Industrial decarbonization is critical for achieving net-zero goals.The carbon dioxide electrochemical reduction reaction(CO_(2)RR)is a promising approach for converting CO_(2)into high-value chemicals,offering the po...Industrial decarbonization is critical for achieving net-zero goals.The carbon dioxide electrochemical reduction reaction(CO_(2)RR)is a promising approach for converting CO_(2)into high-value chemicals,offering the potential for decarbonizing industrial processes toward a sustainable,carbon-neutral future.However,developing CO_(2)RR catalysts with high selectivity and activity remains a challenge due to the complexity of finding such catalysts and the inefficiency of traditional computational or experimental approaches.Here,we present a methodology integrating density functional theory(DFT)calculations,deep learning models,and an active learning strategy to rapidly screen high-performance catalysts.The proposed methodology is then demonstrated on graphene-based single-atom catalysts for selective CO_(2)electroreduction to methanol.First,we conduct systematic binding energy calculations for 3045 single-atom catalysts to identify thermodynamically stable catalysts as the design space.We then use a graph neural network,fine-tuned with a specialized adsorption energy database,to predict the relative activity and selectivity of the candidate catalysts.An autonomous active learning framework is used to facilitate the exploration of designs.After six learning cycles and 2180 adsorption calculations across 15 intermediates,we develop a surrogate model that identifies four novel catalysts on the Pareto front of activity and selectivity.Our work demonstrates the effectiveness of leveraging a domain foundation model with an active learning framework and holds potential to significantly accelerate the discovery of high-performance CO_(2)RR catalysts.展开更多
This work demonstrates a micron-sized nanosecond current pulse probe using a quantum diamond magnetometer.A micron-sized diamond crystal affixed to a fiber tip is integrated on the end of a conical waveguide.We demons...This work demonstrates a micron-sized nanosecond current pulse probe using a quantum diamond magnetometer.A micron-sized diamond crystal affixed to a fiber tip is integrated on the end of a conical waveguide.We demonstrate real-time visualization of a single 100 nanosecond pulse and discrimination of two pulse trains of different frequencies with a coplanar waveguide and a home-made PCB circuit.This technique finds promising applications in the display of electronic stream and can be used as a pulse discriminator to simultaneously receive and demodulate multiple pulse frequencies.This method of detecting pulse current is expected to provide further detailed analysis of the internal working state of the chip.展开更多
Glycyrrhiza uralensis is considered to be one of the most important herbs in traditional Chinese medicine due to its numerous pharmacological effects particularly its ability to relieve cough and act as a mucolytic.Ba...Glycyrrhiza uralensis is considered to be one of the most important herbs in traditional Chinese medicine due to its numerous pharmacological effects particularly its ability to relieve cough and act as a mucolytic.Based on previous research,these effects are mediated by a number of active ingredients,especially glycyrrhizic acid(GA).In the present study,a gene encodingβ-amyrin synthase(β-AS)involved in GA biosynthesis in G.uralensis has been cloned and expressed in Saccharomyces cerevisiae.The cloned enzyme showed similar activity to native enzymes isolated from other Glycyrrhiza species to catalyze the conversion of 2,3-oxidosqualene intoβ-amyrin.In fact theβ-AS gene is particularly important in the GA biosynthetic pathway in G.uralensis.The complete sequence of the enzyme was determined and a phylogenetic tree based on theβ-AS gene of G.uralensis and 20 other species was created.This showed that Glycyrrhiza glabra had the closest kinship with G.uralensis.The results of this work will be useful in determining how to improve the efficacy of G.uralensis by improving its GA content and in exploring the biosynthesis of GA in vitro.展开更多
基金supported by the National Key Research and Development Program of China(2022ZD0117501)the Scientific Research Innovation Capability Support Project for Young Faculty(ZYGXQNJSKYCXNLZCXM-E7)the Tsinghua University Initiative Scientific Research Program and the Carbon Neutrality and Energy System Transformation(CNEST)Program led by Tsinghua University.
文摘Industrial decarbonization is critical for achieving net-zero goals.The carbon dioxide electrochemical reduction reaction(CO_(2)RR)is a promising approach for converting CO_(2)into high-value chemicals,offering the potential for decarbonizing industrial processes toward a sustainable,carbon-neutral future.However,developing CO_(2)RR catalysts with high selectivity and activity remains a challenge due to the complexity of finding such catalysts and the inefficiency of traditional computational or experimental approaches.Here,we present a methodology integrating density functional theory(DFT)calculations,deep learning models,and an active learning strategy to rapidly screen high-performance catalysts.The proposed methodology is then demonstrated on graphene-based single-atom catalysts for selective CO_(2)electroreduction to methanol.First,we conduct systematic binding energy calculations for 3045 single-atom catalysts to identify thermodynamically stable catalysts as the design space.We then use a graph neural network,fine-tuned with a specialized adsorption energy database,to predict the relative activity and selectivity of the candidate catalysts.An autonomous active learning framework is used to facilitate the exploration of designs.After six learning cycles and 2180 adsorption calculations across 15 intermediates,we develop a surrogate model that identifies four novel catalysts on the Pareto front of activity and selectivity.Our work demonstrates the effectiveness of leveraging a domain foundation model with an active learning framework and holds potential to significantly accelerate the discovery of high-performance CO_(2)RR catalysts.
基金Project supported by the National Key R&D Program of China(Grant No.2021YFB2012600)。
文摘This work demonstrates a micron-sized nanosecond current pulse probe using a quantum diamond magnetometer.A micron-sized diamond crystal affixed to a fiber tip is integrated on the end of a conical waveguide.We demonstrate real-time visualization of a single 100 nanosecond pulse and discrimination of two pulse trains of different frequencies with a coplanar waveguide and a home-made PCB circuit.This technique finds promising applications in the display of electronic stream and can be used as a pulse discriminator to simultaneously receive and demodulate multiple pulse frequencies.This method of detecting pulse current is expected to provide further detailed analysis of the internal working state of the chip.
文摘Glycyrrhiza uralensis is considered to be one of the most important herbs in traditional Chinese medicine due to its numerous pharmacological effects particularly its ability to relieve cough and act as a mucolytic.Based on previous research,these effects are mediated by a number of active ingredients,especially glycyrrhizic acid(GA).In the present study,a gene encodingβ-amyrin synthase(β-AS)involved in GA biosynthesis in G.uralensis has been cloned and expressed in Saccharomyces cerevisiae.The cloned enzyme showed similar activity to native enzymes isolated from other Glycyrrhiza species to catalyze the conversion of 2,3-oxidosqualene intoβ-amyrin.In fact theβ-AS gene is particularly important in the GA biosynthetic pathway in G.uralensis.The complete sequence of the enzyme was determined and a phylogenetic tree based on theβ-AS gene of G.uralensis and 20 other species was created.This showed that Glycyrrhiza glabra had the closest kinship with G.uralensis.The results of this work will be useful in determining how to improve the efficacy of G.uralensis by improving its GA content and in exploring the biosynthesis of GA in vitro.
