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Related Concept Videos

Bioplastics01:27

Bioplastics

Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
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Bioremediation is an environmentally sustainable process that employs living organisms—primarily microorganisms—to degrade or neutralize pollutants from contaminated environments. In oil spills and hydrocarbon pollution, bioremediation involves the use of hydrocarbon-degrading bacteria to transform toxic compounds into less harmful substances. This approach leverages natural microbial metabolic processes and is considered both cost-effective and ecologically favorable compared to physical or...
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Polyethylene terephthalate (PET) is a synthetic polymer widely utilized in the packaging industry, particularly for bottles and containers. Due to its chemical stability and durability, PET accumulates in the environment, contributing significantly to plastic pollution. It comprises repeating units of terephthalic acid and ethylene glycol, resulting in a semi-crystalline structure that is resistant to natural degradation processes.A notable breakthrough in plastic biodegradation came with the...
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Lactic acid, an important organic acid extensively applied in food, pharmaceutical, and biodegradable polymer industries, is primarily produced via microbial fermentation. This method is favored over chemical synthesis due to its environmental sustainability and capacity for enantiomerically pure product formation. Among various microbial processes, the fermentation of starch-based substrates stands out due to the abundance and renewability of raw materials like corn and potatoes.Hydrolysis of...
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Pesticides often feature structurally complex chemical architectures, incorporating halogen groups and multiple aromatic rings. These characteristics confer high chemical stability, rendering many pesticides resistant to natural degradation processes. This resistance poses significant environmental concerns, as persistent pesticide residues can accumulate in ecosystems and affect non-target organisms.Despite the inherent stability of many pesticides, certain microorganisms possess the metabolic...

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Machine Learning Prediction of Thermal Properties of PHB/PHBV-Based Materials: A Quantitative Structure-Property Relationship Approach Using an Integrated Polymer Database.

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A Data-Driven Framework for Predicting PHBV Biodegradation-Induced Weight Loss Based on Laboratory and Real-Environment Condition Tests.

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Updated: May 14, 2026

Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste
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Published on: July 18, 2025

Machine Learning Methods for Mineralization-Based Biodegradation Prediction in Polyhydroxyalkanoate-Based

Marianna I Kotzabasaki1, Leonidas Mindrinos1, Nikolaos P Sotiropoulos1

  • 1Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.

Polymers
|May 13, 2026
PubMed
Summary

This study introduces a computational approach to predict the biodegradability of bio-based plastics like polyhydroxyalkanoates (PHAs). This method aids in developing sustainable polymers, crucial for managing plastic waste in challenging environments.

Keywords:
biodegradation predictionmachine learningmineralizationpolyhydroxyalkanoatespolymer informaticsquantitative structure-activity relationship

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Area of Science:

  • Polymer Science
  • Environmental Science
  • Computational Chemistry

Background:

  • Bio-based and biodegradable plastic products (BBpPs) offer solutions to environmental pollution from fossil-based plastics, particularly in crisis situations with limited waste management.
  • Polyhydroxyalkanoates (PHAs) are promising biocompatible biopolymers that do not generate microplastics, but their biodegradation assessment is time-consuming and resource-intensive.
  • Developing predictive models for PHA biodegradability is essential for advancing sustainable materials.

Purpose of the Study:

  • To develop a computational Quantitative Structure-Activity Relationship (QSAR)-based approach for predicting the biodegradability of short chain length (scl)-PHA formulations.
  • To create and validate machine learning models using a novel dataset of poly(-3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) biodegradation parameters.
  • To support the sustainable safe-by-design (SSbD) approach for next-generation biodegradable polymers.

Main Methods:

  • A comprehensive QSAR approach was developed using a curated dataset of PHBV biodegradation parameters from various environmental systems (soil, marine, freshwater, compost).
  • Random Forest (RF) and Extreme Gradient Boosting (XGBoost) machine learning models were optimized and validated through cross-validation and test set predictions.
  • Analysis of descriptor variable importance was performed to understand factors influencing biodegradability.

Main Results:

  • Optimized ML models demonstrated high accuracy (R² values) in predicting biodegradation metrics, indicating strong structure-biodegradability relationships.
  • Biodegradation time was identified as a key factor favorably affecting biopolymer biodegradability.
  • Environmental conditions and additives showed secondary, yet physically consistent, effects on biodegradation.

Conclusions:

  • The developed QSAR framework provides a robust and interpretable web-based tool for predicting the environmental fate of PHBV.
  • This approach facilitates the design and selection of biodegradable polymers with predictable environmental performance.
  • The study supports the advancement of sustainable materials and the SSbD initiative for polymers.