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The identification of green strategies for ammonia production is critical, and nitrate reduction has emerged as a promising approach. In a recent issue of Nature Catalysis, Tayyebi et al. reported unprecedented ammonia production rates via bias-free photochemical nitrate reduction coupled with glycerol oxidation.

Refreshing the record of the electrocatalytic activity for bifunctional oxygen electrocatalysis is the first priority of developing next-generation rechargeable zinc-air batteries. A ΔE indicator to evaluate the bifunctional electrocatalytic activity has stagnated with a record of ΔE > 0.60 V for decades. Herein, a bifunctional oxygen electrocatalyst is developed to afford an ultrahigh bifunctional electrocatalytic activity of ΔE = 0.57 V and realize high-performance rechargeable zinc-air batteries. Specifically, atomically dispersed Fe-N-C sites and NiFeCe layered double hydroxides are integrated to afford a composite FeNC@LDH electrocatalyst, following the guidance of the data-driven analysis. The FeNC@LDH electrocatalyst demonstrates a record-breaking electrocatalytic activity of ΔE = 0.57 V, far exceeding the state-of-the-art level by ca. 60 mV. Practical ampere-hour-scale zinc-air batteries are constructed with a capacity of 6.4 Ah and cycle under 1.0 A and 1.0 Ah conditions. This work affords a record-breaking bifunctional electrocatalyst for ampere-hour-scale zinc-air batteries in future application scenarios.

Amitava Sarkar is a corporate research scientist for North America at TotalEnergies and a resident visiting scientist at Stanford University working to develop disruptive, no/low-carbon sustainable technologies to decarbonize the chemical industry. He is active in TotalEnergies’ open innovation effort through research and development collaborations and strategic partnerships with various research institutions around the world.

Shaffiq Jaffer is the vice president of corporate science and technology projects in North America at TotalEnergies. He is engaged across the research ecosystem with relationships and investments in academia, startups, and private research companies for the purpose of bringing value to TotalEnergies and its partners.

Arun Majumdar is the inaugural dean of the Stanford Doerr School of Sustainability, the Jay Precourt provostial chair professor at Stanford University, a faculty in the Department of Mechanical Engineering, and a senior fellow and former director of the Stanford Precourt Institute for Energy. He served in the Obama administration as the founding director of the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) (2009–2012), the acting undersecretary for energy (2011–2012), and as the vice chair of the secretary of energy advisory board (2014–2017). Dr. Majumdar is a member of the US National Academy of Sciences, US National Academy of Engineering, and the American Academy of Arts and Sciences.

Eddie Sun, Marco Gigantino, Richard Randall, Jimmy Rojas, and Shang Zhai were PhD students/postdoctoral scholars at Stanford University at the time of writing. Jimmy Rojas is now the founder and chief executive officer at EvolOH, and Shang Zhai is now an assistant professor in the Department of Mechanical and Aerospace Engineering (with a joint appointment in the School of Earth Sciences) at Ohio State University.

Large language models (LLMs) as ChatBots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm toward adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this commentary identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.

Sulfurized polyacrylonitrile (SPAN) is emerging as a promising cathode for high-energy Li metal batteries. The transition-metal-free nature, high capacity, good sustainability, and low cost serve as competitive advantages of SPAN over conventional layered-oxide counterparts. The unique structure of SPAN with abundant covalent C–S and N–S bonds enables it to achieve high electrochemical performance even in lean electrolyte conditions. Despite great research progress, the current performance of Li/SPAN batteries still falls far behind its true potential. Here, we thoroughly analyze the energy density and cycle life of practical Li/SPAN cells based on our in-house-developed models. Besides, using Sand’s equation, we derive the requirements for Li/SPAN cells to achieve a reasonable power density and discuss their implications. Our analyses address critical issues of Li/SPAN on both material and cell levels, with an emphasis on particularly crucial details that are often overlooked or misunderstood. Accordingly, the challenges and directions for future Li/SPAN research are indicated.

More than 60% of the energy in the world is wasted during the energy conversion processes but can be potentially collected by thermoelectric (TE) technology. Recently, entropy engineering has been applied in the TE community, yielding numerous high-entropy material systems that exhibit outstanding TE performance. This review outlines the design principles for entropy-stabilized TE materials. Subsequently, it discusses the impact of high entropy on electrical and thermal transport properties. Furthermore, the research advancements of various high-entropy TE material systems are summarized, encompassing IV–VI compounds, half-Heusler (HH) compounds, liquid-like materials, oxide-based ceramics, and other relevant systems. Finally, the conclusion and outlook for high-entropy TE materials are elucidated. Only small regions of high-entropy TE materials have been investigated so far, and there will be vast space that remains to be explored. High-entropy TE materials will be one of the core strategies in the TE community if the optimization mechanism is completely understood.

In zero-gap CO₂ electrolyzers, maintaining the balance of water and cations is crucial. Excessive accumulation at the cathode causes performance degradation, leading to flooding and salt precipitation. Using operando wide-angle X-ray scattering and X-ray fluorescence techniques, we observed the dynamic evolution of H₂O and Cs⁺ inside a membrane electrode assembly. Our findings indicate that Cs⁺ movement across the membrane from the anode to the cathode is governed by migration and drags H₂O via electroosmosis. H₂O diffusion then allows Cs⁺ diffusion further within the gas diffusion electrode. When decreasing the applied voltage, the concentration gradient causes Cs⁺ to quickly diffuse back to the anode. The H₂O content in the macro-porous layer remains at the same level, thus showcasing an origin of gas diffusion electrode (GDE) flooding. By regulating the electrolyte concentration, we deconvolute the correlation of water and cations for selectivity changes. Our work underscores the significance of water/cation management strategies in zero-gap electrolyzers.

Metal aggregation, caused by high current density or long-cycling catalysis, severely affects the stability of ruthenium (Ru)-based catalysts toward hydrogen evolution reaction (HER). Herein, we constructed an anti-growth strategy of phosphorus (P)-induced Ru clusters (1.3 nm) integrated with adjacent Ru single atoms on nitrogen (N)-doped carbon fibers (Ru_SA/NP-PNCFs) for ultra-stable HER. The Ru_SA/NP-PNCFs exhibit outstanding activity (8 and 132 mV at 10 and 1,000 mA cm⁻²) and record durability (100,000 cycles and 1,000 h at 600 mA cm⁻²). Thanks to the optimized binding energy and orbital interaction between Ru and P/N, the size variation is only 0.8 nm, and single atoms are also well preserved. Both experiments and theoretical simulations indicate that the heteroatom P can not only boost the capacity of H₂O dissociation but also suppress the aggregation of Ru clusters and single atoms during HER. This work provides an effective strategy for designing stable metal cluster-single-atom systems for advanced electrocatalysts.

Accurate prediction of battery lifetime is critical for ensuring timely maintenance and safety of batteries. Although data-driven methods have made significant progress, their model accuracy is often hampered by a scarcity of labeled data. To address this challenge, we developed a semi-supervised learning technique named partial Bayesian co-training (PBCT), enhancing the modeling of battery lifetime prediction. Leveraging the low-cost unlabeled data, our model extracts hidden information to improve the understanding of the underlying data patterns and achieve higher lifetime prediction accuracy. PBCT outperforms existing approaches by up to 21.9% on lifetime prediction accuracy, with negligible overhead for data acquisition. Moreover, our research suggests that incorporating unlabeled data into the training process can help to uncover critical factors that impact battery lifetime, which may be overlooked with a limited number of labeled data alone. The proposed semi-supervised approach sheds light on the future direction for efficient and explainable data-driven battery status estimation.