Archives for 2022
Brantley Group Published in JACS
The Brantley group recently published a paper in JACS entitled “Electroediting of Soft Polymer Backbones” Alan Fried, Breana Wilson, and Nick Galan contributed to the research, under the supervision of Johnathan Brantley.
The paper discusses new methodology for degradation and functionalization of olefin-containing polymers through electrochemistry. This method can be carried out in both homogeneous and heterogeneous systems, in addition to using mild conditions and being experimentally simple.
The work was completed in memory of Alan Fried.
Best Group Publishes ATP-Responsive Liposomes in JACS
The research group of Michael Best in Tennessee Chemistry, led by graduate student Jinchao Lou, recently published an article describing the development of ATP-responsive liposomes in the Journal of the American Chemical Society. The nanocarriers reported in this work show strong prospects for enhancing clinical drug delivery applications.
Liposomes are highly effective nanocarriers for therapeutics due to their ability to encapsulate drugs with wide-ranging properties and enhance their circulation and delivery to cells. However, their potential could be improved by achieving control over the release of cargo to maximize drug potency and diseased-cell selectivity. While liposome-triggered release represents a vibrant field of research due to this significance, the toolbox for controlling liposome release remains limited and prior strategies face many challenges that obstruct clinical application.
The Best Group has explored a new paradigm for triggered release in which cargo escape is triggered only when liposomes encounter specific small molecule metabolites that are overly abundant in disease states. This is achieved using synthetic stimuli-responsive lipid switches designed to undergo programmed conformational changes upon the binding of small molecule targets, events that compromise membrane packing and thereby drive release.
In this work, Lou and co-workers developed liposomes that selectively respond to ATP over eleven other structurally similar phosphorylated small molecules. ATP is a critical target for metabolite-mediated drug delivery since this molecule is a universal energy source that is known to be heavily upregulated in-and-around cancer cells. This opens up the potential for selective drug delivery and release driven by overly abundant ATP associated with diseased cells.
This project also entailed a collaboration with the group of Dr. Francisco Barrera in the Tennessee Biochemistry & Cellular and Molecular Biology (BCMB) Department. Through cellular delivery and fluorescence imaging experiments, graduate student Jennifer Schuster showed that modulation of cellular ATP levels using drugs led to direct control of cellular delivery of ATP-responsive liposomes. These results demonstrate the key advancement that liposome delivery can be modulated by the cellular abundance of ATP.
A provisional patent has been filed for this ATP-responsive liposome technology. Additionally, the Best Group is currently working on advancing this platform for clinical delivery applications and developing liposomes that respond to other disease-associated small molecule metabolites.
Dai Group Published in Nature Communications
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.
Kostas Vogiatzis Receives the 2022 NSF CAREER Award
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.