Artificial intelligence chemistry最新文献_第8页

Hidden descriptors: Using statistical treatments to generate better descriptor sets 隐藏的描述符：使用统计处理方法生成更好的描述符集

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-03-30 DOI: 10.1016/j.aichem.2024.100061

Lucía Morán-González , Feliu Maseras

引用次数: 0

Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis 机器学习洞察铁基费舍尔托普什合成中催化剂组成和结构对甲烷选择性的影响

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-04-02 DOI: 10.1016/j.aichem.2024.100062

Yujun Liu , Xiaolong Zhang , Luotong Li , Xingchen Liu , Tingyu Lei , Jiawei Bai , Wenping Guo , Yuwei Zhou , Xingwu Liu , Botao Teng , Xiaodong Wen

{"title":"Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis","authors":"Yujun Liu , Xiaolong Zhang , Luotong Li , Xingchen Liu , Tingyu Lei , Jiawei Bai , Wenping Guo , Yuwei Zhou , Xingwu Liu , Botao Teng , Xiaodong Wen","doi":"10.1016/j.aichem.2024.100062","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100062","url":null,"abstract":"<div><p>Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performance characteristics is a challenging task, as their performance depends on various factors such as precursor, support material, promoters, pretreatment conditions and the catalyst structures. Thus, it remains difficult to understand the structure-performance relationship of FTS and to optimize the catalyst formulations and operating conditions rationally. By integrating traditional chemistry with machine learning, we herein establish intrinsic correlations among reduction, reaction conditions, phase information and the methane selectivity of Fe-based FTS, using high quality experimental data. The content of the iron phases in the post-reaction phase, particularly χ-Fe<sub>5</sub>C<sub>2</sub>, significantly influences the methane selectivity of the catalyst. Four types of additives K, Cu, SiO<sub>2</sub>, and Ca could effectively suppress the methane selectivity, most likely by promoting or stabilizing the iron carbide phases, indicated by their strong correlation. The machine learned structure-performance relationships offers new insights into the design of Fe-based FTS catalysts, and could guide the further optimization of the preprocessing conditions and various parameter factors to minimize the methane selectivity of FTS.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000204/pdfft?md5=062c38e0ee5dbc49728857b869639811&pid=1-s2.0-S2949747724000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140346879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning protocol for geometric information retrieval from molecular spectra 从分子光谱中检索几何信息的机器学习协议

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2023-11-28 DOI: 10.1016/j.aichem.2023.100031

Shijie Tao , Yi Feng , Wenmin Wang , Tiantian Han , Pieter E.S. Smith , Jun Jiang

{"title":"A machine learning protocol for geometric information retrieval from molecular spectra","authors":"Shijie Tao , Yi Feng , Wenmin Wang , Tiantian Han , Pieter E.S. Smith , Jun Jiang","doi":"10.1016/j.aichem.2023.100031","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100031","url":null,"abstract":"<div><p>Geometric information of molecules is closely related to their properties, and vibrational spectroscopy, as a common and powerful analytical tool for determining molecular structure, can assist in gaining precise geometric information. Traditional methods used to delineate spectrum-structure correlations are often expensive, time-consuming, and require extensive professional expertise. In this work, we used a machine learning protocol to construct a map from spectra to molecular geometric structures, and employed Grad-CAM, a convolutional network interpretation technology, to analyze which kinds of chemical information are important for determining our model’s results. The results obtained for six small molecules of differing structures demonstrate that the model is capable of (1) extracting the crucial spectral features that are vital to downstream tasks without necessitating any manual preprocessing, and (2) enabling retrieval of molecular structural information with high precision.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000313/pdfft?md5=8aed6656166ef3e340a5e81d46b42a1c&pid=1-s2.0-S2949747723000313-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138474987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid screening of copper-based bimetallic catalysts via automatic electrocatalysis platform: Electrocatalytic reduction of CO2 to C2+ products on europium-modified copper 通过自动电催化平台快速筛选铜基双金属催化剂：在铕改性铜上电催化还原 CO2 至 C2+ 产物

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-02-18 DOI: 10.1016/j.aichem.2024.100056

Yan Shen, Zihan Wang, Yihan Wang, Cheng Wang

{"title":"Rapid screening of copper-based bimetallic catalysts via automatic electrocatalysis platform: Electrocatalytic reduction of CO2 to C2+ products on europium-modified copper","authors":"Yan Shen, Zihan Wang, Yihan Wang, Cheng Wang","doi":"10.1016/j.aichem.2024.100056","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100056","url":null,"abstract":"<div><p>The electrocatalytic conversion of CO<sub>2</sub> (CO<sub>2</sub>RR) to multi-carbon products has been an appealing strategy to reduce carbon emissions. However, rapid experimental discovery of efficient CO<sub>2</sub>RR electrocatalysts and fast recording of full product distribution information is non-trivial. Herein, we used an electrocatalyst testing platform featuring a home-built automatic flow cell to accelerate catalysts screening. Based on 364 effective data points from 42 Cu-lanthanide bimetallic catalysts obtained within 21 working hours, we found that Eu modification over Cu can promote C<sub>2+</sub> faradaic efficiency (FE). We have previously reported part of the screening data and the optimization of the Mg-Cu catalyst（<em>Angew. Chem.</em> <strong>2022</strong>, <em>134</em>, e202213423）. Here we augmented the dataset by adding the lanthanide modifiers and reported the Eu-Cu catalyst resulted from the high-throughput investigation. Our characterizations revealed that the Eu<sup>2+</sup> reduced from Eu<sup>3+</sup> during the catalyst synthesis prevented the agglomeration of nanoparticles, thus making europium modifications stand out from other lanthanide metal modifiers on FE C<sub>2+</sub> enhancement. We then optimized the Eu-CuO<sub>x</sub> catalyst based on the above understanding to achieve ∼80% C<sub>2+</sub> FE at a high current density of 1.25 A cm<sup>−2</sup>.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000149/pdfft?md5=d1c6b7f6973c2f825f4024a496be4cd7&pid=1-s2.0-S2949747724000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139935586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reaction condition- and functional group-specific knowledge discovery: Data- and computation-based analysis on transition-metal-free transformation of organoborons 特定于反应条件和官能团的知识发现：基于数据和计算的有机硼无过渡金属转化分析

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2023-12-07 DOI: 10.1016/j.aichem.2023.100034

Linke He , Yulong Fu , Shaoyi Hou , Guoqiang Wang , Jiabao Zhao , Yipeng Xing , Shuhua Li , Jing Ma

{"title":"Reaction condition- and functional group-specific knowledge discovery: Data- and computation-based analysis on transition-metal-free transformation of organoborons","authors":"Linke He , Yulong Fu , Shaoyi Hou , Guoqiang Wang , Jiabao Zhao , Yipeng Xing , Shuhua Li , Jing Ma","doi":"10.1016/j.aichem.2023.100034","DOIUrl":"10.1016/j.aichem.2023.100034","url":null,"abstract":"<div><p>Gaining insights into overarching trends in chemical reaction systems is crucial for refining reaction conditions and developing novel reactions. These knowledgements include preferences for certain reagents, solvents, and functional group tolerance rules. Traditionally, synthetic chemists have relied on extensive literature searching to acquire the knowledge, a process that is both time-consuming and laborious. To streamline this process, we construct a standardized dataset and knowledge graph on an emerging domain, transition-metal-free transformations with organoborons. The dataset, compiled from organic reaction literature, includes comprehensive details of reaction scopes and conditions. The subsequent construction of a knowledge graph offers a visual representation of the reactions and their interrelationships. Through knowledge graph-based hierarchical analysis and density functional theory (DFT) calculations, we revealed the currently most frequently used reactants, synthetic conditions, and functional group rules in this field. We anticipate this knowledge graph-based approach will accelerate the acquisition and transfer of chemical reaction knowledge, catalyzing the discovery of new reactions. This work provides an automatic and adaptive framework for extracting key insights from reaction datasets to inform the design of novel reactions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000349/pdfft?md5=c4bedd7068acf7555c4e457d139943df&pid=1-s2.0-S2949747723000349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in machine-learning approaches to RNA-targeted drug design 机器学习方法在 RNA 靶向药物设计方面的进展

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-02-06 DOI: 10.1016/j.aichem.2024.100053

Yuanzhe Zhou , Shi-Jie Chen

{"title":"Advances in machine-learning approaches to RNA-targeted drug design","authors":"Yuanzhe Zhou , Shi-Jie Chen","doi":"10.1016/j.aichem.2024.100053","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100053","url":null,"abstract":"<div><p>RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI’s potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000113/pdfft?md5=300db5aa459794dcdbc0972a40d0ca02&pid=1-s2.0-S2949747724000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning prediction of state-to-state rate constants for astrochemistry 机器学习预测天体化学的态对态速率常数

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-02-03 DOI: 10.1016/j.aichem.2024.100052

Duncan Bossion , Gunnar Nyman , Yohann Scribano

{"title":"Machine learning prediction of state-to-state rate constants for astrochemistry","authors":"Duncan Bossion , Gunnar Nyman , Yohann Scribano","doi":"10.1016/j.aichem.2024.100052","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100052","url":null,"abstract":"<div><p>In this work, we investigate the possibility to use an artificial neural network to predict a large number of accurate state-to-state rate constants for atom-diatom collisions, from available rates obtained at two different accuracy levels, using a few accurate rates and many low-accuracy rates. The H + H<sub>2</sub> → H<sub>2</sub> + H chemical reaction is used to benchmark our neural network, as both low and high accuracy state-to-state rates are available in the literature. Our artificial neural network is a multilayer perceptron, using 8 input neurons including the low-accuracy rate constants, with the high accuracy rate constants as the output neuron. The use of machine learning to predict rate constants is very encouraged, as the rates obtained are accurate, even using as low as 1% of the full dataset to train the neural network, and improve greatly the low accuracy rates previously available. This approach can be used to generate full rate constant datasets with a consistent accuracy, from sparse rates obtained with various methods of different accuracies.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000101/pdfft?md5=be9d938fa5886a1544bcda53427c4f6f&pid=1-s2.0-S2949747724000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KNIME workflows for applications in medicinal and computational chemistry KNIME 工作流程在药物化学和计算化学中的应用

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-04-03 DOI: 10.1016/j.aichem.2024.100063

Ruchira Joshi , Zipeng Zheng , Palak Agarwal , Ma’mon M. Hatmal , Xinmin Chang , Paul Seidler , Ian S. Haworth

{"title":"KNIME workflows for applications in medicinal and computational chemistry","authors":"Ruchira Joshi , Zipeng Zheng , Palak Agarwal , Ma’mon M. Hatmal , Xinmin Chang , Paul Seidler , Ian S. Haworth","doi":"10.1016/j.aichem.2024.100063","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100063","url":null,"abstract":"<div><p>Artificial intelligence (AI) has huge potential to accelerate drug discovery, but challenges remain in implementing AI algorithms that can be used by the broad scientific community. Identification of molecular features and their subsequent use in training of machine learning models may permit prediction of new molecules with enhanced properties. Predictive modeling is particularly applicable to analysis of structure-activity relationships (SARs) and would be a useful tool in the hands of laboratory medicinal chemists. This requires a software platform that is chemically intuitive while providing the user with access to AI methods. The KNIME platform provides such an environment through inclusion of broad chemical toolsets and a user-friendly approach for utilization of machine learning for analysis of SAR data. Here, we illustrate use of KNIME for this purpose, with a focus on discovery of features of highly potent tau inhibitors from a series of structurally diverse polyphenols. Workflows are described that enable implementation of AI tools in KNIME for diverse SAR projects.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000216/pdfft?md5=2753f7e85fd445cdf8e68194d90fd743&pid=1-s2.0-S2949747724000216-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140349865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Intelligent Platforms for High‐Throughput Chemical Synthesis 用于高通量化学合成的自动化智能平台

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2024-02-22 DOI: 10.1016/j.aichem.2024.100057

Jia-Min Lu , Jian-Zhang Pan , Yi-Ming Mo , Qun Fang

{"title":"Automated Intelligent Platforms for High‐Throughput Chemical Synthesis","authors":"Jia-Min Lu , Jian-Zhang Pan , Yi-Ming Mo , Qun Fang","doi":"10.1016/j.aichem.2024.100057","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100057","url":null,"abstract":"<div><p>Automation and high-throughput techniques provide a solid technical foundation for realizing the deep fusion of artificial intelligence and chemistry as well as the full utilization of their advantages. In recent years, with the unique advantages of low consumption, low risk, high efficiency, high reproducibility, high flexibility and good versatility, intelligent automated platforms for high-throughput chemical synthesis aroused widespread concerns of synthetic chemists. In this review, the automated high-throughput chemical synthesis, automated high-throughput sample treatment and characterization technique, as well as the application of artificial intelligence technique in chemical synthesis are introduced. The characteristics of the systems and platforms based on these techniques, including the iChemFoundry platform developed in the ZJU-Hangzhou Global Scientific and Technological Innovation Center, are introduced. The intelligent automated platforms for high-throughput chemical synthesis will reshape the thinking mode of traditional disciplines, promote the innovation of disruptive techniques, redefine the rate of chemical synthesis, and innovate the way of material manufacturing.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000150/pdfft?md5=25909749b7c4a1be6ae263d1cca2abf9&pid=1-s2.0-S2949747724000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine-learning-driven simulations on microstructure, thermodynamic properties, and transport properties of LiCl-KCl-LiF molten salt lcl - kcl - lif熔盐微观结构、热力学性质和输运性质的机器学习模拟

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2023-11-19 DOI: 10.1016/j.aichem.2023.100027

Si-Min Qi , Tao Bo , Lei Zhang , Zhi-Fang Chai , Wei-Qun Shi

{"title":"Machine-learning-driven simulations on microstructure, thermodynamic properties, and transport properties of LiCl-KCl-LiF molten salt","authors":"Si-Min Qi , Tao Bo , Lei Zhang , Zhi-Fang Chai , Wei-Qun Shi","doi":"10.1016/j.aichem.2023.100027","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100027","url":null,"abstract":"<div><p>The thermodynamic and transport properties of high-temperature chloride molten salt systems are of great significance for spent fuel reprocessing in the field of nuclear energy engineering. Here, by using machine learning based deep potential (DP) method, we train a high-precision force field model for the LiCl-KCl-LiF system. During force field training, adding new dataset through multiple iterations improves the accuracy of the force field model and its applicability to more configurations. The comparison of density functional theory (DFT) and DP results for the test dataset indicates that our trained DP model has the same accuracy as DFT. Then, we comprehensively investigate the local structure, thermophysical properties, and transport properties of the LiCl-KCl and LiCl-KCl-LiF molten salt systems using the trained DP model. The effects of temperature and LiF concentration on the above properties are analyzed. This work provides guidance for the training of machine learning force fields in molten salt systems and the study of basic physical properties of high-temperature chloride molten salt systems.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100027"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000271/pdfft?md5=036ccca1e342d34c04c5cc6fb6e73f01&pid=1-s2.0-S2949747723000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138484545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0