Artificial intelligence chemistry最新文献_第3页

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 : 2024-02-22 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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","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

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 : 2024-02-18 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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","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

Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis 有机金属催化化学空间可视化的降维技术比较

Artificial intelligence chemistry Pub Date : 2024-02-17 DOI: 10.1016/j.aichem.2024.100055

Mario Villares , Carla M. Saunders , Natalie Fey

{"title":"Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis","authors":"Mario Villares , Carla M. Saunders , Natalie Fey","doi":"10.1016/j.aichem.2024.100055","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100055","url":null,"abstract":"<div><p>We have used a Ligand Knowledge Base for bidentate P,P-donor ligands of potential interest to homogeneous catalysis to compare three dimensionality reduction techniques, namely Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE). While our previous work on Ligand Knowledge Bases has focused on PCA, here we compare this approach with more recently-published approaches and assess the information retention, visualization, clustering and interpretability which can be achieved for each approach. We find that potential advantages of t-SNE are not realized with a database of the current size (275 entries), and that there is a degree of complementarity between PCA and UMAP. The statistics underlying PCA rely on linear relationships, making interpretation of the resulting plots comparatively straightforward. Since much of chemistry relies on linear structure-property relationships and low-dimensional visualization, the explainability and information retention achieved is attractive. UMAP proved more challenging to interpret, but achieved clear clustering which was often chemically meaningful, and it would be a useful approach for ensuring that distinct subsets of compounds are sampled in a machine-learning context. This analysis also highlighted that the tunability of catalysis achieved through ligand exchange maps well onto some areas of chemical space where closely related ligands cluster, while others represent outliers; these arise from different combinations of steric and electronic effects which chemists will find intuitive.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000137/pdfft?md5=d22dd66b98e698544ad12f66b7d830c4&pid=1-s2.0-S2949747724000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139943030","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 assisted analysis and prediction of rubber formulation using existing databases 利用现有数据库对橡胶配方进行机器学习辅助分析和预测

Artificial intelligence chemistry Pub Date : 2024-02-12 DOI: 10.1016/j.aichem.2024.100054

Wei Deng , Yuehua Zhao , Yafang Zheng , Yuan Yin , Yan Huan , Lijun Liu , Dapeng Wang

{"title":"Machine learning assisted analysis and prediction of rubber formulation using existing databases","authors":"Wei Deng , Yuehua Zhao , Yafang Zheng , Yuan Yin , Yan Huan , Lijun Liu , Dapeng Wang","doi":"10.1016/j.aichem.2024.100054","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100054","url":null,"abstract":"<div><p>Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000125/pdfft?md5=c058446a90f81b469ca59bff1d08c2a1&pid=1-s2.0-S2949747724000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749640","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 : 2024-02-06 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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","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 : 2024-02-03 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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","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

Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations 机器学习分子动力学模拟中固体电解质的锂离子电导率与尺寸有关

Artificial intelligence chemistry Pub Date : 2024-01-24 DOI: 10.1016/j.aichem.2024.100051

Yixi Zhang , Jin-Da Luo , Hong-Bin Yao , Bin Jiang

{"title":"Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations","authors":"Yixi Zhang , Jin-Da Luo , Hong-Bin Yao , Bin Jiang","doi":"10.1016/j.aichem.2024.100051","DOIUrl":"10.1016/j.aichem.2024.100051","url":null,"abstract":"<div><p>Solid-state electrolytes are key ingredients in next-generation devices for energy storage and release. Machine learning molecular dynamics (MLMD) has shown great promise in studying the diffusivity of mobile ions in solid-state electrolytes, with much higher efficiency than conventional ab initio molecular dynamics (AIMD). In this work, we combine an efficient embedded atom neural network (EANN) approach and an uncertainty-driven active learning algorithm that optimally selects data points from high-temperature AIMD trajectories to construct ML potentials for solid-state electrolytes and validate this strategy in a benchmark system, Li<sub>3</sub>YCl<sub>6</sub>, for which several controversy theoretical results exist. Through systematic MLMD simulations, we find that a typically used small supercell in AIMD simulations fails to predict the supersonic transition at a critical temperature, leading to a significant overestimation of the Li<sup>+</sup> conductivity in Li<sub>3</sub>YCl<sub>6</sub> at room temperature. Fortunately, thanks to the scalability of the EANN potential, extended MLMD simulations in a sufficiently large cell does yield a notable change of temperature-dependence in conductivity at ∼420 K and a much lower room-temperature conductivity in excellent with experiment. Interestingly, our results are all based on a semi-local PBE density functional, which was argued unable to predict the superionic transition. We analyze possible reasons of the seemingly inconsistent MLMD results reported in literature with different ML potentials. This work paves the way of simply using high-temperature AIMD data to generate more reliable MLMD results of low-temperature ionic conductivities in solid-state electrolytes.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000095/pdfft?md5=ff9758425c151a024cd1c50e2503eb45&pid=1-s2.0-S2949747724000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635555","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 advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry 有机合成中的机器学习进步：人工智能在化学中应用的重点探索

Artificial intelligence chemistry Pub Date : 2024-01-19 DOI: 10.1016/j.aichem.2024.100049

Rizvi Syed Aal E Ali , Jiaolong Meng , Muhammad Ehtisham Ibraheem Khan , Xuefeng Jiang

{"title":"Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry","authors":"Rizvi Syed Aal E Ali , Jiaolong Meng , Muhammad Ehtisham Ibraheem Khan , Xuefeng Jiang","doi":"10.1016/j.aichem.2024.100049","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100049","url":null,"abstract":"<div><p>Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000071/pdfft?md5=ca6a79f1c6ae5ed3980ec0ff3589b022&pid=1-s2.0-S2949747724000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549589","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

Applying graph neural network models to molecular property prediction using high-quality experimental data 利用高质量实验数据，将图神经网络模型应用于分子特性预测

Artificial intelligence chemistry Pub Date : 2024-01-19 DOI: 10.1016/j.aichem.2024.100050

Chen Qu, Barry I. Schneider, Anthony J. Kearsley, Walid Keyrouz, Thomas C. Allison

引用次数: 0

Exploring the energy landscape of graphynes for noble gas adsorption using swarm intelligence 利用群集智能探索石墨炔吸附惰性气体的能量图谱

Artificial intelligence chemistry Pub Date : 2024-01-11 DOI: 10.1016/j.aichem.2024.100048

Megha Rajeevan, Rotti Srinivasamurthy Swathi

{"title":"Exploring the energy landscape of graphynes for noble gas adsorption using swarm intelligence","authors":"Megha Rajeevan, Rotti Srinivasamurthy Swathi","doi":"10.1016/j.aichem.2024.100048","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100048","url":null,"abstract":"<div><p>Gas adsorption on one-atom-thick membranes is a growing technology for separation applications owing to its excellent energy efficiency. Herein, we investigate the adsorption of the noble gases, Ne, Ar and Kr on graphynes (GYs), a novel class of one-atom-thick carbon membranes using a swarm intelligence technique, namely particle swarm optimization (PSO). Modeling the adsorption of noble gas clusters on two-dimensional substrates requires a thorough examination of the energy landscape. The high dimensionality of the problem makes it tricky to employ ab initio methods for such studies, necessitating the use of a metaheuristic global optimization technique such as PSO. We explored the adsorption of 1–30 atoms of Ne, Ar and Kr on α-, β-, γ- and rhombic-GYs to predict the most suitable form of GY for the adsorption of each of the gases. Employing the dispersion-corrected density functional theory (DFT-D) data for the adsorption of single gas atoms as the reference data, we parametrized two empirical pairwise potentials, namely, Lennard-Jones (LJ) and improved Lennard-Jones (ILJ) potentials. We then analyzed the growth pattern as well as the energetics of adsorption using the parametrized potentials, in combination with the PSO technique, which enabled us to predict the best possible membrane for the adsorption of the noble gases: α-GY for Ne and γ-GY for Ar and Kr. The accuracy of our modeling approach is further validated against DFT-D computations thereby establishing that PSO, when combined with the ILJ potential, can serve as a computationally feasible approach for modeling gas adsorption on GYs.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772400006X/pdfft?md5=13e8fd3ef313b8180bab9c56f7c85352&pid=1-s2.0-S294974772400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504245","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