The Journal of Physical Chemistry Letters最新文献

英文中文

Data-Driven Deciphering Structure–Mössbauer Spectroscopy Relationships in Iron-Based Compounds 数据驱动解码Structure-Mössbauer铁基化合物中的光谱关系

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-09 DOI: 10.1021/acs.jpclett.5c03985

Tao Yang,Jiaqing Zhang,Haotian Chen,Xiaoze Yuan,Yuwei Zhou,Wenping Guo,Xingchen Liu,Xiaotong Liu,Jinjia Liu

Accurate identification of material structures is crucial for establishing reliable structure–property relationships, yet this task is often hindered by the coexistence of multiple metastable phases and their facile transformations under reaction conditions. Although spectroscopic techniques are powerful tools for probing structural motifs, the lack of comprehensive reference data remains a critical bottleneck for experimental analysis. Here, we propose a unified strategy that integrates structure prediction, thermodynamic stability calculations, machine learning models, and Mössbauer spectroscopy simulations, tailored for iron-based intermetallic compounds. By constructing quantitative relationships between local structural features and Mössbauer parameters with machine learning, and by leveraging a theoretical spectral database to interpret experimental spectra, we successfully identified candidate iron sulfide metastable phases that were previously unresolved. This data-driven framework provides a robust complement to experimental approaches, enabling more accurate and efficient identification of metastable phases in dynamically evolving systems.

{"title":"Data-Driven Deciphering Structure–Mössbauer Spectroscopy Relationships in Iron-Based Compounds","authors":"Tao Yang,Jiaqing Zhang,Haotian Chen,Xiaoze Yuan,Yuwei Zhou,Wenping Guo,Xingchen Liu,Xiaotong Liu,Jinjia Liu","doi":"10.1021/acs.jpclett.5c03985","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c03985","url":null,"abstract":"Accurate identification of material structures is crucial for establishing reliable structure–property relationships, yet this task is often hindered by the coexistence of multiple metastable phases and their facile transformations under reaction conditions. Although spectroscopic techniques are powerful tools for probing structural motifs, the lack of comprehensive reference data remains a critical bottleneck for experimental analysis. Here, we propose a unified strategy that integrates structure prediction, thermodynamic stability calculations, machine learning models, and Mössbauer spectroscopy simulations, tailored for iron-based intermetallic compounds. By constructing quantitative relationships between local structural features and Mössbauer parameters with machine learning, and by leveraging a theoretical spectral database to interpret experimental spectra, we successfully identified candidate iron sulfide metastable phases that were previously unresolved. This data-driven framework provides a robust complement to experimental approaches, enabling more accurate and efficient identification of metastable phases in dynamically evolving systems.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"278 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanistic Determinants of Oriented Enzyme Immobilization from Martini Simulations 定向酶固定的机制决定因素从马提尼模拟

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-09 DOI: 10.1021/acs.jpclett.5c03753

Juan Carlos Jiménez-García,Nicoll Zeballos,Fernando López-Gallego,Xabier López,David De Sancho

Although enzyme immobilization is widely used in biotechnology, it still poses challenges as a result of the trade-offs among stability, activity, and surface interactions. Computer simulations offer a promising aid to exploring the effects of different immobilization sites and surface chemistry on both the conformational dynamics and catalytic activity of these biomolecules. Here, we introduce a protocol based on a structure-based version of the Martini coarse-grained simulation model (Go̅Martini) to explore how surface tethering geometry influences the structure and function of immobilized Bacillus stearothermophilus alcohol dehydrogenase (BsADH). We compare traditional His-tag tethering with two engineered histidine cluster variants, analyzing their behavior in both soluble and surface-tethered states. We find that cluster-based immobilization locally restricts flexibility in surface-contacting subunits while preserving the mobility of exposed regions, resulting in an enhanced conformational stability under thermal stress. Functional analyses reveal that the ethanol association rates remain largely unaffected by surface attachment, whereas the dissociation of NADH is significantly slowed, explaining the reduced catalytic efficiency. These trends align with experimental findings and highlight the predictive power of Go̅Martini simulations in capturing key functional trade-offs. Altogether, this work offers mechanistic insight into the rational design of immobilized biocatalysts and outlines a practical framework for in silico exploration of enzyme–surface systems.

{"title":"Mechanistic Determinants of Oriented Enzyme Immobilization from Martini Simulations","authors":"Juan Carlos Jiménez-García,Nicoll Zeballos,Fernando López-Gallego,Xabier López,David De Sancho","doi":"10.1021/acs.jpclett.5c03753","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c03753","url":null,"abstract":"Although enzyme immobilization is widely used in biotechnology, it still poses challenges as a result of the trade-offs among stability, activity, and surface interactions. Computer simulations offer a promising aid to exploring the effects of different immobilization sites and surface chemistry on both the conformational dynamics and catalytic activity of these biomolecules. Here, we introduce a protocol based on a structure-based version of the Martini coarse-grained simulation model (Go̅Martini) to explore how surface tethering geometry influences the structure and function of immobilized Bacillus stearothermophilus alcohol dehydrogenase (BsADH). We compare traditional His-tag tethering with two engineered histidine cluster variants, analyzing their behavior in both soluble and surface-tethered states. We find that cluster-based immobilization locally restricts flexibility in surface-contacting subunits while preserving the mobility of exposed regions, resulting in an enhanced conformational stability under thermal stress. Functional analyses reveal that the ethanol association rates remain largely unaffected by surface attachment, whereas the dissociation of NADH is significantly slowed, explaining the reduced catalytic efficiency. These trends align with experimental findings and highlight the predictive power of Go̅Martini simulations in capturing key functional trade-offs. Altogether, this work offers mechanistic insight into the rational design of immobilized biocatalysts and outlines a practical framework for in silico exploration of enzyme–surface systems.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"132 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nucleation Statistics from Experiments as a Benchmark for Theory and Simulations 实验成核统计作为理论和模拟的基准

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-09 DOI: 10.1021/acs.jpclett.5c03082

Frédéric Caupin,Robert E. Grisenti

The fate of a metastable substance is governed by rare nucleation events, whose full microscopic details still remain elusive despite over 150 years of study. Experimental nucleation rates often differ by many orders of magnitude from theory and simulations, limiting predictive power. In this Perspective, after a general overview on nucleation, we focus on crystallization in three representative systems: metastable water, colloidal suspensions, and Lennard-Jones liquids. The latter, which are well realized by rare-gas liquids, provide a touchstone for nucleation theory. Recent femtosecond X-ray diffraction experiments on supercooled argon and krypton deliver accurate crystal nucleation statistics and direct insight into structural defects such as stacking faults. These advances establish rare-gas liquids as uniquely well-controlled systems bridging experiment, simulation, and theory, and pave the way toward a more complete microscopic understanding of nucleation.

引用次数: 0

Self-Supporting FeOOH/NiFe-LDH Heterostructures with a Built-In Electric Field for Efficient and Durable Alkaline Seawater Oxidation 具有内置电场的FeOOH/NiFe-LDH异质结构高效持久碱性海水氧化

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-08 DOI: 10.1021/acs.jpclett.5c04027

Lin Chen,Fei Ma,Yutong An,Yuning Zhang,Shiqi Yin,Xiaohan Yuan,Kaicai Fan,Lei Wang,Zhiqiang Hu,Tianrong Zhan

Efficient and durable oxygen evolution reaction (OER) catalysts are crucial for the generation of hydrogen via alkaline seawater electrolysis. Herein, an FeOOH/NiFe-LDH heterostructure with a built-in electric field (BEF) has been synthesized on Ni foam through a one-step cathodic electrodeposition. The formed BEF accelerates the OER kinetics by optimizing the interfacial electronic structure and enhancing the mass transfer and stabilizes the structure of the catalyst by Fe–O–Ni–O–Fe coupling bonds. In addition, the BEF and FeOOH mutually reduce the adsorption of Cl– on the catalyst. Accordingly, FeOOH/NiFe-LDH demonstrates an outstanding OER catalytic performance in alkaline seawater electrolytes. In detail, FeOOH/NiFe-LDH displays small η100 values of 265 and 278 mV in alkaline simulated and natural seawater, respectively, and achieves exceptional durability with smooth operation for ∼150 h at 250 mA cm–2, albeit in a high-salt electrolyte (1 M KOH and 2.5 M NaCl). When FeOOH/NiFe-LDH is used as the anode of the AEM electrolyzer, the cell in alkaline simulated seawater delivers low voltages of 1.59 and 1.92 V at 100 and 500 mA cm–2, respectively. The cell also shows excellent durability after operation over 110 h at 250 mA cm–2 with an insignificant voltage increase of only 27 mV (∼0.25 mV h–1). This work provides insight into the catalytic mechanism of the BEF-based heterostructure as anodic catalysts for alkaline seawater electrolysis.

{"title":"Self-Supporting FeOOH/NiFe-LDH Heterostructures with a Built-In Electric Field for Efficient and Durable Alkaline Seawater Oxidation","authors":"Lin Chen,Fei Ma,Yutong An,Yuning Zhang,Shiqi Yin,Xiaohan Yuan,Kaicai Fan,Lei Wang,Zhiqiang Hu,Tianrong Zhan","doi":"10.1021/acs.jpclett.5c04027","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c04027","url":null,"abstract":"Efficient and durable oxygen evolution reaction (OER) catalysts are crucial for the generation of hydrogen via alkaline seawater electrolysis. Herein, an FeOOH/NiFe-LDH heterostructure with a built-in electric field (BEF) has been synthesized on Ni foam through a one-step cathodic electrodeposition. The formed BEF accelerates the OER kinetics by optimizing the interfacial electronic structure and enhancing the mass transfer and stabilizes the structure of the catalyst by Fe–O–Ni–O–Fe coupling bonds. In addition, the BEF and FeOOH mutually reduce the adsorption of Cl– on the catalyst. Accordingly, FeOOH/NiFe-LDH demonstrates an outstanding OER catalytic performance in alkaline seawater electrolytes. In detail, FeOOH/NiFe-LDH displays small η100 values of 265 and 278 mV in alkaline simulated and natural seawater, respectively, and achieves exceptional durability with smooth operation for ∼150 h at 250 mA cm–2, albeit in a high-salt electrolyte (1 M KOH and 2.5 M NaCl). When FeOOH/NiFe-LDH is used as the anode of the AEM electrolyzer, the cell in alkaline simulated seawater delivers low voltages of 1.59 and 1.92 V at 100 and 500 mA cm–2, respectively. The cell also shows excellent durability after operation over 110 h at 250 mA cm–2 with an insignificant voltage increase of only 27 mV (∼0.25 mV h–1). This work provides insight into the catalytic mechanism of the BEF-based heterostructure as anodic catalysts for alkaline seawater electrolysis.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"10 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Process-Mechanized Green Approach to Organic Phosphorescence: From Mechanochemistry Synthesis to 3D Printing 有机磷光的过程机械化绿色方法：从机械化学合成到3D打印

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-08 DOI: 10.1021/acs.jpclett.5c03971

Xinyue Xu,Xinyu Ding,Guangming Meng,Jing Lv,Dong Ding,Feng Li,Tao Zhuang,Erkin Zakhidov,Mingliang Sun

Carbazole-based organic room-temperature phosphorescent (RTP) materials have attracted widespread attention, yet their structural diversification has remained limited due to inherent synthetic constraints. In this work, a dual-mechanical strategy integrating mechanochemical synthesis with 3D-printed processing is introduced. A g-configured benzoindole (Bd[g]) skeleton is efficiently obtained through a solvent-free mechanochemical protocol, enabling rapid and scalable access to high-performance RTP molecular frameworks. When dispersed within a poly(vinyl butyral) (PVB) matrix, Bd[g] derivatives display stable RTP emission as a result of suppressed molecular motion and minimized environmental quenching. Benefiting from the excellent processability of PVB-based composites, the RTP materials are further shaped into customizable 3D-printed architectures featuring persistent phosphorescence, mechanical flexibility, and strong resistance to seawater. This fully mechanical “molecule-to-device” methodology establishes a practical route toward durable organic RTP systems and underscores their potential in marine sensing, underwater imaging, and long-term anticorrosion applications.

{"title":"Process-Mechanized Green Approach to Organic Phosphorescence: From Mechanochemistry Synthesis to 3D Printing","authors":"Xinyue Xu,Xinyu Ding,Guangming Meng,Jing Lv,Dong Ding,Feng Li,Tao Zhuang,Erkin Zakhidov,Mingliang Sun","doi":"10.1021/acs.jpclett.5c03971","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c03971","url":null,"abstract":"Carbazole-based organic room-temperature phosphorescent (RTP) materials have attracted widespread attention, yet their structural diversification has remained limited due to inherent synthetic constraints. In this work, a dual-mechanical strategy integrating mechanochemical synthesis with 3D-printed processing is introduced. A g-configured benzoindole (Bd[g]) skeleton is efficiently obtained through a solvent-free mechanochemical protocol, enabling rapid and scalable access to high-performance RTP molecular frameworks. When dispersed within a poly(vinyl butyral) (PVB) matrix, Bd[g] derivatives display stable RTP emission as a result of suppressed molecular motion and minimized environmental quenching. Benefiting from the excellent processability of PVB-based composites, the RTP materials are further shaped into customizable 3D-printed architectures featuring persistent phosphorescence, mechanical flexibility, and strong resistance to seawater. This fully mechanical “molecule-to-device” methodology establishes a practical route toward durable organic RTP systems and underscores their potential in marine sensing, underwater imaging, and long-term anticorrosion applications.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"23 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Space Series: Leveraging Geometric Features of Heterostructures for Cross-Composition Property Prediction of Core–Shell Quantum Dots 空间序列：利用异质结构的几何特征预测核壳量子点的交叉组成性质

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-08 DOI: 10.1021/acs.jpclett.5c03783

Xinyu Deng,Jinzhe Cao,Shengyang Tao

The spatial arrangement of compositions in functional materials plays a pivotal role in modulating their properties. While various representations exist for predicting the benchmark properties of constituent compositions, geometric factors like heterogeneity beyond the atomic scale, which are crucial to the overall behavior of the material, are not encoded explicitly, leading to poor generalization to materials with scarce training data. In this work, we introduce a novel representation, called a space series, which explicitly encodes the geometric features of heterostructures. As a proof of concept, we apply this approach to the property prediction of core–shell quantum dots (CSQDs). Our model, designed to generate and leverage these geometric-compositional sequences, outperforms traditional machine learning methods in terms of generalization across different data sets. Further analysis reveals that such sequences enable the model to effectively capture spatial dependencies within the system, offering a novel pathway for incorporating geometric factors into functional material predictions.

{"title":"Space Series: Leveraging Geometric Features of Heterostructures for Cross-Composition Property Prediction of Core–Shell Quantum Dots","authors":"Xinyu Deng,Jinzhe Cao,Shengyang Tao","doi":"10.1021/acs.jpclett.5c03783","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c03783","url":null,"abstract":"The spatial arrangement of compositions in functional materials plays a pivotal role in modulating their properties. While various representations exist for predicting the benchmark properties of constituent compositions, geometric factors like heterogeneity beyond the atomic scale, which are crucial to the overall behavior of the material, are not encoded explicitly, leading to poor generalization to materials with scarce training data. In this work, we introduce a novel representation, called a space series, which explicitly encodes the geometric features of heterostructures. As a proof of concept, we apply this approach to the property prediction of core–shell quantum dots (CSQDs). Our model, designed to generate and leverage these geometric-compositional sequences, outperforms traditional machine learning methods in terms of generalization across different data sets. Further analysis reveals that such sequences enable the model to effectively capture spatial dependencies within the system, offering a novel pathway for incorporating geometric factors into functional material predictions.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"22 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Monte Carlo Simulation of Faceted Nanoparticles Using Analytical Interaction Potentials 利用分析相互作用势的高效蒙特卡罗模拟多面纳米颗粒

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-08 DOI: 10.1021/acs.jpclett.5c04083

Şafak Çallıoğlu,Quanpeng Yang,Yuanchuan Shao,Brian H. Lee,Gaurav Arya

Understanding how energetic interactions between faceted nanoparticles (NPs) drive their self-assembly into higher-order architectures is critical for controlling various properties of NP assemblies. Here, we integrate analytical potentials that capture orientation-dependent van der Waals interactions into a Monte Carlo simulation framework for fast and accurate modeling of NP self-assembly. By implementing virtual cluster moves in the framework, we overcome sampling limitations and account for size-dependent diffusion of clusters. Simulations using the analytical potentials are orders of magnitude faster than atomistic and coarse-grained models while producing correct assembly morphologies. Phase behavior calculations of faceted NPs with weak and strong interparticle attractions show that attraction enhances ordering and shifts isotropic-to-semiordered transitions to lower volume fractions, while semiordered-to-crystalline transitions remain largely entropy driven. Overall, this work highlights the importance of enthalpic interactions and the advantages of using analytical potentials for efficient simulations of faceted NPs.

{"title":"Efficient Monte Carlo Simulation of Faceted Nanoparticles Using Analytical Interaction Potentials","authors":"Şafak Çallıoğlu,Quanpeng Yang,Yuanchuan Shao,Brian H. Lee,Gaurav Arya","doi":"10.1021/acs.jpclett.5c04083","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c04083","url":null,"abstract":"Understanding how energetic interactions between faceted nanoparticles (NPs) drive their self-assembly into higher-order architectures is critical for controlling various properties of NP assemblies. Here, we integrate analytical potentials that capture orientation-dependent van der Waals interactions into a Monte Carlo simulation framework for fast and accurate modeling of NP self-assembly. By implementing virtual cluster moves in the framework, we overcome sampling limitations and account for size-dependent diffusion of clusters. Simulations using the analytical potentials are orders of magnitude faster than atomistic and coarse-grained models while producing correct assembly morphologies. Phase behavior calculations of faceted NPs with weak and strong interparticle attractions show that attraction enhances ordering and shifts isotropic-to-semiordered transitions to lower volume fractions, while semiordered-to-crystalline transitions remain largely entropy driven. Overall, this work highlights the importance of enthalpic interactions and the advantages of using analytical potentials for efficient simulations of faceted NPs.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"158 4 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bioinspired Optic-Neural Device with Neuromorphic Spatiotemporal Visual Perception for Dynamic Object Recognition 一种具有神经形态时空视觉感知的生物启发光学神经装置用于动态目标识别

IF 6.475 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-08 DOI: 10.1021/acs.jpclett.5c04067

Bo Wei,Yan Kang,Yongshun Xia,Yuhan Sun,Xiaotong Han,Bujia Liang,Yuanxi Peng,Liang Fang,Yabo Chen,Cheng Li,Xiaokuo Yang

Neuromorphic vision with integrated sensing, memorizing, and computing is considered a potential way to break through the high latency and storage bottlenecks of existing machine vision. However, simulating the human vision system with a single device to achieve spatiotemporal visual information perception remains a challenge. Hence, we demonstrated a bioinspired optic-neural device that achieves dynamic object recognition by simultaneously implementing encoding and sensing functions on molybdenum disulfide (MoS2). This device exhibits excellent optical synaptic plasticity originating from charge trapping and recombination in the device. The electrical output is weighted by the number of optical inputs, which reflects the history information on optical inputs. This mechanism is similar to the spatiotemporal information encoding ability of the lateral geniculate nucleus in the human vision system, enabling the device to remember historical visual information as well as achieve spatial resolution. Benefiting from the optic-neural device nonlinearly encoding and mapping a series of light inputs when arranged in an array, we achieved spatiotemporal visual information perception for dynamic object recognition similar to that of the human vision system. High classification accuracy up to 96.3% in five dynamic object data sets with high energy efficiency and low computational load are achieved based on such neuromorphic vision system. Our research findings have profound significance for promoting the advancement of existing machine vision systems.

{"title":"A Bioinspired Optic-Neural Device with Neuromorphic Spatiotemporal Visual Perception for Dynamic Object Recognition","authors":"Bo Wei,Yan Kang,Yongshun Xia,Yuhan Sun,Xiaotong Han,Bujia Liang,Yuanxi Peng,Liang Fang,Yabo Chen,Cheng Li,Xiaokuo Yang","doi":"10.1021/acs.jpclett.5c04067","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c04067","url":null,"abstract":"Neuromorphic vision with integrated sensing, memorizing, and computing is considered a potential way to break through the high latency and storage bottlenecks of existing machine vision. However, simulating the human vision system with a single device to achieve spatiotemporal visual information perception remains a challenge. Hence, we demonstrated a bioinspired optic-neural device that achieves dynamic object recognition by simultaneously implementing encoding and sensing functions on molybdenum disulfide (MoS2). This device exhibits excellent optical synaptic plasticity originating from charge trapping and recombination in the device. The electrical output is weighted by the number of optical inputs, which reflects the history information on optical inputs. This mechanism is similar to the spatiotemporal information encoding ability of the lateral geniculate nucleus in the human vision system, enabling the device to remember historical visual information as well as achieve spatial resolution. Benefiting from the optic-neural device nonlinearly encoding and mapping a series of light inputs when arranged in an array, we achieved spatiotemporal visual information perception for dynamic object recognition similar to that of the human vision system. High classification accuracy up to 96.3% in five dynamic object data sets with high energy efficiency and low computational load are achieved based on such neuromorphic vision system. Our research findings have profound significance for promoting the advancement of existing machine vision systems.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"387 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking the Potential of Fluoroethylene Carbonate with Lithium Difluoro(oxalate)borate for High-Voltage and High-Rate Lithium Metal Batteries. 利用二氟（草酸）硼酸锂释放碳酸氟乙烯在高压和高倍率锂金属电池中的潜力。

IF 4.6 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-07 DOI: 10.1021/acs.jpclett.5c03897

Changquan Wu, Guangni Ding, Du Liu, Xuerui Yang, Xiaowei Huang, Naigen Zhou

Fluoroethylene carbonate (FEC), as a key electrolyte component, has been extensively employed across diverse electrolyte systems owing to its excellent compatibility with different anode materials. However, its mechanistic role on the cathode side remains under debate due to the strong electron-withdrawing nature of the fluorine incorporation. Here, we demonstrate that lithium difluoro(oxalate)borate (LiDFOB) can effectively trigger the latent cathode-side functionality of FEC through a rationally designed dual-additive electrolyte. At the LiNi_0.9Co_0.05Mn_0.05O₂ cathode, oxidative cleavage of LiDFOB generates BOF₂ intermediates that activate FEC and direct its decomposition toward LiF and B-O/B-F-rich inorganic species, constructing a compact and resilient cathode-electrolyte interphase (CEI). Simultaneously, the coupled reduction of FEC and DFOB^- at the lithium metal anode yields a boron-rich, LiF-enriched solid electrolyte interphase (SEI) that enhances interfacial compatibility and suppresses dendrite growth. These LiDFOB-enabled, FEC-mediated interfacial pathways significantly improve ion transport and durability, delivering 73.4% capacity retention of Li||NCM9055 full cells after 1000 cycles at 4.7 V, versus 55.1% for the base electrolyte.

{"title":"Unlocking the Potential of Fluoroethylene Carbonate with Lithium Difluoro(oxalate)borate for High-Voltage and High-Rate Lithium Metal Batteries.","authors":"Changquan Wu, Guangni Ding, Du Liu, Xuerui Yang, Xiaowei Huang, Naigen Zhou","doi":"10.1021/acs.jpclett.5c03897","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c03897","url":null,"abstract":"Fluoroethylene carbonate (FEC), as a key electrolyte component, has been extensively employed across diverse electrolyte systems owing to its excellent compatibility with different anode materials. However, its mechanistic role on the cathode side remains under debate due to the strong electron-withdrawing nature of the fluorine incorporation. Here, we demonstrate that lithium difluoro(oxalate)borate (LiDFOB) can effectively trigger the latent cathode-side functionality of FEC through a rationally designed dual-additive electrolyte. At the LiNi0.9Co0.05Mn0.05O2 cathode, oxidative cleavage of LiDFOB generates BOF2 intermediates that activate FEC and direct its decomposition toward LiF and B-O/B-F-rich inorganic species, constructing a compact and resilient cathode-electrolyte interphase (CEI). Simultaneously, the coupled reduction of FEC and DFOB- at the lithium metal anode yields a boron-rich, LiF-enriched solid electrolyte interphase (SEI) that enhances interfacial compatibility and suppresses dendrite growth. These LiDFOB-enabled, FEC-mediated interfacial pathways significantly improve ion transport and durability, delivering 73.4% capacity retention of Li||NCM9055 full cells after 1000 cycles at 4.7 V, versus 55.1% for the base electrolyte.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular Representation Matters: Comparative Evaluation of Fingerprints, RDKit Descriptors, and Hashing Effects. 分子表征问题：指纹，RDKit描述符和哈希效果的比较评估。

IF 4.6 2区化学 Q2 CHEMISTRY, PHYSICAL

The Journal of Physical Chemistry Letters

Pub Date : 2026-02-07 DOI: 10.1021/acs.jpclett.5c03797

Shijia Zhou, Junqing Li, Ziyi Liu, Dongqi Wang

Molecular representations largely determine the learnability of quantum-chemical properties with machine learning. In order to find the most appropriate way to represent molecules in chemoinformatic studies, a comparative study of nine two-dimensional molecular fingerprints and three RDKit descriptor sets (PHYS, CONF, and PHCO) was conducted in terms of the prediction of five molecular properties by trained predictive machine learning models. The use of RDKit descriptor sets consistently yields more accurate results than hashed fingerprints across properties. Among fingerprints, Layered Fingerprint outperforms for global energy targets (E_tot, E_ee, E_xc), whereas ECFP6 demonstrates better performance for atom-localized (E_atom) and thermodynamic targets (C_p). We further evaluate how the choice of hash function used during fingerprint construction affects representation quality and identify that noncryptographic hashing preserves locality and leads to better and more consistent outcomes than cryptographic hashing (SHA-256). This work provides mechanistic insights into how different molecular representations encode structural and physicochemical information, highlighting the merits and limits of descriptors for learning quantum-chemical properties. This offers practical guidance for selecting molecular representations and hashing strategies in designing and establishing pipelines for the artificial intelligence study of chemistry.

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