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Hix in Bailey Lab Named Goldwater Scholar

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As the result of a partnership with the Department of Defense National Defense Education Programs (NDEP), Mrs. Peggy Goldwater Clay, Chair of the Board of Trustees of the Barry Goldwater Scholarship and Excellence in Education Foundation, announced that the Trustees of the Goldwater Board have increased the number of Goldwater scholarships it has awarded for the 2021-2022 academic year to 410 college students from across the United States.

“As it is vitally important that the Nation ensures that it has the scientific talent it needs to maintain its global competitiveness and security, we saw partnering with the Goldwater Foundation as a way to help ensure the U.S. is developing this talent,” said Dr. Jagadeesh Pamulapati, Director of the NDEP program, as he explained the partnership. With the 2021 awards, this brings the number of scholarships awarded since 1989 by the Goldwater Foundation to 9457.

From an estimated pool of over 5,000 college sophomores and juniors, 1256 natural science, engineering and mathematics students were nominated by 438 academic institutions to compete for the 2021 Goldwater scholarships. Of students who reported, 198 of the Scholars are men, 207 are women, and virtually all intend to obtain a Ph.D. as their highest degree objective. Fifty-one Scholars are mathematics and computer science majors, 291 are majoring in the natural sciences, and 68 are majoring in engineering. Many of the Scholars have published their research in leading professional journals and have presented their work at professional society conferences.

Goldwater Scholars have impressive academic and research credentials that have garnered the attention of prestigious post-graduate fellowship programs. Goldwater Scholars have been awarded 94 Rhodes Scholarships, 150 Marshall Scholarships, 170 Churchill Scholarships, 109 Hertz Fellowships, and numerous other distinguished awards like the National Science Foundation Graduate Research Fellowships.

Elijah Hix, of Cookeville, Tennessee, is a College Scholar whose major focuses on quantum chemical biology. Hix has pursued research at UT, Tennessee Tech, and Oak Ridge National Laboratory. Currently he is conducting research at UT under Constance Bailey, assistant professor of chemistry. He subsequently plans to pursue a PhD in biophysics to study the intersection of enzymatic synthesis and cellular networking to develop more adaptable antibiotics. “I am honored to be selected as a 2021 Goldwater Scholar,” Hix said. “It is the culmination of years of work with my two mentors, and I look forward to using this opportunity to propel enzyme modeling and engineering to new heights.”

Hix anticipates graduating May 2022. “After obtaining my degree in Quantum Chemical Biology, I plan to earn a Ph.D. in Biophysics in order to continue my research and teach on the molecular dynamics of enzymes,” Hix said.

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Sokolov Group Published in Energy Storage Mater. and ACS Macro Lett.

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The Sokolov Group recently published their work “Anomalously high elastic modulus of a poly(ethylene oxide)-based composite electrolyte” in Energy Storage Materials.

The practical use of lithium metal anodes in solid-state batteries requires a polymer membrane with high lithium-ion conductivity, thermal/electrochemical stability, and mechanical strength. The primary challenge is to effectively decouple the ionic conductivity and mechanical strength of the polymer electrolytes.

They report a remarkably facile single step synthetic strategy based on in-situ crosslinking of poly(ethylene oxide) (xPEO) in the presence of a woven glass fiber (GF). Such a simple method yields composite polymer electrolytes (CPE) of anomalously high elastic modulus up to 2.5 GPa over a broad temperature range (20 °C – 245 °C) that has never been previously documented.

An unsupervised machine learning algorithm, K-mean clustering analysis, was implemented on the hyperspectral Raman mapping at the xPEO/GF interface. Using such a unique means, we show for the first time that the promoted mechanical strength originates from xPEO and GF interactions through dynamic hydrogen and ionic bonding. High ionic conductivity is achieved by the addition plasticizer (e.g. tetraglyme), where trifluoromethanesulfonate anions are tethered to the xPEO matrix and Li+ cations are favorably transported through coordination with the plasticizer.

Further, stringent galvanostatic cycling tests indicates the CPE can be stably cycled for >3000 h in a Li-metal symmetric cell at a moderate temperature (nearly 1500 Coulombs/cm2 Li equivalents), outperforming most of the PEO-based electrolytes. The GF reinforced CPE reported here has multifunctional uses, such as solid electrolytes for all solid-state batteries and membranes for redox-flow batteries.

Although the focus of this study is on lithium-based batteries, the results are equally promising for other alkali metal based batteries such as sodium and potassium.

The Sokolov Group also had their work “Turning Rubber into a Glass: Mechanical Reinforcement by Microphase Separation” published in ACS Macro Letters.

Supramolecular associations provide a promising route to functional materials with properties such as self-healing, easy recyclability or extraordinary mechanical strength and toughness. The latter benefit especially from the transient character of the formed network, which enables dissipation of energy as well as regeneration of the internal structures. However, recent investigations revealed intrinsic limitations in the achievable mechanical enhancement.

This manuscript presents studies of a set of telechelic polymers with hydrogen-bonding chain ends exhibiting an extraordinarily high, almost glass-like, rubbery plateau. This is ascribed to the segregation of the associative ends into clusters and formation of an interfacial layer surrounding these clusters. An approach adopted from the field of polymer nanocomposites provides a quantitative description of the data and reveals the strongly altered mechanical properties of the polymer in the interfacial layer. These results demonstrate how employing phase separating dynamic bonds can lead to the creation of high-performance materials.

Vogiatzis Group’s Recent Publications

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Research in the Vogiatzis Group centers on the development of computational methods based on electronic structure theory and machine learning algorithms for describing chemical systems relevant to clean, green technologies. They are particularly interested in new methods for non-covalent interactions and bond-breaking reactions of small molecules with transition metals. Their overall objectives are to elucidate the fundamental physical principles underlying the magnetic, catalytic, and sorption properties of polynuclear systems, as well as to assist in the interpretation of experimental data.

Recent work in Coordination Chemistry Reviews “Computational catalysis for metal-organic frameworks: An overview” explores Metal-organic frameworks (MOFs), a family of porous hybrid organic/inorganic materials, have shown great promise for many challenging chemical applications including gas separations, catalysis, and sensors.

“This review highlights recent work performed on catalytic reactions promoted by MOFs from a computational and theoretical standpoint. Computational modeling includes the elucidation of reaction mechanisms, the characterization of electronic structure effects of key intermediates and transition states, and the interpretation of experimental data.” said Gavin McCarver, graduate student.

Vogiatzis also published a paper with his undergraduate advisor, Dimitris Georgiadis. “Professor Georgiadis is the person who taught me first how to do research and follow my scientific goals” said Vogiatzis. Their work  “A Carbodiimide-Mediated P-C Bond-Forming Reaction: Mild Amidoalkylation of P-Nucleophiles by Boc-Aminals” in Organic Letters shares the first example of a carbodiimide-mediated P–C bond-forming reaction. 

The reaction involves activation of β-carboxyethylphosphinic acids and subsequent reaction with Boc-aminals using acid-catalysis. Mechanistic experiments using 31P NMR spectroscopy and DFT calculations support the contribution of unusually reactive cyclic phosphinic/carboxylic mixed anhydrides in a reaction pathway involving ion-pair “swapping”. The utility of this protocol is highlighted by the direct synthesis of Boc-protected phosphinic dipeptides, as precursors to potent Zn-aminopeptidase inhibitors.

Inorganic Chemistry published their work “Electrocatalytic Dechlorination of Dichloromethane in Water Using a Heterogenized Molecular Copper Complex.” 

The remediation of organohalides from water is a challenging process in environment protection and water treatment. They report a molecular copper(I) complex with two triazole units, CuT2, in a heterogeneous aqueous system that is capable of dechlorinating dichloromethane (CH2Cl2) to afford hydrocarbons (methane, ethane, and ethylene). Computational studies provided additional insight into the reaction mechanism and the selectivity toward the CH4 formation. The findings in this study demonstrate that complex CuT2 is an efficient and stable catalyst for the dehalogenation of CH2Cl2 and could potentially be used for the exploration of the removal of halogenated species from aqueous systems.

Dai Group Published in Nano Energy and Chem. Commun.

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The Dai Group published their work “Room temperature synthesis of high-entropy Prussian blue analogues” in Nano Energy.

High-entropy Prussian blue analogues (HEPBAs) integrating the highly dispersed active sites of high-entropy materials with intrinsic 3D diffusion channels and the redox-active sites of Prussian blue analogues have great potential in electrochemical applications but have not been realized. In this work, a series of HEPBAs were successfully synthesized under room temperature combining mechanochemistry with wet chemistry for the first time.

High-entropy Prussian blue analogues (HEPBAs) were fabricated by combining mechanochemistry with wet chemistry. As an optimal element combination, high-entropy K(MgMnFeNiCu)Fe(CN)6 exhibited enhanced higher capacitances than all the single-component PBAs.

The group also published their work “Overcoming the phase separation within high-entropy metal carbide by poly(ionic liquid)s†” in Chemical Communications. 

High-entropy crystalline materials are attracting more attention. In principle, high-entropy metal carbides (HMCs) that contain five or more metal ions, possess more negative free energy value during catalysis. But its preparation is challenging because of the immiscibility of multi metal cations in a single carbide solid solution.

Here, a rational strategy for preparing HMC is proposed via a coordination-assisted crystallization process in the presence of Br-based poly(ionic liquids). Through this method, Mo0.2W0.2V0.2Cr0.2Nb0.2C nanoparticles, with a single cubic phase structure, incorporated on porous carbon, are obtained (HMC@NC). By combination of well dispersed small particle size (∼4 nm), high surface area (∼270 m2 g−1), and high-entropy phase, HMC@NC can function as a promising catalyst for the dehydrogenation of ethylbenzene. Unexpected activity (EB conv.: 73%) and thermal stability (>100 h on steam) at 450 °C are observed. Such a facile synthetic strategy may inspire the fabrication of other types of HMCs for more specific tasks.

Dadmun Published in Chemical Reviews

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Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive “designer” solvents exhibiting a host of tunable physicochemical properties.

Mark Dadmun, professor and ORNL-UT joint faculty member, contributed to this collaborative piece “Deep Eutectic Solvents: A Review of Fundamentals and Applications” in Chemical Reviews.

A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure–property relationships in this class of solvents. Complex hydrogen bonding is postulated as the root cause of their melting point depressions and physicochemical properties; to understand these hydrogen bonded networks, it is imperative to study these systems as dynamic entities using both simulations and experiments.

This review emphasizes recent research efforts in order to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding of DESs. It covers recent developments in DES research, frames outstanding scientific questions, and identifies promising research thrusts aligned with the advancement of the field toward predictive models and fundamental understanding of these solvents.

Vogiatzis Wins OpenEye Outstanding Junior Faculty Award

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Kostas Vogiatzis, assistant professor with the Department of Chemistry, is one of the recipients of the American Chemical Society, Computers in Chemistry Division (ACS COMP) OpenEye Outstanding Junior Faculty Award for Spring 2021.

This competitive and prestigious award identifies junior faculty of promise in the area of computational chemistry and modeling. Vogiatzis will present his research in the upcoming (online) National Meeting of the American Chemical Society. The title of his talk is “Data-driven Computational Chemistry for Noncovalent Interactions of CO2”.

For more information about the award visit https://www.acscomp.org/awards/the-comp-acs-outstanding-junior-faculty-award.

For more information about Dr. Vogiatzis’ research visit https://vogiatzis.utk.edu.

Zhao Receives Excellence in Research Award

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Each year, Dean Theresa Lee and members of her cabinet, with help from department heads, recognize faculty in the College of Arts and Sciences for their excellence in teaching, research and creative activity, and lifetime achievements.

Due to the ongoing pandemic, however, we were unable to host the annual awards banquet in-person. Each faculty member received a plaque and congratulations from the dean. We posted a video to the college YouTube channel here, which features each faculty award winner.

We seek to recognize faculty members who excel in scholarship and creative activity while also being fully engaged in the other responsibilities of faculty jobs, primarily teaching and service. To this end, the college honors faculty in three stages of their research careers – early, mid, and senior – with awards for excellence in research or creative achievement, as well as honoring a faculty with an award for Distinguished Research Career at UT.

Bin ZhaoBin Zhao is the Paul and Wilma Ziegler Professor in the Department of Chemistry, received a senior career excellence in research award. He has made significant contributions to the field of macromolecular brush materials, from precise synthesis to fundamental understanding and potential applications of surface brushes, polymer brush-grafted particles (hairy particles), and brush polymers. He is widely recognized, nationally and internationally, as one of the leading figures in this field. His work on stimuli-responsive polymers has also received wide attention. Zhao is a dedicated research mentor who seeks to use research opportunities to cultivate scientific reasoning and spirit in his graduate and undergraduate students.

“I am very excited to receive this great honor and recognition from our college” Zhao said. “I look forward to continuing contributing to the research mission of our university in the years ahead.”

Musfeldt Group Published in Nano Letters

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The Musfeldt Group published their work “Excitations of Intercalated Metal Monolayers in Transition Metal Dichalcogenides” in Nano Letters.

They combine Raman scattering spectroscopy and lattice dynamics calculations to reveal the fundamental excitations of the intercalated metal monolayers in the FexTaS2 (x = 1/4, 1/3) family of materials. Both in- and out-of-plane modes are identified, each of which has trends that depend upon the metal–metal distance, the size of the van der Waals gap, and the metal-to-chalcogenide slab mass ratio.

They test these trends against the response of similar systems, including Cr-intercalated NbS2 and RbFe(SO4)2, and demonstrate that the metal monolayer excitations are both coherent and tunable.

They discuss the consequences of intercalated metal monolayer excitations for material properties and developing applications.

Computational Chemistry and Machine Learning in the Vogiatzis Group

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Research in the Vogiatzis Group centers on the development of computational methods based on electronic structure theory and machine learning algorithms for describing chemical systems relevant to clean, green technologies.

“We are particularly interested in new methods for non-covalent interactions and bond-breaking reactions of small molecules with transition metals,” Vogiatzis said. “Our overall objectives are to elucidate the fundamental physical principles underlying the reactivity and properties of molecules and materials, as well as to assist in the interpretation of experimental data.”

In June 2020, the group was published in Nature Communications for their work “Representation of molecular structures with persistent homology for machine learning applications in chemistry.” This was a unique collaborative opportunity between chemistry department’s Jacob Townsend, graduate student, John Hymel, undergraduate student, Konstantinos Vogiatzis, assistant professor, along with Cassie Micucci and Vasileios Maroulas, Department of Mathematics. The group presents a novel molecular representation method based on persistent homology, an applied branch of topology, which encodes the atomistic structure of molecules.

They began their study by computing with density functional theory (DFT) the CO2 interaction energies of 100 organic molecules. “Since the initial, limited 100 data points were not capturing the diversity of the GDB-9 database, we applied a technique called active learning in order to incrementally obtain data which helped us efficiently screen the 133,885 molecules,” Vogiatzis said. “We found out that the combination of PIs with active learning performed well with data (interaction energies) from only 220 molecules in order to identify new molecules with stronger CO2 binding.”

Their data-driven methodology was able to identify molecular patterns previously unknown to us that increase the CO2 affinity of organic molecules.

The Vogiatzis Group broke a record with their work “Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies.” 

Machine learning methods have enabled the low-cost evaluation of molecular properties such as energy at an unprecedented scale. While many of such applications have focused on molecular input based on geometry, few studies consider representations based on the underlying electronic structure.

Directing the attention to the electronic structure offers a unique challenge that allows for a more detailed representation of the underlying physics and how they affect molecular properties. The target of this work is to efficiently encode a lower-cost correlated wave function derived from MP2 to predict a higher-cost coupled-cluster singles-and-doubles (CCSD) wave function based on correlation-pair energies and the contributing electron promotions (excitations) and integrals.

The new molecular representation explores the short-range behavior of electron correlation and utilizes distinct models that differentiate between two-electron promotions from the same molecular orbital or from two different orbitals. The group presents a re-engineered set of input features that provide an intuitive description of the orbital properties involved in electron correlation. The overall models are found to be highly transferable and size extensive, necessitating very few training instances to approach the chemical accuracy of a broad spectrum of organic molecules.

“Coupled-cluster theory is the level of theory that provides the most accurate quantum chemical results in a reasonable computational time. Typically, we need ~10 minutes for computing the energy of a small molecule with coupled-cluster and for a database with ~133,000 small molecules, we will need ~1,330,000 minutes or ~2.5 years of computations,” Vogiatzis said. “In this work, we demonstrated that we can use the results from only 100 coupled-cluster calculations for training a machine learning model that can predict, without loss of accuracy, the energy of the full 133,000 molecule database a few hours.”