Marie Saade

Maroun Jneid
email: maroun.jneid@ua.edu.lb

Imad Saleh
email: imad.saleh@univ-paris8.fr

PREDICTING TECHNOLOGY SUCCESS BASED ON PATENT DATA, USING A WIDE AND DEEP NEURAL NETWORK AND A RECURRENT NEURAL NETWORK

Keywords: Innovative product development, forecasting technology success, patent analysis, machine learning

The temporal dynamic growth of technology patents for a time sequence is a major indicator to measure the technology power and relevance in innovative technology-based product/service development. A new method for predicting success of innovative technology is proposed based on patent data and using Neural Networks models. Technology patents data are extracted from the United States Patent and Trademark Office (USPTO) and used to predict the future patent growth of two candidate technologies: "Cloud/Client computing" and "Autonomous Vehicles". This approach is implemented using two Neural Networks models for accuracy comparison: a Wide and Deep Neural Network (WDNN) and a Recurrent Neural Network (RNN). As a result, RNN achieves a better performance and accuracy and outperforms the WDNN in the context of small datasets.

Introduction

Product development consists of methods and processes focused on value creation through the development of new and innovative technology-based products. The traditional technical aspects are involved such as engineering design techniques, cost reduction, increase in product quality, etc. However, predicting technologies success is a crucial need and a prerequisite step for investments in product development and value creation. For instance, [START_REF] Jneid | Improving start-ups competitiveness and innovation performance: the case of Lebanon[END_REF] have stated that for start-ups facing a competitive environment, innovation is a key factor contributing to their success. They presented a new approach based on co-innopreneuriat which provides a method for co-innovation and co-creation of values by convergence and collaboration. Furthermore, organizations seek to enhance the Research and development (R&D) activities by increasing the R&D investments on new patents technologies [START_REF] Hall | Patent Data as Indicators[END_REF]. Technology patents can be used as a significant factor for measuring success and relevance in technology-based product 1 development, and therefore, patents growth of a technology can be used as a major indicator to predict its power and its impact on value-creation. Accordingly, the main problematic of this study is to examine how technology success can be forecasted based on patent analysis, using a convenient prediction tool while considering data availability and how to prioritize several trending technologies in order to identify the most appropriate ones for a given investment project. Therefore, in this paper, we initially propose a novel model for forecasting technology success and predicting patents growth, based on two Neural Network models for comparison purpose: a Wide and Deep Neural Network and a Recurrent Neural Network. Noting that according to our research, this approach has not been implemented so far when forecasting patents growth in this specific context.

Literature review

Technologies Patents play a critical role in fostering innovation and making decisions [START_REF] Chen | Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014[END_REF]. Patents data can be used and analyzed to predict promising technology, in order to provide reliable knowledge for decision making [START_REF] Cho | Patent analysis for forecasting promising technology in high-rise building construction[END_REF]. Patent expansion potential and patent power are considered as technology scope indicators [START_REF] Altuntas | Forecasting technology success based on patent[END_REF]. In addition, the National Science Board highlights the importance of patents counts and patents citations (National Science Board, 2014) in measuring the innovation value and the knowledge flows. Professor Bronwyn H. Hall [START_REF] Hall | Patent Data as Indicators[END_REF] identified a positive correlation between the patents grant growth and the industrial R&D, which consequently increases the total R&D.

Patent-based technology forecasting methods:

Patent-based technology forecasting has been considered a reliable method for finding technology scope, identifying trending technologies and improving decision-making. For instance, a patent analysis has been elaborated to quantitatively forecast the technology success in the context of patent expansion potential, patent power, technology life cycle and diffusion potential [START_REF] Altuntas | Forecasting technology success based on patent[END_REF]. Cho, Lim, Lee, Cho and Kyung-InKang analyzed patents data to predict promising technology fields in the building construction [START_REF] Cho | Patent analysis for forecasting promising technology in high-rise building construction[END_REF]. In addition, patent data have been employed in a matrix map and in the quantitative method 'K-medoids clustering, in order to predict the vacant technologies in the management of technology field [START_REF] Jun | Technology forecasting using matrix map and patent clustering[END_REF]. Furthermore, a machine learning approach has been proposed to detect new technologies at early stages, using several patent indicators and a multilayer neural network, while focusing on the pharmaceutical technology [START_REF] Lee | Early identification of emerging technologies: A machine learning approach using multiple patent indicators[END_REF]. Kim and Lee proposed a prediction approach for multitechnology convergence based on patent-citation analysis, a neural network analysis and a dependencystructure analysis (Kim & Lee, 2017).

Research design

In the present study, the analysis of patent data is considered as a quantitative approach to assess its impact on a technology's success. Specifically, this study forecasts the future growth of patents for a given technology, using a Neural Network model. The latter is built using two separate methods for comparison purposes: a Wide and Deep Neural Network (WDNN) model and a Recurrent Neural Network (RNN) model.

Neural Network model:

The artificial neural networks are used for a dynamic [START_REF] Alsheikh | Machine learning in wireless sensor networks:Algorithms, strategies, and applications[END_REF]), unsupervised learning (Schmidhuber, 2015) from historical data in order to estimate future values. In this paper, the neural networks are used specifically for data classification, regression, data analysis and prediction.

Wide and Deep Neural Network model:

Generally, the more data a Neural Network can have, the more its generated predictions are accurate and reliable [START_REF] Thrideep | Artificial Neural Networks -The Future of Airline Sales and Revenue forecasting[END_REF]. However, Big Data analytics issues and challenges can be referred to the fast information retrieval, data representation, data processing, etc. For this reason, this Neural Network is structured as a wide and deep model in order to support such tasks, since it can handle complex large-scale data [START_REF] Elaraby | Deep Learning: Effective Tool for Big Data Analytics[END_REF], integrating heterogeneous input data [START_REF] Najafabadi | Deep Learning applications and challenges in Big Data analytics[END_REF]. In addition, Deep learning algorithms generalize the extracted representation and the relationships in the data, by exploring new or rare features combinations by transitivity of correlations. However, deep neural network can over-generalize and extract less relevant features (Cheng, et al., 2016). For this reason, the memorization of features interactions or correlations in the historical data, the exception rules and the frequent co-occurrence of features is a crucial need to enhance the neural network prediction. Hence the importance of merging a wide linear model for learning exceptions and memorizing specific rules with a deep model for learning interactions within historical data and then generalizing the output on new data (Cheng, et al., 2016).

Recurrent Neural Network model:

RNN is a model of artificial Neural Network where its nodes are connected along sequences. Accordingly, the main reason of using this model of Neural Network is to process sequences of inputs in order to predict a sequence of outputs where theses sequences can be different in lengths for different technologies (Ng, et al., 2018), which is applied in our case as well. In addition, Gated Recurrent Units (GRU) are employed in the RNN as they tend to optimize the performance on smaller datasets [START_REF] Chung | Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[END_REF], which is the case of our study where the data are limited to 163 records.

The above models will be discussed in details in the following sections: Proposed methodology and Neural Network models structures.

Proposed methodology

The proposed methodology is illustrated with a process flow, as presented in Figure 1, covering the following main objectives: data collection, database integration, data manipulation, datasets creation, neural networks implementation, results visualization/analysis and decision-making. It consists of 15 steps, explained in Table 1: 3

Objective

Step Description

Data Collection

Step 1 Searching for old and new trending technologies from several references.

Step 2 Listing the related keywords for each technology.

Database integration

Step 3 Inserting these technologies' data and their related keywords into two separate tables in a new integrated database. These two tables are related according to the identifier "tech_id".

Noting that these tables are considered as dictionary tables which the following steps will depend on.

Data Collection

Step 4 Extracting patents data related to each technology from a patent database based on keywords matching. Several patents databases could be used such as the United States Patent and Trademark Office (USPTO) database. This latter is considered among the richest intellectual property databases, it issued approximately 10 million patents so far (USPTO, 2018).

Database integration

Step 5 Manipulating and inserting data into the integrated DB.

Data manipulation

Step 6 Computing the total number of patents per technology per year.

Datasets creation

Step 7 Querying and grouping the data by year following the axis: [Year-max,

Neural networks implementation

Step 8 Training the Neural Networks.

Step 9 Testing the Neural Networks.

Step 10

Predicting the number of patents.

Step 11

The number of years y that we need to generate is checked at every step during the prediction phase until y is equal to p.

Database integration

Step 12

Inserting the output data of the Neural Network into the integrated database.

Results visualization and analysis

Step 13

Illustrating the patents variation in statistical graphs.

Step 14

Ranking the candidate technologies based on their patents progress.

Decision-making

Step 15

Evaluating and prioritizing the candidate technologies based on the above ranking and based on business perspective.

Neural Network models structures

The Neural Networks have been built using the TensorFlow software library under Python.

Wide and Deep Neural Network

This Neural Network structure is based on the "DNN Linear Combined Regressor" (TensorFlow, 2018).

Noting that "regression" refers to the prediction of a continuous variable as output [START_REF] Bishop | Pattern Recognition and Machine Learning[END_REF], in our case, the estimation of the number of patents. Furthermore, Adagrad is used as an optimization method during the training phase, since it can handle sparse data [START_REF] Ruder | An overview of gradient descent optimization algorithms[END_REF] and enhance the robustness of the Stochastic Gradient Descent [START_REF] Dean | Large scale distributed deep networks[END_REF]. This Neural Network consists of the following layers:  Input Layer: The input layer is composed of two types of nodes:

-Continuous number: represents the number of patents for a technology for each available year. -Bucket: represents ranges of the same data as the continuous number for each available year.

 Hidden Layers: represent the intermediary layers that can make the model more adaptable. This neural network contains a specific number h of hidden layers, and each one contains a specific number no of nodes. Noting that the hidden layers could be adjustable based on the accuracy and the loss results.

 Output layer: represents the data to be forecasted. The number of patents from Year1 till Yearp will be predicted for the technology in question. Noting that in Step 10, during the prediction phase, the neural network predicts the number of patents for each year separately and in Step 11 the number of years y is checked at every step. Therefore, each predicted output is serving as input for the next prediction until the number of years y we need to generate is reached: in other words until y is equal to p.

Figure 3: The outputs of the DWNN.

Recurrent Neural Network

The RNN has been implemented for time series prediction based on an encoder-decoder sub-model using Gated Recurrent Unit (GRU) cells and based on a Sequence-to-Sequence (seq2seq) neuronal architecture, where the RNN can map a variable-length sequence of inputs (x1,…, xT) to a sequence of outputs (y1,…, yT), by encoding this input sequence into a fixed-length vector representation and then decoding a fixedlength vector representation into the target sequence [START_REF] Sutskever | Sequence to Sequence Learning with Neural Networks[END_REF]) (Ng, et al., 2018). In this case, the input size differs according to the data availability of each technology. In addition, this Neural Network is structured as a many-to-many model, where both the input and the output represent sequences of data with same or different lengths (Ng, et al., 2018). Furthermore, regarding the optimization method applied to minimize the cost function during the backward propagation in the training phase, the Root Mean Square Prop (RMSProp) optimizer is applied in order to speed up the gradient descent (Ng, et al., 2018).

The RNN consists of the following layers:  Input Layer: The input layer takes sequences of time series data with variable-length.

In addition, each row in a given dataset represents an input sequence in the RNN.

 Hidden Layers: This neural network can contain a specific number h of stacked recurrent cells on the depth axis and a width of no hidden units or nodes for each cell. Noting that all hidden layers can be merged into one stacked recurrent layer, since all of them have same biases and weights [START_REF] De | Forecasting technology success based on patent[END_REF].

 Output layer: represents the data sequence to be forecasted. The number of patents from Year1 till Yearp will be predicted as a sequence of p cells for the technology in question. Accordingly, in this case, Step 11 is not needed given that the prediction of the number of patents of all y years is executed at a single step: that means y = p.

Figure 5: The output sequence of the RNN.

Experimentation

We collected patent data for 11 trending technologies over a period of time, in order to train and test the neural network. The two Neural Networks models are applied on two candidate technologies to predict the number of patents for the next five years: in this case the parameter p is equal to 5.

Data collection 1.Technologies listing

The selected technologies are listed based on several web sources, such as: IEEE (IEEE, 2016) (IEEE, 2015), Gartner [START_REF] Cearley | Top 10 Strategic Technology Trends for[END_REF]), Elsevier (Peter Edelstein, 2017), Scientific American [START_REF] Meyerson | Top 10 Emerging Technologies of 2015[END_REF], MIT Technology Review (MIT Technology Review, 2018), etc. Therefore, their related keywords are extracted manually using different references, such as "thesaurus" (Thesaurus, 2013), "TechTerms" (Tech Terms, 2018), etc. and inserted in the integrated database.

Patent data Source

A CSV file containing the granted patents applications published until 2016 has been downloaded from the USPTO website and imported into the 'Patents' table in the database. Therefore, given that the patents data extracted from the USPTO are not grouped or categorized by technology, a script has been developed under Python in order to extract the needed data by technology from the 'Patents' table, based on technologies keywords matching. Specifically, patents were categorized by technology based on the title of the patent application: the text analysis can be conducted by searching each keyword or term of each technology in the title of all patents applications [START_REF] Sunghae | IPC Code Analysis of Patent Documents Using Association Rules and Maps -Patent Analysis of Database Technology[END_REF].

Finally, the number of patents were counted and grouped by year and by technology. The result was then inserted into the 'NumberOfPatents' table .  The following figure represents the number of patents variation for a list of technologies:

Training, Testing and Prediction datasets creation

The data in the 'NumberOfPatents' table have been split manually into training and validation datasets based on the correlation between the number of patents of the technologies, since we have a limited amount of data. The following figures illustrate samples of the original data: As per figure 6, 7 and 8, the number of patents of the "Autonomous Vehicles" technology in the testing dataset is correlated with the "Big data and visualization" technology in the training dataset, since the variation of their number of patents is approximately similar through the years. Moreover, the "Cloud/Client computing" technology in the testing dataset is correlated as well with the "Augmented Reality" technology that belongs to the training dataset.

Regarding the Prediction dataset, it contains the patent data related to the same technologies as the testing dataset, in order to be able to compare the predicted values with the actual values, and therefore to visualize the accuracy of the models. Accordingly, the prediction can be applied on any new technology using the most accurate implemented model.

Neural Networks implementation

The implemented neural networks are based on different open source tutorials. The training parameters have been tuned progressively, and the configurations in these experiments have been determined experimentally based on the best obtained results, as suggested by Chevalier [START_REF] Chevalier | seq2seq-signal-prediction[END_REF].

Wide and Deep Neural Network

In order to implement the WDNN, we have referred to the open source tutorial of TensorFlow under GitHub (TensorFlow, 2017), with the following main parameters, as defined in the Proposed Methodology section:

a) Training

The training phase was processed by executing 10000 iterations, with a small learning rate equal to 0.001 in order to decrease the loss function and therefore to accelerate the training convergence (Ng, et al., 2018).

b) Validation and Prediction

In this section, the accuracy of this neural network was calculated as per the following steps and formulas: p = 5 years; y = 1 year (as initial value); max = 45 years; n = 9; h = 5 hidden layers; no1 = 1000 nodes; no2 = 750 nodes; no3 = 500 nodes; no4 = 300 nodes; no5 = 150 nodes i.

First we normalized the expected and the predicted values on a scale from 0 to 100: the highest value (max) in the expected values array was considered as 100, then the other expected (expected) and predicted (pred) values was normalized accordingly:

ii.

Then we calculated the absolute difference between the new normalized values of the expected and the predicted arrays:

iii.

Finally the accuracy was calculated by subtracting the average difference of the obtained values from the total percentage:

Recurrent Neural Network

In order to implement the Sequence-to-Sequence neural network, we have referred to the open source code of the "Signal prediction with a Sequence-to-Sequence Recurrent Neural Network model in TensorFlow" [START_REF] Chevalier | seq2seq-signal-prediction[END_REF], with the following main parameters, as defined in the Proposed Methodology section:

a) Training

The training phase was processed by executing 1000 iterations, with a small learning rate equal to 0.001 in order to prevent divergent losses [START_REF] Ravaut | Faster gradient descent via an adaptive learning rate[END_REF] (Ng, et al., 2018).

b) Validation and Prediction

The accuracy of the RNN was calculated based on the same steps and formulas as for the WDNN.

Results

The graphs in the following subsections represent the obtained results for both candidate technologies in the WDNN and the RNN, illustrating the quality of the prediction, where the abscissa axis represents the time and the ordinate axis represents the number of patents. As per the below results of the two neural networks, we note that the predicted numbers of patents for the "Autonomous Vehicles" technology are following the same pattern as the "Big data and visualization" technology, concluding that the correlation mentioned earlier between these two technologies was detected by the neural networks during the prediction phase with a certain accuracy rate. The same applies to the "Cloud/Client computing" and the "Augmented Reality" technologies.

Wide and Deep Neural Network

Recurrent Neural Network

Conclusion and discussion

The development of new and innovative technology-based products creates business value in today's economy, and therefore, forecasting technologies success becomes a crucial need. However, predicting technologies success is a complex task in terms of prediction accuracy and data availability. Accordingly, a novel method has been proposed in this paper, relying on patent analysis as a quantitative approach, and using Neural Networks models in order to measure the candidate technologies power based on the prediction of patents growth. Addressing this estimation is a necessary prerequisite before proceeding with investments. This method has been implemented using the USPTO Patent Database, with a comparative study of two Neural Networks: a Wide and Deep Neural Network and a Recurrent Neural Network, and experimented on 11 trending technologies to train and test these neural networks then applied on two candidate technologies, "Cloud/Client computing" and "Autonomous Vehicles", for the prediction phase. The findings showed that RNN is more performing and accurate than WDNN. Therefore, the proposed method answers questions related to technology success and appropriate prediction models. Accordingly, it supports decision making of innovative technology-based product/service development.

This study can be further enhanced by tackling its current limitations. The most challenging task faced in our study is the access to accurate Big data, as the data are currently limited to a small dataset extracted only from the USPTO database and based uniquely on the granted patents applications, published until 2016. In addition, patent data are categorized by technology based on keywords matching of only the title of the patent application. They can be queried as well in the application abstracts and other fields. Furthermore, future works can further evolve the proposed research design to include additional factors or dimensions affecting future technology success.

Figure 1 :

 1 Figure 1: Methodology design.

Figure 2 :

 2 Figure 2: Wide and Deep Neural Network.

Figure 4 :

 4 Figure 4: Recurrent Neural Network.

Figure 6 :

 6 Figure 6: Number of patents variation of different technologies from 1965 to 2016.

Figure 7 :

 7 Figure 7: A sample of the training data.

Figure 8 :

 8 Figure 8: A sample of the testing data.

p

  = 5 years; max = 45 years; n = 5; h = 2 recurrent cells; no = 250 hidden units per cell

Figure 9 :

 9 Figure 9: Actual and predicted number of patents in the WDNN for the "Cloud/Client computing" technology from 1995 to 2016.

Figure 10 :

 10 Figure 10: Actual and predicted number of patents in the WDNN for the "Autonomous Vehicles" technology from 1991 to 2016.

Figure 11 :

 11 Figure 11: Actual and predicted number of patents in the RNN for the "Cloud/Client computing" technology from 1995 to 2016.

Figure 12 :

 12 Figure 12: Actual and predicted number of patents in the RNN for the "Autonomous Vehicles" technology from 1991 to 2016.

Table 1 :

 1 Methodology design steps.

  … , Year-1, Year0, Year1, … , Yearp]. Such as: max: represents the maximum number of years per technology where historical data is available in the training dataset. p: represents the number of years for which we are validating results in the testing dataset, and the number of years for which we are predicting results in the prediction dataset. -Year0: represents the most recent year in the training data set per technology, based on data availability.

  Tensorflow is a high level open-source API created by Google and is used for Machine Learning and deep neural network purposes (TensorFlow, 2018). As previously mentioned, two Neural Network designs are proposed based on different models:

Table 2 :

 2 Prediction accuracy for the WDNN.

Wide and Deep Neural Network

Table 3 :

 3 Prediction accuracy for the RNN.

	"Cloud/

Client computing" (Figure 9) "Autonomous Vehicles" (Figure 10) Actual or true values Predicted Values Actual or true values Predicted Values

	114	126	96	53
	180	113	61	43
	173	154	28	32
	169	222	10	22
	98	265	5	22
	Prediction

accuracy: 64.66% Prediction accuracy: 80.41% Average prediction accuracy: 72.53% Recurrent Neural Network "Cloud/Client computing" (Figure 11) "Autonomous Vehicles" (Figure 12) Actual or true values Predicted Values Actual or true values Predicted Values

	114	187	96	69
	180	234	61	67
	173	241	28	34
	169	251	10	11


Pietro Giovanni Santori

Florian Dominik Speck

Serhiy Cherevko

Horie Adabi Firouzjaie

Xiong Peng

William E Mustain
email: mustainw@mailbox.sc.edu

Frédéric Jaouen
email: frederic.jaouen@umontpellier.frw.mustain

High Performance FeNC and Mn-oxide/FeNC Layers for AEMFC Cathodes

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

Introduction

The energy-environment nexus calls for the development of renewable energy sources and more efficient machines. Renewable electric power, produced from solar or wind energies for example, can be efficiently converted to hydrogen via water electrolysis, and hydrogen used as a green fuel, on demand. [START_REF] Staffell | The role of hydrogen and fuel cells in the global energy system[END_REF] To this end, fuel cell devices can efficiently re-convert renewable H 2 and O 2 from air to electric power with high efficiency. This technology is well positioned to compete, among other applications, with internal combustion engines for automotive application. [START_REF] Stephens | Toward sustainable fuel cells[END_REF] Among different types of fuel cells, Proton Exchange Membrane Fuel Cells (PEMFCs) are currently the ones envisioned and developed by the automotive industry for transportation application, due to their high power density, low internal resistance, low maintenance, long-term durability and fast start-up. [START_REF] Cano | Batteries and fuel cells for emerging electric vehicle markets[END_REF][START_REF] Kongkanand | The Priority and Challenge of High-Power Performance of Low-Platinum Proton-Exchange Membrane Fuel Cells[END_REF] In the long term, however, the main drawback of PEMFCs is tied to the kinetics of the Oxygen Reduction Reaction (ORR) that occurs at the cathode side. The sluggishness of the ORR in the acidic PEMFC forces the use of platinum-based catalysts to minimize the losses in energy efficiency and power density that are associated with the ORR overpotential. [START_REF] Wu | A review of PEM fuel cell durability: Degradation mechanisms and mitigation strategies[END_REF][START_REF] Gasteiger | Activity benchmarks and requirements for Pt, Pt-alloy, and non-Pt oxygen reduction catalysts for PEMFCs[END_REF] Platinum is a rare and expensive metal that increases the production cost of the device, threatening the large-scale commercialization of PEMFCs and its sustainability. [START_REF] Wu | A review of PEM fuel cell durability: Degradation mechanisms and mitigation strategies[END_REF][START_REF] Gasteiger | Activity benchmarks and requirements for Pt, Pt-alloy, and non-Pt oxygen reduction catalysts for PEMFCs[END_REF][START_REF] Majlan | Electrode for proton exchange membrane fuel cells: A review[END_REF][START_REF] Thompson | Direct hydrogen fuel cell electric vehicle cost analysis: System and high-volume manufacturing description, validation, and outlook[END_REF] The need to reduce the amount of platinum (at large, platinum group metals (PGMs), Pt and Pd being the least rare PGMs) in high-performance PEMFCs has led the scientific community involved in electrocatalysis to investigate two separate approaches, one leaning on the improvement of PGM (mostly Pt) catalysts and cathode layers to utilize more efficiently such metals, [START_REF] Henning | Unsupported Pt-Ni Aerogels with Enhanced High Current Performance and Durability in Fuel Cell Cathodes[END_REF][START_REF] Chung | Highly Durable and Active Pt-Based Nanoscale Design for Fuel-Cell Oxygen-Reduction Electrocatalysts[END_REF][START_REF] Ding | Nano-engineered intrapores in nanoparticles of PtNi networks for increased oxygen reduction reaction activity[END_REF][START_REF] Asset | A Review on Recent Developments and Prospects for the Oxygen Reduction Reaction on Hollow Pt-alloy Nanoparticles[END_REF][START_REF] Ishikawa | Tomographic analysis and modeling of polymer electrolyte fuel cell unsupported catalyst layers[END_REF][START_REF] Chattot | Surface distortion as a unifying concept and descriptor in oxygen reduction reaction electrocatalysis[END_REF] and another exploring the synthesis of PGM-free catalysts with activity and durability that must, ultimately, become comparable to those of Pt-based catalysts.

Among the PGM-free catalysts investigated since the early 2000's, a class of MNC catalysts with 3d transition metals (M=Fe and Co, in particular) integrated in a N-doped carbon matrix has hitherto shown the most promising activity and stability in acidic media, [START_REF] Banham | Current Status and Future Development of Catalyst Materials and Catalyst Layers for Proton Exchange Membrane Fuel Cells: An Industrial Perspective[END_REF][START_REF] Lefèvre | Iron-Based Catalysts with Improved Oxygen Reduction Activity in Polymer Electrolyte Fuel Cells[END_REF][START_REF] Li | Structure and activity of metal-centered coordination sites in pyrolyzed metal-nitrogen-carbon catalysts for the electrochemical reduction of O2[END_REF][START_REF] Shao | Recent Advances in Electrocatalysts for Oxygen Reduction Reaction[END_REF][START_REF] Shao | PGM-Free Cathode Catalysts for PEM Fuel Cells: A Mini-Review on Stability Challenges[END_REF][START_REF] Wang | Achievements, challenges and perspectives on cathode catalysts in proton exchange membrane fuel cells for transportation[END_REF][START_REF] Martinez | Progress in the Development of Fe-Based PGM-Free Electrocatalysts for the Oxygen Reduction Reaction[END_REF] though oxynitrides of transition metals of group 4 and 5 in the periodic table have also been reported with promising activity. [START_REF] Chisaka | Synthesis of carbon-supported titanium oxynitride nanoparticles as cathode catalyst for polymer electrolyte fuel cells[END_REF][START_REF] Chisaka | Facile Combustion Synthesis of Carbon-Supported Titanium Oxynitride to Catalyse Oxygen Reduction Reaction in Acidic Media[END_REF][START_REF] Ishihara | Emergence of Oxygen Reduction Activity in Zirconium Oxide-Based Compounds in Acidic Media: Creation of Active Sites for the Oxygen Reduction Reaction[END_REF] The nature of the most active catalytic sites in MNC catalysts obtained via pyrolysis has been controversial since their emergence. However, recent studies from several groups are now converging to the same conclusion that, in acidic medium, the most active and stable sites in MNC catalysts are atomically-dispersed MN x moieties. [START_REF] Zhang | Highperformance fuel cell cathodes exclusively containing atomically dispersed iron active sites[END_REF][START_REF] Wagner | Elucidating the Structural Composition of an Fe-N-C Catalyst by Nuclear-and Electron-Resonance Techniques[END_REF][START_REF] Mineva | Understanding Active Sites in Pyrolyzed Fe-N-C Catalysts for Fuel Cell Cathodes by Bridging Density Functional Theory Calculations and 57Fe Mössbauer Spectroscopy[END_REF][START_REF] Kneebone | A Combined Probe-Molecule, Mössbauer, Nuclear Resonance Vibrational Spectroscopy, and Density Functional Theory Approach for Evaluation of Potential Iron Active Sites in an Oxygen Reduction Reaction Catalyst[END_REF][START_REF] Kramm | On an Easy Way To Prepare Metal-Nitrogen Doped Carbon with Exclusive Presence of MeN4-type Sites Active for the ORR[END_REF][START_REF] Jiang | Edge-Site Engineering of Atomically Dispersed Fe-N4 by Selective C-N Bond Cleavage for Enhanced Oxygen Reduction Reaction Activities[END_REF][START_REF] Jia | Spectroscopic insights into the nature of active sites in iron-nitrogen-carbon electrocatalysts for oxygen reduction in acid[END_REF][START_REF] Lai | Metal-Organic-Framework-Derived Fe-N/C Electrocatalyst with Five-Coordinated Fe-Nx Sites for Advanced Oxygen Reduction in Acid Media[END_REF][START_REF] Zhang | Single Atomic Iron Catalysts for Oxygen Reduction in Acidic Media: Particle Size Control and Thermal Activation[END_REF][START_REF] Kumar | Physical and Chemical Considerations for Improving Catalytic Activity and Stability of Non-Precious-Metal Oxygen Reduction Reaction Catalysts[END_REF] This conclusion could be reached with the preparation of model MNC materials, exclusively comprising single-metal-atom structures and no metal clusters (to the detection limit of X-ray absorption and [START_REF] Meng | pH-effect on oxygen reduction activity of Fe-based electro-catalysts[END_REF] Fe Mössbauer spectroscopies as well as scanning transmission electron microscopy). Combined theoretical and experimental interpretation of the spectroscopic response of such model MNC catalysts prepared from different precursors are also converging to the picture of FeN x and CoN x moieties (x ≤ 4) embedded in the carbon matrix as porphyrinic MN 4 C 12 or pyridinic MN 4 C 10 structures, with the transition metal in a +II or +III oxidation state and in different spin states. [START_REF] Li | Structure and activity of metal-centered coordination sites in pyrolyzed metal-nitrogen-carbon catalysts for the electrochemical reduction of O2[END_REF][START_REF] Wagner | Elucidating the Structural Composition of an Fe-N-C Catalyst by Nuclear-and Electron-Resonance Techniques[END_REF][START_REF] Mineva | Understanding Active Sites in Pyrolyzed Fe-N-C Catalysts for Fuel Cell Cathodes by Bridging Density Functional Theory Calculations and 57Fe Mössbauer Spectroscopy[END_REF][START_REF] Zitolo | Identification of catalytic sites for oxygen reduction in iron-and nitrogen-doped graphene materials[END_REF][START_REF] Zitolo | Identification of catalytic sites in cobaltnitrogen-carbon materials for the oxygen reduction reaction[END_REF][START_REF] Kattel | Stability, Electronic and Magnetic Properties of In-Plane Defects in Graphene: A First-Principles Study[END_REF][START_REF] Yang | Unveiling the highactivity origin of single-atom iron catalysts for oxygen reduction reaction[END_REF] Binuclear Fe 2 N 5 sites have also been proposed and could be a minority site in FeNC catalysts, but possibly with higher turnover frequency. [START_REF] Holby | Structure of Fe-Nx-C Defects in Oxygen Reduction Reaction Catalysts from First-Principles Modeling[END_REF] Besides the need to further improve the activity of MNC catalysts for matching the high PEMFC performance currently obtained with Pt-based catalysts, the main bottleneck for the industrial application of MNC catalysts in PEMFC is presently their limited durability during PEMFC operation. Their degradation mechanisms have been intensively explored in rotating disk electrode (RDE), PEMFC and ex situ with non-electrochemical Fenton tests. [START_REF] Shao | PGM-Free Cathode Catalysts for PEM Fuel Cells: A Mini-Review on Stability Challenges[END_REF][START_REF] Kumar | Physical and Chemical Considerations for Improving Catalytic Activity and Stability of Non-Precious-Metal Oxygen Reduction Reaction Catalysts[END_REF][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 | Minimizing Operando Demetallation of Fe-N-C Electrocatalysts in Acidic Medium[END_REF][START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF][START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF][START_REF] Goellner | Degradation by Hydrogen Peroxide of Metal-Nitrogen-Carbon Catalysts for Oxygen Reduction[END_REF][START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF][START_REF] Wang | Iron-Free Cathode Catalysts for Proton-Exchange-Membrane Fuel Cells: Cobalt Catalysts and the Peroxide Mitigation Approach[END_REF][START_REF] Ferrandon | Stability of iron species in heat-treated polyaniline-iron-carbon polymer electrolyte fuel cell cathode catalysts[END_REF][START_REF] Singh | Carbon-based catalysts for oxygen reduction reaction: A review on degradation mechanisms[END_REF][START_REF] Martinez | Durability challenges and perspective in the development of PGM-free electrocatalysts for the oxygen reduction reaction[END_REF][START_REF] Serov | Nano-structured non-platinum catalysts for automotive fuel cell application[END_REF] While some MNC catalysts have shown high stability when load-cycled in deaerated acidic electrolytes, [START_REF] Kumar | Physical and Chemical Considerations for Improving Catalytic Activity and Stability of Non-Precious-Metal Oxygen Reduction Reaction Catalysts[END_REF][START_REF] Choi | Stability of Fe-N-C Catalysts in Acidic Medium Studied by Operando Spectroscopy[END_REF] similar load-cycling O 2 -saturated electrolytes has resulted in irreversible ORR activity loss, assigned to reactive oxygen species formed during ORR, such as hydrogen peroxide, triggering unexpected carbon corrosion at such low potential and the transformation of FeN x sites to iron oxide. [START_REF] Kumar | On the Influence of Oxygen on the Degradation of Fe-N-C Catalysts[END_REF] All MNC catalysts can also be expected to suffer from carbon corrosion during startup/shutdown accelerated stress tests, leading to the destruction of MN x sites and the leaching of the metal cations. [START_REF] Kumar | Physical and Chemical Considerations for Improving Catalytic Activity and Stability of Non-Precious-Metal Oxygen Reduction Reaction Catalysts[END_REF][START_REF] Goellner | Degradation of Fe/N/C catalysts upon high polarization in acid medium[END_REF] Such transient events can be mitigated at system level and are therefore less critical than the degradation occurring during normal PEMFC cathode operation.

Highly microporous FeNC catalysts, often prepared via NH 3 pyrolysis, are the most active MNC catalysts in acidic media, but they are also the least stable in operating PEMFCs. [START_REF] Lefèvre | Iron-Based Catalysts with Improved Oxygen Reduction Activity in Polymer Electrolyte Fuel Cells[END_REF][START_REF] Shao | Recent Advances in Electrocatalysts for Oxygen Reduction Reaction[END_REF][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] Proietti | Ironbased cathode catalyst with enhanced power density in polymer electrolyte membrane fuel cells[END_REF][START_REF] Yang | Activity, Performance, and Durability for the Reduction of Oxygen in PEM Fuel Cells, of Fe/N/C Electrocatalysts Obtained from the Pyrolysis of Metal-Organic-Framework and Iron Porphyrin Precursors[END_REF] Recent studies by Dodelet's group and also our group have shown that NH 3 -pyrolyzed microporous FeNC materials show distinctly faster Fe leaching rates in acidic medium than FeNC catalysts similarly prepared but pyrolyzed in inert gas. [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[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] Our study reported that the leaching rate of iron in O 2 -saturated acid electrolyte during a linear scan between 1 and 0 V vs. RHE was circa 10 times higher for a microporous FeNC catalyst (prepared via a first pyrolysis in Ar and a second pyrolysis in ammonia, labelled Fe 0.5 -NH 3 ) relative to the FeNC catalyst prepared identically except that it was not subjected to the second pyrolysis in ammonia (labelled Fe 0.5 -Ar). [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] Furthermore, the online and operando measurement identified that the Fe leaching rate increased when the potential was decreased from 1 to 0 V vs. RHE, with an onset potential at ca 0.75 V vs. RHE and a peak of dissolution rate at 0.2-0.3 V vs. RHE. It was also shown that both the Fe 0.5 -NH 3 and Fe 0.5 -Ar catalysts are exclusively comprised of FeN x sites. Thus, the distinctly lower stability of FeN x sites in Fe 0.5 -NH 3 in acidic electrolyte could be assigned to either the higher microporosity of Fe 0.5 -NH 3 , or to weaker Fe-N bonds in acidic medium. These new insights must also be connected with previous studies from Dodelet's group where it was shown that NH 3 pyrolysis results in nitrogengroups with high pKa values, and the high initial activity in acid medium was proposed to be due to the protonation of such N-groups in acidic medium, and their fast deactivation due to subsequent anion adsorption on positively-charged NH groups. [START_REF] Herranz | Unveiling Nprotonation and anion-binding effects on Fe/N/C-catalysts for O2 reduction in PEM fuel cells[END_REF] Combined together, those results can be interpreted as the coordination of Fe cations with highly basic N-ligands in Fe 0.5 -NH 3 , with the Metal-N coordination leading to high ORR activity but low stability in acid media due to the competition between Fe cations and protons for such highly basic N-groups. Strongly supporting this hypothesis, Fe 0.5 -NH 3 was as stable as Fe 0.5 -Ar in alkaline electrolyte, both from the perspective of the operando-measured Fe leaching rate in O 2 -saturated electrolyte and from the measured ORR activity before and after 5,000 load cycles. [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] The mass activity of Fe 0.5 -NH 3 at 0.9 V vs. RHE in 0.1 M KOH was 4.6 A•g -1 , among the highest reported mass activities for MNC catalysts thus far. [START_REF] Meng | pH-effect on oxygen reduction activity of Fe-based electro-catalysts[END_REF][START_REF] Yasuda | Iron-Nitrogen-Doped Vertically Aligned Carbon Nanotube Electrocatalyst for the Oxygen Reduction Reaction[END_REF][START_REF] Sarapuu | Electrocatalysis of oxygen reduction by iron-containing nitrogen-doped carbon aerogels in alkaline solution[END_REF][START_REF] Ratso | Enhanced oxygen reduction reaction activity of iron-containing nitrogen-doped carbon nanotubes for alkaline direct methanol fuel cell application[END_REF] The high activity combined with high stability in alkaline medium of the most active FeNC catalysts prepared via ammonia pyrolysis is thus promising for their application in Anion Exchange Membrane Fuel Cells (AEMFCs). AEMFCs have recently received significant attention as a possibly much lower cost alternative to PEMFCs, [START_REF] Varcoe | Anion-exchange membranes in electrochemical energy systems[END_REF][START_REF] Thompson | Perspective-The Next Decade of AEMFCs: Near-Term Targets to Accelerate Applied R&D[END_REF][START_REF] Setzler | Activity targets for nanostructured platinum-group-metal-free catalysts in hydroxide exchange membrane fuel cells[END_REF][START_REF] Dekel | Review of cell performance in anion exchange membrane fuel cells[END_REF][START_REF] Gottesfeld | Anion exchange membrane fuel cells: Current status and remaining challenges[END_REF][START_REF] Wang | Pt-Ru catalyzed hydrogen oxidation in alkaline media: oxophilic effect or electronic effect?[END_REF] particularly since the emergence of very high performing and stable cells over the past two years. [START_REF] Omasta | Beyond catalysis and membranes: visualizing and solving the challenge of electrode water accumulation and flooding in AEMFCs[END_REF][START_REF] Huang | Composite Poly(norbornene) Anion Conducting Membranes for Achieving Durability, Water Management and High Power (3.4 W/cm2) in Hydrogen/Oxygen Alkaline Fuel Cells[END_REF][START_REF] Maurya | Rational design of polyaromatic ionomers for alkaline membrane fuel cells with >1 W cm-2 power density[END_REF][START_REF] Wang | High-Performance Hydroxide Exchange Membrane Fuel Cells through Optimization of Relative Humidity, Backpressure and Catalyst Selection[END_REF][START_REF] Li | The Comparability of Pt to Pt-Ru in Catalyzing the Hydrogen Oxidation Reaction for Alkaline Polymer Electrolyte Fuel Cells Operated at 80 °C[END_REF] However, essentially all modern AEMFCs are operated with PGM-based catalysts despite the fact that the highly touted low cost of future AEMFCs relies on the development of highly active and stable PGM-free catalysts for both the cathode and anode. At the cathode, production of hydrogen peroxide during AEMFC operation on FeN 4 sites or on the N-doped carbon surface might limit device lifetime. [START_REF] Zhang | Detection of Reactive Oxygen Species in Anion Exchange Membrane Fuel Cells using In Situ Fluorescence Spectroscopy[END_REF][START_REF] Parrondo | Reactive oxygen species accelerate degradation of anion exchange membranes based on polyphenylene oxide in alkaline environments[END_REF] Hydrogen peroxide, a reaction intermediate produced by the 2+2e -ORR pathway, is a strong oxidizer, which, at low pH and, in the presence of 3d transition metal cations, undergoes Fenton's reaction, producing reactive oxygen species (ROS) such as •OH and •OOH. [START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF][START_REF] Nidheesh | Heterogeneous Fenton catalysts for the abatement of organic pollutants from aqueous solution: a review[END_REF] In high-pH environment, rather than •OH and

•OOH, it is the superoxide radical (O 2 •-
) that is expected from Fenton reactions between hydrogen peroxide and 3d metal cations, or as an ORR intermediate. The superoxide radical has recently been shown to be positively correlated with the decrease in ion-exchange capacity and conductivity of AEMs in an operating AEMFC with in situ fluorescence spectroscopy. [START_REF] Zhang | Detection of Reactive Oxygen Species in Anion Exchange Membrane Fuel Cells using In Situ Fluorescence Spectroscopy[END_REF][START_REF] Parrondo | Reactive oxygen species accelerate degradation of anion exchange membranes based on polyphenylene oxide in alkaline environments[END_REF] In contrast, Fenton reactions between FeNC catalysts and hydrogen peroxide have recently been shown to lead to strong ORR deactivation when occurring in acid electrolyte, but to no ORR deactivation when occurring in alkaline electrolyte. [START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF] Nevertheless, HO 2 -might strongly bind on the Fe-based active sites and, if not electro-reduced to water sufficiently quickly, might poison the FeN 4 active sites.

This effect would be exacerbated in the confined environment of micropores, where both the O 2 access and removal of formed peroxide are limited. [START_REF] Choi | The Achilles' heel of iron-based catalysts during oxygen reduction in an acidic medium[END_REF] The formation of hydrogen peroxide from 2e - ORR is also unwanted in AEMFCs because it lowers the achievable efficiency and power density.

For those reasons, the aim of this work is to identify a stable and active PGM-free electrocatalyst for the 2e -reduction of HO 2 -to OH -in alkaline electrolyte, and to study such a catalyst in combination with the highly active and stable Fe 0.5 -NH 3 catalyst mentioned previously.

With such a composite catalyst, we aim to decrease the amount of peroxide produced during ORR in alkaline electrolyte below the level observed on Fe 0.5 -NH 3 . It is important to remind here that, in contrast to the case in acidic medium, the ORR activity of metal-free N-doped carbons in alkaline medium is not negligible. [START_REF] Masa | Metal-free catalysts for oxygen reduction in alkaline electrolytes: Influence of the presence of Co, Fe, Mn and Ni inclusions[END_REF][START_REF] Yang | Porous N-Doped Carbon Prepared from Triazine-Based Polypyrrole Network: A Highly Efficient Metal-Free Catalyst for Oxygen Reduction Reaction in Alkaline Electrolytes[END_REF][START_REF] Yang | Efficient Metal-Free Oxygen Reduction in Alkaline Medium on High-Surface-Area Mesoporous Nitrogen-Doped Carbons Made from Ionic Liquids and Nucleobases[END_REF] Thus, metal-free nitrogen functional groups can significantly contribute to the ORR in parallel with FeN x sites, in any FeNC catalyst. Such metal-free N-groups typically catalyse the 2e-ORR to HO 2 -. The role of a co-catalyst that is selected for its high activity toward hydrogen peroxide reduction reaction (HPRR) is thus to scavenge hydrogen peroxide produced either at FeN x or metal-free N-sites, in alkaline electrolyte. Manganese oxides have been well-known for decades for their high heterogeneous catalytic activity toward H 2 O 2 disproportionation, which has been investigated in particular for propulsion of space and undersea vehicles. [START_REF] Broughton | Mechanism of Decomposition of Hydrogen Peroxide Solutions with Manganese Dioxide[END_REF][START_REF] Rophael | II -kinetics of the catalytic decomposition of hydrogen peroxide solution by manganese dioxide samples[END_REF] Surprisingly, however, while various transition-metal-oxides have previously been shown to work in tandem with carbon catalysts (carbon catalysing O 2 electro-reduction to HO 2 -and metal-oxides catalysing the HPRR), [START_REF] Malkhandi | Electrocatalytic Activity of Transition Metal Oxide-Carbon Composites for Oxygen Reduction in Alkaline Batteries and Fuel Cells[END_REF][START_REF] Poux | Dual role of carbon in the catalytic layers of perovskite/carbon composites for the electrocatalytic oxygen reduction reaction[END_REF][START_REF] Kéranguéven | Synthesis of efficient Vulcan-LaMnO3 perovskite nanocomposite for the oxygen reduction reaction[END_REF] the investigation of the specific HPRR activity of different Mn-oxide polymorphs has only recently been investigated by Ryabova et al. [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF][START_REF] Bonnefont | R Challenges in the understanding oxygen reduction electrocatalysis on transition metal oxides[END_REF] Among the different Mn-oxide polymorphs that were investigated (α-Mn 2 O 3 , LaMnO 3 , β-MnO 2 , α-MnOOH and Mn 3 O 4 ), α-Mn 2 O 3 was identified as the initially most active one, followed by β-MnO 2 .

Structure-Activity correlations were established, identifying the Mn IV /Mn III redox potential as a key

descriptor to predict the onset potential for HPRR.

From these previous works, we selected four Mn-oxide polymorphs, i.e. three allotropes of MnO 2 , namely α-MnO 2 , β-MnO 2 and δ-MnO 2 , and one Mn-oxide with a different stoichiometry, i.e.

Mn 2 O 3 , which showed to be the most active for HPRR in Ref. [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF] . The stability of the same four MnO x materials was recently investigated by us in a parallel work using a scanning flow cell coupled to inductively coupled mass spectroscopy (SFC/ICP-MS). [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF]

Experimental

Synthesis of manganese oxides

The synthesis of pure α-, β-, δ-MnO 2 and α-Mn 2 O 3 phases was performed according to literature reports, and as detailed in our recent study. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] A brief description is given here for each phase. The α-MnO 2 phase was obtained by reducing KMnO 4 in ultrapure water (18 M) and fumaric acid at room temperature. The resulting gel was filtered, washed with ultrapure water and dried. [START_REF] Zhang | Highperformance fuel cell cathodes exclusively containing atomically dispersed iron active sites[END_REF] δ-MnO 2 was obtained via the reduction of KMnO 4 in a mixture of water, H 2 SO 4 and ethanol, also at room temperature. [START_REF] Quan | New nanocomposite material as supercapacitor electrode prepared via restacking of Ni-Mn LDH and MnO2 nanosheets[END_REF] To obtain β-MnO 2 , the dried δ-MnO 2 powder was calcined at 450 °C. For preparing α-Mn 2 O 3 , γ-MnOOH was prepared in a first step, and then calcined at 550°C for 12 h. [START_REF] Ryabova | Rationalizing the Influence of the Mn(IV)/Mn(III) Red-Ox Transition on the Electrocatalytic Activity of Manganese Oxides in the Oxygen Reduction Reaction[END_REF] For the first step, γ-MnOOH was synthesized dissolving Mn(CH 3 COO) 2 •4H 2 O and KMnO 4 in deionized water, the solution refluxed for 12 h and then washed with water and dried. [START_REF] Cao | A novel non-enzymatic hydrogen peroxide biosensor based on ultralong manganite MnOOH nanowires[END_REF] Synthesis of Fe 0.5 -NH 3 Fe 0.5 -NH 3 was prepared via the sacrificial metal-organic framework method. [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] Commercial ZIF-8 (Basolite® Z1200, Sigma Aldrich), 1,10-phenantroline (≥ 99%, Sigma Aldrich) and iron (II) acetate (≥99.99%, Sigma Aldrich) were mixed in weight ratios of 4/1 for ZIF-8/phenanthroline and 0.5 wt% of iron in the complete catalyst precursor, using planetary ball milling (400 rpm, four cycles of 30 minutes milling with 5 minutes pause between each cycle). The catalyst precursor powder was then collected from the jar, transferred in a quartz boat and inserted in a quartz tube.

For the first pyrolysis, the oven was pre-equilibrated at 1050°C for 2 h while a continuous flow of Ar passed through the tube, with the quartz boat and catalyst precursor inside the tube but outside the heating zone. After 2 h, the quartz boat was pushed in the heating zone in three steps of 30 seconds ("flash pyrolysis"), with the help of an outer magnet and a quartz rod with magnet attached at one end, located inside the tube. The catalyst precursor was pyrolysed in Ar at 1050°C for exactly 1 h, then the split-hinge oven opened and the tube removed and let to cooldown for 20 min, still under Ar flow. A second pyrolysis was then performed, following the above-mentioned procedure, but using pure NH 3 instead of Ar, an oven temperature of 950°C and a pyrolysis duration of 5 min.

The final product is collected directly from the quartz boat (Fe 0.5 -NH 3 ).

Preparation of MnO x /Vulcan and MnO x /Fe 0.5 -NH 3 composites

Each Mn-oxide phase was mixed with Vulcan carbon black in a weight ratio of 1/4 by manual grinding. The Mn-oxides were mixed with Fe 0.5 -NH 3 with the same procedure and weight ratio.

Physical and morphological characterization

The crystalline structure of the four manganese oxides was verified using X-ray Powder Diffraction (XRPD) with PANAlytical X'pert diffractometer in Bragg-Brentano configuration, using a CuK α source (λ=1.5406 Å) in a 2θ range of 5-80° with a step size of 0.035°. The most intense peaks were compared to the literature using PANalytical X'Pert Highscore Plus (version 3.0e). To analyse the morphology of the Mn-oxides, Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 , scanning electron microscopy (SEM) was applied (Hitachi S4800). Specific surface area of the four Mnoxides was determined using the Brunauer-Emmett-Teller (BET) method, cleaning previously the sample with flowing nitrogen at 200°C for 5 h and then measuring the sorption of nitrogen at 77 K (Micromeritics ASAP 2020). Elemental distribution in the α-Mn 2 O 3 /Fe 0.5 -NH 3 composite was analysed with SEM (ZEISS EVO HD15) coupled with Energy-dispersive X-ray spectroscopy (EDX, Oxford Instruments X-Max N SDD Detector).

Electrochemical characterization

The ORR activity and HO 2 -production during ORR were measured with a RRDE setup (Pine Instruments) in 0.1 M KOH, using a three-electrode configuration. The reference electrode was a Pt-wire immersed in a H 2 -saturated electrolyte separated from the main compartment by a fritted glass, acting as a reversible hydrogen electrode (RHE). The counter electrode was a graphite rod immersed in the electrolyte, at a fixed distance from the working electrode. The latter was a glassy carbon tip (5.6 mm of diameter) where the catalyst is deposited. The peroxide produced at the working electrode was detected at the Pt ring. The HPRR activity was measured in a RDE setup (Pine instruments) in the same conditions (glassy carbon tip of 5 mm diameter). The inks were prepared adding in sequence 54 µL of Nafion® (5% perfluorinated resin solution), 744 µL of ethanol and 92 µL of ultrapure water to 5 mg of FeNC, 20%-MnO x /C or 20%-MnO x /FeNC. The mixture was then sonicated for 1 h for homogenization. Then, 8.8 µL of the dispersion was deposited on the RRDE-GC tip, while 7 µL are drop cast on RDE-GC tip, and dried in air at room temperature, to obtain a total catalyst loading of 0.2 mg•cm -2 . For measuring the ORR activity of the Vulcan carbon black (used for Mn-oxides), an aliquot of 7.0 µL was drop cast on the RRDE-tip and 5.6 µL on the RDE-tip, in order to reach a loading of 0.16 mg cm -2 . This corresponds to the loading of Vulcan in the active layers of 20%-MnO x /C. The electrochemical surface area of the Mnoxides was assessed on 20%-MnO x /C composite layers in N 2 -saturated electrolyte by cycling at 10 mV•s -1 and under 1600 rpm rotation the potential between 0.45 and 1 V vs. RHE (SP-300, BioLogic Potentiostat). The lower range potential of 0.45 V was chosen to minimize Mn leaching. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] To measure the ORR activity and selectivity, the solution was saturated with O 2 and the potential scanned in the range 0 -1 V vs. RHE with a scan rate of 1 mV s -1 , while the potential of 1.2 V vs.

RHE was applied to the Pt ring. To measure the activity toward HPRR, a RDE configuration was used, and the measurement performed in N 2 -saturated 0.1 M KOH with 2 mM H 2 O 2 , scanning the potential at 1 mV•s -1 in the range 0.45 -1.0 V vs. RHE. During ORR and HPRR measurements, the scan rate of 1 mV•s -1 was sufficiently low to neglect non-Faradaic currents.

Operando Scanning Flow Cell -Inductively Coupled Plasma Mass Spectroscopy

To evaluate the stability of the α-Mn 2 O 3 /Fe The dispersion was then sonicated in an ice bath for 1 h and then sprayed on a gas diffusion layer (Toray 60, 5 wt % PTFE wet-proofing) using an airbrush (Iwata Eclipse HP CS). The obtained gas diffusion electrodes and ETFE membrane (50 µm thick in fully swollen conditions) were then soaked for 20 min in 1 M KOH, and this was repeated two times. The electrodes and membrane were combined to create Membrane Electrode Assemblies (MEAs) without prior hot-pressing in single-cell Scribner fuel cell hardware using Teflon gaskets, with the gasket thickness chosen to reach 25% compression. The AEMFC was operated using a Scribner 850e Fuel cell test system, flowing H 2 /O 2 at 1.0 L min -1 with a cell temperature of 60°C. No back pressure was applied on anode and cathode sides. The break-in was performed at 0.5 V, adjusting the relative humidity (RH) at both electrodes. [START_REF] Kumar | Physical and Chemical Considerations for Improving Catalytic Activity and Stability of Non-Precious-Metal Oxygen Reduction Reaction Catalysts[END_REF][START_REF] Zitolo | Identification of catalytic sites for oxygen reduction in iron-and nitrogen-doped graphene materials[END_REF] AEMFC tests were carried out with three different cathodes: a) 40wt%Pt/C, with a loading of 0.45 mg Pt cm -2 (total catalyst loading of 1.125 mg cm -2 ); b) Fe 0.5 -NH 3 ; and c) α-Mn 2 O 3 /Fe 0.5 -NH 3 (20 wt % α-Mn 2 O 3 ). Both b) and c) had a total catalyst loading of 1.5 mg cm -2 .

All AEMFC experiments were carried out using 40wt%[PtRu]/C at the anode side (Pt:Ru 2:1 mass ratio) with a loading of 0.9 mg PGM cm -2 .

Results and discussion

Physical and morphological characterization of Mn-oxides

The XRPD patterns shown in Figure 1a demonstrate the formation of pure or almost pure Mn-oxide phases. All diffraction peaks could be assigned to the targeted oxide phase (Figure S1), except for the α-Mn 2 O 3 pattern which also shows minor peaks that can be assigned to α-MnO 2 (5% of α-MnO 2 was estimated, [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] ). Figure 1a also underlines the higher crystallite dimension of α-Mn 2 O 3 compared to MnO 2 structures. The δ-MnO 2 phase, especially, shows broad peaks indicating small crystallite. The high crystallinity of the present α-Mn 2 O 3 material may be related to the high temperature of 550°C at which it was formed (see Experimental section), while the higher crystallinity of β-MnO 2 relative to δ-MnO 2 may also be explained from the viewpoint of the synthesis temperature, namely room temperature for δ-MnO 2 and then calcination at 450°C to obtain β-MnO 2 . Figure 1b shows the N 2 adsorption isotherms of the Mn-oxide materials, revealing the coexistence of micro and mesopores. The isotherm of δ-MnO 2 shows the highest amount of N 2 adsorbed, especially at low P/P 0 and also a strong hysteresis closing at P/P 0 of ca 0.4. The shape of the isotherms of β-MnO 2 and α-MnO 2 are similar up to P/P 0 = 0.7, with the one for β-MnO 2 vertically translated by ca +10 cm 3 •g -1 due to a higher amount of N 2 adsorbed at low P/P 0 . The adsorption isotherm for α-Mn 2 O 3 clearly shows a much lower amount of N 2 adsorbed relative to all others, as well as little hysteresis, suggesting it is mostly non-porous. The BET specific surface areas are, in increasing order, 14 (α-Mn 2 O 3 ), 98 (α-MnO 2 ), 136 (β-MnO 2 ) and 214 (-MnO 2 ) m 2 •g -1 .

In line with the very different BET areas, SEM micrographs provide evidence of substantial morphological differences. Macroscopically, δ-MnO 2 and β-MnO 2 share a common particle morphology, with a size in the range of 300-600 nm (Figure 2a,c), in line with the fact that β-MnO 2 was simply derived from δ-MnO 2 via calcination. However, the high resolution SEM micrographs show that the δ-MnO 2 surface has a higher roughness, with a sand-rose morphology and chambers of 30-60 nm, which partially collapsed in the case of the β-MnO 2 particles (Figure 2 b, d). The macroscopic morphology of α-MnO 2 is different, with large chunks that are several µm in size, composed of numerous well-defined rod-shaped sub-structures with ~130-300 nm length and 30-60 nm width, and also of even smaller objects with less-defined particle-like morphology (Figure 2 e,f). Last, α-Mn 2 O 3 has an molten-like, interconnected particle morphology with a smooth surface and an average particle dimension of ~150 nm (Figure 2 g, h), in line with the higher temperature of 550°C used for its synthesis. The BET area ranking can be reasonably well explained from the surface roughness as identified by the higher magnification SEM images. The highest BET area was measured for δ-MnO 2 (214 m 2 •g -1 ) with highest surface roughness (Figure 2b), then slightly decreased BET area of 136 m 2 •g -1 for β-MnO 2 due to partial collapse of surface tortuosity (Figure 2d), still a high surface area of 98 m 2 •g -1 for α-MnO 2 with smooth but small rod substructures (Figure 2f) and, last, drastically lowered BET area of 14 m 2 •g -1 for α-Mn 2 O 3 due to interconnected and molten particles morphology (Figure 2h).

To further verify the apparent correlation between surface roughness and BET area, we calculated the expected size of Mn-oxide nanostructures from their measured BET areas and assuming either a slab, rod, or spherical geometry (depending on samples) for the non-porous oxide substructures assumed to contribute mainly to the surface area of the Mn-oxides (Table 1). For δ-MnO 2 and β-MnO 2 , the assumed slab geometry results in an estimated slab thickness of ca 3 nm, in line with the very thin walls of the sand rose morphology observed by SEM.

For α-MnO 2 , the assumed rod morphology results in ca 9 nm diameter for the rods, which is significantly smaller than observed by SEM. Therefore, the surface of α-MnO 2 seems to result from a contribution of the observed rods but also from the smaller and less defined objects seen in the SEM images (Figure 2f). Last, for α-Mn 2 O 3 , the estimated particle size of ca 95 nm is in line with the smooth particles observed in SEM images (Figure 2h). Due to these large morphological differences, the apparent activity that will be measured for a given loading of Mn-Oxides during RDE measurements needs to be normalized to the active area, which will be approximated by the BET area.

Electrochemical characterization of Mn-oxides

The HPRR activity of the four Mn-oxide polymorphs mixed either with Vulcan (Figure 3a)

or Fe 0.5 -NH 3 (Figure 3b) was investigated by RDE in deaerated 0.1 M KOH with 2 mM HO 2 -. The polarization curves seen in Figure 3a-b show a cathodic branch that can mainly be assigned to HPRR (Eq. 1), and an anodic branch that can be assigned to the hydrogen peroxide oxidation reaction (HPOR) (Eq. 2). In addition, a minor fraction of the cathodic current might also be related to the ORR, with O 2 being produced in situ via non-electrochemical disproportionation of peroxide on the Mn-oxide surface (Eq. 3).

HO 2 -+ H 2 O + 2 e -→ 3 OH - ; E 0 ~ 1.73 V vs. RHE at pH 13 (1)
HO

Considering the electrochemical reactions, it is important to note that reaction 2 is not the reverse of reaction 1, and the HPRR and HPOR have therefore very different equilibrium potentials, at pH 13 (see the equations above). [START_REF] Pourbaix | Atlas of Electrochemical Equilibria in Aqueous Solutions NACE International[END_REF] This expresses that, in the entire potential region between 0.72 and 1.73 V vs. RHE, HO 2 -should be highly unstable and could, theoretically, be at the same time electro-oxidized to O 2 and electro-reduced to OH -. In practice, however, a net oxidation current is generally observed at potentials above 0.8-0.9 V, depending on the electrocatalyst investigated, and assigned to HPOR. [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF][START_REF] Poux | Electrocatalysis of hydrogen peroxide reactions on perovskite oxides: experiment versus kinetic modeling[END_REF] The oxidation reaction (2) may be kinetically much faster than the reduction reaction (1), explaining why a net positive current is observed on all types of catalysts in that potential region. A net reduction current is observed for MnO x /Vulcan only when the HPOR becomes thermodynamically unfavored, i.e. at potentials lower than 0.83-0.85 V vs. RHE (Figure 3a). At those conditions, peroxide anions at the electrode surface can no longer be oxidized, and 3a). In the anodic branch (where the HPOR dominates), in contrast, no such effect was reported by Ryabova et al, and the HPOR was limited by peroxide diffusion at potentials above 1 V vs. RHE for both α-Mn 2 O 3 and β-MnO 2 . [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF] A similar observation is made here for oxidation current densities at 1 V vs.

RHE on the present set of Mn-oxides mixed with Vulcan, though the anodic branches at this potential have not yet reached their plateau. This may be due to the lower OH -concentration used in our work compared to Ref. [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF] , 0.1 and 1.0 M respectively. As one can see from reaction (2), the HPOR involves OH -as a reactant, and this could therefore impact the HPOR reaction rate.

As discussed in the previous section, the different Mn-oxides possess very different BET areas, α-Mn 2 O 3 in particular having a lower BET area than the other materials. To assess their intrinsic activity for HPRR/HPOR, we normalized the geometric current density by the surface area of the oxides in the active layer (from the known loading and the known BET area of each oxide).

Since Vulcan has negligible activity for HPRR/HPOR (Figure 3a, black diamonds), this normalization should correctly represent the intrinsic specific surface activity of the Mn-oxides in a three-electrode environment where the liquid electrolyte should wet the entire surface of all of the catalysts. Figure 3c highlights the superior intrinsic activity of α-Mn 2 O 3 compared to the three MnO 2 allotropes. In this graph, α-Mn 2 O 3 not only reaches higher reduction currents at low potential (due to non-limitation by a slow chemical step) but also higher oxidation and reduction currents near OCP, i.e. faster intrinsic electrochemical kinetics. To quantify the latter, we fitted the polarization curves in the region close to OCP (-30/+50 mV) with a straight line and report the derivative dj/dE (units of mA cm oxide -2 V -1 ) in Figure 3d. The figure shows a seven-fold higher intrinsic activity for α-Mn 2 O 3 compared to β-and α-MnO 2 , and fourteen-fold higher intrinsic activity compared to -MnO 2 . These results are in line with the five-fold higher kinetic rate constants k 2 and k 3 for Mn 2 O 3 vs. β-MnO 2 reported in Ref. [START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF] , and describing the kinetics of reaction (2). In conclusion, α-Mn 2 O 3 is shown to be intrinsically more active toward HPRR and HPOR than the other Mn-oxides. Furthermore, our recent study showed that Mn 2 O 3 is also more stable than the other Mn-oxides in the ORR region, even after normalization for the oxide area. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] Therefore, both from activity and stability viewpoints, α-Mn 2 O 3 is clearly identified as the most promising Mnoxide material for scavenging peroxide species produced during ORR. The full potential of α-Mn 2 O 3 vs. other Mn-oxides is, however, challenged by its generally lower BET area, related to the high temperature calcination usually required to form this phase. [START_REF] Zhang | Synthesis of highly efficient Mn2O3 catalysts for CO oxidation derived from Mn-MIL-100[END_REF][START_REF] Saputra | Shape-controlled activation of peroxymonosulfate by single crystal α-Mn2O3 for catalytic phenol degradation in aqueous solution[END_REF][START_REF] Qiu | Morphologyconserved transformation: synthesis of hierarchical mesoporous nanostructures of Mn2O3 and the nanostructural effects on Li-ion insertion/deinsertion properties[END_REF] In the next step, the Mn-oxides were mixed with Fe 0. in both alkaline and acid electrolytes. [START_REF] Jaouen | O2 Reduction Mechanism on Non-Noble Metal Catalysts for PEM Fuel Cells. Part I: Experimental Rates of O2 Electroreduction, H2O2 Electroreduction, and H2O2 Disproportionation[END_REF][START_REF] Muthukrishnan | Kinetic Approach to Investigate the Mechanistic Pathways of Oxygen Reduction Reaction on Fe-Containing N-Doped Carbon Catalysts[END_REF][START_REF] Choi | Unraveling the Nature of Sites Active toward Hydrogen Peroxide Reduction in Fe-N-C Catalysts[END_REF][START_REF] Shah | Exploring Fe-Nx for Peroxide Reduction: Template-Free Synthesis of Fe-Nx Traumatized Mesoporous Carbon Nanotubes as an ORR Catalyst in Acidic and Alkaline Solutions[END_REF][START_REF] Ramaswamy | Fundamental Mechanistic Understanding of Electrocatalysis of Oxygen Reduction on Pt and Non-Pt Surfaces: Acid versus Alkaline Media[END_REF][100][101] The maximum HPOR and HPRR current densities for Fe 0.5 -NH 3 in the potential range 0.5 -1.0 V vs. RHE are ca 0.2 and 0.4 mA cm -2 , respectively (Figure 3b). These values are significantly increased for the MnO x /Fe 0.5 -NH 3 composites. In the cathodic region, the limiting current density increases in the same order than for the MnO x /Vulcan catalysts, i.e. α-MnO 2 < -MnO 2 = β-MnO 2 < α-Mn 2 O 3 , demonstrating the role of Mn-oxides in increasing the HPRR activity of MnO x /Fe 0.5 -NH 3 composites. In the anodic region of 0.95 -1.0 V vs. RHE, the HPOR current density is also higher for all MnO x /Fe 0.5 -NH 3 composites relative to Fe 0.5 -NH 3 (Figure 3b). The composites MnO x /Fe 0.5 -NH 3 also show higher OCP values than the corresponding MnO x /Vulcan layers, with OCP values of 0.86 and 0.91 V vs. RHE for α-MnO 2 /Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 , respectively. This shift of OCP toward higher values seems driven by the even higher OCP of Fe 0. Overall, the results confirm the higher HPRR activity of α-Mn 2 O 3 /Fe 0.5 -NH 3 relative to Fe 0.5 -NH 3 itself, at potentials of 0.50 -0.85 V vs. RHE. At the cathode of an AEMFC, peroxide may be produced in this potential region due to high current density and relatively low selectivity of metal-free N-doped sites (along with some peroxide produced from FeN x sites).

The demonstration of the concept of scavenging HO 2 -formed during ORR with the addition of Mn-oxides to Fe 0.5 -NH 3 requires RRDE measurements in O 2 -saturated electrolyte. Figure 4a,b and c shows, for reference, the ORR selectivity, activity, and Tafel plots of MnO x /Vulcan layers, respectively. The ORR onset of MnO x /Vulcan layers is ca 0.9 V vs. RHE for α-Mn 2 O 3 , α-and β-MnO 2 and ca 20 mV lower for -MnO 2 . The ORR polarization curves show otherwise a similar shape and reach comparable O 2 -diffusion-limited current densities (Figure 4b). The latter are close to the value expected for an overall apparent four-electron ORR, correlated by low % HO 2 -detected at the ring (Figure 4a). A closer detail at the % HO 2 -reveals fine differences, with decreasing % HO 2 -in the order α-MnO 2 , -MnO 2, β-MnO 2 , α-Mn 2 O 3 . This is in the exact same order as the absolute value of limiting current density observed on MnOx/C layers in the HPRR measurement (Figure 3a). This suggests that ORR on these MnO x proceeds via a 2e + 2e pathway (subsequent 2 electron reduction on same MnO x sites) and/or via 2 electron pathway to peroxide followed by catalytic HO 2 -decomposition (reaction 3), which could also lead to apparent overall near fourelectron ORR.

Then, the ORR selectivity, polarization curves and Tafel plots were recorded for Fe 0.5 -NH 3 and the four MnO x /Fe 0.5 -NH 3 composite layers (Figure 5a, b andc). Fe 0.5 -NH 3 alone shows high ORR activity of ca 1 mA•cm -2 at 0.9 V vs. RHE (Figure 5c), i.e. a mass activity of ca 5 A•g -1 , similar to our recent study on Fe 0.5 -NH 3 in alkaline electrolyte. [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] The ORR activity (in mA per cm 2 geometric area of electrode) at 0.9 V of the four MnO x /Fe 0.5 -NH 3 composite layers is ca 30 % lower (Figure 5c), as expected from the slightly lower loading of Fe 0.5 -NH 3 in the composite cathodes(only 0.16 mg FeNC cm -2 , while the reference Fe 0.5 -NH 3 layer has 0.2 mg FeNC cm -2 ). Last but not least, Figure 5a demonstrates higher selectivity for all MnO x /Fe 0.5 -NH 3 composite layers relative to Fe 0.5 -NH 3 . This result can be interpreted as a bifunctional effect of the composite layers, the FeN x sites mainly driving the ORR to water, with minute amount of peroxide produced (either on some FeN x sites, or on the N-C surface), and the MnO x particles mainly acting as electrochemical scavengers of the minute amount of peroxide produced.

Then, the stability of the composite catalyst α-Mn 2 O 3 /Fe 0.5 -NH 3 was evaluated in operando SFC-ICP/MS. α-Mn 2 O 3 was selected for the composite catalyst due its higher oxide-surface-normalized HPRR activity, as shown in this study and previously, as well as its slightly higher stability among this set of four oxides, as recently reported by Speck et al. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] Figure 6a (middle panel) in the region from 0 to ca 500 s shows similarly low Mn dissolution rates (< ca 0.1 ng•cm -2 •s -1 ) for the composite α-Mn 2 O 3 /Fe 0.5 -NH 3 catalyst in both Arand O 2 -saturated electrolytes when the potential is cycled for the first time between 1 and 0.6 V vs.

RHE. This is due to the relative thermodynamic stability of α-Mn 2 O 3 in that restricted potential range. In contrast, when the potential is cycled between 1 and 0 V (time between 500 and 1500 s in Figure 6a, middle panel), the Mn leaching rates increased when compared to the 1-0.6 V cycling and a higher Mn leaching rate is observed in O 2 -saturated electrolyte vs. Ar-saturated electrolyte.

This trend is similar to that observed for α-Mn 2 O 3 /C. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] An accelerated stress test protocol showed that the overall dissolution of manganese in O 2 -saturated electrolyte is higher than in Ar-saturated electrolyte (Figure 6b, middle panel), which is explained by the presence of hydrogen peroxide produced in O 2 -saturated solution, as already reported by us. [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] Concerning Fe dissolution, the catalyst does not show evident dissolution peaks during the first slow scan between 1 and 0 V in O 2 -saturated electrolyte (Figure 6b, bottom panel). The dissolution rate of Fe is 0.05-0.08 ng cm - 2 •s -1 , comparable to the Fe leaching rate observed for Fe 0.5 -NH 3 and previously reported by us. [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] Figure 6b (middle panel) shows that the Mn dissolution rate from α-Mn 2 O 3 /Fe 0.5 -NH 3 is comparable to the dissolution rate previously reported for the same Mn-oxide supported on Vulcan, [START_REF] Speck | Mechanisms of Manganese Oxide Electrocatalysts Degradation During Oxygen Reduction and Oxygen Evolution Reactions[END_REF] with maximum leaching rates of 0.15-0.30 ng•cm -2 •s -1 in O 2 -saturated electrolyte during the slow-scan from 1 to 0 V vs. RHE.

Finally, Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 were evaluated as cathode catalysts in operating AEMFCs and compared to Pt/C (Figure 7a). The initial activity at 0.9 V of the PGM-free cathodes is comparable with that of Pt/C. Fe 0.5 -NH 3 shows a higher current density at 0.9 V (65 mA•cm -2 ) than the composite catalyst, which shows 52 mA•cm -2 , almost identical to Pt/C (55 mA•cm -2 ) (Figure 7b). At lower cell voltages, the PGM-free cathodes do not perform as well as the Pt/C cathode, due to mass transport issues. The latter may be assigned to a more difficult path for oxygen to reach the active centers in such cathodes (possibly due to the microporous nature of the carbon in Fe 0.5 -NH 3 ), but also to local O 2 starvation due to the much lower site density in Fe 0.5 -NH 3 (ca 1.6 wt % Fe only) [START_REF] Santori | Effect of Pyrolysis Atmosphere and Electrolyte pH on the Oxygen Reduction Activity, Stability and Spectroscopic Signature of FeNx Moieties in Fe-N-C Catalysts[END_REF] compared to Pt/C (40 wt% Pt). Nevertheless, the absolute performance obtained with these PGM-free cathodes is promising: the Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 cathodes are able to support a current density of 1.43 A•cm -2 and 1.25 A•cm -2 at 0.6 V, respectively. This corresponds to 59 and 52 % of the current density at 0.6 V obtained with 0.45 mg Pt •cm -2 (2.41 A•cm -2 ). The peak power density is 1.04 W•cm -2 for Fe 0.5 -NH 3 and 0.98 W•cm -2 for the α-Mn 2 O 3 /Fe 0.5 -NH 3 composite, compared to 1.53 W•cm -2 for Pt/C (Figure 7c). The higher current density at low potential (below 0.4 V) obtained using the composite α-Mn 2 O 3 /Fe 0.5 -NH 3 cathode relative to Fe 0.5 -NH 3 can be attributed either to the effect of the Mn-oxide helping in reducing peroxide into water at those potentials, or to the slightly lower loading of Fe 0.5 -NH 3 in the composite cathode (80% the loading compared to that in the Fe 0.5 -NH 3 layer), helping the mass-transport of oxygen.

Table S2 gives an overview of AEMFC performance previously reported in H 2 and O 2 conditions with PGM-free cathodes, indicating three key metrics corresponding to pure kinetic control (current density at 0.9 V), mixed kinetic and mass-transport control (current density at 0.6

V) and mainly mass-transport control (peak power density) of the cell performance. From Table S2, it can be seen that the current densities reached at 0.9 V with the present Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 cathodes (52-65 mA•cm -2 ) are significantly above those reported for PGM-free metal oxides (≤15 mA•cm -2 ) 102-106 and other FeNC catalysts with atomically-dispersed FeN x sites (≤25 mA•cm -2 ), [107][108][109][110][111] with the exception of the material labelled Fe-S-Phen/CNT in Ref. 112 and that resulted in a current density of ca 120 mA•cm -2 . The latter was however reached at a cell temperature of 80°C, significantly above 60°C used in the present study. The current density at 0.6

V is an important operating point of fuel cells, often resulting in a balanced compromise between energy efficiency (proportional to the ratio of operating to thermodynamic cell voltage) and power density. The current density obtained with Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 cathodes (1430 and 1250 mA•cm -2 ) are above those reported for other FeNC cathodes (190-977 mA•cm -2 ), 107,108,[110][111][112] with two exceptions. The first, is a FeCoPc/C material in Ref. 109 , that achieved 1670 mA•cm -2 at 0.6 V, though this was obtained at a higher cell temperature (80°C vs. 60°C here) and with a thinner AEM (15 µm, vs. 50 µm here). Both factors are known to significantly improve the cell performance, due to lower Ohmic resistance but also improved water management. The second exception was by our team using a similar FeNC material to Fe 0.5 -NH 3 113 , where a current density of 1830 mA•cm -2 was achieved at 65°C and 0.6 V. However, the data reported there was taken with a much higher IEC, thinner AEM based on high density polyethylene. 114 The current density obtained with Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 cathodes at 0.6 V (1430 and 1250 mA•cm -2 ) are also above those reported recently for CoO and Co 3 O 4 (320 and 520 mA cm -2 ), 105,106 and comparable to those reported for CoO x or cobalt ferrite (1330 and 1560 mA cm -2 ) 103 and MnCo 2 O 4 spinel (970 mA cm -2 at 60°C) 104 . The CoO x and cobalt ferrite were tested in the same cell housing and with the same FC test bench as in the present study, although at slightly higher cell temperatures of 65 and 70°C (Table S2). Thus, in spite of a much higher ORR activity at 0.9 V for the present cathodes based on Fe 0.5 -NH 3 vs. those based on cobalt-rich oxides, similar current densities are hitherto observed at 0.6 V. This reveals that mass-transport must be further optimized in Fe 0.5 -NH 3 cathodes, in order to improve the catalyst utilization at high current density.

This could be achieved with improved water management (thinner membrane, optimized cell and O 2 gas temperature and RH) or improved layer structure (pore size distribution, ionomer content and distribution, optimized catalyst loading). Finally, the peak power densities achieved with PGMfree cathodes are also reported in Table S2. A graph of peak power density vs. current density at 0.6

V of the data shown in

Conclusions

This study demonstrates that the addition of Mn-oxides to FeNC helps in scavenging hydrogen peroxide formed during ORR. The lower yields of hydrogen peroxide observed with MnO x /Fe 0.5 -NH 3 layers compared to Fe 0.5 -NH 3 alone derives from the high HPRR kinetics of the four Mn-oxides. α-Mn 2 O 3 was shown to have higher HPRR activity than the other three Mn-oxide polymorphs, once normalized per oxide surface area. The present α-Mn 2 O 3 material has however lower BET area than the other Mn-oxides, and further progress could thus be made by synthesizing α-Mn 2 O 3 powders with higher BET area. α-Mn 2 O 3 was selected for stability study in operando SFC-ICP/MS. The results show similar dissolution rates for Mn and Fe from the mixed layer 20 wt% α-Mn 2 O 3 / 80 wt% Fe 0.5 -NH 3 when compared to 20wt% α-Mn 2 O 3 /C and Fe 0.5 -NH 3 , respectively. Thus, no synergy effect is observed regarding the metal leaching rates. The results also show similar trends as for the separate materials with respect to the saturating gas, with higher Mn dissolution rates in O 2 vs. Ar-saturated electrolytes, due the production of hydrogen peroxide that triggers the Mn leaching. Finally the mixed layer α-Mn 2 O 3 /Fe 0.5 -NH 3 was tested at the cathode of an AEMFC, showing comparable activity at 0.9 V to the Fe 0.5 -NH 3 cathode. The activity at 0.9 V in AEMFC is comparable to that achieved with a Pt/C cathode and significantly higher than that reported hitherto for cathodes based on cobalt-rich oxides. At 0.6 V cell voltage, current densities close to 1000 mA•cm -2 were achieved with α-Mn 2 O 3 /Fe 0.5 -NH 3 and Fe 0.5 -NH 3 cathodes at 60°C in H 2 /O 2 conditions. This is comparable to the best results recently reported with cathodes based on cobalt-rich oxides. Further optimization of the mass-transport is necessary to increase the catalyst utilization at high current density, in order to fully exploit the kinetic advantage of FeNC vs. other PGM-based and PGM-free cathodes.

5 -

 5 Figure S3a, showing an alveolar structure in that region of the catalyst. Due to the commensurate

5 -

 5 NH 3 (0.94 V vs. RHE). In fact, due to the negligible activity of Vulcan in MnO x /Vulcan layers, and the comparable loading of Fe 0.5 -NH 3 in the pure Fe 0.5 -NH 3 layer and in the composite MnO x /Fe 0.5 -NH 3 layers (0.20 and 0.16 mg cm -2 , respectively), one may expect the polarization curves of MnO x /Fe 0.5 -NH 3 layers to behave, in first approximation, as the linear superposition of the polarization curves of MnO x /Vulcan and Fe 0.5 -NH 3 .

  at the anode). Improved cathode and cell performance by operating to higher temperature and with optimized conditions should, in the near future, leverage the reduction of the PGM loading at the anode while maintaining the same overall cell performance. The investigation of the durability of FeNC layers during AEMFC operation and their optimization for operation with air (CO 2 -free) feed are the next important steps for increased maturity of FeNC-based AEMFC cathodes.

Figures 2 -MnO 2 -MnO 2  3 -MnO 2 -MnO 2 -MnO 2 Figure 1 .

 22232221 Figures

Figure 2 . 2 -Mn 2 O 3 -MnO 2 / 3 -MnO 2 / 3 -MnO 2 / 3 -Mn 2 O 3 / 3 FeFigure 3 . 2 mg cm - 2 . 2 -MnO 2 -MnO 2 

 223232323233322222 Figure 2. SEM micrographs of the different Mn-oxide polymorphs, in two different magnifications.

Figure 4 .

 4 Figure 4. ORR selectivity, polarisation curves and Tafel plots for the four Mn-oxides supported onVulcan carbon (20 wt% MnO x / Vulcan). The total catalyst loading is 0.2 mg•cm -2 , except for Vulcan alone, with a carbon loading of 0.16 mg•cm -2 mimicking the amount of Vulcan deposited

5 - 3 FeFigure 5 .

 535 Figure5. ORR selectivity, polarisation curves and Tafel plots for the four Mn-oxides supported on Fe 0.5 -NH 3 (20 wt% MnO x / Fe 0.5 -NH 3 ). The total catalyst loading is 0.2 mg cm -2 (i.e. 0.2 mg FeNC cm - 2 for the pure layer, and only 0.16 mg FeNC cm -2 for the composite layers). All experiments were

Figure 6

 6 Figure 6 Operando SFC-ICP/MS of the composite catalyst 20% α-Mn 2 O 3 /80%Fe 0.5 -NH 3 in

Figure 7 .

 7 Figure 7. AEMFC polarization curves measured for Pt/C, Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3

  and iron during electrochemical protocols. Electrochemical measurements have been carried out in a three electrode configuration using a graphite counter electrode, Ag/AgCl reference electrode (Metrohm) and glassy carbon as working electrode. Electrochemistry is applied with a

	Potentiostat (Gamry, Reference 600) in 0.05 M NaOH (Merck, Certipur), reaching a controlled pH
	(Mettler Toledo, SevenExcellence) of 12.7, used to convert the potentials of the Ag/AgCl to the
	RHE.

0.5 -NH 3 catalyst, a custom made polycarbonate SFC coupled with ICP-MS (Perkin Elmer, NexION 350X) is used to measure in situ the dissolution of manganese

AEMFC testing

The catalytic inks were prepared following the procedure described in Omasta et al., manually grinding the catalyst and ETFE (ethylene tetrafluoroethylene) powder ionomer

[START_REF] Biancolli | ETFEbased anion-exchange membrane ionomer powders for alkaline membrane fuel cells: a first performance comparison of head-group chemistry[END_REF]

, and then dispersing the solids in a mixed solvent of H 2 O and 1-propanol (10 vol % H 2 O). All anodes were prepared from a 60% PtRu/C commercial catalyst (Johnson Matthey, 2:1 Pt:Ru mass ratio), mixed with Vulcan to reach 40 wt% PtRu/C. 150 mg of 40 wt% PtRu/C and 37.53 mg of ETFE were weighed to prepare the anode ink (ionomer/carbon ratio 0.41). Pt/C cathodes were prepared from a 60 wt% Pt/C commercial catalyst (Johnson Matthey) mixed with Vulcan to reach 40 wt% Pt/C. The catalyst and AEI mass were the same as used for the anode ink. For Fe 0.5 -NH 3 cathodes, 82.59 mg of catalyst was mixed with 17.35 mg ETFE AEI (ionomer/carbon ratio 0.21). For α-Mn 2 O 3 / Fe 0.5 -NH 3 cathodes, 20.91 mg α-Mn 2 O 3 , 82.59 mg Fe 0.5 -NH 3 and 17.35 mg ETFE were mixed together.

  become available for HPRR. No clear redox process associated with Mn-oxides is observed near the open circuit potential of 0.83-0.85 V vs. RHE, except for -MnO 2 , as shown by cyclic voltammetry expected from the Levich equation.[START_REF] Ryabova | Study of Hydrogen Peroxide Reactions on Manganese Oxides as a Tool To Decode the Oxygen Reduction Reaction Mechanism[END_REF] In contrast, the limiting current density of HPRR on α-Mn 2 O 3 obeyed the Levich equation and increased proportionally with the square root of the electrode rotation rate. The authors concluded that for MnOOH and Mn 3 O 4 (as well as β-MnO 2 ,

	in a N 2 -saturated electrolyte free of peroxide (Figure S2). Therefore, the experimental values of
	open circuit potential (OCP) should be close to the equilibrium potential of reaction (2). With 2 mM
	HO 2	-in the electrolyte and assuming an activity of 1 for O 2 (i.e. assuming that reaction 3 can
	saturate with O 2 in a very thin layer of electrolyte near the catalyst surface), one can calculate from
	the Nernst equation that the equilibrium potential for reaction (2) should be 0.801 V vs. RHE,
	partially) the rate of the HPRR at low potential is not controlled by the diffusion of HO 2	-species
	toward the electrode, but by a slow chemical step. The latter was proposed to be the chemical
	dissociation of the O-O bond of an adsorbed HO 2

assuming a pH of 13. This is close to the experimentally observed values of OCP, ranging from 0.83 to 0.85 V vs. RHE for -MnO 2 , β-MnO 2 , α-MnO 2 and α-Mn 2 O 3 , in the order of increasing OCP values. Looking at the cathodic branch, assigned mainly to HPRR, the current density reaches different values of plateau depending on the Mn-oxide considered (Figure

3a

), which is in contradiction with a same diffusion-limited current density expected for the two-electron HPRR.

During the HPRR, the current density plateau is highest for α-Mn 2 O 3 , followed by β-MnO 2 and -MnO 2 , and lowest for α-MnO 2 . It was shown by Ryabova et al that this limiting current density can be assigned entirely to a rate-determining chemical step in the HPRR on Mn 3 O 4 and MnOOH (with the limiting current density showing no variation with the rotation rate), while β-MnO 2 was in an intermediate situation in which the limiting current density slightly varied with rotation rate, but much less than -on a Mn III site. Our experimental results are in line with this, showing that other MnO 2 allotropes behave similarly to β-MnO 2 (Figure

  TableS2would reveal a straight proportional relationship, such that a separate discussion is not necessary for the peak power density. A similar linear relationship is also visible in Figure7bof Ref.108 .Overall, this overview of recent AEMFC data with PGM-free cathodes highlights the kinetic advantage of the present Fe 0.5 -NH 3 and α-Mn 2 O 3 /Fe 0.5 -NH 3 cathodes over other PGM-free cathodes, and its state-of-the-art power density. Compared to the 2022 milestone of initial AEMFC performance recently defined by the U.S. Department of Energy (≥ 1000 mA cm -2 at 0.65 V on H 2 /O 2 , T ≥80°C and total PGM loading ≤ 0.2 mg cm -2 , see TableIIin Ref. 8), the cell performance achieved at 60°C and at 0.65 V in the present work (1110 mA cm -2 ) already slightly exceeds this milestone with a PGM-free cathode, but with a higher PGM loading than targeted in the MEA (i.e.

Table 1 .

 1 Measured BET areas of the Mn-oxides and estimated object size of the oxide nanostructures forming the BET area. A slab geometry was assumed for δ-MnO 2 and β-MnO 2 and spherical geometry for α-Mn 2 O 3 .

	Tables
	Oxide	BET (m 2 •g -1 )	Density (g•cm -3 )	Theor. specific	Calculated object
				surface area	size / nm
	δ-MnO 2	214	3.00	2 / (ρ•t ) (slab)	3.1
	β-MnO 2	136	5.03	2 / (ρ•t ) (slab)	2.9
	α-MnO 2	98	4.36	4 / (ρ•d) (rod)	9.4
	α-Mn 2 O 3	14	4.50	6 / (ρ•d) (sphere)	95.2

Acknowledgments

This work was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement CREATE [721065]. We are grateful to Prof. John R. Varcoe (Univ. Surrey, UK) for providing the ETFE ionomer and membrane.