Since the 1950s, plastic production has steadily increased with millions of metric tons accumulating in the environment.¹ Much of the waste stream is single use plastics, and the need for biodegradable alternatives is pressing. Polyhydroxyalkanoates (PHA), a class of polymers produced by many bacteria as a carbon source, presents a viable biodegradable replacement for many commodity thermoplastics such as polypropylene and polyethylene.

Eric Ferreira’s own early fascination with solving puzzles helps him inspire students to piece together the logic behind organic chemistry.

Dr. Melanie Reber, assistant professor in the UGA Department of Chemistry, has been named one of two Innovation Fellows for Spring 2021. Launched as part of the University of Georgia’s campus-wide Innovation District initiative, the semester-long fellowship helps faculty align their research activities with industry needs and bring their discoveries to the marketplace.

Harshani Rathnaweera, a UGA Chemistry graduate student in the Salguero laboratory, was one of the Materials Research Society’s “Best Poster Winners” at the 2020 Virtual Fall Meeting. Her poster “2D Nanostructured Tetrasilicates with Cr(II) and Cr(II)/Fe(II) in Square-Planar Coordination” described her research on the synthesis, crystal growth, properties, and nanostructuring of metal chromium tetrasilicate materials ACrSi₄O₁₀ (A = Ca, Sr, Ba) with interesting magnetic properties.

Laser desorption time-of-flight mass spectrometry (LD-ToF-MS) experiments on pressed-pellet samples of polycyclic aromatic hydrocarbons (PAHs) produce covalently-bonded dimers at masses (m/z) of 2M-2 and 2M-4 (where M is the parent mass). Through replication of these LD-ToF-MS conditions at higher throughput, PAH dimers have been produced and collected in milligram quantities. For collected samples of pyrene, perylene, and coronene, differential sublimation has isolated the dimer sample from residual monome

In the past, catalyst design has been highly characterized by trial and error based on previous experience and knowledge of catalysis.¹ Due to the number of variables involved in catalyst performance, it is difficult to manually optimize highly active catalysts. In the past few years, there has been a movement toward the use of machine learning as a tool in experimental catalyst design.