Efficient energy conversion and storage technologies are critical for achieving China's carbon peak and carbon neutrality goals. Solid oxide electrolysis cells (SOECs) offer a promising route by converting renewable electricity into storable chemical fuels through high-temperature carbon dioxide electrolysis. However, the sluggish oxygen evolution reaction (OER) at the anode poses a major challenge due to its complex four-electron transfer process.Perovskite oxides are regarded as promising candidates for SOEC anodes due to their high mixed ionic–electronic conductivity and tunable electronic structures. Previous studies have revealed a volcano-shaped correlation between the occupancy of the 3d electron with eg symmetry in perovskite oxide and intrinsic OER activity in alkaline solution, with peak activity occurring at a near-unity eg occupancy. Yet, the intrinsic connection between eg occupancy and high-temperature OER activity has remained unclear due to the mechanistic differences between high- and low-temperature OER processes.Spin-state tuning in PrFeO3-δ perovskite for high-temperature oxygen evolution reaction (Image by YU Jingcheng)In a study published in Journal of the American Chemical Society, Assoc. Prof. SONG Yuefeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. WANG Guoxiong from Fudan University, developed a series of alkaline-earth-metal-doped perovskites, Pr0.5Ae0.5FeO3−δ (Ae = Ca, Sr, Ba, labeled as PCF, PSF, PBF), to investigate the impact of electronic structure tuning on high-temperature OER performance.Researchers found that OER activity increases with larger dopant ionic radius, and PBF achieves a current density of 3.33 A cm−2 at 2.0 V and 800 °C. Detailed analyses revealed that alkaline earth metal doping enhances Fe 3d-O 2p hybridization, lowers charge-transfer energy, and promotes oxygen ions migration and surface spillover, thereby accelerating the OER process. Moreover, magnetic measurements showed that Ba doping induces a spin-state transition from high-spin Fe3+ (t2g3eg2) to low-spin Fe4+ (t2g4eg0), resulting in reduced eg occupancy and accelerated oxygen kinetics."Our study establishes spin-state tuning as a key strategy to boost high-temperature OER activity and provides fundamental guidance for electronic structure engineering in the design of advanced SOEC anode materials," said Dr. SONG.

Contemporary industrial nitrogen fixation largely relies on the energy-intensive Haber-Bosch process, which operates under extremely high temperatures and pressures. Metal carbides, as a promising class of catalytic materials, have recently attracted attention in heterogeneous catalysis research. Understanding their nitrogen activation mechanisms at the molecular and electronic levels is crucial for developing next-generation catalysts and advanced single-atom materials.In a study published in Chemical Science, a research team led by Prof. JIANG Ling and Prof. XIE Hua from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) revealed the competitive mechanism of dual-mode nitrogen fixation in negatively charged metal tricarbon cluster, MC₃⁻ (M = Os, Ir, Pt).Nitrogen fixation mechanism diagram of MC3– (M = Os, Ir, Pt) clusters (Image by DU Shihu)By combining photoelectron spectroscopy with quantum chemical calculations, researchers investigated the reactivityof MC3– clusters in activating nitrogen molecules. They demonstrated that these clusters exhibited two competing nitrogen activation pathways: cleavage of the N≡N triple bond with the formation of a stable C–N bond, and chemisorption of nitrogen onto the metal center. Among the studied clusters, OsC3− primarily facilitates N≡N bond cleavage, IrC3− displays the coexistence of dual nitrogen activation mechanisms, while PtC3− favors nitrogen fixation mainly through chemisorption.Further theoretical analysis revealed that the activation of nitrogen by MC3– decreases as the 5d orbital energy of the metal atoms lowers, while the predominance of chemisorption correspondingly increases."Our study provides molecular-level insightsinto dinitrogen activation by mononuclear metal carbide clusters and establishes a new paradigm for developing efficient catalysts for dinitrogen fixation," said Prof. XIE.

Anthropogenic perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widespread and persistent pollutants that are increasingly subject to stringent regulatory thresholds in water resources. However, current nonthermal defluorination strategies face critical limitations, including incomplete mineralization, leaving behind short-chain PFAS byproducts and residual fluoride ions, thereby hindering compliance with water quality standards.Recently, a research group led by Prof. WANG Feng and Assoc. Prof. JIA Xiuquanfrom the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. JIANG Guibin's group from the Research Center for Eco-Environmental Sciences (RCEES) of CAS, has realized complete perfluorooctanoic acid (PFOA) mineralization and fluoride immobilization using a microcloud enriched with wollastonite-bearing microdroplets. This study was published in the Journal of the American Chemical Society.The microcloud is characterized by fast phase switching of water among the bulk phase, microdroplets, and vapor phase, driven by ultrasonic spray. During this process, positively charged larger droplets and negatively charged smaller droplets are generated – a phenomenon known as the Lenard effect.Electrostatic attraction between the oppositely charged droplets drives their fast coalescence, causing them to rapidly migrate or fall back into the bulk phase. These fast spray-fusion cycles facilitate sustained electron transfer between charged droplets, thereby making the redox reactions thermodynamically feasible.Researchers found that wollastonite-bearing microdroplets prioritize defluorination over C-C scission in perfluoroalkyl chains through liquid-solid-gas triple-phase contact electrification. This process results in complete PFOA mineralization with hardly detectable shorter-chain anionic PFAS byproducts. Moreover, microdroplet-mediated weathering of wollastonite triggers the formation of CaF2-SiO2 interfacial structures through Si-F-Ca bonding interactions, thereby enabling fluoride immobilization with negligible leaching.Concept of the fluorine-first approach to full mineralization of perfluoroalkyl substances in a charged cloud of microdroplets and mineral particles (Image by YANG Yifan)Researchers further demonstrated that the defluorination reactions are initiated by electron attachment coupled to proton or H• transfer during hydrodefluorination, as well as the •OH-mediated C-H bond oxidation process. This approach reduces PFOA concentration to well below the U.S. Environmental Protection Agency's (EPA) Maximum Contaminant Level (MCL) of 4 ppt, while producing shorter-chain anionic PFAS byproducts with concentrations far below the European draft recast Drinking Water Directive's proposed 500 ppt limit for total PFAS in drinking water.Furthermore, microdroplet-mediated weathering of CaSiO3 and in situ generated silica leads to the formation of CaF2-SiO2 interfacial structures, resulting in F– residue concentration fluctuating around the 1 ppm regulatory limit set by surface water quality standards. Additionally, the interaction between microdroplets and minerals enables efficient C-C bond cleavage, producing syngas with a carbon yield larger than 98% and tunable H2/CO ratios of 0.5 to 1. "Our study indicated that, beyond potential applications of microdroplets in practical water treatment under ambient conditions, microdroplets from clouds and sea spray may possess a significant yet overlooked, self-cleaning capacity for PFAS pollutants on a global scale," said Prof. WANG.

Accurate estimation of battery state of health (SOH) during fast charging is crucial for the management of electric vehicle batteries. However, challenges remain due to the lack of training data for individual target batteries, and there is a need for personalized models to account for variations in charging and discharging behavior.In a study published in IEEE Transactions on Transportation Electrification, a research team led by Prof. CHEN Zhongwei and Assoc. Prof. MAO Zhiyu from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. FENG Jiangtao from Xi'an Jiaotong University, developed a novel two-stage federated transfer learning framework for accurate SOH prediction using fast charging segments while preserving the user privacy.The proposed FTL framework for fast charging battery SOH estimation (Image by LIU Yunpeng)In this framework, multiple distributed batteries first collaborate to train a global model through federated learning, sharing only model parameters. This allows the system to learn general battery behavior without exposing private data. The global model is then fine-tuned with a small amount of local data from a target battery, generating a personalized model that captures its unique charging and discharging characteristics.This federated transfer framework is built on a lightweight convolutional neural network enhanced with a channel attention mechanism. Experimental results on the public fast charging battery dataset showed that it outperforms both locally trained models and traditional federated learning methods.In addition, the framework has been integrated into the second-generation battery digital brain (PBSRD Digit core model), enabling intelligent battery management. It also has been applied to launch a vertical intelligent customer service system in the energy storage field for Shuangdeng Group, advancing automation and intelligence in the energy storage industry."This federated transfer learning technology provides solid technical support for our intelligent customer service system," said Prof. CHEN.

Compared with the energy-intensive Haber-Bosch process, renewable energy-driven electrocatalytic nitrate reduction reaction (NO3−RR) provides a low-carbon route for ammonia synthesis under mild conditions. Using nitrate from wastewater as the nitrogen source and water as the hydrogen source, this route has the potential to produce ammonia sustainably while mitigating water pollution.Copper (Cu)-based catalysts show a good performance for NO3−RR to ammonia. However, they still suffer from challenges including high overpotential, competing nitrite (NO2–) formation, and low overall energy efficiency.In a study published in ACS Catalysis, a research team led by Profs. BAO Xinhe and GAO Dunfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. WANG Guoxiong from Fudan University, proposed hydroxyl (*OH) adsorption as a selectivity descriptor for ammonia synthesis via NO3−RR over Cu catalysts.Hydroxyl adsorption as a selectivity descriptor for electrocatalytic nitrate reduction over Cu catalysts (Image by WANG Yi)Using a size- and shape-selected Cu nanocube model catalyst, researchers investigated how electrode potential and NO3− concentration affect NO3−RR product distribution. They found that more negative potentials and lower NO3– concentrations can improve ammonia selectivity over NO2– formation, which is an effect strongly correlated with weakened *OH adsorption.Moreover, researchers proposed *OH adsorption as a descriptor for product selectivity, as it controls the availability of surface adsorbed hydrogen (*H) generated during water dissociation. They further demonstrated that using *OH adsorption as a descriptor can guide the design of both catalysts and electrolytes to lower NO3−RR overpotentials."Our work underscores the importance of *OH adsorption as a selectivity descriptor for designing more efficient systems toward ammonia electrosynthesis from nitrate," said Prof. GAO.

Brewer's yeast (Saccharomyces cerevisiae) has served as a cornerstone of industrial biotechnology for centuries. Its exceptional adaptability to diverse natural and industrial environments has led to the emergence of a wide variety of strains, each with unique genetic and metabolic characteristics. However, most research and applications still rely on a few laboratory strains such as S288c and CEN.PK—limiting the discovery of optimal strains for high-performance cell factory development.A research team led by Prof. ZHOU Yongjin from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Associate Prof. LU Hongzhong from Shanghai Jiao Tong University, has developed a powerful systems biology method to address this limitation. Their study, published in Proceedings of the National Academy of Science, leverages strain-specific metabolic modelling to uncover how yeast adapts to different environments at a systems level.Strain-specific metabolic modelling workflow for understanding the adaptive evolution of yeast (Image by WANG Haoyu)Researchers created a high-quality digital resource of the yeast pan-genome and constructed metabolic models tailored to individual strains. Based on this, they developed a novel analysis pipeline that integrates genomic data, metabolic models, and multi-omics information to systematically evaluate different strains.As a proof of concept, the researchers applied this framework to industrial ethanol-producing strains. Their analysis revealed—at the genetic, transcriptional, and metabolic levels—that enhancing downstream pathways of glycolysis is key to efficient ethanol synthesis. This discovery provides key insights for the rational design of yeast cell factories for ethanol and its derivatives."Our work not only provides a comprehensive digital resource of yeast strains for academia and industry," said Prof. ZHOU, "but also offers new methods for evaluating and selecting optimal chassis strains for biomanufacturing applications."