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Hazari Online

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Dr. Al Hazari Magic ShowAl Hazari, retired UT professor, has been conducting experiments on Zoom to teach and inspire chemistry enthusiasts.

“I have always enjoyed teaching and sharing something of myself and my knowledge with anyone ages 2 to 102,” Hazari said. “What we’re really after are science-literate citizens. Everyone should know about science, be comfortable with science, and never stop being curious and inquisitive about the wonders of science.”

This summer, Hazari has utilized the Zoom platform to teach Forensic Chemistry Camp for middle school students, ORAU workshops, the Harriman Public Library Chemistry Magic Show, and local WBIR presentations. 

Hazari WBIR 1

Hazari WBIR 2

Hazari WBIR 3

Larese Named ACS Fellow

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John Larese, professor in the Department of Chemistry, has been named an 2020 ACS Fellow.

The primary purpose of the ACS Fellows Program is to recognize and honor members of the American Chemical Society for their outstanding achievements in and contributions to the science and the profession and for their equally exemplary service to the Society.

“I have truly benefited by my service to ACS and LSAC and by all of the great individuals who have contributed to my scientific endeavors and career,” Larese said. “I’m especially proud of my effort in leading the science/design/funding case for the VISION Spectrometer at the SNS and the training of future scientists in the use of neutrons and novel materials. Clearly without the support and patience of my wife Maryann and children such pursuits are impossible.”

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Vogiatzis Lab Published in Nature Communications

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Machine learning applications for chemical problems have been rapidly increasing. Their popularity is justified since they have led to the discovery of new molecules and materials with enhanced properties, new reactions, or have contributed to the reduction of computational effort needed of complex calculations and simulations.

The Vogiatzis Lab seeks to address how a computational algorithm can efficiently “read” and “learn” patterns from molecular structures in their research “Representation of molecular structures with persistent homology for machine learning applications in chemistry” recently published in Nature Communications.

In this collaborative work between Jacob Townsend, John Hymel and Konstantinos Vogiatzis (Chemistry, University of Tennessee) and Cassie Micucci and Vasileios Maroulas (Mathematics, University of Tennessee), the group is presenting a novel molecular representation method based on persistent homology, an applied branch of topology, which encodes the atomistic structure of molecules.

A molecule is mapped into a persistence diagram, a two-dimensional point summary, which demystifies the connected components and the empty space that exist in a molecule based on the atom types and the distances among them. A persistence diagram is further vectorized to a persistence image (PI), a weighted representation of the diagram, which captures the chemically driven uncertainty. The PI in that sense is a “molecular fingerprint”, and when used with machine learning, offers an efficient and reliable approach to screen large molecular databases when compared to other popular molecular representation schemes.

The efficiency arises from the low computation effort needed to compare a large number of fingerprints, and the similar-size representations that are generated, independently of the molecular sizes.

The group demonstrates the applicability of the PI method by screening a large molecular database (GDB-9) with 133,885 organic molecules. Their target was to identify novel molecular units that selectively interact with CO2 and can be used as building blocks of materials, such as polymeric membranes.

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.

 

Two Journal Covers from the Sokolov Group

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The Sokolov Group’s primary focus of research on dynamics of soft materials, including dynamics of biological macromolecules, and nano-composite materials.

Their recent publication “Correlation between the temperature evolution of the interfacial region and the growing dynamic cooperativity length scale” was featured on cover of The Journal of Chemical Physics. They presented an analyses that revealed a clear correlation between the temperature dependence of the characteristic relaxation time, ln(τα(T)/τ0), and the interfacial layer thickness, Lint(T), in nanocomposite materials.

The group also published their work “Perspectives for Polymer Electrolytes: A View from Fundamentals of Ionic Conductivity” which was on the cover of Macromolecules. This research analyzes fundamental mechanisms controlling ionic conductivity and suggests design of novel polymer electrolytes with enhanced conductivity.

Zhao Group Published in Nature Communications

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The Zhao Group is a polymer chemistry research group, focusing on responsive, functional soft and hybrid materials.

The group recently published their work “Breaking translational symmetry via polymer chain overcrowding in molecular bottlebrush crystallization” in Nature Communication.

The research focuses on  the fundamental laws in crystallization is translational symmetry. This piece reports on the spontaneous formation of spherical hollow crystals with broken translational symmetry in crystalline molecular bottlebrush (mBB) polymers. This study unravels a new principle of spontaneous translational symmetry breaking, providing a general route towards designing versatile nanostructures.

First Publication from Bailey Lab

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The Bailey Lab just published their first review article “Site directed mutagenesis as a precision tool to enable synthetic biology with engineered modular polyketide synthases” in Synthetic and Systems Biotechnology. 

This article is an overview on a way to target genetic changes to change as little as one amino acid to change the function of polyketide synthases.  

“Polyketides have a correspondence between their sequence and the structure of the small molecules they create, which are often important pharmaceuticals,” said Assistant Professor Constance Bailey. “Finding ways to subtly alter the structure of the metabolite that forms is a way to enable the discovery of new important drugs.”

The group reviews examples of targeted point mutagenesis to one or a few residues harbored within the PKS that alter domain specificity or selectivity, affect protein stability and interdomain communication, and promote more complex catalytic reactivity.

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Calhoun Lab Publishes Online Detection Method for Microfluidics

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The Calhoun Lab published their article “Total Internal Reflection Transient Absorption Microscopy: An Online Detection Method for Microfluidics” in The Journal of Physical Chemistry A. Brandon Colon, a graduate student in the Calhoun Lab, is the primary author of this work.

Microreactors have garnered widespread attention for their tunability and precise control of synthetic parameters to efficiently produce target species. Despite associated advances, a lack of on-line detection and optimization methods has stalled the progression of microfluidic reactors.

“Here we employ and characterize a total internal reflection transient absorption microscopy (TIRTAM) instrument to image excited state dynamics on a continuous flow device,” Colon said. “The experiments presented demonstrate the capability to discriminate between different chromophores as well as in differentiating the effects of local chemical environments that a chromophore experiences.”

This work presents the first such on-line transient absorption measurements and provides a new direction for the advancement and optimization of chemical reactions in microfluidic devices. 

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Do Lab Published in Physical Chemistry Chemical Physics

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The Do Group’s research combines ion-mobility mass spectrometry (IM-MS), mass spectrometry imaging (MSI), and computational modeling to bring a chemical physics outlook to problems of broad chemical interest. 

The group recently published their work “Selective host–guest chemistry, self-assembly and conformational preferences of m-xylene macrocycles probed by ion-mobility spectrometry mass spectrometry” in the journal Physical Chemistry Chemical Physics.

 The group demonstrates ion-mobility spectrometry mass spectrometry (IMS-MS) as a powerful tool for interrogating and preserving selective chemistry including non-covalent and host–guest complexes of m-xylene macrocycles formed in solution. 

Their experiments collectively unravel multiple facets of macrocycle chemistry including conformational flexibility, self-assembly and ligand binding; all in one analysis. Their findings illustrate an inexpensive and widely applicable approach to investigate weak but important interactions that define the shape and binding of macrocycles.

Sharma Lab Published in Analyst

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The Sharma Raman lab is an interdisciplinary research group working in the areas of Analytical and Physical Chemistry, Biology, and Materials Science. The group focuses on probing and characterizing the underlying chemistry and physics of biological processes. The long range research goal of the group is the use of innovative Raman spectroscopic methods to create new approaches to understand biology. Specifically, we are developing methods for early detection of disease (both in vitro and in vivo detection), as well as methods for chemical and biological sensing.

The Sharma Lab published their work “Raman spectroscopy and neuroscience: from fundamental understanding to disease diagnostics and imaging” in Analyst. 

Neuroscience would directly benefit from more effective detection techniques, leading to earlier diagnosis of disease. The specificity of Raman spectroscopy is unparalleled, given that a molecular fingerprint is attained for each species. It also allows for label-free detection with relatively inexpensive instrumentation, minimal sample preparation, and rapid sample analysis. This review summarizes Raman spectroscopy-based techniques that have been used to advance the field of neuroscience in recent years.