one but is performed with N2 as opposed to air as the cathode feed, and therefore serves to isolate the exclusive effect of carbon electro-oxidation on the catalyst' performance decay (and, possibly, metal-dissolution) [START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Choi | Minimizing Operando Demetallation of Fe-N-C Electrocatalysts in Acidic Medium[END_REF] at this potential. We started investigating the NNMC's performance decay in the protocols performed at 0.5 V and using air vs. N2 as the cathode feeds (i.e., 'Air-0.5V' vs. 'N2-0.5V', respectively), for which the corresponding polarization curves and Tafel plots are displayed in Figure 2a-d.

The mass-normalized ORR-activities at 0.8 V at the beginning-of-life (≈ 8 A•gNNMC -1 at 80 °C and an O2-partial pressure (pO2) of 0.21 bar, corresponding to ≈ 27 A•gNNMC -1 at pO2 = 1 bar) [START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] and at the end of each protocol are also summarized in Figure S5, and the corresponding activity losses normalized with regards to the beginning-of-life value are featured in Figure 3.

The latter unveils that, after the 'Air 0.5V' protocol, the kinetic performance decreased by ≈ 70 % of its initial value, and this was accompanied by a significant drop of the high current density performance that is further discussed below (Figure 2a,b). By comparison, when the same protocol was performed in the absence of O2 at the cathode (i.e., in 'N2-0.5V'), the massnormalized ORR-activity only decreased by ≈ 30 %, and the potential at the highest measured current of 0.8 mA•cm -2 even increased slightly (Figure 2c,d). Notably, the non-negligible ORR-activity decrease observed even in the 'N2-0.5V test' suggests that the relatively low potential of 0.5 V in this protocol leads to a certain extent of demetallation from the Fe-based nanoparticles' and/or FeNx sites' responsible for this catalyst's ORR-activity, and/or to a decrease of their TOF. 10, 18 To evaluate Fe-demetallation and H2O2-related degradation effects during the 'Air-0.5

V' protocol in terms of morphology and elemental composition of the NNMC layer, we investigated a pristine CL and the post-mortem, end-of-life sample with STEM and XRF microanalysis. Figure 4a (top) shows a cross-section of a catalyst layer and indicates a typical region that was analysed with STEM-EDS and HAADF to access the spatial Fe-distribution in the CLs. Elemental mapping of the weight fraction of Fe acquired across the thickness of the end-of-life CL (shown in Figure 4b) indicates the presence of Fe-rich clusters in the NNMC layer after the 'Air-0.5V' degradation protocol. The corresponding HAADF image unveils that these clusters correlate with high-brightness NPs that are observed at higher resolution in Figure S1 and S6a-b, and are typically embedded in thick, semi-graphitized carbon shells. A comparison with the pristine samples reveals that the spatial distribution of Fe clusters is qualitatively similar in the pristine and 'Air-0.5V' CLs (see the maps in Figure S6d-e). This is further confirmed by measurements of the average Fe weight fraction in six 100 2 µm 2 areas of each sample (Figure S6j), where no significant changes or trend was detected with the error bars from standard deviation largely overlapping for the pristine and post-mortem CLs. We note, however, that the weight fractions were computed using a standardless method (namely, the k-factors, see Methods) which is semi-quantitative in nature.

Changes in the Fe-rich agglomerates were then investigated with respect to their morphology in the pristine and post-mortem CLs using HAADF-STEM images (displayed in Figure S7a-b) of a total area of 60-70 µm 2 with a resolution allowing to resolve single particles down to a 5-10 nm size. The complete results inferred from these measurements are presented in Table S1. Within the accumulated statistics (> 730 particles for each sample), no significant decrease in the number of particles per image (Figure 4c) nor changes in the distribution of their size (Figure 4d) or aspect ratio (major over minor diameter of a fitted ellipse, Table S1) as a result of the degradation protocol could be detected. Thus, the electron microscopy and spectroscopy results seem to indicate that most Fe-particles have withstood the 'Air-0.5V' degradation test, which is not surprising considering that the majority of them appear to be fully protected by thick carbon shells (cf. Figure S1). An alternative explanation is that the "Air-0.5" protocol caused leaching of the Fe cations from the catalyst that precipitated along the CL in the form of Fe-based NPs (under these conditions, expected to be Fe-oxides) with a shape and size similar to that of the carbon-encapsulated agglomerates present in the pristine catalysta possibility that cannot be ruled out, but that appears highly unlikely based on the good agreement among beginning-and end-of-life results and on the large statistics intrinsic to these measurements. This is endorsed by the fact that, in our microscopic examination of multiple locations along the post mortem CL, we did not find any metallic nanoparticles devoid of a protecting carbon shell (as one would have expected from the non-encapsulated Fe-oxides reported to form during potential-cycling ASTs). [START_REF] Kumar | On the Influence of Oxygen on the Degradation of Fe-N-C Catalysts[END_REF] Finally, the analysis by EDS of these endof-test particles strongly suggests that they preponderantly consist of the non-oxidic, Fe3Cphase featured in the pristine catalyst (see Fig. S8).

Complementarily to this, we carried out a synchrotron-based high-resolution x-ray fluorescence (XRF) nano imaging study in order to quantify more precisely the total Fe contents in the same pristine and post-mortem, 'Air-0.5V' samples. These experiments were conducted at a dedicated instrument of the ID16A-NI beamline at the European Synchrotron Radiation Facility (ESRF), operating in vacuum and cryogenic conditions that facilitate quantitative measurements of the atomic distribution of Fe with a sensitivity down to few tens of atoms. 2D XRF mapping at 20 nm resolution of the pristine and post mortem samples after 'Air-0.5V' was conducted are shown in Figure S9, and unveil that the recorded images look fairly similar to those of the STEM-EDS study. However, XRF's enhanced sensitivity provides additional and quantitative information on the CLs' Fe-content, and thus a comparison of different positions from the microtomed CLs unveils a ≈ 15 % decrease in the Fe-content of the end-of-life sample compared to the pristine one. This apparent decrease is limited to a statistically small volume of 30-40 µm [START_REF] Staffell | The role of hydrogen and fuel cells in the global energy system[END_REF] , so additional 3D XRF-CT measurements were performed to overcome these limitations with a beamsize of 200 nm that translates into proved volumes of ≈ 6000 µm 3 for both CLs. The tomographic reconstructions in Figure 4e We then integrated the intensity of all voxels within the CL to assess the average number of Fe atoms per voxel in both layers, and found an overall loss of ≈ 18 % of the Fe atoms (with regards to the pristine CL's content, and depicted in the inset of the histogram) that agrees very well with the results of the 2D nano-XRF analysis. We note in passing that the ≈ 15-20 % lower Fe-content in the post mortem sample is not related to differences between the catalysts loadings of the pristine and 'Air-0.5V' CLs used for these analysis, since their loadings were 1.3 vs. 1.4 mgNNMC•cm -2 , respectively.

If one combines this confirmation of Fe-dissolution with the shift in the Fe distribution towards smaller sizes inferred from the histograms, Fe-leaching in the course of this 'Air-0.5V' protocol must occur mainly from within voxels with fewer metal atoms. Indeed, Fe precipitates with a size around 20-50 nm like the ones imaged by HAADF would consist of at least a couple of thousand Fe atoms within one voxel (assuming a ferrite body-centered cubic crystal structure and sizes as those seen by HAADF). Importantly, from integrating the total Fe content of voxels with intensities > 1000 atoms, no significant differences between the pristine and post-mortem sample after the 'Air-0.5V' protocol are inferred. In other words, the absence of significant changes in the Fe contents of the pristine and end-of-life samples within the range of high intensity voxels assignable to Fe-based nanoparticles implies that these side-phases did not undergo any demetallation (in agreement with what was qualitatively inferred from the HAADF images), and indirectly suggests that the loss of Fe deduced from the histograms' integration likely stems from the partial demetallation of the atomically dispersed FeNx sites in this NNMC.

Beyond its specific origin, the confirmation that Fe dissolves upon PEMFC operation at 0.5 V may imply the presence within the CL of Fe-cations that could aid the decomposition of ORR-produced H2O2 into aggressive radicals that would in turn chemically degrade the membrane and ionomer. On top of this, such cationic species could additionally placeexchange with the protons in the ionomer and membrane's sulfonic acid groups, effectively decreasing their H + -conduction capabilities. Either of these effects should therefore be detectable in the impedance spectra of the post-mortem sample, which essentially featured the same RHFR as the pristine CL and a ≈ 10 % higher RH+, eff value (see Figures S10 andS11 for the impedance spectra and corresponding RHFR and RH+, eff values derived from their fits, respectively). These minor changes in the impedance results indicate that the operative release of Fe did not result in noticeable H2O2-related degradation of the membrane and/or the ionomer, at least within the rather short time scale (1 h) of this 'Air-0.5V' protocol. Notably, the lack of ionomer degradation was also confirmed by the F-EDS cross-section images of the CLs acquired by STEM, where there was no clear difference in fluorine distribution between the pristine CL and the sample submitted to the 'Air-0.5V' protocol (Figure S6g,h). On the other hand, the slight increase in RH+, eff observed for the post mortem CL may be partially caused by the poisoning of the ionomer's sulfonate groups with leached Fe-ions. More precisely, based on the CL's NNMC-loading (1 mg•cm -2 ) and ICR (0.7), along with the ionomer's equivalent weight (1100 g/molSO3-), the ≈ 10 % higher RH+, eff value would result from the release of ≈ 6 % of the CL's iron content as Fe 3+ (assuming that each of these ferric ions poisons three sulfonate groups). Notably, this would correspond to leached iron that would be trapped within the CL, implying that the total amount of dissolved Fe would be larger than the ≈ 15 % decrease of the metal inventory estimated by the above XRF measurements (which would correspond to Fe-ions leaving the cell). Following this investigation of the processes in the degradation tests at 0.5 V, we now assess the performance changes in the protocols performed at a higher potential of 0.8 V that, as discussed above, is likely to entail the electrochemical corrosion of the catalyst's carbonaceous matrix. Since the onset potential of this reaction is expected to strongly depend on the graphitization degree of the NNMC type, [START_REF] Wang | Surface Fluorination to Boost the Stability of the Fe/N/C Cathode in Proton Exchange Membrane Fuel Cells[END_REF][START_REF] Zhang | Is iron involved in the lack of stability of Fe/N/C electrocatalysts used to reduce oxygen at the cathode of PEM fuel cells?[END_REF] we performed a preliminary PEMFC experiment to assess the corrosion-resistance of our catalyst by submitting a pristine NNMC CL to the same protocol and conditions used for the recording of the polarization curves, but using N2 as cathode feed instead of air. The resulting current-potential plot is displayed in Figure 5a, and shows that the electrochemical oxidation currents recorded on this NNMC become significant at potentials ≥ 0.7 V. Moreover, these oxidative currents appear to also be responsible for the systematic deviation from a 70 mV•dec -1 linear slope observed at high potentials in the Tafel plots in Figure 2b,f,d,h, which has been proposed to be a consequence of the ORR-mechanism on NNMCs. [START_REF] Chlistunoff | RRDE and Voltammetric Study of ORR on Pyrolyzed Fe/Polyaniline Catalyst. On the Origins of Variable Tafel Slopes[END_REF] To assess this possibility, the currents in the beginning-of-life, H2/Air polarization curve (iORR,measured) were corrected for the oxidative currents in this H2/N2 test (ioxidation) using the equation:

iORR,corrected = iORR,measured -ioxidation (3)
yielding a corrected current (iORR,corrected) that, as showcased in Figure 5b, now features a fully linear slope of 70 mV•dec -1 at all potentials > 0.8 V. As such, this result suggests that the curvature observed in the Tafel plots at high potentials is exclusively caused by the contributions of a simultaneous oxidative process to the overall measured currents. On the other hand, at the current stage we cannot exclusively ascribe this oxidative current to the electrooxidation of the catalyst to CO2. More precisely, if one assumes that all of the oxidative charge in the 'N2-0.8V' test (accounting to ≈ 33 mAh•cm -2cf. Figure S4d) were to correspond to the oxidation of the carbon in the NNMC to CO2, roughly a third of the catalyst's carbon would have been fully corroded. By comparison, a comparatively smaller loss of ≈ 10 % of the carbon inventory in Pt/C CLs is known to result in the collapse of their porous network and cell failure [START_REF] Gasteiger | Catalyst Degradation Mechanisms in PEM and Direct Methanol Fuel Cells[END_REF] a complete loss of performance that is not observed at the end-of-life of the 'N2-0.8V' protocol, and that suggests that an undetermined fraction of the charge in that test may result from reactions other than the complete oxidation of carbon to CO2 (e.g., the formation of C-O surface functionalities). Having confirmed that the NNMC undergoes an oxidative process at the 0.8 V at which this second set of degradation protocols is carried out, we analyzed the performance decay at this potential in both the presence and absence of O2. The recorded polarization curves and the corresponding Tafel plots are displayed in Figure 2e-h and show that the 'Air-0.8V' protocol (expected to entail carbon electro-oxidation together with H2O2-related effects) leads to drastic performance losses both in the kinetic and high current density regions (whereby the former experienced a ≈ 95 % drop with regards to the beginning-of-life valuesee Figure 3). In the 'N2-0.8V' test, however, there was no change in high current density performance, while a ≈ 60 % decrease in the ORR activity was determined from the Tafel plot at 0.8 V (see Figure S5 for the ORR activities). This indicates that carbon electro-oxidation alone is highly detrimental to the NNMC's ORR-activity even in the absence of O2 and concomitant H2O2related effects. It is also worth noting that cyclic voltammograms recorded after these two protocols at 0.8 V featured a slightly larger double layer current (possibly relatable to the corrosion of the carbon [START_REF] Povia | Combining SAXS and XAS To Study the Operando Degradation of Carbon-Supported Pt-Nanoparticle Fuel Cell Catalysts[END_REF] ) along with a partial disappearance of the redox peaks at ≈ 0.75 V present in the CV of the pristine NNMC layer (see Figure S12b). These redox peaks have been correlated to these catalysts' ORR activity by some studies, [START_REF] Tylus | Elucidation of Fe-N-C electrocatalyst active site functionality via in-situ X-ray absorption and operando determination of oxygen reduction reaction kinetics in a PEFC[END_REF] but other works have opposed this interpretation. [START_REF] Elvington | Communication-On the Lack of Correlation between the Voltammetric Redox Couple and ORR Activity of Fe-N-C Catalysts[END_REF] Notably, that lack of redox-peak vs. ORR-activity correlation would also be endorsed by our results, since the CVs of the end-of-life NNMC layers submitted to the protocols at 0.5 V are essentially identical to that of the pristine catalyst (see Figure S12a), even if both CLs suffered from a significant drop of their ORR activity in both tests (i.e., ≈ 70 vs. ≈ 30 % losses in 'Air-0.5V' vs. 'N2-0.5V', respectivelysee Figure 3). On top of this, we also assessed the effect of this high potential hold on the catalyst's carbonaceous matrix by performing Raman spectroscopy measurements on pristine and post mortem CLs (i.e., prior to and after the 'N2-0.8V' protocol, respectively). As shown in Fig. S13, the two layers feature almost indistinguishable Raman spectra with a line shape typical of carbon materials submitted to a heat treatment at temperatures < 1000 °C. In the first-order region, the spectra are characterized by two maxima at ≈ 1350 and ≈ 1600 cm -1 assigned to the so-called "D" and "G" lines. In this regard, the intensity of the "D" line and the width of both the "D" and "G" lines clearly indicate that the layers are formed by of a highly defective carbon material with a low average in-plane crystallite size (< 2-3 nm) and that also contains a high amount of point defects. In such cases, the direct use of the intensity ratio between these lines (ID/IG) does not allow the determination of the average in-plane crystallite size. However, it is clear that these results do not indicate a massive corrosion of the carbonaceous material, and this is also the case when considering the second-order region of the spectra (not shown here), which also showed no significant changes between pristine and post mortem CLs. On the other hand, the results cannot exclude the formation of different carbon-oxygen functionalities that are always very difficult to detect analyzing carbon-based materials. Specifically, this could be regarded as indicative of a C-corrosion mechanism in which the electro-oxidation of the catalyst's graphene domains leads to an enhancement of their hydrophilicity that in turn causes their dissolution in the aqueous electrolyte, as previously proposed for the electrochemical oxidation of glassy carbon in alkaline medium. [START_REF] Yi | Electrochemical corrosion of a glassy carbon electrode[END_REF] An STEM analysis was also performed on the end-of-life CL submitted to the 'Air-0.8V' protocol to assess the morphological and compositional changes caused by this test. As before, F and Fe elemental mapping (Figure S6 f, i), semi-quantitative analysis (Figure S6 j-k)

and HAADF imaging and analysis of the Fe agglomerates (Figure S7 c-e) were performed.

Similar to the post-mortem CL that had undergone the 'Air-0.5V' test, the end-of-life CL after the 'Air-0.8V' protocol contained numerous Fe clusters, whose signal did not display an appreciable change compared to that of the pristine CL, and the number and morphology of the Fe agglomerates imaged by HAADF showed no significant changes. As showed by the STEM and XRF measurements carried out on the 'Air-0.5V' sample, though, these observations do not rule out a subtle change in the Fe content of the layers; we note in passing that, unfortunately, it was not possible to perform such the time-demanding, synchrotron-assisted measurements on the post mortem 'Air-0.8V' sample. On the other hand, the impedance measurements of the end-of-life CLs after the protocols at 0.8 V featured no significant changes in the RHFR and RH+,eff values (with regards to the beginning-of-life) regardless of the cathode feed (see Figure S10c,d and Figure S11). This indicates that, and unlike in the case of the sample submitted to the 'Air-0.5 V' protocol (that featured a ≈ 10 % increase of the proton transport resistance), none of these higher potential samples should have suffered from the poisoning of the ionomer by leached Fe-ions discussed above for the lower-potential analogue (i.e., 'Air-0.5V'). Instead, the H + -conduction properties of the membrane and ionomer in the CL were completely unaffected by the presence or absence of O2 in these tests, even if in this case their duration added up to ≈ 250 h. Notably, this stability of the ionomer was also confirmed by F-EDS cross-section images of the CLs acquired by STEM, from which no clear differences in fluorine distribution were observed between the pristine CL and the sample submitted to the 'Air-0.8V' protocol (Figure S6g, i).

Having discussed all degradation protocols, we now revise the ORR activity losses caused by each of them and plotted in Figure 3. The latter clearly illustrates that the two tests entailing the use of air at the cell's cathode are unambiguously more detrimental to the ORR-activity than those performed in N2, as already indicated by some recent studies. [START_REF] Kumar | On the Influence of Oxygen on the Degradation of Fe-N-C Catalysts[END_REF][START_REF] Zhang | Standardized protocols for evaluating platinum group metal-free oxygen reduction reaction electrocatalysts in polymer electrolyte fuel cells[END_REF] Interestingly, when one isolates the effects of the potential hold at 0.5 V and of carbon electrooxidation at 0.8 V based on the extents of degradation observed in the complementing N2-tests (i.e., 'N2-0.5V' and 'N2-0.8V', respectively), the remaining activity losses in the air protocols and ascribable to H2O2-related degradation account for ≈ 40 % of the overall activity decay.

This fraction is irrespective of the potential at which these tests were performed, and would therefore imply that these H2O2-effects are only dependent of the ORR charge transferred in the course of the test. Such a conclusion would in turn be at odds with the potential-dependent H2O2-yields generally observed for NNMCs in the rotating ring disk electrode (RRDE) experiments customarily used to assess their ORR-selectivity. [START_REF] Primbs | Establishing reactivity descriptors for platinum group metal (PGM)-free Fe-N-C catalysts for PEM fuel cells[END_REF] This contradiction could stem from the significant differences between the testing environments in these RRDE tests and actual PEMFC MEAs (e.g., liquid vs. solid electrolyte, different mass transport limitations, etc.), which has been widely discussed in the literature in the specific context of Pt-based catalyst [START_REF] Lazaridis | Capabilities and limitations of rotating disk electrodes versus membrane electrode assemblies in the investigation of electrocatalysts[END_REF] and also observed for NNMCs. [START_REF] Jaouen | Oxygen reduction activities compared in rotating-disk electrode and proton exchange membrane fuel cells for highly active FeNC catalysts[END_REF] As for the results observed in the tests using N2 at the cathode, the smaller ORR-activity losses observed in the protocol at 0.5 V vs. 0.8 V imply that carbon electro-oxidation at the latter potential is more detrimental to the ORR-activity than the Fe-demetallation caused by the former, even if the effects of that test at 0.5 V were by no means negligible.

To finalize this study, we delve in the effects of the different stability tests and corresponding degradation mechanisms on the mass transport properties of these CLs. To this end, the mass transport overpotentials( 𝜂 𝑡𝑥 s) of the pristine CL and of the CLs submitted to each of the degradation protocols were estimated from the potential difference between the extrapolated Tafel lines and the polarization curves corrected for all ohmic losses (i.e., 𝑅 𝐻𝐹𝑅 and 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff ), and displayed in Figure S14. Figure 6 then features the change in mass transport overpotential (∆𝜂 𝑡𝑥 ) inferred by each testing condition, which was calculated as the difference between the 𝜂 𝑡𝑥 of the pristine CL and that of each end-of-life sample (i.e., ηpristineηpost-mortem), in all cases at a current of 0.8 A•cm -2 (see Figure S15). The results in Figure 6 indicate that the mass transport worsened after the two protocols performed in air, in which the increase in 𝜂 𝑡𝑥 qualitatively scales with the loss of the kinetic activity (see Figure 3). In other words, the increase in the 𝜂 𝑡𝑥 was ≈ 25 mV larger in the post-mortem sample submitted to the 'Air-0.8V' protocol, for which the ORR-activity loss was also higher compared to sample submitted to the 'Air-0.5V' protocol (≈ 95 vs. ≈ 70 % with regards to the beginning-of-life value, respectively). This observation qualitatively confirms the correlation between the number of ORR-active sites in this type of NNMC and its CLs' mass transport (whereby more vs. less sites imply a lower / higher 𝜂 𝑡𝑥 value) postulated in our recent work, [START_REF] Ünsal | Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes[END_REF] and originally reported for Pt-based ORR-catalyst. [START_REF] Kongkanand | The Priority and Challenge of High-Power Performance of Low-Platinum Proton-Exchange Membrane Fuel Cells[END_REF] Additionally, in the case of the 'Air-0.8V' test, the electro-oxidation of the catalyst's surface expected at high potential is known to increase the hydrophilicity of the CL, further worsening its mass transport properties. In the case of both N2 protocols, however, the mass transport properties of the end-of-life CLs systematically improved when compared to the pristine sample. For the N2-test at a low potential of 0.5 V, this improvement can be attributed to the potential-driven reduction of the catalyst's surface (e.g., hydroquinone groups), [START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF] which would entail the removal of hydrophilic O-functionalities and increase the CL's hydrophobicity, thus leading to a better water management. On the other hand, the enhancement of the mass transport observed for the CL having undergone the N2protocol at 0.8 V is more puzzling, since we would expect that the above discussed electrooxidation of the carbon surface at this high potential would increase the CL's hydrophilicity and worsen its mass transport properties. We hypothesize that this effect may be counteracted by localized C-electro-oxidation to CO2, which may expose more graphitic (and thus hydrophobic) domains and eventually lead to an additional re-distribution of the ionomer that may lead to a net improvement in mass transport along this CL. better mass transport properties, respectively.

CONCLUSIONS

In this study, we aimed to disentangle the relative contributions to the overall PEMFCperformance losses of the three main NNMC-deactivation paths proposed in the literature (i.e., demetallation, electrochemical carbon oxidation and H2O2-related effects) by using four degradation protocols implying different cathode gas feeds (i.e., air vs. N2), potential holds and durations. Our results indicated that H2O2-effects on the catalyst's ORR-activity appear to depend only on the ORR-charge, and not on the test's operative potential. Interestingly, the chemical degradation of the ionomer and/or membrane that might originate from H2O2-derived species does not seem to significantly contribute to the performance decay observed under air operation, at least within the duration of protocols presented here. Complementarily, we found that electrochemical carbon oxidation in the protocols at a higher potential of 0.8 V contributes to the ORR-activity loss more significantly than Fe-demetallation at 0.5 V, even if the effect of the latter mechanism was not negligible. Notably, this observation is likely to be highly dependent on the potentials chosen for these tests, since demetallation vs. C-oxidation are expected to be exacerbated as the potential decreases or increases, respectively. [START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Choi | Minimizing Operando Demetallation of Fe-N-C Electrocatalysts in Acidic Medium[END_REF] Moreover, the CLs' mass transport properties worsened after both of the protocols performed using air at the cathode, and the observed increase of the ηtx qualitatively matched with the extent of ORR activity loss. On the other hand, the mass transport systematically improved after the protocols performed under N2 both at 0.5 and 0.8 V; the former potential possibly implies a reduction of the catalyst's surface that would enhance the CL's hydrophobicity leading to this decreased ηtx, while the reason for the improvement obtained in the 0.8 V protocol remains unclear.

We believe that the combination of durability tests presented herein can help to differentiate and quantify the impact of these degradation mechanisms on NNMCs' stability. Specifically, the combined protocols may prove extremely useful for comparing catalysts with wellestablished metal speciations (e.g., Co vs. Fe-based, featuring other single atom vs. metal NP ratios) and physicochemical features (e.g., surface composition, carbon graphitization extent), as it could serve to establish ties between these properties and the relative impact of different instability mechanisms. The knowledge gained through such comparisons could in turn be used to guide the design of NNMCs with the sufficiently high durability required for their implementation in commercials PEMFCs.

)

  The polarization curves corrected for the 𝑅 𝐻𝐹𝑅 and 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff terms over the entire current density range after each degradation protocol (provided in FigureS10) were used to estimate the 𝜂 𝑡𝑥 values by quantifying the difference in potential between the extrapolated Tafel lines and the polarization curves corrected for these ohmic losses (i.e., 𝑅 𝐻𝐹𝑅 and 𝑅 H + ,𝑐𝑎𝑡ℎ𝑜𝑑𝑒 eff Sample preparation for scanning transmission electron microscopy (STEM) and X-ray fluorescence (XRF) analyses: STEM/EDS measurements of the Fe-based nanoparticles' composition were performed on catalyst particles scrapped from the NNMC MEAs and dispersed in 1:4 DI water:isopropanol in an ultrasonic bath. 2-4 µL of this ink was placed on ultrathin lacey carbon TEM copper gris (Electron Microscopy Science) and allowed to dry.

  Nano-XRF imaging: 2D X-ray fluorescence (XRF) mapping and 3D XRF computer tomography (XRF-CT) were performed at the ID16A-NI beamline of the European Synchrotron Radiation Facility (ESRF). The experiments were performed at 17 keV with the X-ray beam focused by two multilayer-coated Kirkpatrick-Baez (KB) mirrors. The mirrors focused the beam down to a 68 nm (h) by 37 nm (v) spot size. The incident flux used was 4*10 11 photons/s. The XRF data was acquired in scanning mode with a 20 nm step size for 2D

Figure 1 .

 1 Figure 1. Schematic representation of the various stability protocols used in this study, along

Figure 2 .

 2 Figure 2. PEMFC H2/Air polarization curves recorded at 1.5 barabs, 80 °C and 100% relative humidity with a cathode loading of 1 mgNNMC•cm -2 (a, c, e, g) before and after the degradation protocols in Figure 1, along with their corresponding Tafel plots corrected for HFR and H + -

Figure 3 .

 3 Figure 3. Summary of the losses in mass-normalized ORR-activity at 0.8 V (expressed as a

  represents the 3D rendering of the Fe distribution (based on the Fe Kα line, and for a representative subvolume of 6x30x30 µm 3 ) using a color scale that allows identifying subtle changes in the content and distribution of Fe atoms. In case of the pristine sample, a subtle shift of intensity towards higher Fe-contents is represented by the yellow color within the CL. These changes are evenly distributed across the sample, indicating that this pristine CL has a higher content of Fe-atoms per voxel compared to the almost uniformly colored post-mortem sample. While the 3D rendering visualizes the spatial distribution, a histogram representing the average Fe content can better evidence the potential leaching of Fe. Thus, we carried out a simple segmentation protocol based on gradient and signal strength to mask out air voxels and extract the Fe-content in units of atoms per 200 nm 3 voxels. The results of this approach are shown as a histogram in Figure4f, in which the pristine sample exhibits a broader peak centered at ≈ 220 Fe-atoms while the post-mortem CL features a sharper distribution centered at ≈ 140 Fe-atoms.

Figure 4 .

 4 Figure 4. STEM and XRF analysis of pristine vs. end-of-life samples before and after the 'Air-

Figure 5 .

 5 Figure 5. Current density vs. potential response recorded in a PEMFC in H2/N2 at 1.5 barabs, 80 °C and 100 % relative humidity using an NNMC cathode loading of 1 mgNNMC•cm -2 (a) and

Figure 6 .

 6 Figure 6. Summary of the changes in mass transport overpotential (∆ηtx = ηpristine -ηpost-mortem)

ASSOCIATED CONTENT Supporting Information.

AUTHOR INFORMATION Corresponding Author

*Juan Herranz (email: juan.herranz@psi.ch)

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Funding Sources

S.Ü., R.G., V.S., T.J.S. and J.H. gratefully acknowledge funding from the Swiss National Science Foundation, under the funding scheme Sinergia (project grant number 180335). C.A.

has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 884104. C.A. is also supported by funding from Chalmers's initiative for advancement of neutron and X-ray techniques.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENT

We would like to thank the staff of the beamline ID16A-NI at the European Synchrotron Radiation Facility (ESRF), Grenoble, France for providing beamtime for nano-XRF imaging experiments under the proposal number MA-5294. For the nano-XRF sample preparation, we also acknowledge the ScopeM staff at ETH Zurich: Joakim Reuteler and Stephan Handschin for embedding and Eszter Barthazy for microtome cutting. We are also grateful to Dr. Jinzhen Huang for his help with the XRD measurements.


Thibaut Bellanger

Alexandre Guitton

Razvan Stanica

Fabrice Valois

Everyone can slice LoRaWAN

Long-Range Wide Area Networks (LoRaWAN) enable low-power data collection over long distances, and they are thus widely used for Internet of Things applications, despite their limitations to meet some traffic requirements (e.g., reliability). To achieve the quality of service required by the applications, service differentiation is a promising approach which can be provided by network slicing. In this work, we show that related works are either incompatible with the LoRaWAN specifications, or do not isolate traffic, or assume an a priori known and stable traffic. In this paper, we propose a lightweight approach to achieve slicing in LoRaWAN, compatible with the LoRaWAN specifications. Our approach allows to isolate traffic, to protect confirmed traffic, and to deal with unsolicited traffic.

I. INTRODUCTION

Long-Range Wide Area Network (LoRaWAN) is a wireless communications standard dedicated to data collection from low-power end-devices over long distances. It is widely used in Internet of Things (IoT) applications for three main reasons: (i) it can trade-off communication range with data rate, (ii) it enables the deployment of low-cost networks, and (iii) it is simple to implement. Applications using LoRaWAN typically include smart city monitoring, smart farming, and supply chain management and optimization.

Because of the simple design of its medium access control (MAC) protocol, LoRaWAN is not able to support the Quality of Service (QoS) requirements of some IoT applications. For this reason, several proposals ( [1]- [START_REF] Mai | Transfer reinforcement learning aided distributed network slicing optimization in industrial IoT[END_REF]) were made to use slicing in order to implement service differentiation over LoRaWAN, i.e., to provide different levels of QoS. The idea of slicing comes from 5G networks. It uses independent resources, including radio resources, to create slices which are isolated from each other, then allocates nodes to slices, and manages these slices independently. In theory, a slicing approach applied to LoRaWAN could provide different QoS levels for various types of traffic: confirmed, unsolicited, traffic from mobile end-devices, etc.

However, current slicing approaches for LoRaWAN fail to meet key requirements: (i) several approaches are not entirely compatible with the LoRaWAN specifications from the LoRa Alliance, (ii) most approaches do not isolate traffic, which does not allow to provide QoS guarantees, (iii) they require an a priori knowledge of the traffic, and assume stable traffic conditions, and (iv) they often assume that traffic parameters are controlled by the IoT node, which is not practical for real operated networks. Moreover, the current slicing approaches for LoRaWAN are unable to deal with unsolicited traffic, for instance coming from end-devices associated to an application server that has been terminated: such a situation is unfortunately likely to happen increasingly in the future, as some old application servers will eventually be switched off without removing the corresponding IoT nodes.

In this paper, we propose a lightweight slicing approach for LoRaWAN that: (i) is compatible with the LoRaWAN specifications, (ii) isolates traffic, (iii) is dynamic, and (iv) is controlled by the network-server. Our approach is flexible, and it can handle slices based on radio channels, spreading factors or other radio/network parameters. We show that we can take benefit from the adaptive data rate (ADR) mechanism to create slices and to allocate end-devices to slices. In other words, with our approach, everyone can slice LoRaWAN.

The remainder of this paper is organized as follows. Section II gives a brief overview of LoRaWAN and of slicing in 5G networks. Section III presents the related works on slicing for LoRaWAN, as well as on the ADR mechanism. Section IV introduces our lightweight approach to perform ADR-based slicing. Section V evaluates our approach through simulations. Finally, Section VI concludes our work.

II. BACKGROUND A. LoRaWAN network architecture and medium access

LoRaWAN defines the network architecture and MAC protocol of low-power wide area networks. It is based on the Long Range (LoRa) physical layer modulation. In LoRaWAN, enddevices send LoRa frames, which are received by (possibly multiple) gateways. The gateways forward them to a centralized network server, which deduplicates them. The network server then determines the target application from the headers, and forwards the frames to the corresponding application server. Both the application server and the network server can also send frames to end-devices: in this case, the network server decides through which gateway the frame should be sent. Note that a join procedure is required before an enddevice can communicate.

LoRaWAN defines three classes of end-devices. We focus here on Class A, as it is the only mandatory class, and is used by a large majority of applications. In Class A, the enddevices use an Aloha MAC mechanism: they can transmit a frame without waiting or assessing the channel, but have to comply with a duty-cycle. Each transmission is followed by the opening of two short reception windows, where enddevices can receive acknowledgments in the case the frame they transmitted belongs to the confirmed traffic class.

The LoRa modulation integrates three main parameters: the spreading factor (SF), the channel bandwidth (BW) and the coding rate (CR). These three parameters have a direct impact on the robustness of the modulation: a long communication range can be reached with a small BW, a large SF and a large CR. LoRaWAN can dynamically adapt both BW and SF through a mechanism called ADR. When ADR is active, the network server constantly monitors the received signal strength of frames reaching the gateways, for each end-device. Then, it chooses LoRa parameters for each end-device. Finally, the network server uses a control message to request the enddevice to switch to this set of parameters.

Typical LoRaWAN applications produce traffic with different QoS requirements: (i) the traffic might be confirmed (that is, each uplink frame is requesting for an acknowledgment) or unconfirmed, (ii) the end-devices can be mobile (in which case, the ADR has to be disabled and end-devices use SF12) or static (in which case, the ADR is usually enabled), and (iii) there might be unsolicited end-devices producing traffic, typically when the application server has been switched off while some end-devices are still deployed and running.

The simple Aloha mechanism used in LoRaWAN does not allow for service differentiation to provide QoS guarantees. However, LoRaWAN uses several orthogonal frequency channels and orthogonal SF at the physical layer. These two orthogonalities are used by most slicing approaches, as we will see later.

B. Network slicing

Slicing is a key feature in 5G networks, allowing the support of a wide range of applications with very different QoS requirements. This is made possible by the virtualization of all resources (e.g., radio, backhaul, computation) and of all the network functions in the cellular network. The main idea is to use orthogonal (or independent) resources and independent functions to design slices based on the application QoS requirements. Each slice is isolated from the others, monitored and managed independently, and can evolve dynamically according to the network demand. Because of this isolation, there is no impact of the traffic from a given slice on another one, which allows to enforce the key performance indicators.

To provide slicing in a network architecture, it is mandatory to identify independent resources. Then, a slice is defined as the list of the necessary virtual network functions, the dedicated radio resources, and the required network hardware.

III. RELATED WORKS

There are only a few existing slicing approaches for Lo-RaWAN. In this section, we compare them based on three crucial properties: the isolation guarantees, the support of dynamic traffic, and the compatibility with the LoRaWAN specifications (especially on the end-device side). We also give a brief overview of proposed ADR enhancements.

A. Slicing in LoRaWAN

In [START_REF] Dawaliby | Adaptive dynamic network slicing in LoRa networks[END_REF], the authors propose a three-step approach to perform slicing in LoRaWAN, while assuming an a priori knowledge of the traffic. First, they group end-devices with similar QoS requirements, which determines the slices. Second, they compute the capacity for each slice and allocate channels accordingly. Third, each end-device is managed by the best gateway. The authors study the performance of their approach according to various SF allocation strategies for the third step.

In [START_REF] Aggarwal | QoS based spreading factor assignment for LoRaWAN networks in IoT applications[END_REF], the authors propose to change the SF allocation based on the device priority. They propose two greedy algorithms that allocate the SF to high-priority end-devices first: one algorithm allocates the smaller SFs first, while the other allocates the larger SFs first. However, the approach assumes that all end-devices can use the smallest SF, which is not realistic in a real deployment.

In [START_REF] Aimi | Packet delivery ratio guarantees for differentiated LoRaWAN services[END_REF] and [START_REF]Traffic Control and Channel Assignment for Quality Differentiation in Dense Urban LoRaWANs[END_REF], the authors propose two similar approaches to provide different QoS, expressed as differentiated targets of frame delivery rate. They propose to separate the traffic based on frequency channels, and to further limit the number of devices per SF. The authors also propose to reduce the dutycycle of low-priority nodes in order to limit the traffic. In both papers, some end-devices are forbidden to send traffic, which requires to modify the behavior of end-devices, and thus does not follow the LoRaWAN specifications.

In [START_REF] Dandachi | A frequency-based intelligent slicing in LoRaWAN with Admission Control Aspects[END_REF], the authors explain that slicing on LoRaWAN channels does not provide a fine QoS granularity, as the number of channels in LoRaWAN is typically very small (from 3 to 8 in the European regional settings). Thus, they propose end-devices of different priorities to be allocated to the same channel. However, they introduce a blocking probability on end-devices, in order to reduce the traffic of low-priority ones. This proposal requires to modify the end-devices to support priorities and a blocking probability, which is not supported by the LoRaWAN specifications.

In [START_REF] Mai | Transfer reinforcement learning aided distributed network slicing optimization in industrial IoT[END_REF], the authors propose an approach using Deep Deterministic Policy Gradient to optimize resource allocation within frequency-based slices. For each gateway, an agent attempts to find the optimal pair of SF and transmission power for each node. To reduce the learning time, the authors use Transfer Learning. However, the proposal has two main drawbacks. First, there is no isolation since the slices are defined by SF and transmission power. Second, the authors assume that each gateway can allocate channels to end-devices independently, while the broadcast nature of LoRa communications generates interference when two neighboring end-devices communicate on the same channel for different gateways.

In [START_REF] Tellache | Deep reinforcement learning based resource allocation in dense sliced LoRaWAN networks[END_REF], the authors define three classes of traffic, with reliability and urgency components. Those three classes are isolated in frequency-based slices. The authors then use Deep-Q Networks to allocate efficiently SF and transmission power to each node. Each slice uses a different reward function which enables to define different allocations to ensure QoS requirements. However, the authors achieve slightly better scores than the ADR allocation scheme.

Table I summarizes these approaches by considering the three properties which appear imperative for providing slices in LoRaWAN: the ability to isolate slices, the ability to deal with dynamic traffic without an a priori knowledge, and the Table I: Summary and comparison of related works.

Reference

Slicing approach Isolation Dyamic traffic support LoRaWAN Specification compatibility [START_REF] Dawaliby | Adaptive dynamic network slicing in LoRa networks[END_REF] frequency-based + -+ [START_REF] Aggarwal | QoS based spreading factor assignment for LoRaWAN networks in IoT applications[END_REF] SF-based + -+ [START_REF] Aimi | Packet delivery ratio guarantees for differentiated LoRaWAN services[END_REF] SF-based and frequency-based + -- [START_REF]Traffic Control and Channel Assignment for Quality Differentiation in Dense Urban LoRaWANs[END_REF] SF-based and frequency-based + -- [START_REF] Dandachi | A frequency-based intelligent slicing in LoRaWAN with Admission Control Aspects[END_REF] frequency-based --- [START_REF] Mai | Transfer reinforcement learning aided distributed network slicing optimization in industrial IoT[END_REF] frequency-based and power-based --+ [START_REF] Tellache | Deep reinforcement learning based resource allocation in dense sliced LoRaWAN networks[END_REF] frequency-based + + + Our approach SF-based and frequency-based + + + compatibility with the LoRaWAN specifications.

B. ADR modifications

The ADR mechanism enables the network server to allocate the SF and transmission power to each end-device, based on the quality of the links with gateways. However, several major weaknesses were identified in this mechanism. First, the ADR allocates parameters only based on the received signal strength, without taking into account the other enddevices. This causes congestion in the network, typically when too many end-devices are allocated to the same SF. Second, the ADR is unstable when channel conditions are volatile or for mobile end-devices. Consequently, many researchers have proposed modifications to the ADR mechanism, typically to improve the throughput, to reduce the energy consumption, and to increase capacity. details on these ADR enhancements can be found in [START_REF] Kufakunesu | A survey on adaptive data rate optimization in LoRaWAN: Recent solutions and major challenges[END_REF].

IV. ADR-BASED SLICING FOR LORAWAN

A. Motivations

We aim to design a slicing architecture for LoRaWAN, while remaining compatible with the LoRaWAN specifications. In other words, our proposal should be supported by any real LoRaWAN deployment, without requiring any modification on either the end-devices, the join server or the application servers. We propose to leverage the ADR mechanism to allocate and manage the slices, in addition to its usual role of improving the link budget of LoRa devices.

The goal of using slices is to protect the traffic through isolation in order to meet the QoS requirements of the applications. In the context of LoRaWAN, we claim that the confirmed traffic suffers from a lack of reliability in practice. Thus, we propose to create a slice dedicated to end-devices sending confirmed traffic. Moreover, in LoRaWAN networks, we also observe unsolicited traffic, i.e. traffic from end-devices whose application server is stopped. Indeed, when an application server is switched off, thousands of end-devices may continue to send traffic on their own, which drastically impacts the performance of the whole network. Thus, we propose to dedicate a slice to isolate such unsolicited traffic. Finally, we dedicate a third slice to the end-devices which send only nonconfirmed traffic, which we refer to as normal traffic. Note, that we can extend these three slices to a larger number of slices, e.g., a slice to isolate mobile end-devices.

B. How to slice LoRaWAN?

According to the design principles of slices in 5G, we should slice a LoRaWAN network both in the back-end network (network server, application servers, join server), and in the radio access part (end-devices, gateways).

1) The back-end network side: Because of the low traffic intensity of IoT applications, originating from small IoT packet sizes (typically in the order of tens of bytes), we claim that slicing the back-end is not necessary. If it is requested by some applications, for privacy issues or performance reasons, a virtual local area network can easily isolate the traffic, satisfying these QoS constraints.

2) The radio access side: Slicing the radio resource part of a LoRaWAN network can be done either by using LoRa frequency channels, or by using LoRa spreading factors. Without loss of generality, we propose to consider a slicing approach based on the use of spreading factors. Our work can be easily extended to frequency channel slicing, or to both frequency channel and SF slicing.

We chose to use SF to achieve slicing for two key reasons:

• Quasi-orthogonality: SF are often claimed to be orthogonal and independent. In practice, the SF are quasiorthogonal to one another [START_REF] Seller | LoRaWAN link layer[END_REF]. Table II summarizes the impact of each SF on the others. If chosen correctly, a specific SF can be used to protect the confirmed traffic, a second one to isolate unsolicited traffic, and all the others for the non-confirmed traffic. • Allocation and reallocation: LoRaWAN typically uses the ADR mechanism to optimize the link budget through a dynamic adaptation of the SF used by the end-devices. Without modifying the end-device behavior, we extend the ADR to also change the SF according to the slicing strategy. Based on the header of LoRa frames, the network server is able to detect devices sending mostly confirmed traffic. Based on the application_id of each frame, it is also able to determine if the corresponding application server is running: if not, we assume that the traffic is unsolicited. C. ADR-based slicing for LoRaWAN 1) Description of the slices: Our proposal supports the following three slices: (i) the Normal slice manages all the traffic coming from end-devices which do not have strong reliability requirements, and whose application server is still running, (ii) the Confirmed slice is dedicated to support only the confirmed traffic, i.e., the traffic which requires to be acknowledged, (iii) the Unsolicited slice is a slice which receives all the unsolicited traffic, coming from end-devices which are still running despite having an inactive application server. Note that these latter nodes produce unwanted traffic.

2) Frames management and slice allocation: Each LoRa frame is received by gateways, then forwarded to the network server (NS). The NS analyzes the header of each LoRa frame to determine the corresponding slice. For this, we use information already existing in the LoRaWAN messages. If the message type in the MAC header of the LoRa frame is set to 100, the traffic is confirmed. During a join procedure, which allows an end-device to be associated to a LoRaWAN, the application_id is set. We use this field to keep track of end-devices sending data to application servers that are not reachable, and thus to identify unsolicited traffic. The traffic generated by the other nodes is classified as normal. In each case, the NS uses our ADR-based slice mechanism to allocate the corresponding SF to the end-devices.

3) How to slice using SF?: We investigate two strategies to create the three slices:

• Lower SF Slicing (LSF-Slicing): We can see from Table II that SF7 is the SF being the least impacted by other SFs, whereas SF11 is the most impacted by the other SFs. For this reason, in this strategy, we use SF7 only for confirmed traffic, and SF11 only for unsolicited traffic. The remaining SFs are shared among normal enddevices, and are allocated using the usual ADR algorithm to improve the link budget. • Higher SF Slicing (HSF-Slicing): SF12 is known to provide the better coverage and the higher robustness, despite its lower data-rate. We also recall that SF7 provides the lower time-on-air value, at the cost of a very limited radio range. Moreover, SF7 leads to a lower impact on the other traffic and generates low interference. For these reasons, in this strategy, we use SF12 only for confirmed traffic, and SF7 only for unsolicited traffic. The remaining SFs are shared among normal end-devices, and are again allocated using the usual ADR algorithm. Note that normal end-devices cannot use either SF7/SF11 (LSF-Slicing case), or SF7/SF12 (HSF-Slicing case). In the event that the legacy ADR algorithm would recommend one of these forbidden SFs to a normal device, we allocate a larger SF if available, or a smaller SF otherwise. Table III: Summary of SF allocation per slice (Normal, Confirmed, Unsolicited), for the two strategies.

Strategy SF7 SF8 SF9 SF10 SF11 SF12 LSF-Slicing C N N N U N HSF-Slicing U N N N N C V. PERFORMANCE EVALUATION
We used the FLoRa module [START_REF] Slabicki | Adaptive configuration of LoRa networks for dense IoT deployments[END_REF] for the OMNeT++ simulator [START_REF]Omnet++ discrete event simulator[END_REF] to run our simulations. We deployed either 100 or 250 end-devices in a square area of 300 m×300 m. The single gateway is deployed at the center of this area, in a square of 50 m×50 m. Traffic is generated using a Poisson process, where the inter-arrival rate follows an exponential distribution of intensity 1/600 (including for the first frame). We randomly selected either 5% or 30% of the end-devices to produce confirmed traffic, and either 5% or 30% of the end-devices to produce unsolicited traffic. We used a single frequency channel with a duty-cycle of 1%, in order to focus on the impact of the SF. All the other end-devices produce normal traffic. Each simulation lasts for eight hours. Each scenario is repeated ten times, with random positions for the end-devices and gateway.

A. Slices can be isolated

Figure 1 shows the distribution of the SF as a function of time, for the baseline LoRaWAN (on the left), the LSF-Slicing strategy (on the middle) and the HSF-Slicing strategy (on the right), on a single instance of 100 nodes with 5% confirmed traffic and 5% unsolicited traffic. As expected, the baseline LoRaWAN allocates the SF independently of the type of traffic. However, both our strategies can isolate the traffic in the correct slices, as soon as the join procedure has been finished. Thus, our approach is able to isolate slices using independent resources. The allocation of the correct slice requires the end-device to receive a message from the network server. However, this requires some time, as downlink opportunities are scarce in LoRa and are heavily influenced by the duty-cycle limitation (1% in Europe). We call this delay the slicing completion delay, and we define it as the longest time required by a node to be allocated to its correct slice, once it has initiated the join procedure. We can see from Figures 1b and1c that the slicing completion delay is lower than 3 hours, which is reasonable. Note that the slice allocation does not require the network server to collect 20 frames of history, unlike the usual ADR, but only a single one.

B. The confirmed traffic is protected

Figure 2 shows the spatial distribution of the SF, for all strategies, on a single instance of 100 nodes with 5% confirmed traffic and 5% unsolicited traffic. The baseline ADR of LoRaWAN allocates SF according to the received power at the gateway, that is based on an approximation of the distance. The LSF-Slicing strategy allocates SF7 (in light green) to enddevices generating confirmed traffic (represented with stars), and SF11 (in red) to end-devices generating unsolicited traffic (represented with a hyphen). The other SFs are allocated based on an approximation of the distance. Similarly, the HSF-Slicing strategy allocates SF12 (in brown) to confirmed enddevices (with stars), and SF7 (in light green) to unsolicited end-devices (with hyphen).

Figure 3 shows the packet delivery ratio (PDR) per type of traffic and per strategy, for three configurations: 100 nodes, 5% confirmed traffic and 5% unsolicited traffic (on the left), 250 nodes, 5% confirmed traffic and 5% unsolicited traffic (on the middle), and 250 nodes, 30% confirmed traffic and 30% unsolicited traffic (on the right). The LSF-Slicing strategy achieves a very low performance for the confirmed traffic, in all configurations. Indeed, while SF7 enables high throughput, end-devices producing confirmed traffic are not necessarily close to the gateway, and thus might not be able to communicate with it using SF7. The HSF-Slicing strategy is able to improve the PDR of the confirmed traffic compared to the baseline LoRaWAN, for two reasons: it uses a robust SF for this traffic, while ensuring that the number of end-devices using this SF is not too large. The HSF-Slicing strategy also improves the PDR of the normal traffic, by completely removing the congestion due to unsolicited traffic. Finally, it yields a low PDR for unsolicited traffic, which is not an issue. Figure 4 shows the spatial distribution of the PDR for the three strategies, on a single instance of 100 nodes, 5% confirmed traffic and 5% unsolicited traffic. With the LSF-Slicing strategy, it can be seen that a low PDR is experienced by confirmed end-devices that are not located close to the gateway. With the HSF-Slicing strategy, a low PDR is experienced by normal end-devices that are far away from the gateway. Those normal end-devices would have been allocated to SF12 with the baseline ADR, but SF12 is not available for normal end-devices in HSF-Slicing.

C. Limitations of our approach

The main limitation of our approach is the identification of end-devices sending unsolicited traffic. In our case, we considered only one gateway, connected to a NS who is aware of the state of all application servers (active or not). In practice, some application servers might be reachable only through some specific NS associated to their gateways. Therefore, not all NS have knowledge about all application servers, meaning that our classification solution for unsolicited traffic might generate false positives. A way to solve this issue would be to consider a federated approach in which application designers register their end-devices with an expiration date.

VI. CONCLUSION AND PERSPECTIVES

In this paper, we proposed a lightweight slicing architecture which is fully compatible with the LoRaWAN specifications. We have shown that it is possible to exploit the ADR mechanism in order to manage and to allocate slices. We have shown that the HSF-Slicing strategy is able to perform traffic differentiation with a small convergence time, and it can improve the performance of both the confirmed traffic and the normal traffic.

In the future, we will consider end-devices joining and leaving the network at any time, as well as mobile end-devices. We will also add a slice monitor in order to dynamically adapt the size of the slices to the number of end-devices they serve.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Usage of SF as a function of time, on a single instance of 100 nodes, 5% confirmed traffic and 5% unsolicited traffic.

Figure 4 :

 4 Figure 4: Spatial distribution of PDR, on a single instance of 100 nodes, 5% confirmed traffic and 5% unsolicited traffic.

Table II :

 II Interference matrix of Spreading Factor[START_REF] Seller | LoRaWAN link layer[END_REF].

		SF7	SF8	SF9	SF10 SF11	SF12
	SF7 victim	76% 5%	1%	0%	0%	0%
	SF8 victim	17% 80%	1%	0%	0%	0%
	SF9 victim	17% 6%	80% 0%	0%	0%
	SF10 victim 18% 6%	2%	80%	0%	0%
	SF11 victim 18% 6%	2%	0%	81%	0%
	SF12 victim 19% 6%	2%	0%	0%	64%