Operating anion-exchange membrane fuel cells (AEMFCs) with CO2-containing ambient-air feed is conventionally seen as a difficult challenge detrimental to cell performance, because CO2 reacts with hydroxide ions generated at the cathode, forming (bi)carbonate ions that reduce AEM ionic conductivity. In this Perspective, we discuss the effect of CO2 in operando AEMFCs, which involves a complex interplay of multi-anion transport, concentration polarization, back-diffusion, changes in water distribution, cation stability and variations in local pH. We argue that the negative effects of CO2 may be managed to minimize them, and even exploited to advantage. We introduce two concepts: CO2 management and the positive stabilizing effect of CO2. The first, analogous to the water-management effect, relates to phenomena and strategies to balance, transport and utilize CO2-related species to enhance AEMFC performance. The second, although counterintuitive, relates to the potentially positive effects that CO2 may impart to ambient-air cell operation, in particular towards improving long-term performance stability.

The hydrogenation of carbon dioxide (CO2) to methanol (CH3OH) offers a sustainable pathway for storing renewable energy in chemical form, yet conventional thermocatalytic routes suffer from high activation barriers and intrinsic selectivity limits. Here, we report a one-step-synthesized indium oxide-cerium oxide (In2O3-CeO2) heterojunction catalyst that achieves benchmark CO2-to-CH3OH conversion rates under visible-light irradiation at ambient pressure. The optimized 1:1 composition attains a methanol production rate of 1,054.6 μmol g−1 h−1 with 53.1% at 247.7°C, ranking it among the highest reported for non-noble photothermal systems. Operando DRIFTS, isotopic labeling, kinetic analyses, and density functional theory (DFT) calculations reveal that oxygen vacancies, surface hydroxyls, and Ce3+/Ce4+ redox cycling collectively promote CO2 activation and drive a carbonate-mediated photochemical pathway distinct from thermally accessible routes. These results demonstrate a synergistic light-heat mechanism and outline a mechanistically guided, scalable strategy for selective CO2 valorization under realistic reaction conditions.

Ammonia is an efficient hydrogen carrier that can be easily converted into H ${}_2$-rich gas. In this study, a numerical investigation of the ammonia decomposition process within a small-scale plate reformer is presented to understand the effect of catalyst layer thickness on intra-catalyst diffusion limitations. Simulations are conducted using a RANS approach coupled with chemical kinetics, diffusion, and porous medium models. The different catalyst layer thicknesses ranging from $100\mu m$ to $2000\mu m$ are examined over a temperature range of 400 to 700 °C. The numerical results are compared with experimental data obtained for unmodified Ni-Al ${}_2$O ${}_3$ catalyst, revealing a strong correlation with an average deviation of less than 4%. The results reveal the significant influence of catalyst layer thickness, particularly up to $1000\mu m$, on the ammonia decomposition process due to intra-layer diffusion limitations. Beyond $1000\mu m$ in thickness at temperatures between 500–600 °C, further increases in thickness showed minimal impact on ammonia decomposition and hydrogen yield. The effect of temperature on diffusion limitations is minimal. Additionally, at temperatures exceeding 650 °C, close-to-stoichiometric product gas composition was observed for low Reynolds numbers, even with a catalyst layer thickness of $100\mu m$.

Abstract. The study explores acoustic flow control in high-lift, high-work turbine blades (L3FHW and L4FHW) tested in a transonic linear cascade facility to tackle flows with open separations at low Reynolds numbers caused by high adverse pressure gradients. Acoustic excitation aims to focus on frequencies associated with the Kelvin–Helmholtz instability, which scales with the 3/2 power of the exit Mach number. Two different acoustic sources were utilized: a siren disk and a passive perforated tailboard, which functioned as a Helmholtz resonator. Experiments across various Mach numbers, supported by Schlieren imaging and static pressure measurements. Key findings demonstrate that siren disk excitation increased peak suction pressure by up to 60%, while reducing the angle of the separated shear layer by 10 deg. The tailboard excitation yielded a similar reduction of up to 11 deg in the shear layer angle, albeit with modest improvements in pressure recovery near the trailing edge. Spectral analysis showed enhanced vortical coherence under excitation, with energy concentrating at target frequencies. Numerical simulations indicated up to 40% variation in peak suction pressure along the blade span due to end-wall vortices, suggesting secondary flows influencing the efficacy of flow control on midspan pressure distribution. Acoustic excitation demonstrated the ability to mitigate the adverse effects of end-wall vortices on pressure recovery. These findings highlight the potential of acoustic flow control to improve aerodynamic performance while underscoring the need to address secondary flow effects and integration challenges in advanced turbine designs.

Porous materials with pore sizes spanning the range from molecular to macroscopic dimensions (from angstroms to centimeters) are essential in electrochemical, thermoelectric, nuclear, and solar power sources and in the extraction of oil, gas, and geothermal heat. To enable the clean, fast, and efficient conversion of energy, the porous structure must be designed to allow, modulate, or block the flow of energy transfer vectors. The most important energy streams are mass, charge, heat, radiation, and pressure, and they must be optimized while packing the optimal surface area per device volume. In this Review, we analyze the physical processes that enable energy transfer in porous structures, highlighting recent advances in the design, characterization, modeling, and fundamental understanding of porosity that have enabled breakthroughs across the landscape of energy technologies.

The desalination fuel cell (DFC) represents a novel electrochemical technology that simultaneously desalinates water and generates electricity by coupling oxidation–reduction reactions. This study introduces a theoretical framework to support experimental findings and further elucidates DFC performance mechanisms. Unlike traditional electrodialysis (ED), where an externally applied electric potential drives desalination, the DFC employs redox half-reactions to achieve ion separation and energy conversion. The proposed H ${}_2$-O ${}_2$ DFC system, previously tested only experimentally, is examined here theoretically for the first time. The system features a desalination channel, bordered by anion and cation exchange membranes (AEM and CEM), with reacting catholyte and anolyte streams driving the desalination process. To evaluate cell performance, the model incorporates electrolyte composition variations, ionic interactions, resistances, and electrode kinetics. Mass transport is governed by the Nernst–Planck equation under the electroneutrality assumption, while electrochemical kinetics account for both activation and concentration overpotentials. The results provide insights into the interplay between desalination efficiency, ionic fluxes, and electrochemical processes, advancing the understanding of DFC operation and optimization strategies.

Anion exchange membrane fuel cells (AEMFCs) have attracted much attention due to their bipolar design that enables the use of all Pt-group metal (PGM)-free catalysts. As the only PGM-free metal with the anode hydrogen oxidation reaction (HOR), the performance of Ni-based metals in fuel cells is too low to match well with the cathode oxygen reduction reaction (ORR) due to the low utilization rate of the active sites. Here, we activated Ni atoms with single-atom Fe up to its third nearest-neighbor site, and the prepared catalyst Fe₁Ni single-atom alloy (named Fe₁Ni_SAA–O@CN) exhibited a high mass activity of 67.58 A g_Ni⁻¹. More importantly, the peak power density (PPD) of the AEMFC with an Fe₁Ni_SAA–O@CN/C anode reached 729.4 mW cm⁻², which was 4.4 times that of pure Ni, exceeding most of the previously reported Ni-based anode catalysts. Furthermore, when this catalyst was paired with a PGM-free cathode catalyst, a remarkable PPD of 420.9 mW cm⁻² was obtained, highlighting the outstanding performance of the catalyst. Atomic resolution electron energy-loss spectroscopy (EELS) analysis and density functional theory (DFT) calculations revealed that the surrounding Ni atoms up to the third nearest neighbor around the Fe dopants were tuned into active sites via charge transfer from the Fe, thereby increasing the HOR activity. This study provides a new design paradigm for further improving the performance of PGM-free AEMFCs.

Anion-exchange membranes (AEMs) enable electrochemical energy devices to operate in alkaline environments, allowing the use of earth-abundant, platinum group metal-free catalysts. This makes them highly attractive for applications such as AEM fuel cells (AEMFCs), water electrolyzers (AEMWEs), and oxygen separators (AEMOSs). However, two key challenges still hinder their commercialization: chemical degradation of the membranes and carbonation from atmospheric CO₂, both of which impair conductivity and reduce long-term performance. Building upon our ex situ novel technique to measure the alkaline stability of quaternary ammonium salts (QAs) and AEMs at different hydroxide hydration levels, in this study we investigate the effect of CO₂ on the stability of the AEMs. Specifically, we evaluate the stability of AEM standard benzyltrimethyl ammonium cations (BTMA⁺) in the presence of mixtures of hydroxide and (bi)carbonate anions at different ratios and hydration levels. Additionally, we introduce a novel technique to simultaneously evaluate (bi)carbonate impact and hydroxide conductivity in AEMs. Our results reveal that the presence of (bi)carbonate anions surrounding BTMA⁺ significantly reduces QA degradation. This phenomenon is further supported by molecular dynamics simulations, which suggest a “salting-out” effect, where water more strongly hydrates hydroxide ions, isolating them from the organic QA sites and reducing degradation probability. These findings provide new insight into AEM behavior in CO₂-containing environments such as ambient air. This study provides new, valuable data to overcome the critical challenge of AEM alkaline stability and opens new avenues for the design of more durable AEM materials, advancing the development of AEMFCs, AEMWEs, and AEMOSs for real-world applications.

Predictive chemical kinetic modeling is foundational to areas ranging from energy and environmental science to pharmaceuticals and advanced materials. While significant progress has been made in automating individual steps, the development of a complete predictive model remains a human-intensive effort to orchestrate existing software tools and revise models. This Perspective outlines a practical path to improved chemical kinetic model development using agentic AI. A dual-lane architecture is introduced: a fast execution lane handles mechanism generation and parameter refinement, while a deliberative agentic lane plans, refines, and revises while executing experiments and computations. The proposed outcome is a robust pathway toward decision-grade models. Humans remain central: researchers set objectives and priors, approve high-impact actions, and adjudicate new chemical insights. Creativity, complex judgment, and strategic thinking remain in the human domain. Ultimately, this approach aims to accelerate trustworthy, transparent, decision-grade model development.

The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights.