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January 2022

Archives for January 2022

Dai Group Published in Nature Communications

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The Dai Group published their latest research “Intra-crystalline mesoporous zeolite encapsulation-derived thermally robust metal nanocatalyst in deep oxidation of light alkanes” in Nature Communications.

Zeolite-confined metal nanoparticles (NPs) have attracted much attention owing to their superior sintering resistance and broad applications for thermal and environmental catalytic reactions. However, the pore size of the conventional zeolites is usually below 2 nm, and reactants are easily blocked to access the active sites.

In this work, a facile in situ mesoporogen-free strategy is developed to design and synthesize palladium (Pd) NPs enveloped in a single-crystalline zeolite (silicalite-1, S-1) with intra-mesopores (termed Pd@IM-S-1). Pd@IM-S-1 exhibited remarkable light alkanes deep oxidation performances, and it should be attributed to the confinement and guarding effect of the zeolite shell and the improvement in mass-transfer efficiency and active metal sites accessibility. The Pd–PdO interfaces as a new active site can provide active oxygen species to the first C-H cleavage of light alkanes. “This work exemplifies a promising strategy to design other high-performance intra-crystalline mesoporous zeolite-confined metal/metal oxide catalysts for high-temperature industrial thermal catalysis”, said Honggen Peng, a previous visiting scholar in the Dai Group.

Filed Under: Dai

Kostas Vogiatzis Receives the 2022 NSF CAREER Award

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The Chemistry department is proud to announce that Kostas Vogiatzis has received this year’s National Science Foundation’s Faculty Early Career Development Program (CAREER) Award, the organization’s most prestigious grant in support of early-career faculty. Dr. Vogiatzis research centers on the development of new computational methods that interface quantum chemistry with machine learning. The title of his award is “CAREER: CAS-Climate: Data-driven Coupled-Cluster for Biomimetic CO2 Capture”.

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Dr. Vogiatzis is developing data-driven computational methodologies for the biomimetic capture of carbon dioxide. Carbon dioxide (CO2) overload in the atmosphere generates a significant greenhouse gas (GHG) layer, a major contributor to climate change in the United States and around the globe. Climate change presents a growing challenge to human health and safety, quality of life, and economic growth. Direct air capture (DAC) refers to technologies that capture CO2 directly from the air. One approach to DAC agent design relies upon chemical compositions that lead to favorable CO2 binding. Computational studies can examine different chemical environments and suggest new CO2-philic groups. Dr. Vogiatzis and his research group will develop new hybrid quantum chemical/machine learning models for the exploration of novel DAC approaches that are based on how enzymes can selectively capture CO2. Dr. Vogiatzis will also develop a new course offered at the upper undergraduate or early graduate level that aims to bridge data science with chemistry and provide important skills to undergraduate and graduate students. This course aims to reach students from underserved groups and provide a stimulating view of chemistry while training students in more expansive use of data science in chemistry.

The primary objective of his project is to develop computational methodologies that capitalize on recent progress in data science for expanding the applicability of accurate quantum chemistry methods. Dr. Vogiatzis’ approach is based on the recycling of molecular wave functions obtained at low computational cost to help train machine-learning models which will provide fast and reliable energies and geometries of complex molecular systems without loss of accuracy. Coupled-cluster singles-and-doubles with perturbative triples (CCSD(T)) is a wave function method that balances accuracy with efficiency. Dr. Vogiatzis and his research group will develop transferable machine learning models that learn highly accurate CCSD(T) wave functions by utilizing data from low-cost methods such as Hartree-Fock (HF) and second-order perturbation theory. This data-driven coupled-cluster (DDCC) scheme is based on electron correlation, a property that has a local, short-range character across all molecular species, independent of their size. DDCC models can effectively encode the local nature of electron correlation and, after thorough testing and benchmarking, can be used for the examination of CO2-oligopeptide systems for biomimetic CO2 capture. Furthermore, the advances made here in combining quantum chemical methods with machine-learning are expected to be applicable to a significant variety of other chemical challenges.