Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction

2021 ◽  
Vol 143 (15) ◽  
pp. 5755-5762
Author(s):  
Ying Guo ◽  
Xinru He ◽  
Yuming Su ◽  
Yiheng Dai ◽  
Mingcan Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Co2 Reduction ◽  
Guided Discovery ◽  
Cu Catalysts

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

2021 ◽  
Author(s):  
Marcus Reis ◽  
Filipp Gusev ◽  
Nicholas G. Taylor ◽  
Sang Hun Chung ◽  
Matthew D. Verber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Guided Discovery ◽  
19F Mri

Machine learning guided discovery of super-hard high entropy ceramics

2021 ◽  
pp. 130899
Author(s):  
Russlan Jaafreh ◽  
Yoo Seong Kang ◽  
Jung-Gu Kim ◽  
Kotiba Hamad
Keyword(s):  
Machine Learning ◽  
Guided Discovery ◽  
High Entropy

scholarly journals Facet-Dependent Selectivity of Cu Catalysts in Electrochemical CO2 Reduction at Commercially Viable Current Densities

ACS Catalysis ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 4854-4862 ◽  
Author(s):  
Gian Luca De Gregorio ◽  
Thomas Burdyny ◽  
Anna Loiudice ◽  
Pranit Iyengar ◽  
Wilson A. Smith ◽  
...  
Keyword(s):  
Co2 Reduction ◽  
Electrochemical Co2 Reduction ◽  
Cu Catalysts ◽  
Current Densities

scholarly journals Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB2 near the hydrogen liquefaction temperature

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Pedro Baptista de Castro ◽  
Kensei Terashima ◽  
Takafumi D Yamamoto ◽  
Zhufeng Hou ◽  
Suguru Iwasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetocaloric Effect ◽  
Guided Discovery ◽  
Liquefaction Temperature

scholarly journals Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

ACS Catalysis ◽  
2017 ◽  
Vol 7 (10) ◽  
pp. 6600-6608 ◽  
Author(s):  
Zachary W. Ulissi ◽  
Michael T. Tang ◽  
Jianping Xiao ◽  
Xinyan Liu ◽  
Daniel A. Torelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Site ◽  
Co2 Reduction ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

2021 ◽  
Author(s):  
Marcus Reis ◽  
Filipp Gusev ◽  
Nicolas Taylor ◽  
Sang Hun Chung ◽  
Matthew Verber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Guided Discovery ◽  
19F Mri

Electrolyte Competition Controls Surface Binding of CO Intermediates to CO2 Reduction Catalysts

2019 ◽  
Author(s):  
Anna Wuttig ◽  
Jaeyune Ryu ◽  
Yogesh Surendranath
Keyword(s):  
Variable Temperature ◽  
Co Adsorption ◽  
Co2 Reduction ◽  
Adsorbed Water ◽  
Fine Tuning ◽  
Design Parameters ◽  
Surface Binding ◽  
Cu Catalysts ◽  
Reduction Catalysts

Adsorbed CO is a critical intermediate in the electrocatalytic reduction of CO<sub>2</sub> to fuels. Contemporary methods for probing the thermodynamics of CO adsorption ignore the role of the electrolyte. Using in situ infrared spectroelectrochemistry, we disclose the contrasting influence of electrolyte competition on reversible CO binding to Au and Cu catalysts. Whereas reversible CO binding to Au surfaces is driven by substitution and reorientation of adsorbed water, CO binding to Cu requires the reductive displacement of adsorbed carbonate anions. Through variable temperature studies, we find that CO binding to Cu is enthalpically favored by ~36 kJ mol<sup>–1</sup> relative to CO adsorption on Au. The divergent CO adsorption stoichiometry on Au and Cu explains their disparate reactivity: water adsorption drives CO liberation from Au surfaces, impeding further reduction, whereas carbonate desorption drives CO accumulation on Cu, allowing for further reduction to hydrocarbons. These studies provide direct insight into how electrolyte constituents can serve as powerful design parameters for fine-tuning of CO surface populations and, thereby, CO2-to-fuels reactivity. <br>

(Invited) A Full Carbon Balance Analysis on High Current Density Electrochemical CO2 Reduction Reactors with Cu Catalysts

2020 ◽  
Vol MA2020-01 (36) ◽  
pp. 1497-1497
Author(s):  
Brian Seger ◽  
Ming Ma ◽  
Ezra L Clark ◽  
Kasper Therkildsen ◽  
Ib Chorkendorff
Keyword(s):  
Current Density ◽  
Carbon Balance ◽  
High Current Density ◽  
Co2 Reduction ◽  
High Current ◽  
Electrochemical Co2 Reduction ◽  
Balance Analysis ◽  
Cu Catalysts

Formation of C–C bonds during electrocatalytic CO2 reduction on non-copper electrodes

2020 ◽  
Vol 8 (44) ◽  
pp. 23162-23186
Author(s):  
Yansong Zhou ◽  
Boon Siang Yeo
Keyword(s):  
Electrochemical Reduction ◽  
Bond Formation ◽  
Co2 Reduction ◽  
Formation Mechanisms ◽  
Copper Electrodes ◽  
Recent Advances ◽  
Cu Catalysts

Recent advances in non-Cu catalysts for electrochemical reduction of CO2 to multi-carbon products are summarized, focusing on C–C bond formation mechanisms.

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