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| 1 | Machine learning accelerated calculation and design of electrocatalysts for CO_(2) reduction显示文摘In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML. | Zhehao Sun Hang Yin Kaili Liu Shuwen Cheng Gang Kevin Li Sibudjing Kawi Haitao Zhao Guohua Jia Zongyou Yin | 2022 | SmartMat2022，3，1： | 2 |
| 2 | Two-Dimensional Metal-Organic Frameworks with Unique Oriented Layers for Oxygen Reduction Reaction:Tailoring the Activity through Exposed Crystal Facets显示文摘As one of the most important families of porous materials,metal–organic frameworks(MOFs)have well-defined atomic structures.This provides ideal models for investigating and understanding the relationships between structures and catalytic activities at the molecular level.However,the active sites on the edges of two-dimensional(2D)MOFs have rarely been studied,as they are less exposed to the surfaces.Here,for the first time,we synthesized and observed that the 2D layers could align perpendicular to the surface of a 2D zeolitic imidazolate framework L(ZIF-L)with a leaf-like morphology.Owing to this unique orientation,the active sites on the edges of the 2D crystal structure could mostly be exposed to the surfaces.Interestingly,when another layer of ZIF-L-Co was grown heteroepitaxially onto ZIF-L-Zn(ZIF-L-Zn@ZIF-L-Co),the two layers shared a common b axis but rotated by 90°in the ac plane.This demonstrated that we could control exposed facets of the 2D MOFs.The ZIF-L-Co with more exposed edge active sites exhibited high electrocatalytic activity for oxygen reduction reaction.This work provides a new concept of designing unique oriented layers in 2D MOFs to expose more edge-active sites for efficient electrocatalysis. | Yanzhi Wang Tu Sun Amir H.B.Mostaghimi Tiago J.Goncalves Zuozhong Liang Yuye Zhou Wei Zhang Zhehao Huang Yanhang Ma Rui Cao Samira Siahrostami Haoquan Zheng | 2022 | CCS Chemistry2022，4，5： | 1 |
| 3 | Monitoring the in vivo siRNA release from lipid nanoparticles based on the fluorescence resonance energy transfer principle显示文摘The siRNA-loaded lipid nanoparticles have attracted much attention due to its significant gene silencing effect and successful marketization.However,the in vivo distribution and release of siRNA still cannot be effectively monitored.In this study,based on the fluorescence resonance energy transfer(FRET)principle,a fluorescence dye Cy5-modified survivin siRNA was conjugated to nanogolds(Au-DR-siRNA),which were then wrapped with lipid nanoparticles(LNPs)for monitoring the release behaviour of siRNA in vivo.The results showed that once Au-DR-siRNA was released from the LNPs and cleaved by the Dicer enzyme to produce free siRNA in cells,the fluorescence of Cy5 would change from quenched state to activated state,showing the location and time of siRNA release.Besides,the LNPs showed a significant antitumor effect by silencing the survivin gene and a CT imaging function superior to iohexol by nanogolds.Therefore,this work provided not only an effective method for monitoring the pharmacokinetic behaviour of LNP-based siRNA,but also a siRNA delivery system for treating and diagnosing tumors. | Lei Sun Jinfang Zhang Jing-e Zhou JingWang Zhehao Wang Shenggen Luo Yeying Wang Shulei Zhu Fan Yang Jie Tang Wei Lu Yiting Wang Lei Yu Zhiqiang Yan | 2023 | Asian Journal of Pharmaceutical Sciences2023，18，1： | 0 |
| 4 | The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction显示文摘Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed. | Zhuo Wang Zhehao Sun Hang Yin Honghe Wei Zicong Peng Yoong Xin Pang Guohua Jia Haitao Zhao Cheng Heng Pang Zongyou Yin | 2023 | eScience2023，3，4： | 0 |
| 5 | First-principles investigation of the significant anisotropy and ultrahigh thermoelectric efficiency of a novel two-dimensional Ga_(2)I_(2)S_(2) at room temperature显示文摘Two-dimensional(2D)thermoelectric(TE)materials have been widely developed;however,some 2D materials exhibit isotropic phonon,electron transport properties,and poor TE performance,which limit their application scope.Thus,exploring excellent anisotropic and ultrahigh-performance TE materials are very warranted.Herein,we first investigate the phonon thermal and TE properties of a novel 2D-connectivity ternary compound named Ga2I2S2.This paper comprehensively studies the phonon dispersion,phonon anharmonicity,lattice thermal conductivity,electronic structure,carrier mobility,Seebeck coefficient,electrical conductivity,and the dimensionless figure of merit(ZT)versus carrier concentration for 2D Ga_(2)I_(2)S_(2).We conclude that the in-plane lattice thermal conductivities of Ga_(2)I_(2)S_(2) at room temperature(300 K)are found to be 1.55 W mK^(−1) in the X-axis direction(xx-direction)and 3.82 W mK^(−1)in the Y-axis direction(yy-direction),which means its anisotropy ratio reaches 1.46.Simultaneously,the TE performance of p-type and n-type doping 2D Ga2I2S2 also shows significant anisotropy,giving rise to the ZT peak values of p-type doping in xx-and yy-directions being 0.81 and 1.99,respectively,and those of n-type doping reach ultrahigh values of 7.12 and 2.89 at 300 K,which are obviously higher than the reported values for p-type and n-type doping ternary compound Sn2BiX(ZT∼1.70 and∼2.45 at 300 K)(2020 Nano Energy 67104283).This work demonstrates that 2D Ga_(2)I_(2)S_(2) has high anisotropic TE conversion efficiency and can also be used as a new potential room-temperature TE material. | Zheng Chang Ke Liu Zhehao Sun Kunpeng Yuan Shuwen Cheng Yufei Gao Xiaoliang Zhang Chen Shen Hongbin Zhang Ning Wang Dawei Tang | 2022 | International Journal of Extreme Manufacturing2022，4，2： | 0 |
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