First principles and machine learning based superior catalytic activities and selectivities for N 2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts
Высокие каталитическая активность и селективность при восстановлении N2 в MBenes, дефектных двумерных материалах и одноатомных катализаторах на основе двумерных π-сопряжённых полимеров: расчёты из первых принципов и машинное обучение
2021-01-01
SCID: 54.1/vuakxhnu
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2D materialsmachine learning descriptornitrogen reductionsingle-atom catalystsvacancy defects
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Abstract (AI)
The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.
Key Findings
1
A machine-learning descriptor accelerates the screening of efficient electrocatalysts for N₂ reduction.
2
Hollow sites and vacancy defects in two-dimensional materials can improve catalytic activity and selectivity for nitrogen fixation.
3
The study investigates MBenes, defective two-dimensional materials, and π-conjugated polymer-supported single-atom catalysts as N₂-reduction platforms.
Research Object
MBenes, defective 2D materials, and 2D π-conjugated polymer-supported single-atom catalysts for N₂ reduction
Research Subject
Catalytic activity and selectivity for nitrogen fixation, including the effects of hollow sites and vacancy defects and machine-learning-assisted catalyst screening
Publication Details
Publication Date
2021-01-01
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