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The growing environmental concerns associated with petroleum-based plastics require the development of sustainable, biodegradable alternatives. Polyhydroxyalkanoates (PHAs), a family of biodegradable bioplastics, offer a promising potential as eco-friendly substitutes due to their renewable origin and favorable degradation properties. This research investigates the use of synthetic condensate, mimicking the liquid fraction from drying and shredding of household food waste, as a viable substrate for PHA production using mixed microbial cultures. Two draw-fill reactors (DFRs) were operated under different feed organic concentrations (2.0 ± 0.5 and 3.8 ± 0.6 g COD/L), maintaining a consistent carbon-to-nitrogen ratio to selectively enrich microorganisms capable of accumulating PHAs through alternating nutrient availability and deficiency. Both reactors achieved efficient organic pollutant removal (>95% soluble COD removal), stable biomass growth, and optimal pH levels. Notably, the reactor with the higher organic load (DFR-2) demonstrated a modest increase in PHA accumulation (19.05 ± 7.18%) compared to the lower-loaded reactor (DFR-1; 15.19 ± 6.00%), alongside significantly enhanced biomass productivity. Polymer characterization revealed the formation of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), influenced by the substrate composition. Microbial community analysis showed an adaptive shift towards Proteobacteria dominance, signifying successful enrichment of effective PHA producers.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12349330 | PMC |
http://dx.doi.org/10.3390/polym17152042 | DOI Listing |
BMC Biotechnol
September 2025
Botanical Garden, Ulm University, Hans-Krebs-Weg, 89081, Ulm, Germany.
Environ Sci Pollut Res Int
September 2025
Faculdade de Engenharia da Universidade do Porto, INESC TEC, Porto, Portugal.
Food waste generated throughout the food supply chain raises several environmental, social, and economic issues. Quantitative methods can aid in managing food waste by describing current contexts, predicting future scenarios, and improving related operations. However, a literature review on the use of quantitative methods, specifically the descriptive, predictive, and prescriptive dimensions, to assess and prevent food waste is lacking.
View Article and Find Full Text PDFBiol Trace Elem Res
September 2025
Department of Environmental Sciences, Faculty of Biological Sciences, Kohat University of Science and Technology Kohat, Khyber Pakhtunkhwa, 26000, Pakistan.
The aim of the study was to evaluate the toxic metals (TMs) pollution, bioaccumulation and its potential health risk via consumption of different vegetables irrigated by different water sources released from industrial estates of Khyber Pakhtunkhwa. Water (fresh and waste), soil and vegetables samples were collected in triplicates and acid digested. Digestion of samples were followed by evaporation and filtration and then assessed for TMs via atomic absorption spectrophotometer.
View Article and Find Full Text PDFWaste Manag Res
September 2025
Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, Vietnam.
This study investigates plastic food packaging (PFP) recycling symbols in Vietnam through field surveys, questionnaires and statistical and machine-learning models. Results show that 68.2% of shoppers correctly identified the recycling symbol, whereas 87.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
School of Food & Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013 Jiangsu Province, China.
Pectinases are indispensable biocatalysts for pectin degradation in food and bioprocessing industries, yet natural enzymes often lack tailored functionalities for modern applications. While a previous review discussed pectinases in terms of production and application, this review particularly discusses an integrated framework for robust pectinases. This framework combines enzyme mining, protein engineering, and AI-assisted design to systematically discover, optimize, and customize pectinases.
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