Quantum-enhanced absorption and gain measurements and their applications in bio-sensing
Assistant Professor of Physics, The University of Tennessee at Chattanooga
Absorption and gain measurements are routinely used in science and engineering. For such measurements using laser light, the sensitivity is fundamentally limited by the shot noise due to the Poisson distribution of photon number in laser radiation. In this presentation, I will talk about our most recent experiments using bright squeezed light to show that both absorption and gain measurements can be performed with sensitivity beyond the shot-noise limit. We report direct sub-shot-noise limited measurements of absorption and gain that require neither homodyne/lock-in nor logic coincidence detection schemes. I will also show that the significantly improved measurement sensitivity can provide a powerful non-invasive bio-sensing and bio-imaging tool for probing properties of biological samples prone to phototoxicity and thermal effects.
Collaborative Work Toward the Identification of Novel Antibiofilm Agents
Daniel L. Baker
Associate Professor, Chemistry Department, University of Memphis
Biofilms result when bacteria attach to surfaces and generate an extracellular polymeric matrix. This matrix helps to provide a physical barrier that prevents antibiotic access and reverts some planktonic bacteria to a persister phenotype that further impacts antibiotic effectiveness. Biofilms are significant problems in many hospital acquired infections, in cases with serious wounds and burns, and with medical implants. Biofilm associated infections result in significant increases in morbidity and mortality. Previously, it has been shown that medium chain fatty acid analogs (known as diffusible signaling factors- DSF) can act to prevent and disburse biofilms and act synergistically with standard antibiotics.
Over the past three years, I have collaborated with a diverse group of investigators at the University of Memphis, J. Amber Jennings, Biomedical Engineering and Tomoko Fujiwara, Chemistry, to evaluate novel DSF as potential antibiofilm and combination antibiotic agents. This work encompasses the optimization of synthetic approaches, stereochemical synthesis, quantitative analytical method development, stability determination, and bioassays. This work has been funded in part by the NIH, DOD, and NSF.
Machine learning assisted design and synthesis of industrial polyester resins for bisphenol-A-Non-Intent (BPA-NI) coating applications
In food contact coating applications, the need to feed a growing world population with safer, healthier packaging material choices is the primary driver for growing innovation in BPA-NI technology. Originated from the initial success of 2,2,4,4-tetramethyl-1,3-cyclobutanediol (TMCD) in BPA-free thermoplastics, a new family of TMCD-based polyester resins has emerged as a promising candidate to lead the conversion from BPA to BPA-NI in packaging coatings due to their superior hydrolytic stability, corrosion resistance and high temperature resistance, in combination with good solubility, compatibility and lower viscosity. To deliver first-to-market solutions that can meet or exceed the performance of BPA benchmarks, Eastman must innovate with complex design to meet significant performance and numerous technical challenges while going up against aggressive timelines. From the molecular and structural perspective, polyester properties can be tuned by taking advantage of the enormous design space including complex monomer selections, compositions, specific end-group and molecular weight distributions, and polymer architectures. However, such a complex design space makes developing structure-process-property relationships and deciding where to focus limited laboratory resources challenging. Owing to the continued development of machine learning, we effectively free up laboratory resources for only the most promising candidates through initial digital exploration of this vast chemical design space. Here, we detail our strategies for construction of machine learning models that accelerate the planning and execution of specialty polyester resin syntheses for BPA-NI food contact coating applications using a combination of theoretical inputs, properties from historical datasets, and molecular descriptors.