Hanning Chen

High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

As Li-ion batteries are increasingly being deployed in electric vehicles and grid-level energy storage, the demand for Li is growing rapidly. Extracting lithium from alternative aqueous sources such as geothermal brines plays an important role in meeting this demand. Electrochemical intercalation emerges as a promising Li extraction technology due to its ability to offer high selectivity for Li and its avoidance of harsh chemical regenerants. In this work, we design an economically feasible electrochemical process that achieves selective lithium extraction from Salton Sea geothermal brine and purification of lithium chloride using intercalation materials, and conversion to battery grade (>99.5% purity) lithium hydroxide by bipolar membrane electrodialysis. We conduct techno-economic assessments using a parametric model and estimated the levelized cost of LiOH•H₂O as 4.6 USD/kg at an electrode lifespan of 0.5 years. The results demonstrate the potential of our technology for electro-driven, chemical-free lithium extraction from alternative sources.

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C₃N₄) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications; however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C₃N₄. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development.

Short-wave infrared (SWIR) imaging systems offer remarkable advantages, such as enhanced resolution and contrast, compared to their optical counterparts. However, broader applications demand improvements in performance, notably the elimination of cryogenic temperature requirements and cost reduction in manufacturing processes. In this manuscript, we present a new development in SWIR photodetection, exploiting the potential of metal halide perovskite materials. Our work introduces a cost-effective and easily fabricated SWIR photodetector with an ultrabroad detection range from 900 to 2500 nm, a room-temperature responsivity of 1.57 × 10² A/W, and a specific detectivity of 4.18 × 10¹⁰ Jones at 1310 nm. We then performed comprehensive static and time-resolved optical and electrical measurements under ambient conditions, complemented by extensive density functional theory simulations, validating the formation of heterojunctions within the intrinsic n-type and extrinsic p-type perovskite structures. The potential of our perovskite-based SWIR materials extends from photodetectors to photovoltaic cells and introduces a possibility for high SWIR responsivity at room temperature and atmospheric pressure, which promotes its economic efficiency.

The escalating global energy predicament implores for a revolutionary resolution—one that converts sunlight into electricity—holding the key to supreme conversion efficiency. This comprehensive review embarks on the exploration of the principle of generating multiple excitons per absorbed photon, a captivating concept that possesses the potential to redefine the fundamental confines of conversion efficiency, albeit its application remains limited in photovoltaic devices. At the nucleus of this phenomenon are two principal processes: multiple exciton generation (MEG) within quantum-confined environments, and singlet fission (SF) inside molecular crystals. The process of SF, characterized by the cleavage of a single photogenerated singlet exciton into two triplet excitons, holds promise to potentially amplify photon-to-electron conversion efficiency twofold, thereby laying the groundwork to challenge the detailed balance limit of solar cell efficiency. Our discourse primarily dissects the complex nature of SF in crystalline organic semiconductors, laying special emphasis on the anisotropic behavior of SF and the diffusion of the subsequent triplet excitons in single-crystalline polyacene organic semiconductors. We initiate this journey of discovery by elucidating the principles of MEG and SF, tracing their historical genesis, and scrutinizing the anisotropy of SF and the impact of quantum decoherence within the purview of functional mode electron transfer theory. We present an overview of prominent techniques deployed in investigating anisotropic SF in organic semiconductors, including femtosecond transient absorption microscopy and imaging as well as stimulated Raman scattering microscopies, and highlight recent breakthroughs linked with the anisotropic dimensions of Davydov splitting, Herzberg–Teller effects, SF, and triplet transport operations in single-crystalline polyacenes. Through this comprehensive analysis, our objective is to interweave the fundamental principles of anisotropic SF and triplet transport with the current frontiers of scientific discovery, providing inspiration and facilitating future ventures to harness the anisotropic attributes of organic semiconductor crystals in the design of pioneering photovoltaic and photonic devices.

As Li-ion batteries are increasingly being deployed in electric vehicles and grid-level energy storage, the demand for Li is growing rapidly. Extracting lithium from unconventional aqueous sources such as geothermal brines plays an important role in meeting this demand. Electrochemical intercalation offers high Li selectivity and avoids the use of harsh chemical regenerants, thus holding great promise in directly extracting lithium from unconventional sources. In this work, we design an integrated electrochemical process that achieves selective lithium extraction from geothermal brine, purification of lithium chloride, and conversion to lithium hydroxide. The lithium extraction process utilizes a lithium-intercalation electrode, LiFePO₄ (LFP), as the working electrode coupled with an activated carbon electrode as the counter electrode. A 91% purity LiCl is extracted from simulated Salton Sea geothermal brine containing 42 mM Li⁺, 3.1 M Na⁺ (Li/Na molar ratio 1:74), 1070 mM Ca²⁺, and 540 mM K⁺, and a further purification step achieves a pure LiCl solution with no Na detected. Subsequently, lithium hydroxide is further produced through a bipolar membrane electrodialysis system and finally crystallized to obtain battery grade (> 99.5% purity) LiOH•H₂O solid. We investigated the selectivity of lithium separation in solutions with different cation concentration ratios as well as in synthetic geothermal brines. We further conducted density-functional theory (DFT) calculations to elucidate the mechanisms underlying the high Li selectivity of olivine FePO₄ in aqueous solution. Finally, we conducted techno-economic assessments using a parametric model and estimated the levelized cost of produced LiOH•H₂O (LCOL) as 4.1 $/kg LiOH•H₂O, which is 6 times lower than the current market price. The results demonstrate the great potential of our technology for electro-driven, chemical-free lithium extraction from unconventional sources.

Peroxides find broad applications for disinfecting environmental pathogens particularly in the COVID-19 pandemic; however, the extensive use of chemical disinfectants can threaten human health and ecosystems. To achieve robust and sustainable disinfection with minimal adverse impacts, we developed Fe single-atom and Fe-Fe double-atom catalysts for activating peroxymonosulfate (PMS). The Fe-Fe double-atom catalyst supported on sulfur-doped graphitic carbon nitride outperformed other catalysts for oxidation, and it activated PMS likely through a nonradical route of catalyst-mediated electron transfer. This Fe-Fe double-atom catalyst enhanced PMS disinfection kinetics for inactivating murine coronaviruses (i.e., murine hepatitis virus strain A59 (MHV-A59)) by 2.17-4.60 times when compared to PMS treatment alone in diverse environmental media including simulated saliva and freshwater. The molecular-level mechanism of MHV-A59 inactivation was also elucidated. Fe-Fe double-atom catalysis promoted the damage of not only viral proteins and genomes but also internalization, a key step of virus lifecycle in host cells, for enhancing the potency of PMS disinfection. For the first time, our study advances double-atom catalysis for environmental pathogen control and provides fundamental insights of murine coronavirus disinfection. Our work paves a new avenue of leveraging advanced materials for improving disinfection, sanitation, and hygiene practices and protecting public health.

Graphitic carbon nitride (g-C₃N₄) nanomaterials hold great promise in diverse applications; however, their stability in engineering systems and transformation in nature are largely underexplored. We evaluated the stability, aging, and environmental impact of g-C₃N₄ nanosheets under the attack of free chlorine and reactive chlorine species (RCS), a widely used oxidant/disinfectant and a class of ubiquitous radical species, respectively. g-C₃N₄ nanosheets were slowly oxidized by free chlorine even at a high concentration of 200-1200 mg L^-1, but they decomposed rapidly when ClO· and/or Cl₂^•- were the key oxidants. Though Cl₂^•- and ClO· are considered weaker oxidants in previous studies due to their lower reduction potentials and slower reaction kinetics than ·OH and Cl·, our study highlighted that their electrophilic attack efficacy on g-C₃N₄ nanosheets was on par with ·OH and much higher than Cl·. A trace level of covalently bonded Cl (0.28-0.55 at%) was introduced to g-C₃N₄ nanosheets after free chlorine and RCS oxidation. Our study elucidates the environmental fate and transformation of g-C₃N₄ nanosheets, particularly under the oxidation of chlorine-containing species, and it also provides guidelines for designing reactive, robust, and safe nanomaterials for engineering applications.