Engineering topics

Majumder, Erica L.-W.

Publications and source records attributed to Majumder, Erica L.-W..

3 records

DOE OSTI2023

A Review of Biodegradable Plastics: Chemistry, Applications, Properties, and Future Research Needs

Environmental concerns over waste plastics’ effect on the environment are leading to the creation of biodegradable plastics. Biodegradable plastics may serve as a promising approach to manage the issue of environmental accumulation of plastic waste in the ocean and soil. Biodegradable plastics are the type of polymers that can be degraded by microorganisms into small molecules (e.g., H 2 O, CO 2 , and CH 4 ). However, there are misconceptions surrounding biodegradable plastics. For example, the term “biodegradable” on product labeling can be misconstrued by the public to imply that the product will degrade under any environmental conditions. Such misleading information leads to consumer encouragement of excessive consumption of certain goods and increased littering of products labeled as “biodegradable”. Here this review not only provides a comprehensive overview of the state-of-the-art biodegradable plastics but also clarifies the definitions and various terms associated with biodegradable plastics, including oxo-degradable plastics, enzyme-mediated plastics, and biodegradation agents. Analytical techniques and standard test methods to evaluate the biodegradability of polymeric materials in alignment with international standards are summarized. The review summarizes the properties and industrial applications of previously developed biodegradable plastics and then discusses how biomass-derived monomers can create new types of biodegradable polymers by utilizing their unique chemical properties from oxygen-containing functional groups. The terminology and methodologies covered in the paper provide a perspective on directions for the design of new biodegradable polymers that possess not only advanced performance for practical applications but also environmental benefits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Large-scale genetic characterization of the model sulfate-reducing bacterium, Desulfovibrio vulgaris Hildenborough

Sulfate-reducing bacteria (SRB) are obligate anaerobes that can couple their growth to the reduction of sulfate. Despite the importance of SRB to global nutrient cycles and their damage to the petroleum industry, our molecular understanding of their physiology remains limited. To systematically provide new insights into SRB biology, we generated a randomly barcoded transposon mutant library in the model SRB Desulfovibrio vulgaris Hildenborough (DvH) and used this genome-wide resource to assay the importance of its genes under a range of metabolic and stress conditions. In addition to defining the essential gene set of DvH, we identified a conditional phenotype for 1,137 non-essential genes. Through examination of these conditional phenotypes, we were able to make a number of novel insights into our molecular understanding of DvH, including how this bacterium synthesizes vitamins. For example, we identified DVU0867 as an atypical L-aspartate decarboxylase required for the synthesis of pantothenic acid, provided the first experimental evidence that biotin synthesis in DvH occurs via a specialized acyl carrier protein and without methyl esters, and demonstrated that the uncharacterized dehydrogenase DVU0826:DVU0827 is necessary for the synthesis of pyridoxal phosphate. In addition, we used the mutant fitness data to identify genes involved in the assimilation of diverse nitrogen sources and gained insights into the mechanism of inhibition of chlorate and molybdate. Our large-scale fitness dataset and RB-TnSeq mutant library are community-wide resources that can be used to generate further testable hypotheses into the gene functions of this environmentally and industrially important group of bacteria.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗