Eunsuk Kim

About

Eunsuk Kim is a Professor of Chemistry at Brown University, specializing in bioinorganic chemistry, coordination chemistry, redox signaling, nitric oxide signaling, energy, and carbon dioxide conversion. His research aims to develop fundamental inorganic chemistry solutions to address challenging biological and environmental problems. The Kim lab employs a multidisciplinary approach, drawing from synthetic inorganic chemistry, spectroscopy, biochemistry, and toxicology to advance understanding in these areas. He earned his Ph.D. in Inorganic Chemistry from Johns Hopkins University in 2004, and holds a M.S. and B.S. in Chemistry from Korean University and Sangmyung University, respectively. His work has contributed to the understanding of unusual synthetic pathways for iron complexes, nitric oxide reactivity, and the transformation of metal complexes relevant to biological processes. His research continues to focus on developing inorganic chemistry methods to explore and solve complex biological and environmental issues.

Research topics

• Computer Science
• Combinatorial chemistry
• Chemistry
• Database

Selected publications

• Multicomponent molecular memory

  Nature Communications · 2020 · 72 citations

  Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital d…

  PublisherOA PDFDOI

• Lewis Acid Assisted Nitrate Reduction with Biomimetic Molybdenum Oxotransferase Complex

  Inorganic Chemistry · 2018-02-14 · 47 citations

  articleSenior authorCorresponding

  The reduction of nitrate (NO3–) to nitrite (NO2–) is of significant biological and environmental importance. While MoIV(O) and MoVI(O)2 complexes that mimic the active site structure of nitrate reducing enzymes are prevalent, few of these model complexes can reduce nitrate to nitrite through oxygen atom transfer (OAT) chemistry. We present a novel strategy to induce nitrate reduction chemistry of a previously known catalyst MoIV(O)(SN)2 (2), where SN = bis(4-tert-butylphenyl)-2-pyridylmethanethi…

  PublisherDOI

• Generation of H₂S from Thiol-Dependent NO Reactivity of Model [4Fe-4S] Cluster and Roussin’s Black Anion

  Inorganic Chemistry · 2021-06-28 · 12 citations

  articleSenior authorCorresponding

  Iron–sulfur clusters (Fe–S) have been well established as a target for nitric oxide (NO) in biological systems. Complementary to protein-bound studies, synthetic models have provided a platform to study what iron nitrosylated products and byproducts are produced depending on a controlled reaction environment. We have previously shown a model [2Fe-2S] system that produced a dinitrosyl iron complex (DNIC) upon nitrosylation along with hydrogen sulfide (H2S), another important gasotransmitter, in t…

  PublisherDOI

• Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling

  Nature Communications · 2023-01-30 · 11 citations

  articleOpen access

  Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of "0" and "1." Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as…

  PublisherOA PDFDOI

• Leveraging autocatalytic reactions for chemical domain image classification

  Chemical Science · 2021-01-01 · 11 citations

  articleOpen access

  Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of…

  PublisherOA PDFDOI

Recent grants

• Chemistry of Iron-sulfur clusters in redox signaling

  NSF · $466k · 2018–2022

• CAREER: Chemistry of Iron-Sulfur Clusters in Nitric Oxide Signaling

  NSF · $550k · 2013–2018

Frequent coauthors

• Kenneth D. Karlin

  Johns Hopkins University

  84 shared

• Andreas D. Zuberbühler

  University of Basel

  75 shared

• Susan Kaderli

  62 shared

• Arnold L. Rheingold

  University of California, San Diego

  62 shared

• Pierre Moënne‐Loccoz

  Oregon Health & Science University

  58 shared

• Matthew E. Helton

  54 shared

• Kady Oakley

  Providence College

  46 shared

• Christopher D. Incarvito

  45 shared

Similar researchers at Brown University

• Shouheng Sunh-193
• Lai-Sheng Wangh-168
• Ming Xianh-70