Thomas Dursap

Mariam Fadel

Philippe Regreny

Cristina Tapia

Céline Chevalier

Hai Son Nguyen

Emmanuel Drouard

Solène Brottet

Michel Gendry

Alexandre Danescu

Alexandru Danescu

Matthieu Koepf

Vincent Artero

Matthieu Bugnet

José Penuelas

Enhanced Light Trapping in GaAs/TiO2-Based Photocathodes for Hydrogen Production

Keywords: hydrogen evolution reaction, photocathode, GaAs, nanowires, cobalt catalyst

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

Introduction

Concomitantly to the development of industrialized societies, the use of fossil fuels such as natural gas, oil, and coal as primary energy sources has drastically increased since the beginning of last century 1 leading to the release of ever-larger quantities of CO2, which are at the origin of the current global warming effect. The search for alternative strategies to provide energy at a large scale with a lower carbon-footprint has become a critical issue for the scientific community.

Among all devices with high potential of applicability, intense efforts are carried out for the development of photoelectrochemical cells (PEC). Following the basic principles of photosynthesis, these devices are able to harvest and use solar energy to drive specific chemical reactions, and convert abundant resources (H2O, CO2, N2, etc.) into energy-rich compounds (H2, CH3OH, NH3, etc.) that are stable, can be stored, and are easily distributed [START_REF] Lewis | Introduction: Solar Energy Conversion[END_REF][START_REF] Detz | The Future of Solar Fuels: When Could They Become Competitive?[END_REF] . In this context, solardriven water splitting is a major target as it involves rather simple chemical reactions and water, which is a widespread and easy-to-handle reagent [START_REF] Kim | Toward Practical Solar Hydrogen Production -an Artificial Photosynthetic Leaf-to-Farm Challenge[END_REF] . Electrochemical devices for water splitting typically combine an anode material (or combination of materials) where water is oxidized to O2 through the oxygen evolution reaction (OER), and a cathode material (or combination of materials) where protons are reduced to H2 through the hydrogen evolution reaction (HER). In the case of PEC, at least one of the two electrode materials must be able to absorb sunlight to generate the electrochemical potential required to drive the OER and HER reactions [START_REF] Walter | Solar Water Splitting Cells[END_REF] . TiO2 has been the first effective photoanode material reported for water splitting, in 1972, by A. Fujishima and K. Honda [START_REF] Fujishima | Electrochemical Photolysis of Water at a Semiconductor Electrode[END_REF] . This pioneering study triggered intense efforts by the scientific community to improve the efficiency of the solar-to-hydrogen (STH) conversion [START_REF] Ni | A Review and Recent Developments in Photocatalytic Water-Splitting Using TiO2 for Hydrogen Production[END_REF] . However, despite being a cheap and robust material for the design of photoanodes, the large bandgap of TiO2 (~3.2 eV) restricts its absorption to a very small fraction of the solar spectrum (λabs < 400 nm), strongly limiting the maximum STH efficiency reachable with devices based only on this oxide [START_REF] Sivula | Semiconducting Materials for Photoelectrochemical Energy Conversion[END_REF] . In the quest for alternative solutions, III-V semiconductors (SCs) such as GaAs emerged as advantageous material constituents of photoelectrodes due to their narrower bandgap (1.4 eV in the case of GaAs) and adequately positioned valence band, allowing for the use of a substantially larger portion of the solar spectrum to drive the OER and HER reactions [START_REF] Walter | Solar Water Splitting Cells[END_REF][START_REF] Standing | Efficient Water Reduction with Gallium Phosphide Nanowires[END_REF][START_REF] Alqahtani | Gallium Phosphide Photoanode Coated with TiO 2 and CoO X for Stable Photoelectrochemical Water Oxidation[END_REF] . Thus, III-V SCs have attracted a fair amount of attention for solar fuels applications.

Besides the choice of the SC material implemented within PEC devices, the control of the morphology of the electrodes was recently recognized as a critical factor controlling both the light absorption properties as well as the efficiency of the electrochemical reaction occurring at the interface with the electrolyte [START_REF] Wang | Crystal Facet Engineering of Photoelectrodes for Photoelectrochemical Water Splitting[END_REF] . III-V nanowires (NWs) present a high aspect-ratio improving their light-trapping efficiency and have a strong ability to be grown on low-cost silicon substrate, making them particularly attractive to design innovative photoelectrodes for light-driven water splitting [START_REF] Deng | Nanowire Photoelectrochemistry[END_REF] . While many studies have been focusing on the use of III-V NWs as photoanodes for the OER, only few reports are available on the use of these materials as photocathodes for the HER [START_REF] Kornienko | Growth and Photoelectrochemical Energy Conversion of Wurtzite Indium Phosphide Nanowire Arrays[END_REF][START_REF] Cui | Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO 2 Layer[END_REF][START_REF] Zhou | Solarto-Hydrogen Efficiency of More than 9% in Photocatalytic Water Splitting[END_REF] . A recent work by Zhou et al. indicated a significant enhancement of the STH efficiency, reaching performances close to 10% using the GaP NW combined with a complex heterostructure and bandgap engineering [START_REF] Zhou | Solarto-Hydrogen Efficiency of More than 9% in Photocatalytic Water Splitting[END_REF] . If the corrosion of these materials has limited the viability of their use in actual devices thus far, Cui et al. recently demonstrated the possibility to overcome this issue by protecting the surface of the nanowires with a thin TiO2 oxide layer [START_REF] Cui | Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO 2 Layer[END_REF] . While this latter study suggests that III-V NW-based photoelectrodes could still be used for solar fuel production, the current state-of-the-art also shows that several open questions remain to be answered. For instance, the influence of morphological parameters of the nanowires such as their aspect-ratio, and their crystallinity or verticality on the PEC performance is seldom investigated. Additionally, the influence of the doping profile of the semiconductor, such as a p-n junction, remains unexplored.

The use of non-noble metal catalysts, instead of commonly used Pt [START_REF] Cui | Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO 2 Layer[END_REF] , is also a meaningful approach for cost-reduction and use of abundant resources that should be privileged [START_REF] Segev | Solar Fuels Roadmap[END_REF] . Furthermore, the TiO2 protective layer quality could possibly introduce variations of the PEC efficiency. Therefore, it appears necessary to study these different pathways with the aim to further optimize the efficiency of PEC cathode components for HER.

In this work, the influence of the NW geometry on the photocathode efficiency is investigated by comparing the photocurrent generated by a 2D GaAs thin film, and by self-assisted GaAs NWs protected with a thin TiO2 shell, both grown on low-cost silicon (111) substrates by molecular beam epitaxy. After confirming the benefit of using a nanostructured electrode compared to flat GaAs substrate, the performances of photoelectrodes featuring different NW morphologies are systematically evaluated. For the best architecture, the benefit of including a buried p-n junction within NWs is studied. Finally a Co-based catalyst is integrated at the surface of the NWs, which leads to photocathodes exhibiting comparable photocurrent densities as Pt-decorated TiO2protected electrodes previously reported in literature [START_REF] Cui | Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO 2 Layer[END_REF] . A schematic illustrating the different steps leading to the final nanostructures is shown in Figure 1 This study demonstrates that finely tuning the NW morphology and doping profile provides efficient photocathodes for HER at neutral pH without the need of using a noble metal catalyst.

Results and discussion

Geometry

In order to evaluate the influence of the geometry of the active material on the photocurrent density generated under irradiation, a thin 2D layer of B-doped GaAs and a sample of self-assisted B-doped GaAs NWs were deposited on separate p-doped silicon substrates. Details of the growth are available in Methods. The NWs were ~2.3 ± 0.3 µm long, for a mean diameter of 125 ± 20 nm, reaching a density of around 1.1 NWs/µm². The total volume of GaAs deposited during the NWs growth hence corresponds to 3.1 x 10 -6 cm 3 for an effective surface, i. e. the surface in contact with the electrolyte, of around 1.01 cm², which is equivalent to the effective surface of the GaAs thin film counterpart.

The samples were placed in 0.1 M potassium phosphate buffer (pH 7) and linear sweep voltammograms (LSV) were recorded under chopped-light irradiation using a Xe lamp equipped with an AM 1.5 filter (simulated 1 sun irradiation, 100 mW/cm²; see Methods for details). As shown in Figure 2.a, photocurrents were observed for both NW and thin film samples, although with distinct onset potentials and current densities. The NWs led to photocurrents with an onset potential of + 0.35 V vs RHE, while a lower onset potential of + 0.25 V vs RHE was observed in the case of the thin film indicating that the NWs are able to drive more efficiently (ie with higher photopotential) reductive processes under irradiation. Moreover, the NWs delivered significantly higher photocurrent than the thin film, with almost two to three times higher current recorded over the full range of the explored potentials, although the volume of the photoactive material (GaAs) deposited is ten times higher for the thin film. Since the geometric surface area for light collection of the two samples were both equal to ~1.0 cm², it is likely that the enhancement of the photocurrent is largely associated to the improved light-trapping efficiency of the NWs compared to the thin film. Indeed, as shown in Figure 2.b, the NW geometry induces a reduction of the reflectivity down to 2%, compared to around 30% for the thin film, indicating an enhancement of the sample light absorption efficiency, and thus demonstrating the strong potential of the nanostructuration of GaAs into NWs to design improved photoelectrodes for PEC applications. The NWs grown for this comparison are shown in Figure 3.a. Besides the light trapping effect, several other factors can influence the overall efficiency of charge carrier generation and collection upon nanostructuration of the absorbing material [START_REF] Deng | Nanowire Photoelectrochemistry[END_REF] . Additional experiments that are outside the scope of this study would, however, be necessary to assess the contribution of each individual factors. In any case, our observations are in agreement with the observations performed by A. Javay's group [START_REF] Lee | P-Type InP Nanopillar Photocathodes for Efficient Solar-Driven Hydrogen Production[END_REF] or E. P. A.

M. Bakkers's group [START_REF] Standing | Efficient Water Reduction with Gallium Phosphide Nanowires[END_REF] on InP or GaP NWs, respectively, and C. Cornet's group [START_REF] Piriyev | Dual Bandgap Operation of a GaAs/Si Photoelectrode[END_REF] for GaAs/Si photoelectrodes.

Morphology

The impact of the morphology of these objects on the photocurrent density were further investigated under simulated 1 sun irradiation. Three NW samples were grown with different morphologies as illustrated in Figure 3.a-c, and described in Table 1. The different morphologies were obtained for different radial growth times (more details in Method section). Sample 1, shown in Figure 3.a, presents the largest aspect ratio (length/diameter) but the smallest effective surface 1), the total effective surface area of GaAs materials exposed to the electrolyte increases, for a comparable NW density.

The sample bearing the shortest and thinnest NWs (Figure 3.a) delivers a photocurrent of around 0.10 mA/cm² at 0.0 V vs RHE, while a value close to 0.31 mA/cm² was obtained at 0.0 V vs RHE for the thickest NWs (Figure 3.c). The improvement of the photocurrent density can be attributed to the increase of the effective surface area of GaAs material in contact with the electrolyte (Figure 3.e), and to enhanced light trapping efficiency by the largest NWs as shown in Figure 3.f. The reflected light in the 350-850 nm spectral range drops from about 2% for the shortest and thinnest NWs (blue lines) to about 0.5% for the thickest ones (green lines). While the reflectance of the different samples was experimentally measured, it was not possible to obtain information on the absorptance of those samples. Finite difference time domain (FDTD) simulations were used to further investigate on the optical properties of our samples, using ANSYS Lumerical FDTD solutions.

The simulated reflectance thus confirms the tendencies observed in the experiment (see Supporting Information for details). Simulations also show the contribution of the GaAs material and of the Si substrate on the light absorptance (see Supporting Information for details), confirming the optimal sample morphology.

p-n junction

In order to drive the targeted chemical reactions at the solid-liquid interface, efficient charge migration must occur from the core of the NWs to its surface. This phenomenon requires the establishment of an adequate gradient of potential between the bulk SC material, here GaAs, and the electrolyte. Ensuring that electrons reach the edge of the GaAs NWs should improve the overall efficiency of the photocathodes. For this purpose, an n-doped shell was grown using Si as dopant around the p-doped core NW, as illustrated in Figure 4.a, aiming for the same dimensions as the NWs of sample 3. The implementation of such p-n junction leads to band edge bending at the interface between the p-core and n-shell, as shown in Figure 4.b, which should favor the separation of the electron-hole pairs and reduce their recombination rate within the NWs. On the bare NWs (sample 3), the LSV measurements performed on samples featuring the buried p-n junction reveal an increase of the photocurrent density of about 30%, up to around 0.4 mA/cm² at 0 V vs RHE, compared to 0.3 mA/cm² for the monolithic p-doped NWs, as shown in Figure 4.c. Furthermore, another significant enhancement of the current density delivered by the samples featuring the NWs containing the p-n junction is observed after the integration of Co-based nanoparticles on the electrodes, a well-known catalyst for the hydrogen evolution reaction (HER) [START_REF] Cobo | A Janus Cobalt-Based Catalytic Material for Electro-Splitting of Water[END_REF] . Indeed, after the photoassisted deposition of the nanoparticles (see the Methods for the experimental details), the current density reaches up to 1.15 mA/cm² at 0 V vs RHE in neutral electrolyte (Figure 5.b and c).

This contrasts with the samples based on monolithic p-doped NWs, which only deliver 0.38 mA/cm² (Figure 5.a and c) even after Co deposition, under the same conditions. Noteworthy, the Co-decorated NW samples featuring the buried p-n junction appear to outperform the recently reported monolithic GaAs NW photocathodes decorated with Pt nanoparticles tested under acidic conditions (pH 1, simulated 1 sun irradiation, 0.87 mA/cm 2 at 0 V vs RHE) [START_REF] Cui | Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO 2 Layer[END_REF] . These results demonstrate that the careful tuning of the NW dimensions and structure, combined with non-noblemetal catalysts can provide viable alternatives for the design of efficient photocathodes for HER in neutral electrolyte.

Enhancement of the stability through TiO2 protection

Despite their promising performances for the photoelectrocatalytic water splitting, III-V SCs suffer from degradation through photocorrosion when placed in aqueous electrolytes. To overcome this issue, an amorphous TiO2 protective layer of about 5-7 nm thickness was deposited by atomic layer deposition (ALD) on the p-core / n-shell NWs. This deposition technique was chosen to ensure a conformal deposition [START_REF] Chandrasekaran | A Robust ALD-Protected Silicon-Based Hybrid Photoelectrode for Hydrogen Evolution under Aqueous Conditions[END_REF][START_REF] Gu | Water Reduction by a P-GaInP2 Photoelectrode Stabilized by an Amorphous TiO2 Coating and a Molecular Cobalt Catalyst[END_REF][START_REF] Hu | Amorphous TiO 2 Coatings Stabilize Si, GaAs, and GaP Photoanodes for Efficient Water Oxidation[END_REF][START_REF] Moehl | Investigation of (Leaky) ALD TiO 2 Protection Layers for Water-Splitting Photoelectrodes[END_REF] . Scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy (EELS) were performed on the cross section of a typical NW, as shown in Figure 6. The TiO2 protective shell is visible in Figure 6.e, which is a higher magnification of the Z-contrast high-angle annular dark field (HAADF) image in Figure 6.a. Ga and Ti EELS elemental maps shown in Figure 6.b (Ga core), Figure 6.c (Ti shell) and Figure 6.d (Ga core + Ti shell) confirm the core-shell geometry of the NWs. Figure 6.e-h, provide a closer view of the 5-7 nm thick protective TiO2 shell.

LSV measurements performed on p-core/n-shell sample, with and without the protection of the TiO2 layer and after deposition of the Co nanoparticles are shown in Figure 7.a. Despite the slight decrease of the current density from about 1.15 mA/cm² down to 0.8 mA/cm² at 0 V vs RHE, which is attributed to the induced resistivity of the oxide layer covering the NWs, the stability of the measured photocurrent over time was enhanced with TiO2. Indeed, Figure 7.b shows the chronoamperometric (CA) measurements recorded under simulated 1 sun illumination over 13 h.

During the first 30 minutes, the sample without the protection layer (purple line) delivered a higher current density compared to the sample with a protective layer (dark blue line). However, after less than one hour, the photocurrent of the sample without TiO2 shell dropped and stabilized at around 0.06 mA/cm². Meanwhile, the sample with the protective shell stabilized after few hours at around 0.15 mA/cm². These results show that the addition of this protective oxide shell induces a significant enhancement of the stability of the photocathode over time likely due to the reduced photocorrosion of the GaAs NWs. It is worth noting that the transient photocurrents recorded at the start of the chronoamperometry is a common feature observed for photoelectrodes [START_REF] Dumortier | Combined Experimental-Numerical Analysis of Transient Phenomena in a Photoelectrochemical Water Splitting Cell[END_REF][START_REF] Laskowski | Transient Photocurrents on Catalyst-Modified N-Si Photoelectrodes: Insight from Dual-Working Electrode Photoelectrochemistry[END_REF][START_REF] Formal | The Transient Photocurrent and Photovoltage Behavior of a Hematite Photoanode under Working Conditions and the Influence of Surface Treatments[END_REF] . The rapid rise and decay of photocurrent when irradiation is switched on, is mostly representative of non-faradaic processes, like charge accumulation, occurring mostly at the liquid-semiconductor and TiO2-GaAs interfaces . The distinct interfaces of the samples w/o TiO2 shell with the electrolyte explain the various transient photocurrents measured.

Importantly, a substantial increase of the Faradaic efficiency (FE) measured for H2 production is observed for the samples bearing the TiO2 protective layer, with a yield reaching 59% when unprotected samples yield about 27% FE only. These measurements are limited by the use of single compartmented cell that was necessary to accommodate the samples, allowing for the diffusion of traces of O2 produced at the anode to the photocathode where O2 is reduced competitively against protons. The FE values reported here, are thus likely underestimated.

After the reaction, a cross section lamella was extracted from the electrode and characterized using STEM-EELS, as shown in Figure 8.a-d. As expected the TiO2 shell successfully protected the GaAs core, and remained intact with no distinct degradation, confirming the efficiency of the protection. The improved FE observed for TiO2-coated samples is, thus, attributed to the reduced photocorrosion of the GaAs semiconductor otherwise leading to an evolution of the chemical composition of the NWs surface likely promoting competitive catalytic or non-catalytic side reactions (O2 reduction, electrochemical surface etching…).

Conclusions

In this study, the influence of the geometry of GaAs semiconductor as photocathodes for the water reduction was investigated. A strong improvement of the cell efficiency was demonstrated for the 1-dimensional NW geometry, which is mainly due to a significant reduction of light reflected by the electrode. The photoelectrochemical activity was strongly enhanced using the combination of a p-n junction buried within the GaAs NW and of the deposition of Co nanoparticles acting as catalysts for the HER. Furthermore, the stability of the NW-based photocathode over time was significantly improved with the deposition of a thin TiO2 protective layer, leading to an increase of the efficiency of the cells by a factor 3 after 13 hours. The fabricated photocathodes display photocurrent density up to 1.15 mA/cm² at 0 V vs RHE under neutral conditions. Such performance is at the state of the art for similar GaAs photoelectrodes but would lead to tandem photoelectrochemical cells with solar-to-hydrogen performances below 1%. This calls for future tenfold improvement of the performances of such scalable materials to allow for a viable solar hydrogen technology.

Methods

All the GaAs NWs samples were grown on 2 inches epi-ready p++ Si(111) substrates (Sil'tronix silicon technologies) using a solid-source molecular beam epitaxy (MBE) reactor. The SiO2 native oxide on the Si substrates was preserved in order to enable the self-assisted growth [START_REF] Fontcuberta I Morral | Prismatic Quantum Heterostructures Synthesized on Molecular-Beam Epitaxy GaAs Nanowires[END_REF] . The wafers were cleaned in acetone and ethanol during 10 minutes in ultrasonic bath, and were degassed at 200°C under ultra-high vacuum before their introduction inside the MBE chamber. The thin film was synthetized for 20 minutes at a growth rate of about 0.25 nm/s, leading to a thickness of around 300 nm on a 1 x 1 cm² Si(111) substrate, leading to a total GaAs volume of around 3.0  10 -5 cm 3 , and an effective surface of 1 cm². For the self-assisted GaAs NWs, the substrate was heated to 440°C and 1 monolayer (ML) [START_REF] Küpers | Ga Predeposition for the Ga-Assisted Growth of GaAs Nanowire Ensembles with Low Number Density and Homogeneous Length[END_REF][START_REF] Fouquat | X-Ray Photoelectron Spectroscopy Study of Ga Nanodroplet on Silica-Terminated Silicon Surface for Nanowire Growth[END_REF] of Ga was pre-deposited to form the Ga catalyst droplet, at a deposition rate of 0.08 ML/s, measured in units of equivalent growth rates of GaAs 2D layers by RHEED oscillations on a GaAs substrate [START_REF] Rudolph | Direct Observation of a Noncatalytic Growth Regime for GaAs Nanowires[END_REF] . The sample was then heated to 570°C and the Ga and As4 fluxes were opened simultaneously to initiate the growth of the self-assisted GaAs NWs. A precise control of the incoming fluxes, valves and shutters was operated via a homemade software.

The GaAs NWs were grown with a Ga and As4 flux of 0.5 ML/s and 1.2 ML/s, respectively, corresponding to a V/III flux ratio of 2.4. The NWs were doped with Be, ensuring the p-doping of the objects. In order to investigate the influence of the NW morphology on the PEC efficiency, the GaAs NWs were radially grown during 1 and 2 hours with a V/III flux ratio of 2.4, at a thermocouple temperature of 400°C. The p-n junction was elaborated with a 1 hour growth of pdoped (Be) core, followed by the growth of the n-doped (Si) shell during 1 hour. All the growths were monitored by RHEED at 20 keV, to obtain information on the crystal structure evolution of the NWs. The samples were rotating all along the growth to ensure the homogeneity of the NWs.

Each sample was imaged and characterized with a JEOL SEM using a 10 kV acceleration voltage.

The effective surface of the samples was determined as follows: The surface of a single NW was calculated with an average length of 2.35 µm (2.35  10 -4 cm) and an average diameter of 125 nm (1.25  10 -5 cm) giving SNW = 9.22  10 -9 cm². The NW density (1.1 NW/µm² = 1.1  10 8 NW/cm²) was finally used to calculate the total effective area Seff = 1.01 cm².

The reflectivity measurements were performed with a home-built reflectivity setup. The excitation is a white light (Halogen lamp) that is collimated and focused onto a 200 µm-diameter spot on the sample. The reflected light is collimated and is either i) directly imaged by a CMOS camera for a quick visualization (inset in Figure 3.e), or is ii) projected onto the entrance slit of the spectrometer then collected by a CCD camera at the output of the spectrometer to obtain the reflectivity spectra. The TiO2 protective shell was deposited by PE-ALD at 250°C and around 200 mTorr using tetrakis(dimethylamido)titanium (TDMAT) as metal precursor and gaseous O2 as oxygen precursor.

The degradation of the NWs after the PEC and the homogeneity of the TiO2 shell deposition were investigated in high-resolution by HAADF-STEM. The chemical study of the GaAs/TiO2 interface was highlighted by elemental maps using EELS, in STEM mode. The HAADF-STEM and STEM-EELS analyses were performed in a JEOL JEM-ARM200F NeoARM, equipped with a cold-FEG, a last generation aberration-corrector (CEOS ASCOR) of the probe-forming lenses, a Gatan GIF Quantum, and operated at 200 kV. The cross-section lamellae were extracted from the electrode using focused ion beam (FIB) lift-out. STEM-EELS datasets were acquired using the spectrum imaging technique implemented in Gatan microscopy suite. Elemental maps of 158  242 pixels were obtained from the Ga-L23 and Ti-L23 edges, with an exposure times of 5.0 ms and a dispersion of 1.0 eV/channel. The noise level was minimized by post-processing using principal component analysis implemented in Gatan microscopy suite [START_REF] Lucas | Multivariate Statistical Analysis as a Tool for the Segmentation of 3D Spectral Data[END_REF] .

All chemicals used for the (photo)electrochemical measurements were obtained from Merck and used as received. The measurements were conducted in the three-electrode configuration using a BioLogic SP300 potentiostat. They were run in a single compartment gas-tight cell in N2-purged 0.1 M potassium phosphate buffer (pH 7). GaAs samples were used as working electrodes (WE) and mounted on FTO-coated glass (3.1 mm thickness; 7 Ω/cm -2 , SOLEMS S.A.) using silver paste to ensure the electrical contact between the back of the GaAs samples and the FTO coating. An Oring was used to define the active surface area of the electrode material in contact with the electrolyte (geometrical surface area of 0.384 cm²). A titanium wire was used as counter electrode (CE) and a home-made Ag/AgCl (3 M KCl) electrode as reference electrode (RE). The [Fe(CN)6] 3- /[Fe(CN)6] 4-couple (E = 0.425 V vs NHE in 0.1 M potassium phosphate buffer at pH = 7) was used to standardize the measurements in aqueous solution [START_REF] O'reilly | Oxidation-Reduction Potential of the Ferro-Ferricyanide System in Buffer Solutions[END_REF] . Potentials are referred to RHE by using the equation ERHE = EAg/AgCl + (E 0 Ag/AgCl + 0.059 х pH) where ERHE is the reported potential vs. RHE, EAg/AgCl the potential measured vs the Ag/AgCl RE, and E 0 Ag/AgCl the standard potential of the RE (0.223 V vs NHE) 35 . Co nanoparticles were deposited form a solution of 0.5 mM Co(NO3)2.6H2O in 0.5 M phosphate buffer at pH 7, by applying a potential of -0.65 V vs Ag/AgCl (-0.01 V vs RHE), for 45 minutes under 1 sun simulated irradiation (see below).

Linear sweep voltammetry (LSV) measurements were performed at 10 mV/s scan rate under chopped light irradiation. Photoelectrocatalytic experiments were performed under continuous light irradiation, by running chronoamperometry (CA) measurement at an applied potential of -0.8 V vs Ag/AgCl (-0.18V vs RHE). During the LSV and CA measurements the samples were frontilluminated using a class 3A solar simulator (Newport) equipped with an ozone-free 300 W Xe Arc lamp operated at 280 W and an AM 1.5 G filter. A calibrated reference cell (model 91150V,

  area. Samples 2 and 3, corresponding to NWs after 1 hour and 2 hours of radial p-doped GaAs growth, respectively, are shown in Figure 3.b and Figure 3.c. By increasing the length and the diameter of the NWs (values reported in Table

FiguresFigure 1 .

 1 Figures

Figure 2 .

 2 Figure 2. (a) Linear sweep voltammograms (10 mV/s) recorded in nitrogen-saturated 0.1 M

Figure 3 .

 3 Figure 3. (a) -(c) SEM images of the samples showing the different morphologies obtained for

Figure 4 .

 4 Figure 4. (a) Schematic representation of the core-shell p-n (B-doped GaAs/ Si-doped GaAs) NW

Figure 5 .

 5 Figure 5. (a)-(c) LSV measurements performed at 10 mV/s in 0.1 M potassium phosphate buffer

Figure 6 .

 6 Figure 6. (a) and (e) STEM-HAADF image of the cross-section of a core/shell GaAs NW with a

Figure 7 .

 7 Figure 7. (a) LSV measurements performed at 10 mV/s in 0.1 M potassium phosphate buffer (pH

Figure 8 .

 8 Figure 8. (a) STEM-HAADF image of the cross-section of a GaAs NW with a TiO2 protective

Table 1 .

 1 Measured length and diameter for the samples shown in Error

! Reference source not found..a-c, here

  named as sample 1-3, respectively.

		Sample 1	Sample 2	Sample 3
	Length (µm)	2.3 ± 0.3	5.2 ± 0.4	9.4 ± 0.3
	Diameter (nm)	125 ± 20	380 ± 20	740 ± 0.4
	Density (NWs/µm²)	1.1	1.0	0.9
	Aspect ratio (L/D)	18.4	13.7	12.7
	Surface area (cm 2 )	1.0	7.2	17.8

Newport) was used to adjust the irradiance at the surface of the sample to 100 mW/cm 2 (simulated 1 sun irradiation). The H2 accumulated in the headspace during the experiments was quantified by gas chromatography (GC) on a Clarus ® 580 gas-chromatograph (PerkinElmer) by direct injection of 50 μL of a sample of the headspace gas, at the end of the CA. A calibration curve was previously established using N2-H2 mixtures of known ratio. Before the photoelectrocatalytic measurement, the electrolyte was thoroughly purged with nitrogen gas, until no traces of oxygen could be detected by GC measurements (ca 20 mins). The % Faradaic efficiency was calculated according to the equation: F(%) = [(mols of H2  2F)/Q]100, where F is the Faraday constant (96485 Cmol -1 ) and Q is the charged passed during the experiment.

Acknowledgments 23

The authors acknowledge access to equipments at the NanoLyon platform and thank J. B. Goure for technical assistance. The authors acknowledge funding from the French Agence Nationale de la Recherche (ANR) for project BEEP 18-CE05-0017-01, Labex ARCANE and CBH-EUR-GS (ANR-17-EURE-0003). The (S)TEM work was performed at the consortium Lyon-St-Etienne de microscopie. The authors are grateful to Y. Lefkir and S. Reynaud for technical assistance using the Jeol NeoARM instrument.

Associated content

Supporting information available: Details of the FDTD simulation of the optical properties of the different samples studied, including Figures S1-S3. (DOCX)

Author information

Corresponding Author *Address correspondence to matthieu.koepf@cea.fr (MK), matthieu.bugnet@insa-lyon.fr (MB), jose.penuelas@ec-lyon.fr (JP)


Jian Tan

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Introduction

Over the past few decades, the prevalence of non-communicable diseases in Western societies has risen dramatically. Environmental exposures, specifically the consumption of a diet low in dietary fiber is accepted as a significant risk factor. Low dietary fiber intake is associated with increased risk of all-cause mortality in humans,1,2 while preclinical studies have demonstrated that high dietary fiber intake protected against colitis, asthma, food allergies, arthritis, experimental autoimmune encephalitis, colorectal cancer, type 1 diabetes, and influenza infection.3-10 . Consequently, the potential of fiber supplements to be utilized for disease prevention and/or intervention is an attractive strategy.11 Dietary fiber is a loosely defined term that refers to complex carbohydrates that are resistant to host digestion. Dietary fiber is utilized by gut bacteria as an energy source, and as prebiotics, they promote the growth of beneficial bacteria such as Bifidobacteria and Lactobacilli. The fermentation of dietary fiber by gut bacteria leads to the release of metabolites as by-products, particularly the short-chain fatty acids (SCFA) acetate, propionate, and butyrate.12 Butyrate is the major source of energy for colonocytes and maintains an anaerobic environment optimal for the survival of beneficial bacteria in the lumen. [START_REF] Litvak | Colonocyte metabolism shapes the gut microbiota[END_REF] Butyrate and propionate are mostly metabolized in the liver, whereas acetate reaches the circulation and can exert systemic effects.12 There is considerable evidence from preclinical and clinical studies that SCFA support many of the health benefits of dietary fiber on the host. For example, daily treatment with propionate restored immune balance in multiple sclerosis patients and reduced their clinical symptoms,14 and acetate and butyrate have been shown to lower blood pressure in hypertensive patients. [START_REF] Jama | Gut microbial metabolites lower blood pressure in patients with hypertension[END_REF] Furthermore, detrimental changes in gut microbiota composition, or dysbiosis, have been reported in most non-communicable diseases. Although direct causality between dysbiosis and disease is yet to be established, preclinical models have confirmed that changes in gut microbiota composition can contribute to the development of these diseases. Mice recolonized with dysbiotic microbiota isolated from patients' stool develop exacerbated disease in models of allergies, autoimmunity, and during cancer therapy16-18. Thus, restoring gut microbiota composition and function, particularly by supporting optimal SCFA production through dietary fiber consumption, offers an attractive therapeutic approach.

There are conflicting results from clinical trials testing the impact of the dietary fiber supplement inulin and/or fructo-oligosaccharide on disease with either improvement or no effects. [START_REF] Mcloughlin | Soluble fibre supplementation with and without a probiotic in adults with asthma: a 7-day randomised, double blind, three way cross-over trial[END_REF][START_REF] Visuthranukul | Effects of inulin supplementation on body composition and metabolic outcomes in children with obesity[END_REF][START_REF] Kok | Predicting personalized responses to dietary fiber interventions: opportunities for modulation of the gut microbiome to improve health[END_REF][START_REF] Mcburney | Establishing what constitutes a healthy human gut microbiome: state of the science, regulatory considerations, and future directions[END_REF][START_REF] Gill | Dietary fibre in gastrointestinal health and disease[END_REF] The reasons why certain individuals do not respond to inulin are unknown but is likely due to differences in their gut microbiota composition.24-26 Dietary fiber is composed of structurally diverse classes of polysaccharides, varying in their monosaccharide composition and chemical bonds27. As such, the repertoire of enzymes needed to break down these polysaccharides, such as carbohydrate-active enzymes, can differ widely between types of fiber. These carbohydrate-degrading enzymes are broadly dispersed across microbial strains. [START_REF] El Kaoutari | The abundance and variety of carbohydrate-active enzymes in the human gut microbiota[END_REF]. Hence, gut microbiota comprising distinct collections of strains will exhibit different responses to a given type of fiber due to differences in the enzymes they collectively possess. This was highlighted in a study by Kovatcheva-Datchary et al., which showed that the consumption of bread enriched in dietary fiber improved postprandial glucose metabolism only in participants with a microbiota characterized by an elevated Prevotella/Bacteroides ratio.29 A landmark study demonstrated that individuals' specific postprandial blood glucose responses to varying foods could be predicted by their preexisting gut microbial communities.30 These studies raise the possibility of personalized nutrition, such as offering individuals tailored dietary fiber interventions to yield maximal SCFA production.

In this study, we examined individual responses to three different dietary fiber supplements and tested the hypothesis that an individual's SCFA profile in response to a given dietary fiber can be predicted based on their gut microbiota composition. This was achieved through a cross-over intervention study to test the response of given individuals to the three different fiber supplements. We showed that although the gut microbiota was largely unchanged, there were supplement-specific effect between longitudinal samples of individuals on specific bacterial taxa, SCFA production, and the immune system. We also identified taxa that may be predictive of an individual's likelihood of responding to inulin supplementation; however, a larger-scale study is required to confirm our findings.

Results

Impact of different fibre supplements on clinical characteristics in healthy women

Thirty-four healthy females were recruited for a crossover study, with all participants asked to intake three different fiber supplements for a period of 1 week per supplement, with a 2-week washout period between supplements (Figure 1). The three commercially available fiber supplement products used were Inulin (100% fructo-oligosaccharide), Vitafiber (100% Isomalto-oligosaccharide; IMO), and Fibremax (a mix of soluble and insoluble fiber consisting of chicory root extract, psyllium husk, soy fiber, oat bran and pectin). We chose structurally distinct dietary fibers: Inulin is a polymer of fructose with β-(2,1) glycosidic bonds and Vitafiber a polymer of glucose with α-D-(1,6)-linkages and Fibremax which contains a diverse mix of dietary fiber. These dietary fibers are commonly used in the food industry and as supplements,31 and inulin (including FOS) are approved by FDA as a dietary fiber32 and have been used in clinical trials to tests its effect on various conditions such as obesity and asthma. [START_REF] Mcloughlin | Soluble fibre supplementation with and without a probiotic in adults with asthma: a 7-day randomised, double blind, three way cross-over trial[END_REF][START_REF] Hiel | Link between gut microbiota and health outcomes in inulin -treated obese patients: lessons from the Food4Gut multicenter randomized placebo-controlled trial[END_REF] Of those enrolled, 28 participants completed the study. The median age at screening interview was 37 (IQR 15.25) years and BMI was 22.1 (IQR 4.1) kg/m 2 (Table 1). Reasons for withdrawing from the study included pregnancy (n = 1), intolerance to supplements (n = 2), inability to attend clinical assessments due to injury (n = 1), or lack of time (n = 2). Overall compliance with fiber supplementation was 93.1% for Fibremax, 96% for Inulin, and 95.6% for Vitafiber (calculated as the number of doses reported/total number of doses prescribed for each supplement). Self-reported symptoms in response to each supplement (bloating or gas) during the 7-day of intervention was 54.5% for Fibremax, 48% for Inulin and 25% for Vitafiber. There was one report of diarrhea for both Fibremax and Inulin but none for Vitafiber. Baseline and intervention dietary fiber (excluding supplements), energy, protein, fat, and carbohydrate intakes were similar across all groups (Table S1).

Body weight, waist and hip circumference, waist-to-hip ratio, and systolic and diastolic blood pressure were measured before and after each dietary fiber intervention (Table 2). There was no major impact on all measured clinical characteristics, although waist circumference was significantly increased with the supplement Fibremax (median +1.0 cm [IQR -0.5 to 2.13], p =.014) and decreased with the supplement Vitafiber (median -0.5 cm [IQR -2.13 to 1.5], p =.024). After Inulin supplementation, we observed an increase in hip circumference (median +1.0 cm [IQR -1 to 2], p =.012) while Vitafiber supplementation increased diastolic blood pressure (median +2.5 mmHg [IQR -0.63 to 6.5], p =.017).

Gut microbiota alteration in response to fibre supplements is constrained by individual characteristics

We assessed whether the 1-week intervention with the three fiber supplements had a discernible impact on gut microbial communities. To do this, we performed 16S rRNA sequencing from pre-and postintervention stool samples from participants in which stool samples were collected from all time points (n = 24 out of 28 and baseline characteristics from this cohort is presented in Table 1). Sequencing output generated sufficient reads from each sample to capture all amplicon sequence variants (ASV) (Fig. S1A), and no significant differences in reads per sample between fiber supplements, either pre-or postintervention, were observed (Fig. S1B).

A global analysis of all samples by non-supervised clustering of principal component analysis (PCA) of Aitchison's distance revealed that the principal driver of microbiota composition variation was the individual participant (Figure 2a). No evidence of a generic response to fiber was found, with no significant clustering of post-supplement gut microbiota composition (Figure 2b). Similarly, there was no significant clustering between pre-and post-supplementation microbiota for any of the three supplements (Fig. S1C). Altogether, one week of dietary fiber supplementation, regardless of the type of supplement, did not alter the overall gut microbiota composition to become significantly more similar.

Fibre supplementation induced signature changes to the gut microbiota composition

While none of the supplements altered the overall gut microbiota composition as measured using nonsupervised clustering by PCA of Aitchison's distance, we next investigated whether the consumption of fiber supplements led to more specific changes in the gut microbiota. Consistent changes to the relative abundance of taxa at the phylum level were observed in response to a specific fiber supplementation, regardless of the individuals' pre-supplement microbiome (Figure 2c). Notably, Actinobacteria were significantly increased following Inulin (23/24 patients, p=<0.0001) and Fibremax (18/24 patients, p = .0017) supplementation (Figure 2d). In contrast, Bacteroidetes level was significantly increased by Fibremax (17/24 patients, p = 0.0172) and decreased by Inulin (15/24 patients, p = .0112) (Figure 2e), and Firmicutes decreased following Fibremax intervention (16/24 patients, p = .0286) (Figure 2f). Euryarchaeota, Verrucomicrobia and Proteobacteria were not affected by any supplement (Fig. S2A), and Vitafiber supplementation induced no changes to any phyla (Figure 2d-f, Fig. S2A). A summary of these changes is provided in Table 3. At the genus level (Figure 2g), differential abundance analysis of the top 30 genera (encapsulating 95% of total taxa) between pre-and post-supplement microbiota revealed that only Inulin induced a signature change, with a significant enrichment of Bifidobacterium (effect size = 1.47, p = .0002) and Anaerostipes (effect size = 1.25, p = 0.0017) after one week of supplementation (Figure 2h). When comparing post-supplement microbiota, Inulin had a significant enrichment in Bifidobacterium (effect size = 1.12, p = 0.0045) and Anaerostipes (effect size = 0.75, p =.037) over Vitafiber, but not over Fibremax.

We then next asked whether each intervention induced a specific bacterial taxonomic signature at the ASV level, using supervised machine learning (Sparse partial least squares discriminant analysis; sPLS-DA) to build models classifying samples in accordance with post-intervention microbiota. This approach considers the overall complexity of the gut microbiota, rather than by comparing individual taxa. Postsupplementation samples could be discriminated against each other with an accuracy of 77.8% (Fig. S2B) (p =.02). Within the model, an Inulin supplemented microbiota had the most distinguishing features, with ASVs assigned as Anaerostipes hadrus, Holdemanella spp., and Mogibacterium spp. having the greatest contribution to the separation of the different post-supplementation microbiota (Fig. S2C and Table S2). In contrast, ASVs corresponding to Blautia, Clostridium IV species, and others were associated with Fibremax, while only Blautia faecis was marginally predictive of a Vitafiber supplemented microbiome (Fig. S2C and Table S2).

Together, we show that the composition of the gut microbiota can be selectively influenced by just one week of fiber supplementation. This effect was fiber specific with a significant increase in Bifidobacteria, a group of bacteria known for their ability to utilize dietary fiber, following the consumption of Inulin and Fibremax in the majority of participants. Fiber supplement-induced changes were substantial enough that post-supplement signatures could be reliably differentiated using a machine learning approach.

The effect of fibre supplementation on gut microbiota composition can be effectively eliminated with a two-week washout period.

To confirm whether a two-week washout period was sufficient, we applied supervised machine learning to build a model that classified post-washout (pre-supplement) samples in accordance with the preceding supplement. Pre-supplement samples from the first batch were excluded because there was no preceding intervention (Figure 3a). The resultant model correctly classified only 43.7% (vs. 33% for random guessing) of samples based on the preceding supplement (Figure 3b), which was not statistically significant (p =.22), indicating that preceding supplements did not induce any distinguishing changes in microbiomes that persisted past the washout period.

Thus, the 2 week-long washout period between the 1-week supplement interventions is sufficient for fiber supplement-induced effects on the gut microbiome to be lost. It also suggests that dietary fiber supplementation induced short-term and reversible alterations to gut microbiota.

Inulin and fibremax but not vitafiber supplementation reduced gut microbiota diversity.

Different fiber supplements had differential effects on gut microbial community alpha diversity metrics. When comparing post-supplement microbiota, we found that inulin induced a significant decrease in bacterial richness (Observed ASV), Shannon's index, and the Inverse Simpson's index compared to other interventions (Figure 4a-c). Importantly, no such differences were evident in pre-intervention samples (Fig. S3A), confirming that a 2-week washout period was sufficient to normalize the gut microbiota of each participant. When comparing changes induced by specific fiber interventions, we found that Inulin had the most consistent effect across individuals on alpha diversity measures, with approximately 75% of the participants showing a decrease in observed ASV (Figure 4d), while Fibremax had an intermediate impact and Vitafiber had no impact. Overall, Inulin significantly decreased post-intervention bacterial richness (Observed ASV), Shannon's and Inverse Simpson's diversity indices relative to pre-intervention (Figure 4d and Fig. S3B). Fibremax decreased Shannon's and Inverse Simpson's diversity indices relative to pre-intervention values (Figure 4d and Fig. S3C), while Vitafiber had no impact on gut microbiota diversity across all measures (Figure 4d. and Fig. S3D).

The alpha diversity results support our previous observations that, while Inulin and Fibremax affected the gut microbiota, Vitafiber had little or no effect. To explore this, we used supervised machine learning to build a model to classify post-intervention microbiota (as done in Fig. S2B), but with the inclusion of baseline samples. In this model, Inulin and Fibremax could be differentiated with high accuracy, whereas the model could not distinguish between baseline samples and Vitafiber-supplemented gut microbiota (Fig. S3E). The inability of the sPLS-DA model in finding features that could differentiate between postsupplement Vitafiber and baseline microbiome suggests that Vitafiber supplementation does not have a prominent impact on gut microbiota composition.

Overall, both Inulin and Fibremax supplementation reduced gut microbiota alpha diversity, with inulin having the most significant effect. This is consistent with our previous analysis showing that Inulin has the greatest impact on the gut microbiota (Figure 2c-h).

Short-chain fatty acid production in response to dietary fibre is individual and fibrespecific.

We next investigated how the different fiber supplements altered stool SCFA profiles, a proxy measure of the effectiveness of microbe-stimulation by fiber supplements, in study participants. When comparing the change in stool SCFA levels between pre-and postintervention samples, there was no significant difference between any of the supplements (Fig. S4A). Consistent with these findings, post-intervention stool SCFA levels were similar across the three fiber interventions (Fig. S4B). These data indicate that, on average, there was no increase or decrease in specific SCFAs when comparing the entire study population. However, we found variation around zero change, consistent with fiber supplementation having personalized effects. To test this explicitly, we normalized SCFA changes (acetate, butyrate, and propionate) to lie on similar ranges of values, and then performed a nonparametric analysis of variance to test whether changes to SCFA were due to the participant irrespective of SCFA type or supplement; the supplement irrespective of SCFA type or participant; the interaction between participant and SCFA type or supplement; or the interaction between SCFA type and supplement. We found a statistically significant effect by participants (R2 = 0.23, p =.001), indicating that participants were differentially predisposed to SCFA level changes under fiber supplementation (Figure 5a). Furthermore, we found a significant effect of the interaction between participant and supplement type, meaning that changes in SCFA in individuals are supplement specific (R2 = 0.61, p =.001). We found no evidence of: 1) specific supplements affecting overall SCFA changes (irrespective of SCFA type or specific participant), 2) specific supplements on specific SCFA types, or 3) participants being differentially predisposed to changes in a specific SCFA type.

Plasma acetate, the prominent SCFA in blood, exhibited similar patterns to stool SCFA, with no significant change in plasma acetate levels between pre-and post-intervention samples (Fig. S4C), as well as when comparing post-intervention samples (Fig. S4D). There was a significant effect of the interaction between participant and supplement type (R2 = 0.228, p =.046), indicating that changes in plasma SCFA in individuals are also supplement specific (Fig. S4E).

Collectively, our data suggest that changes in SCFA levels in response to fiber supplementation depend on both the individual and the type of fiber supplement.

Specific microbial taxa are associated with SCFA production in response to inulin supplementation

It is evident that the response to dietary fiber (related to SCFA) depends on the composition of the gut microbiota and the type of fiber supplement consumed. Thus, we next asked whether an individual's unique gut microbiota composition would dictate their response to different fiber supplements. To do this, we first stratified individuals as either responders (increase in total stool SCFA concentration) or non-responders (no change or decrease in total stool SCFA concentration) for each supplement. Next, we performed supervised sPLS-DA analysis on pre-supplement microbiota to identify features (taxa) associated with either responders or non-responders to each fiber supplement. Using this approach, a satisfactory model for Fibremax and Vitafiber could not be produced, resulting in over-fitted models that included most of the taxa present and had poor predictive accuracy (Fig. S5A and Fig. S5B). However, Inulin could produce a satisfactory model that could predict whether an individual would respond to Inulin supplementation with 75.7% accuracy (Figure 5b), although this was not statistically significant (p =.12). Within this model, the butyrate producer Alistipes marseilloanorexicus was predictive of responders, whereas non-responders were characterized by lower Senegalimassilia anaerobia and Oscillibacter spp (Figure 5b and Table 4). No satisfactory models could be produced for any of the supplement when stratifying responders and non-responders based on plasma acetate levels (data not shown).

To examine if incorporating predicted metabolic functions of the gut microbiota could improve our classification approach, we utilized PICRUSt2, 34 a methodology that allows for predicting gene or pathway abundances based on 16S data. Predicted pathway abundances did not perform better compared to 16S data in classifying responders from non-responders for all three supplements (Fig. S5C). Of note, while an optimally tuned model for Inulin had comparable classification accuracy compared to 16S data, the model is likely not meaningful due to overfitting (i.e., component 1 of the model utilizes 305 pathways out of the included 394 pathways). As both 16S and predicted functional pathway abundance data indicate that it may be possible to predict responders to Inulin, we then looked at whether responders to Inulin had a different predicted baseline pathway abundances to non-responders. To do this, we performed differential abundance analysis, however, we did not find any significant pathways (after adjustment for multiple comparison) that was different between responders and non-responders (top 20 most significant pathways shown in Table S3).

Our results indicate that predicting an individual's response to specific dietary fiber based on SCFA quantification is not straightforward and potentially not feasible for all fiber types. Nevertheless, we successfully developed a model that could predict an individual's responsiveness to Inulin with some degree of certainty.

Fibre supplements differentially alter PBMC population and immune cell functional output

As dietary fiber and SCFA modulate host immunity, we examined how short-term dietary fiber supplementation affects the immune system in healthy individuals, and whether these effects are explicitly linked to an increase in SCFA. To do this, we took a subset of participants (n = 12) with increased stool SCFA post-supplementation and examined their blood T cells, B cells, monocytes, macrophages, dendritic cells, and natural killer cells using flow cytometry (gating strategy presented in Fig. S6). To determine whether each supplement influenced these immune parameters, we compared PBMC pre-and post-fiber supplementation for each supplement (where pre-supplement refers to PBMC samples collected immediately prior to intervention, i.e., at baseline or following washout).

We found that each fiber supplement had a unique effect on immune cells pre-vs. post supplementation (Table 5), indicating a differential effect of fiber supplements on immune cell activity. Consistent with its impact on the gut microbiota, Inulin had the strongest effect on the immune system, with a decreased proportion of circulating dendritic cells (Figure 6a) and an increased proportion of classical monocytes (Figure 6b). Inulin supplementation also affected the activation profile of immune cells with decreased expression of CD86, a costimulatory molecule, on CD16hiCD14+ intermediate monocytes (Figure 6c) and on immune cell cytokine production, with inulin supplementation leading to increased production of TNF in both CD4+ and CD8+ cells (Figure 6d-e), and IFNγ in CD4 T cells (Figure 6f). Some effects were not fiber specific, with all supplements leading to higher CD86 expression on non-classical monocytes (Figure 6g). Thus, fiber supplementation can differentially affect both the proportion and function of blood immune cells, with Inulin having the greatest impact.

Discussion

We show that one week of fiber supplementation in healthy women is sufficient to induce specific changes both at the levels of the gut microbiota and of the immune system. These responses were individualized and depended on the type of dietary fiber used. The product Inulin (fructo-oligosaccharide) elicited the most consistent and predictable effect across individuals, whereas Vitafiber had the least effect. Using a machine learning approach, we were able to identify taxa that were associated with SCFA production in response (Alistipes marseilloanorexicus) or not (Senegalimassilia anaerobia and Oscillibacter spp.) to supplementation with Inulin. Future large-scale studies are required to confirm these findings and to confirm that these taxa could be used to select the most appropriate dietary fiber for an individual to maximize SCFA production. Resolving the gut microbiota at the species level may further improve this approach, as 16S sequencing does not provide adequate species and strain level resolution.35

Dysbiosis is associated with many diseases. Dietary fiber can restore gut microbiota composition and function, and therefore holds promise as a tool to prevent or alleviate diseases. However, human clinical trials using dietary fiber have shown varying success in recapitulating the beneficial effects of fiber demonstrated in preclinical studies.19-21 This is likely explained by the high inter-individual heterogenicity of the human gut microbiota, each possessing a unique repertoire of enzymes with varying abilities to break down different fiber. Indeed, the ability to generate SCFA from specific fiber is dependent on an individual's microbiome36 and improvement in glucose metabolism following fiber intake was only shown in individuals with increased abundance of Prevotella.29 However, a recent study reported no correlation between microbial metagenomic gene abundance and microbiotaderived metabolite levels.37 This suggests that it may not be possible to create a reliable model for predicting SCFA production based on either gut microbiota composition or its functional profile. Other strategies, such as examining the presence of interactions between different bacteria, may be required.

Despite its overall stability, our study showed that aspects of the human gut microbiome are modifiable. One week of inulin supplementation was sufficient to promote an increase in the relative abundance of specific bacterial taxa, particularly Actinobacteria, mostly due to increases in Bifidobacterium species. These are widely considered potent probiotics and high SCFA producers.38 This is consistent with the notion that dietary fiber affects the gut microbiota within days.39 Furthermore, known SCFA producers were signature to postsupplement microbiome, including Anaerostipes hadrus40 for Inulin, and taxa belonging to the genera Blautia41 and Clostridium cluster IV42 for Fibremax. These characteristics were lost following a 2-week washout period, suggesting that this timeframe was sufficient to revert diet-induced microbiome changes in healthy adults. This is consistent with previous studies, whereby inulin supplementation increased Bifidobacteria that peaked within a week and progressively normalized within a one-to-two-week period once supplementation stops. [START_REF] Roberfroid | Introducing inulin-type fructans[END_REF] Our results indicate that the consumption of prebiotics must be continuously maintained for their beneficial effects on the gut microbiota composition to persist.

Gut microbiota diversity was most reduced by Inulin supplementation, followed by Fibremax, whereas Vitafiber had no effect. High-fiber diet has been shown to reduce microbiota diversity in mice5 and in humans. [START_REF] Zhao | Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes[END_REF] This may be explained by the selection of bacteria that thrive on the source of energy substrates provided by a particular diet. [START_REF] Johnson | Daily sampling reveals personalized dietmicrobiome associations in humans[END_REF] In the present study, participants were asked to maintain their usual diet, suggesting that fiber supplementation was sufficient to recapitulate the effects of a high-fiber diet on gut microbiota diversity, at least with Inulin. These results are contradictory to those of other studies, which reported an increase in gut microbiome diversity with high fiber intake, including in mice24 and humans.25 A recent study reported that microbiota diversity did not increase with a high-fiber diet, but rather with a diet high in fermented foods26. One differentiating factor between studies may be the dose and type of fiber used. Very high fiber intake creates an environment that selects for fiber-degrading bacteria, allowing them to outcompete other bacteria46 and reduce the overall microbiome diversity. As microbial diversity is often quoted as beneficial, our study indicates that this may be context-dependent, and reduced diversity is not necessarily an indicator of poor health. [START_REF] Zhao | Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes[END_REF] For example, a high-fiber diet intervention was shown to improve glucose homeostasis in type 2 diabetes patients despite a decreased richness of the gut microbiota. [START_REF] Zhao | Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes[END_REF] A more important readout may be the functional profile of the gut microbiome and its ability to degrade fiber that provide maximal SCFA for the host. Our study found that all three supplements had differential impact on PBMCs. SCFA typically have anti-inflammatory effects, including induction of tolerogenic dendritic cells and immunosuppressive regulatory T cells.5,47,48 We did not observe changes in regulatory T cells in PBMC but instead found that Inulin increased the proinflammatory potential of T cells, including increased TNF and IFNγ production. Regulatory T cells have been reported to increase with fiber and SCFA; however, these were in the gastrointestinal tract.5,48,49 While dietary fiber is typically associated with anti-inflammatory effects, inulin has been shown to drive type 2 inflammation in a mouse model of allergic airway inflammation, mediated by gut microbial production of secondary bile acids.50 In contrast, our observed pro-Th1 effect of inulin under healthy conditions is consistent with the finding that inulin could prevent allergy development in mice.51 Inulin may have differential effects in mice and humans or its impact on the immune system depends on the challenge encountered. Indeed, our data suggests that the beneficial effects of short-term dietary fiber supplementation on immunity might be context dependent. For example, Inulin could be beneficial in cancer as previously reported based on its impact on IFNγ and TNF response.52,53 However, these cytokines are known to aggravate autoimmune diseases, highlighting the importance of personalized interventions to avoid disease aggravation.

Differences in responses to fiber may also be explained by the status of the host. In our study, Vitafiber had the least observable impact on the gut microbiota composition, diversity, SCFA production, and immune phenotypes. This is likely because Vitafiber, composed of corn starch rich in soluble fiber, closely represents common dietary ingredients. Success with fiber supplementation, therefore, also relates to the individual's dietary habits and microbiome profile. It also highlights a potential limitation of our study, which was performed in healthy participants and did not control for other aspects of diet. It also raises the question of whether individuals with dysbiosis would respond similarly to fiber supplements such as inulin. This aspect is particularly relevant as dysbiotic mice supplemented with inulin developed icteric hepatocellular carcinoma54 and fiber supplementation in patients with inflammatory bowel disease elicited a proinflammatory effect due to the inability of a dysbiotic microbiota to ferment the fiber, leading to increased intact β-fructan that activates NLRP3 and TLR2 pathways. [START_REF] Armstrong | Unfermented β-fructan fibers fuel inflammation in select inflammatory bowel disease patients[END_REF] These findings suggest that the health status of individuals and of their gut microbiota can switch the effects of supplements from beneficial to detrimental, which could raise concerns regarding the safety of these food products. Long-term deprivation of fiber, particularly across generations, can lead to irreversible loss of fiber digesting bacterial species.24 Therefore, alternative approaches, such as synbiotic intervention, or prior fecal microbiota transplant, may be required to rescue individuals with a dysbiotic gut microbiota. For example, while the treatment success of fecal microbiota transplant has only been reliably demonstrated for Clostridium difficile infections, the impact of fecal microbiota transplant on recipients' gut microbiota composition appears to be long-lasting, with close resemblance to donor microbiota composition even after 6 months.56 As we have shown that gut microbiota composition can be predictive of a response to fiber supplement (at least with Inulin), this technique holds potential promise in restoring a healthy gut microbiome. Whether transplanting a fiber-responsive microbiota into individuals can improve their response to fiber and to promote higher SCFA production would need to be investigated in future studies.

By utilizing a cross-over design involving three different fiber supplements, we were able to show that fiber supplements have both universal and individualized effects across clinical, microbiota, and immune characteristics in healthy individuals. Our study indicates that different fiber supplementation should be personalized based on an individual's gut microbiota composition and on the desired outcome.

Methods

Subjects and sampling

A total of 34 female volunteers aged between 30 and 65 years old, with a body mass index (BMI) ranging from 18 to 30 kg/m2 were recruited, and n = 28 completed the study. Exclusion criteria were type 1 or type 2 diabetes mellitus, renal or liver disease, cancer or active neoplasms, hyperthyroidism (unless treated or under control), use of medications known to affect weight or energy expenditure, unintentional weight loss (>10% body weight) over the past 5 years, smoking, alcohol consumption (>3 drinks/day), food allergies and/or intolerances, and when changes in diet are contraindicated by the treating doctor.

Volunteers were recruited through electronic and paper media advertisements, including social media and pamphlets, and assessed for eligibility through a screening questionnaire and a screening visit at the Clinical Research Facility at the Charles Perkins Centre. The screening questionnaire included questions regarding medical history, medications, supplements, allergies, and intolerances. Weight and height were measured according to standardized protocol, and BMI was calculated (weight/height)2. Rolling recruitment and assessment were conducted between February 2018 and April 2019.

Ethics

The trial was conducted in accordance with the Declaration of Helsinki and all procedures were reviewed and approved (Protocol Number X17-0130 & HREC/17/RPAH/192) by the Ethics Review Committee (Royal Prince Alfred Hospital Zone) of the Sydney Local Health District. The trial was registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12617001139369) on the 21st of November 2016. All participants signed a consent form.

Fibre supplements

Three different dietary fiber supplements were used for this study and were purchased commercially: Vitafiber (Isomalto-oligosaccharide; Myprotein), Inulin (fructooligosaccharide; Myprotein), and Fibremax (47% chicory root extract, 23.5% psyllium husk, 23.5% soy Fiber, 5% oat bran, and 1% Pectin; New Image). Participants were asked to consume the fiber supplements three times daily, a total of 15 g of Vitafiber, 34 g of Fibremax, or 15 g of inulin, equating to approximately 15 g of soluble fiber per day. During the intervention, participants were provided a 7-day dairy where they indicated the time(s) they took the supplement each day. They were also asked to record any adverse symptoms (bloating, gas and diarrhea) and their dietary intake. Dietary intake (habitual and during intervention) was determined through collection of a four-day estimated food record (4dEFR). Participants were required to record their dietary intake for four consecutive days (including one weekend day) giving as much detail about food consumed as possible. This included brands, preparation technique, leftovers (bones, skin, core), recipes and food consumed outside of home. Dietary intake data was analyzed using FoodWorks 10 Professional, v10.0. Brisbane: Xyris Pty Ltd, 2019 with the AUSNUT 2011-13 database (Food Standards Australia New Zealand (2014). AUSNUT 2011-13 -Australian Food Composition Database. Canberra: FSANZ. Available at www.foodstandards.gov.au).

Stool collection

Stool samples were collected within 48 hours prior to each clinical assessment. Participants were provided with a thermal bag (Thermabag), a small icepack, and written instructions on sample collection, storage, and transportation. Participants were asked to record the time of the collection and to place the sample in the freezer immediately after collection. Participants were requested to minimize the time out of the freezer to less than 2 hours when delivering to the clinic on the assessment day. Once received, the samples were stored in -80°C freezers.

Blood collection and processing

Four-hour fasting blood samples were collected on the day of the clinical assessments. Blood samples were collected into 10 mL BD Vacutainer K2-EDTA collection tubes (Becton Dickinson, #367525) for plasma and 8.5 mL BD Vacutainer SSTII advance collection tubes (Becton Dickinson, #367958) for serum, centrifuged immediately (4500 rpm, 15 min at 4°C), pipetted into CryoPure cryogenic tubes (Sarstedt AG & Co, #72.380), and stored at -80°C. For the isolation of peripheral blood mononuclear cell (PBMC) for immune phenotyping, the SepMate™ PBMC Isolation Tubes (StemCell Technologies, #85460) with Lymphoprep™ (StemCell Technologies, #07861) was used to isolate PBMC from whole blood, according to the manufacturer's instructions. Purified PBMC were cryopreserved in 10% DMSO (Sigma-Aldrich, #276855) in fetal bovine serum (FBS) (Bovogen, #SFBS-AU).

Metabolite quantification from stool and plasma

SCFA levels in plasma and stool were measured by nuclear magnetic resonance (NMR) analysis. Briefly, samples were filtered through a 3 kDa membrane filter (Merck Millipore, #UFC500396), and polar metabolites was extracted from the aqueous phase of a water:chloroform:methanol mixture (Sigma-Aldrich, #151858 and #151947) and diluted in trisodium phosphate (Sigma-Aldrich, #342483) buffer (pH 7). Samples were analyzed on a Bruker 600 MHz NMR machine containing 0.5 mM 4,4-dimethyl-4-silapentane-1-sulfonic acid (Sigma-Aldrich, #178837) as an internal standard. Data were analyzed using the Chenomx Profiler software (8.31). Stool samples were first homogenized in deuterium oxide at a final concentration of 100 mg/ml before filtration.

Immune phenotyping of PBMC

Cryopreserved PBMC were thawed and washed with RPMI medium (Gibco, #21870092) containing 10% FBS, 2 mM L-glutamine (Gibco, #25030081), 100 units/mL penicillin, and 100 μg/mL streptomycin (Gibco, #15140122). 1 × 106 cells were stained with Fc block (BioLegend, #422302) and the Live/Dead Fixable Blue Dead Cell Stain Kit (Invitrogen, #L34962) for 30 min at 4°C and washed with FACS buffer (2% FBS in PBS). For each participant, samples from each timepoint were first barcoded by staining with a combination of different fluorophore-conjugated CD45, washed, and then pooled for extracellular and intracellular staining. For staining of intracellular targets, PBMC were first stimulated with 50 ng/ml phorbol 12-myristate 13-acetate (Sigma-Aldrich, #P8139), 500 ng/ml ionomycin (Sigma-Aldrich, #I0634) and 5 μg/ml brefeldin A (Sigma-Aldrich, #B6542) for 4 h prior to staining. The Foxp3/Transcription Factor Staining Buffer Set (eBioscience, #00-5523-00) was used for intracellular cytokine staining. The following fluorescent-conjugated anti-human antibodies were used in this study: CD24-BUV395 (ML5), CD27-BUV661 (M-T271), CD45-BUV737 (HI30), CD38-BUV805 (HB7), CD19-V450 (HIB19), CD16-BV510 (3G8), CD86-PE/Cy5 (FUN-1), CD56-PE/Cy7 (B159), CD31-R718 (L133.1), CD3-APC-H7 (SK7) from BD Biosciences, CD45-BV570 (RA3-6B2), CD11c-BV605 (3.9), CD45-BV650 (HI30), CD45RA-BV711 (HI100), CD4-BV785 (OKT4), FoxP3-AF488 (259D), CD8-PerCP/Cy5.5 (SK1), CD14-APC (HCD14), IL-10-BV421 (JES3-9D7), TNF-PE (MAb11), IFNγ-PE/Cy7 (4S.B3) from BioLegend, CD25-PE (BC96) from Invitrogen and IL-6-APC (REA1037) from Miltenyi Biotec. Samples were acquired on a Cytek 5 L Aurora spectral flow cytometer, and data were analyzed using FlowJo v10.

Gut microbiota analysis

DNA was extracted using the FastDNA™ SPIN Kit for Feces (MP Biomedicals #116570200), following the manufacturer's instructions. Amplifiability and DNA concentration were verified by PCR and the Qubit assay kit (Invitrogen). Illumina sequencing of the V4 region (515f-806 r) of the 16S rRNA gene was performed commercially at the Ramaciotti Centre for Genomics (The University of New South Wales). Paired-end reads (2 × 250 bp) were processed with the dada2 package (1.12.1) using R software (version 3.6.1). Briefly, forward and reverse reads were trimmed (F240; R200) and quality-filtered (using default parameters) to remove low sequence bases, and error model was determined (108 bases). Sequences were then de-replicated and exact amplicon sequence variants (ASVs) were inferred before merging of paired-end reads and removal of chimeric sequence. Taxonomy was assigned using the Ribosomal Database Project (RDP) training set (rdp_train_set_16) with specie-level taxonomy (dp_species_assignment_16) assignment (10.5281/zenodo.801828). The microbiome was explored using the following R packages phyloseq (1.42.0), microbiome (1.20.0), ALDEx2 (1.30.0), and mixOmics (6.22.0). Sequencing data were deposited in the European Nucleotide Archive under accession number PRJEB61931.

Statistical analysis

To calculate the number of participants required for the study, we estimated a dropout rate of approximately 10% and a within-subject correlation of changes to stool acetate of 0.5 (SD between supplement group). A total of 30 subjects will be required to detect a difference with 80% power and a bilateral alpha risk of 5%. For the analysis of clinical characteristics, fixed effects of treatment (supplement) and time (before versus after intervention) were performed using linear mixed-effects models, with each individual participant as a random effect. To compare pre-vs. post-supplement characteristics for an individual supplement, a two-tailed paired t-test was used. For the comparison of post-supplement characteristics between supplements, one-way ANOVA with Tukey's multiple comparison test was used. To evaluate whether the overall microbiome composition differed between groups, a PERMANOVA test was used. For differential abundance analysis of microbiome composition between pre-and post-supplement microbiome, and between post-supplement microbiome between supplements, paired or unpaired ALDEx2 tests were used, respectively. Sparse partial least squares discriminant analysis (sPLSDA) was used for feature selection and classification, and the models were tuned using M-fold cross-validation with five folds for each validation and 100 repeats. The Balanced Error Rate was used as a misclassification measure and "max.dist" as the distance metric. Multilevel analysis (repeated measurement of participants across different interventions) was used, where appropriate. An alpha cutoff of 0.05 was used for all statistical tests. Data were analyzed using R Software (4.2.2) or GraphPad Prism 9 software.

Acknowledgments

We acknowledge the Sydney Cytometry facility for providing access to flow cytometer analysers. This research was facilitated by access to Sydney Analytical, a core research facility at the University of Sydney.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

The work was supported by the Australian Research Council (LP160100627), National Health and Medical Research Council (APP1068890), Cooper Foundation, The Medical Foundation and the Kick Start Grant (University of Sydney).

Author contributions

JT conducted research, analyzed data and wrote the paper. RVR conducted the clinical study and assisted with data analysis. CB and ES assisted with data analysis. CD, AH, RN and SJS participated to the design of the study. LM received fundings, initiated and designed the study. All authors have read and approved the final manuscript.