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Integrating Ai And Microbial Biodegradation For Sustainable Solutions To Plastic Pollution
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This study developed a local infrared spectroscopy approach for in situ characterization of nanoplastic particles at surfaces, combining near-field optical techniques with conventional infrared spectroscopy. The method achieved spatial resolution below the diffraction limit, enabling chemical identification of nanoplastic particles too small for conventional FTIR analysis.
Introduction/Importance of Study: Pollution by plastics is a rapidly rising problem in the world, and conventional approaches to waste disposal are not effective. Another issue is to find new ways how to address the increase in the quantities of plastic in ecosystems. Novelty Statement: This research examines the coupling of artificial intelligence with microbial biodegradation, thus offering new concepts for optimizing plastics biodegradation solutions. Material and Methods: The use of artificial intelligence was applied in determining the relative efficiency of microbial strains, and enzymes used in the degradation of plastic. AI’s applications were examined for the ability to review cases and experimental data regarding the predictability of best conditions for biodegradation and the enhancement of microbial activity. Results and Discussion: AI was very effective in not only identifying and enhancing the microbial strains for degrading plastics but also in enhancing the economy. Est deposited for specific key case examined how AI tools enhance enzyme activity, strain identification, and ways to adapt to the environment; this gave more impetus to touch with scalability and prospects for industrial use. Conclusion: The application of AI in microbial biodegradation shifts the current management techniques from volumetric, energy, and resource–consuming present options to ecological and efficient models with huge potential to reduce the environmental degradation of plastics.
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Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation
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This review examines how artificial intelligence is being combined with microbial biotechnology to improve the detection and breakdown of persistent environmental pollutants including microplastics. Researchers found that AI models achieve over 90 percent accuracy in classifying microplastics and have helped design enzymes that degrade PET plastic up to 46 times faster than conventional approaches. The integration of AI with biotechnology represents a significant advance in developing sustainable pollution remediation strategies.
Plastic pollution and its global environmental impacts: a comprehensive review of microbial biodegradation mechanisms, plastic-degrading enzymes, emerging biotechnological strategies and artificial intelligence-driven approaches for sustainable plastic waste management
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This review pulls together current research on using bacteria and enzymes to break down plastic waste instead of relying on landfills or incineration, which are costly and create their own pollution. Scientists are now pairing this "microbial recycling" with artificial intelligence to speed up the discovery of plastic-eating enzymes, though the technology still needs more real-world testing before it can be scaled up. This matters because plastic waste breaks down into micro- and nanoplastics that are turning up in our water, food and even our bodies, so finding better ways to eliminate plastic waste could reduce our long-term exposure to these particles.
A study on AI solutions for plastic degradation, its benefits, and challenges
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This review surveys how artificial intelligence and machine learning tools are being applied to accelerate the discovery and optimization of plastic-degrading processes, including enzyme engineering and biodegradation pathway modeling. It highlights the potential for AI to significantly shorten the timeline from laboratory discovery to scalable plastic breakdown solutions.
AI-Integrated Chemistry Solutions for Sustainable Plastic Waste Management
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This paper reviews how artificial intelligence is being applied to improve plastic waste recycling efficiency, expand recycling technologies, and support development of biodegradable plastic alternatives. Real-world case studies illustrate AI's potential to transform plastic waste management and advance sustainable solutions.
Advances in microplastic mitigation: current progress and future directions
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This review synthesizes recent advances in biotechnology-based approaches to microplastic remediation, including microbial degradation, engineered enzyme systems, and AI-driven monitoring. Researchers found that while promising enzymes and engineered biofilm systems have been demonstrated in the lab, translating these solutions to diverse polymer types and real-world field applications remains a major challenge. The study proposes a unified roadmap for scaling sustainable biotechnology solutions to address the global microplastic crisis.
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