Grenoble Institute of Technology

As part of the DAEMOHYC project funded by the French government, we are developing alkaline anion exchange membrane electrolysis without platinum group materials, down to the 25cm2 single cell scale. Our consortium comprises five university laboratories and two industrial partners. Our strategy is to explore different ways of shaping materials, from the simplest, cheapest and most robust to the most performing, expensive and complex. For example, for the anode, we began by considering the use of simple activated stainless steel felts, without the use of ionomer [1]. We then textured these felts and decorated them with cobalt spinels by a combustion method using an amino acid ignition agent. The advantage of this shaping method is the robustness of the resulting electrodes and their good electronic conductivity, compared with electrodes obtained by ink deposition. The disadvantage is their low ionic conductivity, which essentially depends on the KOH concentration within the electrode. We have demonstrated that it is possible to increase the KOH concentration within the electrode thanks to the system architecture (dry cathode operation) and the good management of mass transport (PTL permeability), so as to maintain a pH gradient between the electrode and the feeding channels. Experimental studies combined with modeling work have enabled us to define stability domains for the various materials used, and to deduce optimal system control parameters (temperature, electrolyte pH, pressures, flow rates, clamping stress). In this presentation, we propose to take stock of the various advances made in the DAEMONHYC project in terms of materials, shaping, modeling and testing on the scale of the 25cm2 single cell. The idea is to share some insights that will help those involved in anion exchange membrane electrolysis to define their R&D strategy.The authors would like to thank the PEPR-H2 (ANR-22-PEHY-0010) for the financing support [1] Magnier, L., Cossard, G., Martin, V. et al. Fe–Ni-based alloys as highly active and low-cost oxygen evolution reaction catalyst in alkaline media. Nat. Mater. 23, 252–261 (2024) Figure 1

This paper presents the first implementation of a Time-to-First-Spike (TFS) pixel based on an uncooled microbolometer for infrared (IR) imaging. To the best of our knowledge, no TFS pixels based on thermal sensors have been demonstrated, while TFS pixels have been explored in the visible spectrum using photodiodes. In this work, the microbolometer is modeled as a temperature-dependent resistor whose voltage is compared against a reference ramp. The crossing time, which varies with temperature, is captured as a digital spike using a C-element and converted to a digital signal via a Time-to-Digital Converter (TDC). This temporally encoded scheme enables efficient, event-driven readout and naturally suppresses spatial redundancy, as pixels responding to similar IR radiation spike simultaneously. To validate this approach, a proof-of-concept (PoC) testchip was designed in 130nm CMOS, integrating a single TFS pixel, a 3×3 TFS pixel array, a ramp voltage generator, and a bias current source. The readout circuitry, including the TDC and control logic, will be implemented on an FPGA to support experimentation with both global and rolling shutter strategies. This work opens a new path toward low-power, event-based IR sensors and neuromorphic thermal imaging systems.

Dealing with extreme values is a key challenge in probabilistic modeling, relevant to economics, engineering, and life sciences. Standard GANs, built on light-tailed noise, fail to capture heavy-tail behavior and dependence in extreme regions. We propose HTGAN, a modified GAN with heavy-tailed input noise. Using the stable tail dependence function (stdf) from extreme-value theory, we derive a worst-case error bound for approximating the stdf of the target, scaling as N − 1 / ( d − 1 ) , where N is the latent noise dimension and the data dimension. Thus, higher latent dimension improves dependence estimation. Experiments on synthetic and real heavy-tailed datasets confirm that HTGAN better reproduces extreme dependence than classical light-tailed GANs, with accuracy improving as latent dimension increases.

This paper presents simulation-based insights into Joint Communication and Sensing (JCAS) optimization for direct-to-satellite access networks. We investigate three complementary approaches: (i) a hierarchical deep reinforcement learning framework for satellite beamforming, achieving an 18% throughput gain over heuristic baselines; (ii) a multi-modal federated learning architecture fusing 10 m Sentinel-1 SAR with terrestrial sensing, providing a ~15% detection accuracy improvement under (ɛ, δ) = (1.2, 10-5) differential privacy guarantees; and (iii) a ground-based LLM-driven resource management system that reduces handover latency by 25% in vehicular mobility scenarios. Evaluations using NS-3, STK, TensorFlow, and OMNeT++ validate improvements with 95% confidence intervals across realistic traffic, IoT, and mobility settings. These results highlight opportunities and challenges for integrating AI techniques into satellite-terrestrial access networks, with implications for next-generation intelligent connectivity.

Despite their widespread adoption, cellular networks face growing vulnerabilities due to their inherent complexity and the integration of advanced technologies.One of the major threats in this landscape is Voice over IP (VoIP) to GSM gateways, known as SIMBox devices.These devices use multiple SIM cards to route VoIP traffic through cellular networks, enabling international bypass fraud with losses of up to $3.11 billion annually.Beyond financial impact, SIMBox activity degrades network performance, threatens national security, and facilitates eavesdropping on communications.Existing detection methods for SIMBox activity are hindered by evolving fraud techniques and implementation complexities, limiting their practical adoption in operator networks.This paper addresses the limitations of current detection methods by introducing SigN , a novel approach to identifying SIMBox activity at the cellular edge.The proposed method focuses on detecting remote SIM card association, a technique used by SIMBox appliances to mimic human mobility patterns.The method detects latency anomalies between SIMBox and standard devices by analyzing cellular signaling during network attachment.Extensive indoor and outdoor experiments demonstrate that SIMBox devices generate significantly higher attachment latencies, particularly during the authentication phase, where latency is up to 23 times greater than that of standard devices.We attribute part of this overhead to immutable factors such as LTE authentication standards and Internet-based communication protocols.Therefore, our approach offers a robust, scalable, and practical solution to mitigate SIMBox activity risks at the network edge.

Fuel cell technologies such as Proton Exchange Membrane Fuel Cells (PEMFCs) are amongst the most advantageous solutions for clean energy production due to their high efficiency, low maintenance cost and high energy density. To date, even though PEMFCs have been identified for decarbonized heavy mobility applications, massive efforts are still needed to make them competitive with the internal combustion engines, that presently govern the market [1]. A particular endeavor is currently to increase the temperature of operation from ca. 70-80°C to ca. 90-100°C, to meet the requirements of the heavy-duty mobility. Conceiving and fabricating catalyst materials that show high performance and durability in these conditions is still a big challenge, notably on the cathode side where the oxygen reduction reaction (ORR) proceeds.To that goal, it has been decided to alloy Pt to a 3d metal (Ni) to enhance the intrinsic ORR activity and to cap these PtNi nanoparticles by carbon to enhance their resistance to metal dissolution and particles sintering. Such carbon-capped PtNi nanoalloys catalysts (see Figure 1.A,B) have been elaborated using a novel and simple dry synthesis pathway [2] consisting of the blending of a commercial Pt/Gr.C catalyst (Pt nanoparticles supported on graphitized carbon black) with a Ni-chelate precursor, followed by heat-treatment [3]. This strategy enables both structure and composition flexibility for the alloyed PtNi nanoparticles and the surrounding carbon shell. The activations of these novel carbon-capped catalysts, either electrochemically, or chemically through suitable acid treatments, which are crucial steps to allow the accessibility of reactants to the catalytic sites, will be briefly presented. These materials demonstrate good activity and durability compared to commercial catalysts in a liquid electrolyte [4]. Furthermore, they have been integrated into a PEMFC differential single cell. The break-in procedure and polarization curves under different relative humidity and cell temperature conditions will be presented. After such necessary steps, the PtNi@C/Gr.C catalysts outperform the mother commercial benchmark (Pt/Gr.C), especially in dry and hot PEMFC conditions (Figure 1.C). Accelerated stress tests under various conditions have also been conducted, and the results, along with post mortem characterizations, will be discussed. Acknowledgement: This work was funded by the “France 2030” government investment plan managed by the French National Research Agency, under the reference “ANR-22-PEHY-0005” (PEMFC95).

While proton exchange membrane fuel cells (PEMFC) are already commercial, developing highly-efficient oxygen reduction reaction (ORR) electrocatalysts, is still needed. The scientific community is successful in developing new (in principle more active) electrocatalysts but still struggles to obtain the best performance of these materials in operating PEMFCs: their practical performance in membrane electrode assembly (MEA) do not reach those expected from an analytical approach performed using the rotating disk electrode setup, RDE (the most widely-used setup to assess a material’s ORR performance) [1-6].Several reasons explain why RDE data may not be relevant to account for MEA data. Firstly, in RDE, the O2 reactant is fed as gas dissolved in the electrolyte (slow diffusivity and solubility), hence the mass-transfer rate and related limiting current density are ca. 3 orders of magnitude lower than in real PEMFC conditions. Secondly, the activity data can only be extracted at high potential values in RDE (E > 0.85 V vs. RHE), although PEMFC optimal performance is reached at 0.6 < E < 0.8 V vs. RHE (cathode potential). Thirdly, the proton conduction can be limiting in the thin layer of ionomer covering the electrocatalyst nanoparticles in MEA, which could limit their operation, especially at the high current densities at stake in MEA. In essence, one is not sure that the electrocatalysts are used in optimized conditions in MEA, as emphasized mass-transport of reactants (both H+ and O2) to the catalytic sites is not granted for present ionomers and MEA structure [7, 8]. Recent results obtained in newly-developed electrochemical characterization setups like the floating electrode (FE) [9], the gas-diffusion electrode (GDE) [10] or differential cell (DC), could enable to reach much higher ORR current densities at the lab scale and therefore better match MEA data, a prerequisite for relevant benchmarking.In the present contribution, the GDE (Fig. 1a), that enables high current density measurements with a small amount of electrocatalyst [11], will be particularly studied. The combination of experimental measurements with numerical simulations is a promising approach to understand the factors affecting the performance of an electrocatalyst in a given electrode and assembly. In this study, a 3D multiphysics and multicomponent model is developed on the commercial software COMSOL Multiphysics® to study the ORR kinetics in a GDE, considering an acidic electrolyte. The model includes mass, species and momentum transports, as well as the Butler-Volmer's law for the electrochemical reaction, current distribution and transport of charged ions. The numerical results are then compared to the experimental data for three widely-used commercial benchmark ORR electrocatalysts: Pt/Vulcan XC72 carbon, PtCo/Vulcan XC72 carbon and Pt/graphitized carbon black (Fig. 1b). These results will be discussed, which will enable to isolate what phenomenon is the most limiting in this GDE configuration.This work was funded by the Centre of Excellence of Multifunctional Architectured Materials “CEMAM” no. AN-10-LABX-44-01 and by France’s “Programme d’Investissements d’Avenir” operated by the French National Research Agency (grant ANR-22-PEHY-0005). Some of the work was performed in the frame of the CNRS-Michelin joint Laboratory Alcal’HyLab. Keywords: Proton exchange membrane fuel cells, Oxygen reduction reaction, Gas diffusion electrode, Kinetics Modelling

We study Josephson junctions based on InSb nanowires with Sn shells. We observe skewed critical current diffraction patterns: the maxima in forward and reverse current bias are at different magnetic flux, with a displacement of 20-40 mT. The skew is greatest when the external field is nearly perpendicular to the nanowire, in the substrate plane. This orientation suggests that spin-orbit interaction plays a role. We develop a phenomenological model and perform tight-binding calculations, both methods reproducing the essential features of the experiment. The effect modeled is the \phi_0ϕ0-Josephson junction with higher-order Josephson harmonics. The system is of interest for Majorana studies: the effects are either precursor to or concomitant with topological superconductivity. Current-phase relations that lack inversion symmetry can also be used to design quantum circuits with engineered nonlinearity.

The role of persistent snow covers in wintertime urban air pollution chemistry remains largely unexplored. The interactions of chemistry and transport processes are complex and the physicochemical structure of snow is uncertain. For instance, it is still unclear to what extent uptake and chemistry occur on ice, a disordered interface layer on the ice, or in brine pockets at grain boundaries. We use a process-based one-dimensional coupled atmosphere-snow model to gain initial insight into the interaction of snow with high concentrations of SO2 and NO2 in polluted wintertime Fairbanks, AK, USA. Snow can act as a reservoir for both gases, allowing for fluxes into the snow (during polluted periods) and out of the snow (during cleaner periods). The geometrical distribution of liquid on ice is varied to approximate the conceptual difference between the disordered ice interface and brine in localized pockets. The behavior of SO2 is more sensitive to these differences, mostly due to its greater stickiness on ice and solubility in water compared to NO2, which remains mostly in the snow interstitial air. Liquid-phase chemical processing of both compounds is almost insensitive to the distribution of the liquid phase in the snow and mostly determined by the volume of liquid. Our study highlights the value of comprehensive process-based modeling to further our understanding of snow chemistry. Our model platform can serve as a tool to inform and support future research efforts on improving our understanding of the liquid content of snow, chemical processing on ice surfaces, and, in general, the influence of snow on atmospheric chemistry.