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Kernel-based Actor-Critic Learning Framework for Autonomous Brain Control on Trajectory

IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 554-563页

作者： Song, Zhiwei Zhang, Xiang Chen, Shuhang Tan, Jieyuan Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

Reinforcement learning (RL)-based Brain-Machine Interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multi-step BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this paper, we propose a kernel-based actor-critic learning framework for multi-step BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial Prefrontal Cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multi-step brain control tasks. © 2016 IEEE.

关键词： Adversarial machine learning

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"Weakly Adsorbed CO2 and H2O Species on Monoethanolamine Films in a Room-Temperature CO2 and Direct Air Capture Cycle: An In Situ Infrared Study"

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INDUSTRIAL & engineering CHEMISTRY RESEARCH 2024年第10期63卷 4742-4742页

作者： Oduntan, Aderinsola Chuang, Steven S. C. School of Polymer Science and Polymer Engineering Department of Chemical Biomolecular and Corrosion Engineering The University of Akron 200 E Buchtel Commons Akron Ohio 44325-3909 United States

The binding states of adsorbed CO2 species on monoethanolamine (MEA) thin films in a CO2 and direct air capture cycle at 25 °C have been studied by in situ infrared spectroscopy. CO2 adsorption produced two types of adsorbed CO2: (i) hydrogen-bonded ammonium carbamate, as strongly adsorbed CO2, which can only be removed by heating above 25 °C and (ii) carbamic acid, as weakly adsorbed CO2, which can be removed at room temperature from the MEA film. The infrared results suggest that carbamic acid is on an amine site, which is isolated by OH from neighboring MEA molecules and further clarify the sequence of the formation of adsorbed CO2. Weakly adsorbed CO2 and H2O species were observed for the first time on MEA and nonaqueous MEA thin films. Decreasing the concentration of CO2 from 100% to 420 ppm of CO2/1.5% H2O (atmospheric CO2) for direct air capture decreased the CO2 capture capacity and increased the adsorption half-time while the CO2 desorption half-time (i.e., rate) remained at the same level. Controlling the type and structure of adsorbed CO2 could be effective in bringing down the energy needed for the regeneration of amine solvents and sorbents through enhancing the density of amine sites for carbamic acid.

关键词： Adsorption Amines Noncovalent interactions Organic compounds Thin films

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New materials for lithium-sulfur batteries: challenges and future directions

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chemical Communications 2025年第43期61卷 7770-7794页

作者： Sawangphruk, Montree Centre of Excellence for Energy Storage Technology Department of Chemical and Biomolecular Engineering School of Energy Science and Engineering Vidyasirimedhi Institute of Science and Technology Rayong21210 Thailand

This review explores recent advances in lithium-sulfur (Li-S) batteries, promising next-generation energy storage devices known for their exceptionally high theoretical energy density (∼2500 W h kg−1), cost-effectiveness, and environmental advantages. Despite their potential, commercialization remains limited by key challenges such as the polysulfide shuttle effect, sulfur's insulating nature, lithium metal anode instability, and thermal safety concerns. This review provides a comprehensive and forward-looking perspective on emerging material strategies—focusing on cathode, electrolyte, and anode engineering—to overcome these barriers. Special emphasis is placed on advanced sulfur-carbon composites, including three-dimensional graphene frameworks, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and MXene-based materials, which have demonstrated significant improvements in sulfur utilization, redox kinetics, and cycling stability. Innovations in electrolytes—particularly solid-state and gel polymer systems—are discussed for their roles in suppressing polysulfide dissolution and enhancing safety. This review also examines lithium metal anode protection strategies, such as use of artificial SEI layers and 3D lithium scaffolds and lithium alloying. Finally, it discusses critical issues related to large-scale manufacturing, safety, and commercial scalability. With ongoing innovation in multifunctional materials and electrode design, Li-S batteries are well positioned to transform energy storage for electric vehicles, portable electronics, and grid-scale systems. © 2025 The Royal Society of Chemistry.

关键词： Lithium sulfur batteries

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Property-guided generation of complex polymer topologies using variational autoencoders

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npj Computational Materials 2024年第1期10卷 1825-1837页

作者： Shengli Jiang Adji Bousso Dieng Michael A.Webb Department of Chemical and Biological Engineering Princeton UniversityPrincetonNJUSA Vertaix Department of Computer SciencePrinceton UniversityPrincetonNJUSA

The complexity and diversity of polymer topologies,or chain architectures,present substantial challenges in predicting and engineering polymer *** machine learning is increasingly used in polymer science,applications to address architecturally complex polymers are ***,we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties.

关键词： properties variational polymer

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Unassisted overall water splitting with a solar-to-hydrogen efficiency of over 10% by coupled lead halide perovskite photoelectrodes

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Carbon Energy 2023年第1期5卷 134-143页

作者： Ryan Rhee Tae G.Kim Gyu Y.Jang Gwangmin Bae Jung H.Lee Sunje Lee Sungsoon Kim Seokwoo Jeon Jong H.Park Department of Chemical and Biomolecular Engineering Yonsei UniversitySeoulRepublic of Korea Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea

Hydrogen is a promising future sustainable fuel candidate with boundless *** into photoelectrochemical(PEC)water splitting based on a lead halide perovskite(LHP)has progressed significantly with the aim of more efficient solar hydrogen ***,we unite a well-known photo-absorbing LHP with cost-effective water-splitting catalysts,and we introduce two types of monolithic LHP-based PEC devices that act as a photocathode and a photoanode for the hydrogen evolution reaction and oxygen evolution reaction,leading to efficient unbiased overall water *** the integration of these two monolithic LHP-based photoelectrodes,an unbiased solar-to-hydrogen conversion efficiency of 10.64%and a photocurrent density of 8.65 mA cm^(−2) are achieved.

关键词： perovskite photoelectrochemical cell photoelectrode unassisted

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Modeling Task Engagement to Regulate Reinforcement Learning-based Decoding for Online Brain Control

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 606-614页

作者： Zhang, Xiang Shen, Xiang Wang, Yiwen Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Brain-Machine Interfaces (BMIs) offer significant promise for enabling paralyzed individuals to control external devices using their brain signals. One challenge is that during the online Brain Control (BC) process, subjects may not be completely immersed in the task, particularly when multiple steps are needed to achieve a goal. The decoder indiscriminately takes the less engaged trials as training data, which might decrease the decoding accuracy. In this paper, we propose an alternative kernel RL-based decoder that trains online with continuous parameter update. We model neural activity from the medial prefrontal cortex (mPFC), a reward-related brain region, to represent task engagement. This information is incorporated into a stochastic learning rate using an exponential model, which measures the relevancy of neural data. The proposed algorithm was evaluated in the experiment where rats performed a cursor-reaching BC task. We found the neural activities from mPFC contained the engagement information which was negatively correlated with trial response time. Moreover, compared to the RL method without task engagement modeling, our proposed method enhanced the training efficiency. It used half of the training data to achieve the same reconstruction accuracy of the cursor trajectory. The results demonstrate the potential of our RL framework for improving online brain control tasks. © 2016 IEEE.

关键词： Reinforcement learning

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Unraveling the Harmonious Coexistence of Ruthenium States on a Self-Standing Electrode for Enhanced Hydrogen Evolution Reaction

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Energy & Environmental Materials 2024年第6期7卷 303-311页

作者： Joonhee Ma Jin Hyuk Cho Chaehyeon Lee Moon Sung Kang Sungkyun Choi Ho Won Jang Sang Hyun Ahn Seoin Back Soo Young Kim Department of Materials Science and Engineering Korea UniversitySeoul 02841Korea Department of Chemical and Biomolecular Engineering Institute of Emergent MaterialsSogang UniversitySeoul 04107Korea Department of Cogno-Mechatronics Engineering College of Nanoscience and NanotechnologyPusan National UniversityBusan 46241Korea Department of Materials Science and Engineering Research Institute of Advanced MaterialsSeoul National UniversitySeoul 08826Korea School of Chemical Engineering and Material Science Chung-Ang UniversitySeoul 06974Korea

The development of cost-effective,highly efficient,and durable electrocatalysts has been a paramount pursuit for advancing the hydrogen evolution reaction(HER).Herein,a simplified synthesis protocol was designed to achieve a self-standing electrode,composed of activated carbon paper embedded with Ru single-atom catalysts and Ru nanoclusters(ACP/Ru_(SAC+C))via acid activation,immersion,and high-temperature *** initio molecular dynamics(AIMD)calculations are employed to gain a more profound understanding of the impact of acid activation on carbon ***,the coexistence states of the Ru atoms are confirmed via aberration-corrected scanning transmission electron microscopy(AC-STEM),X-ray photoelectron spectroscopy(XPS),and X-ray absorption spectroscopy(XAS).Experimental measurements and theoretical calculations reveal that introducing a Ru single-atom site adjacent to the Ru nanoclusters induces a synergistic effect,tuning the electronic structure and thereby significantly enhancing their catalytic ***,the ACP/Ru_(SAC+C)exhibits a remarkable turnover frequency(TOF)of 18 s^(−1)and an exceptional mass activity(MA)of 2.2 A mg^(−1),surpassing the performance of conventional Pt *** self-standing electrode,featuring harmoniously coexisting Ru states,stands out as a prospective choice for advancing HER catalysts,enhancing energy efficiency,productivity,and selectivity.

关键词： electrocatalysis electronic coupling effect hydrogen evolution reaction selfstanding electrode

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Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis

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npj Computational Materials 2024年第1期10卷 628-637页

作者： Hoje Chun Jaclyn R.Lunger JeungKu Kang Rafael Gómez-Bombarelli Byungchan Han Department of Chemical and Biomolecular Engineering Yonsei UniversitySeoul03722Republic of Korea Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA02139USA Department of Materials Science&Engineering and NanoCentury Institute Korea Advanced Institute of Science and Technology291 Daehak-roYuseong-guDaejeon34141Republic of Korea

Single-atom catalysts(SACs)with multiple active sites exhibit high activity for a wide range of sluggish reactions,but identifying optimal multimetallic SAC is challenging due to the vast design ***,we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network(GNN)to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions(ORR/OER).Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active *** GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and *** computational predictions of promising Co-Fe,Co-Co,and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the *** approach can be extended to a broader class of multi-element high entropic materials systems.

关键词： space metallic property

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Descriptor-augmented machine learning for enzyme-chemical interaction predictions

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Synthetic and Systems Biotechnology 2024年第2期9卷 259-268页

作者： Yilei Han Haoye Zhang Zheni Zeng Zhiyuan Liu Diannan Lu Zheng Liu Department of Chemical Engineering Tsinghua UniversityBeijing100084China Department of Computer Science and Technology Tsinghua UniversityBeijing100084China

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals,as they could characterize enzymes and chemicals from the physicochemical and evolutionary *** study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships *** curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold *** influence of protein and chemical descriptors was assessed in three scenarios,which were predicting the activity of unknown relations between known enzymes and known chemicals(new relationship evaluation),predicting the activity of novel enzymes on known chemicals(new enzyme evaluation),and predicting the activity of new chemicals on known enzymes(new chemical evaluation).The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes,whereas chemical descriptors appear no effect.A variety of sequence-based and structure-based protein descriptors were constructed,among which the esm-2 descriptor achieved the best *** enzyme families as labels showed that descriptors could cluster proteins well,which could explain the contributions of descriptors to the machine learning *** a counterpart,in the new chemical evaluation,chemical descriptors made significant improvement in four out of the seven datasets,while protein descriptors appear no *** attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the *** results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein

关键词： Enzyme-substrate interaction Enzyme design Machine learning Descriptor

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Age-hardening behavior,corrosion mechanisms,and passive film structure of nanocrystalline Al-V supersaturated solid solution

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Journal of Materials science & Technology 2023年第4期135卷 1-12页

作者： J.Christudasjustus M.R.Felde C.S.Witharamage J.Esquivel A.A.Darwish C.Winkler R.K.Gupta Department of Materials Science and Engineering North Carolina State UniversityRaleighNC 27606United States Department of Chemical Biomolecular and Corrosion EngineeringThe University of AkronAkronOH 44325United States

The effect of age-hardening on microstructure,hardness,and corrosion of an Al-5at.%V alloy,produced us-ing high-energy ball milling and subsequent cold compaction,has been *** alloy exhibited a grain size below 100 nm and extremely high solid solubility of V in Al(3.1 at.%).The age-hardening was carried out at 150,200,and 250℃.The peak-aged condition of 150℃ demonstrated the highest hardness-transpired from grain refinement,precipitation,and solid solution *** corrosion re-sistance of the Al-5at.%V alloy was studied as a function of aging *** peak-aged condition retained the corrosion resistance while it deteriorated in the over-aged ***,the cor-rosion resistance of the ball-milled Al alloys in all the aging conditions was superior to that of pure *** passive film structure and origin of corrosion were studied using scanning/transmission electron mi-croscopy(S/TEM).The high corrosion resistance of the alloy was attributed to the V enrichment at the film/metal interface and deposition of V on the cathodic phases,which suppresses the dissolution of Al within the pit and therefore promotes repassivation in the early stages of corrosion.

关键词： Al alloys Nanocrystalline alloy Age-hardening Ball milling Corrosion Hardness

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