首页|New Machine Learning Study Results Reported from Henan University (Rational Design of Single Transition-metal Atoms Anchored On a Ptse2 Monolayer As Bifunctional Oer/orr Electrocatalysts: a Defect Chemistry and Machine Learning Study)
New Machine Learning Study Results Reported from Henan University (Rational Design of Single Transition-metal Atoms Anchored On a Ptse2 Monolayer As Bifunctional Oer/orr Electrocatalysts: a Defect Chemistry and Machine Learning Study)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Current study results on Machine Learning have been published. According to news reporting originating from Kaifeng, People's Republic of China, by NewsRx correspondents, research stated, "Searching for highly efficient, economical, and environmentally friendly bifunctional electrocatalysts for the oxygen reduction reaction (OER) and oxygen evolution reaction (ORR) is crucial in developing renewable energy conversion and storage technology. In this study, we systematically investigate the effect of defect charges on the electrocatalytic performance of transition metal (TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag) single atoms anchored on a PtSe2 monolayer (TM@PtSe2) using first-principles calculations." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Guizhou Provincial Basic Research Program (Natural Science), Functional Materials and Devices Technology Innovation Team of Guizhou Province University. Our news editors obtained a quote from the research from Henan University, "Based on our formation energy calculation, we find that Pt-rich conditions can promote the anchoring of TM atoms on PtSe2 and demonstrate that 29 types of TM@PtSe2 in different charge states are stable. Among these materials, Pd-center dot@PtSe2 (eta(OER/ORR) = 0.31/0.43 V) and Pd-x@PtSe2 (eta(OER/ORR) = 0.36/0.74 V) systems not only have low formation energy but also exhibit excellent catalytic performance, due to their ultralow overpotential (eta). Interestingly, our results reveal that adjusting the charge states of TM@PtSe2 is a new effective method for designing low overpotential bifunctional OER/ORR electrocatalysts. This adjustment can tune the interaction strength between the oxygenated intermediates and TM@ PtSe2. Additionally, we employ machine learning (ML) models to investigate the origin of activity in OER/ORR processes. Our results reveal that the first ionization energy (I-m), the electronegativity (N-m), the number of TM-d electrons (N-e), the d-band center (epsilon(d)), the electron affinity (chi(m)), and the charge transfer of TM atoms (Q(e)) of TM@PtSe2 are the primary descriptors characterizing the adsorption behavior."
KaifengPeople's Republic of ChinaAsiaChemistryCyborgsEmerging TechnologiesMachine LearningHenan University
NETL
NSTL
