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The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction

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eScience 2023年第4期3卷 1-11页

作者： Zhuo Wang Zhehao Sun Hang Yin Honghe Wei Zicong Peng yoong xin pang Guohua Jia Haitao Zhao Cheng Heng pang Zongyou Yin Department of Chemical and Environmental Engineering The University of Nottingham Ningbo ChinaNingbo315100PR China Research School of Chemistry The Australian National UniversityACT2601Australia Curtin Institute of Functional Molecules and Interfaces School of Molecular and Life SciencesCurtin UniversityPerthWA6845Australia Materials Interfaces Center Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055GuangdongPR China Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province University of Nottingham Ningbo ChinaNingbo315100PR China Municipal Key Laboratory of Clean Energy Conversion Technologies University of Nottingham Ningbo ChinaNingbo315100PR China New Materials Institute University of Nottingham Ningbo ChinaNingbo315042PR China

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 *** carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical ***,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)*** 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 *** this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR *** a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR *** we explore ML-aided prediction of catalytic activity and *** is followed by a discussion about ML-guided catalyst and electrode ***,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.

关键词： Carbon neutrality Carbon dioxide reduction reaction Machine learning Catalyst Rational design

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烟草雄性不育突变株Y12的初步研究

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四川大学学报（自然科学版） 2000年第3期37卷 485-487页

作者：徐莺倪为民卫志明许智宏贾勇炯陈放四川大学生物系成都610064 中国科学院上海植物生理研究所上海200032

植物雄性不育材料，不仅在生产上为育种提供资源，同时也是研究植物雄性生殖发育分子机制的有用材料.雄性不育的材料通常是由自然突变或通过化学及物理诱导等方法获得的，这些方法尽管容易得到大量的突变子，但所获得的突变基因却不易分... 详细信息

植物雄性不育材料，不仅在生产上为育种提供资源，同时也是研究植物雄性生殖发育分子机制的有用材料.雄性不育的材料通常是由自然突变或通过化学及物理诱导等方法获得的，这些方法尽管容易得到大量的突变子，但所获得的突变基因却不易分离.目前已经发展了一种基因标签法，通过插入已知序列的外源片段或转座子作为标签，使突变基因的克隆相对容易，该方法已成为某些实验室分离发育基因的常规方法[1～3].我们报道利用转基因方法获得烟草雄性不育突变株Y12及对其初步研究情况.1 材料和方法1.1 材料1.1.1 烟草品种革新1号(Nicotiana tabaccum *** xin No.1).1.1.2 根癌农杆菌菌株 LBA4404(Agrobacterium tumfaciens LBA4404).1.1.3 质粒植物表达载体pBAL1带有卡那霉素抗性基因(细菌中表达)，新霉素磷酸转移酶基因(nptⅡ)(在植物中组成性表达)和吲哚乙酰胺赖氨酸合成酶基因(iaaL)(在植物种子中特异表达).1.2 方法1.2.1 烟草的转化按照文[4]进行.2.2.2 转基因烟草的鉴定微量提取抗性植株(100mg)的总DNA溶入50μL TE缓冲液中，取5μL进行30μL体积的PCR扩增反应，引物顺序为P1:5’CGC AAG CCT TTG GCG AACAAG TCT C 3’，P2.5’AAT CAT CAT CGC AAG ACC GGC AAC AGG 3’.反应参数为：95℃，5min，一个循环； 93℃，40s；56℃，40s；72℃，60s；30个循环；72℃，***扩增反应阳性植株按照文[5]进行Southern杂交鉴定.

关键词：烟草雄性不育突变株育种 Y12

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