東北大学 · Engineering
Hao Li 교수의 연구실은 전이금속 산화물 및 이종합금 나노촉매를 중심으로, 산소 발생 반응(OER)과 산소 환원 반응(ORR)을 비롯한 에너지 변환 반응의 고성능 촉매 설계를 목표로 합니다. 원자적으로 분산된 촉매 활성 부위의 기초 메커니즘 규명과 함께, 밀도함수이론(DFT) 및 인공신경망(ANN)을 활용한 이론적 모델링과 기계학습 기반 촉매 선별 전략을 융합한 연구를 수행합니다. 특히, 희토류 기반 산화물, 이종합금, 불안정한 상을 가진 나노구조물의 합성 및 안정화 기법 개발에도 주력하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Rare-earth (RE)-based transition metal oxides (TMO) are emerging as a frontier toward the oxygen evolution reaction (OER), yet the knowledge regarding their electrocatalytic mechanism and active sites is very limited. In this work, atomically dispersed Ce on CoO is successfully designed and synthesized by an effective plasma (P)-assisted strategy as a model (P-Ce SAs@CoO) to investigate the origin of OER performance in RE-TMO systems. The P-Ce SAs@CoO exhibits favorable performance with an overp
Alloying elements with strong and weak adsorption properties can produce a catalyst with optimally tuned adsorbate binding. A full understanding of this alloying effect, however, is not well-established. Here, we use density functional theory to study the ensemble, ligand, and strain effects of close-packed surfaces alloyed by transition metals with a combination of strong and weak adsorption of H and O. Specifically, we consider PdAu, RhAu, and PtAu bimetallics as ordered and randomly alloyed (
Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems an
Nitrate (NO3–) is a ubiquitous contaminant in groundwater that causes serious public health issues around the world. Though various strategies are able to reduce NO3– to nitrite (NO2–), a rational catalyst design strategy for NO2– removal has not been found, in part because of the complicated reaction network of nitrate chemistry. In this study, we show, through catalytic modeling with density functional theory (DFT) calculations, that the performance of mono- and bimetallic surfaces for nitrite
A Ag/graphene-like g-C<sub>3</sub>N<sub>4</sub>photocatalyst was synthesized<italic>via</italic>a simple thermal oxidation exfoliation-photodeposition technique.
The catalytic properties of bulk-immiscible alloys are less explored than their bulk-miscible counterparts due to inherent difficulties in their synthesis and stabilization. The development of alternative synthetic methods can, however, provide routes toward bulk-immiscible nanoparticles with metastable randomly alloyed structures. In this study, we combine computational screening and a microwave-based synthesis method to target bulk-immiscible alloys that show enhanced reactivity over pure meta
