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| 1 | Integration of data-intensive,machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions显示文摘Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors. | Oyawale Adetunji Moses Wei Chen Mukhtar Lawan Adam Zhuo Wang Kaili Liu Junming Shao Zhengsheng Li Wentao Li Chensu Wang Haitao Zhao Cheng Heng Pang Zongyou Yin Xuefeng Yu | 2021 | Materials Reports(Energy)2021，1，3： | 3 |
| 2 | Morphology and reactivity characteristics of char biomass particles显示文摘 | Claudio Avila Cheng Heng Pang Tao Wu Ed Lester | 2011 | Bioresource Technology2011，，8： | 1 |
| 3 | Rather than interleukin‐27, interleukin‐6 expresses positive correlation with liver severity in na?ve hepatitis B infection patients显示文摘 | Jung‐Ta Kao Hsueh‐Chou Lai Shu‐Mei Tsai Pei‐Chao Lin Po‐Heng Chuang Cheng‐Ju Yu Ken‐Sheng Cheng Wen‐Pang Su Ping‐Ning Hsu Cheng‐Yuan Peng Yi‐Ying Wu | 2012 | Liver Int2012，，6： | 1 |
| 4 | Rather than interleukin‐27, interleukin‐6 expresses positive correlation with liver severity in na?ve hepatitis B infection patients显示文摘 | Jung‐Ta Kao Hsueh‐Chou Lai Shu‐Mei Tsai Pei‐Chao Lin Po‐Heng Chuang Cheng‐Ju Yu Ken‐Sheng Cheng Wen‐Pang Su Ping‐Ning Hsu Cheng‐Yuan Peng Yi‐Ying Wu | 2012 | Liver Int2012，，6： | 1 |
| 5 | An advanced ash fusion study on the melting behaviour of coal,oil shale and blends under gasification conditions using picture analysis and graphing method显示文摘This study investigates the potential of solid fuel blending as an effective approach to manipulate ash melting behaviour to alleviate ashrelated problems during gasification,thus improving design,operability and safety.The ash fusion characteristics of Qinghai bituminous coal together with Fushun,Xinghua and Laoheishan oil shales(and their respective blends)were quantified using a novel picture analysis and graphing method,which incorporates conventional ash fusion study,dilatometry and sintering strength test,in a CO/CO_(2)atmosphere.This imagebased characterisation method was used to monitor and quantify the complete melting behaviour of ash samples from room temperature to 1520℃.The impacts of blending on compositional changes during heating were determined experimentally via Xray diffraction and validated computationally using FactSage.Results showed that the melting point of Qinghai coal ash to be the lowest at 1116℃,but would increase up to 1208℃,1161℃and 1160℃with the addition of 30%50%of Laoheishan,Fushun,and Xinghua oil shales,respectively.The formation of highmelting anorthite and mullite structures inhibits the formation of lowmelting hercynite.However,the sintering point of Qinghai coal ash was seen to decrease from 1005℃to 855℃,834℃,and 819℃in the same blends due to the formation of lowmelting aluminosilicate.Results also showed that blending directly influences the sintering strength during the various stages of melting.The key finding from this study is that it is possible to mitigate against the severe ash slagging and fouling issue arising from high calcium and iron coals by cogasification with a high silicaalumina oil shale.Moreover,blending coals with oil shales can also modify the ash melting behaviour of fuels to create the optimal ash chemistry that meets the design specification of the gasifier,without adversely affecting thermal performance. | Yang Meng Peng Jiang Yuxin Yan Yuxin Pan Xinyun Wu Haitao Zhao Nusrat Sharmin Edward Lester Tao Wu Cheng Heng Pang | 2021 | Chinese Journal of Chemical Engineering2021，34，4： | 1 |
| 6 | Data-driven structural descriptor for predicting platinum-based alloys as oxygen reduction electrocatalysts显示文摘Owing to increasing global demand for carbon neutral and fossil-free energy systems,extensive research is being conducted on efficient and inexpensive electrocatalysts for catalyzing the kinetically sluggish oxygen reduction reaction(ORR)at the cathode of fuel cells.Platinum(Pt)-based alloys are considered promising candidates for replacing expensive Pt catalysts.However,the current screening process of Pt-based alloys is time-consuming and labor-intensive,and the descriptor for predicting the activity of Pt-based catalysts is generally inaccurate.This study proposed a strategy by combining high-throughput first-principles calculations and machine learning to explore the descriptor used for screening Pt-based alloy catalysts with high Pt utilization and low Pt consump-tion.Among the 77 prescreened candidates,we identified 5 potential candidates for catalyzing ORR with low overpotential.Furthermore,during the second and third rounds of active learning,more Pt-based alloys ORR candidates are identi-fied based on the relationship between structural features of Pt-based alloys and their activity.In addition,we highlighted the role of structural features in Pt-based alloys and found that the difference between the electronegativity of Pt and heteroatom,the valence electrons number of the heteroatom,and the ratio of heteroatoms around Pt are the main factors that affect the activity of ORR.More importantly,the combination of those structural features can be used as structural descriptor for predicting the activity of Pt-based alloys.We believe the findings of this study will provide new insight for predicting ORR activ-ity and contribute to exploring Pt-based electrocatalysts with high Pt utiliza-tion and low Pt consumption experimentally. | Xue Zhang Zhuo Wang Adam Mukhtar Lawan Jiahong Wang Chang-Yu Hsieh Chenru Duan Cheng Heng Pang Paul.K.Chu Xue-Feng Yu Haitao Zhao | 2023 | InfoMat2023，5，6： | 0 |
| 7 | 生物质生产碳材料的可行性研究显示文摘随着能源问题的进一步加剧,生物质和碳材料这两个名词不断的被提起,承载这新能源的希望。生物质资源是一种无污染的可再生资源,它的用途及其广大。新兴的碳材料受到社会各界的广泛关注,而其中石墨烯作为一种新型的二维碳材料,因其独特的结构特征和其在于光学、电学、力学及热学等方面优异的性质,更是具有代表性。但碳材料的制备仍有许多瓶颈,其中一处就是原料成本高。本文就利用低廉的生物质制取碳材料的可行性进行了分析。 | 王寿意 金伟东 张宁 闫誉馨 吴滔 Pang Cheng Heng | 2018 | 化学工程与装备2018，，7： | 0 |
| 8 | 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 |
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