노인수 교수
Insoo Ro
고려대학교 화공생명공학과 · 재료과학
연구실 소개
노인수 교수의 연구실은 나노구조 금속 촉매와 고체 산화물 지원체 간의 상호작용을 깊이 있게 이해하고 제어하는 데 초점을 맞추고 있습니다. 특히 원자적으로 분산된 금속 촉매나 나노입자-산화물 인터페이스에서의 활성 부위를 정밀하게 설계함으로써 선택적 화학변환 반응의 효율을 극대화하는 데 기여하고 있습니다. 최근에는 기계학습 기반의 해석 가능한 예측 프레임워크와 지속 가능한 수소 공급 방식을 접목한 플라스틱 폐기물의 촉매적 분해 및 재활용 기술 개발에도 주력하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15Heterogeneous supported metal catalysts are critical for a wide range of chemical conversion technologies. While the fundamental properties of extended metal surfaces are well understood and active sites on such systems can be designed for targeted applications, much less is known about the properties of active sites formed at the interface of nanometer-scale metal structures and their underlying oxide support. The goal of this Perspective is to highlight recent progress in understanding and con
Well-defined Cu catalysts containing different amounts of zirconia were synthesized by controlled surface reactions (CSRs) and atomic layer deposition methods and studied for the selective conversion of ethanol to ethyl acetate and for methanol synthesis. Selective deposition of ZrO 2 on undercoordinated Cu sites or near Cu nanoparticles via the CSR method was evidenced by UV–vis absorption spectroscopy, scanning transmission electron microscopy, and inductively coupled plasma absorption emissio
Atomically dispersed late-transition-metal catalysts exhibit distinct catalytic reactivity and selectivity compared to metal clusters in many reactions. Realizing the potential benefits of these catalysts requires active site uniformity and control of their local environment. Here, we propose a catalyst synthesis route for manipulating the local environment of atomically dispersed metal-active sites. This was achieved via the targeted deposition of Rh precursors near atomically dispersed ReOx on
Conventional methods for developing heterogeneous catalysts are inefficient in time and cost, often relying on trial-and-error. The integration of machine-learning (ML) in catalysis research using data can reduce computational costs and provide valuable insights. However, the lack of interpretability in black-box models hinders their acceptance among researchers. We propose an interpretable ML framework that enables a comprehensive understanding of the complex relationships between variables. Ou
The surge in global plastic production, reaching 400.3 million tons in 2022, has exacerbated environmental pollution, with only 11% of plastic being recycled. Catalytic recycling, particularly through hydrogenolysis and hydrocracking, offers a promising avenue for upcycling polyolefin plastic, comprising 55% of global plastic waste. This study investigates the influence of water on polyolefin depolymerization using Ru catalysts, revealing a promotional effect only when both metal and acid sites,
Abstract Depolymerizing plastic waste through hydrogen‐based processes, such as hydrogenolysis and hydrocracking, presents a promising solution for converting plastics into liquid fuels. However, conventional hydrogen production methods rely heavily on fossil fuels, exacerbating global warming. This study introduces a novel approach to plastic waste hydrogenolysis that utilizes in situ hydrogen generated via the aqueous phase reforming (APR) of methanol, a biomass‐derived chemical offering a mor
Plastics are widely used materials in our daily lives and various industries due to their affordability and versatility. The massive production of plastic waste, however, has recently emerged as a pressing environmental concern across all media. To address this, emerging technologies are being explored for the sustainable valorization of postconsumer plastic wastes including thermochemical, physical, and catalytic processes aimed at transforming them into higher value-added products. However, th
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