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Papers
61,005 resultsShowing papers similar to AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging
ClearLeveraging Artificial Intelligence for Accelerated Polymer Synthesis and Design
This review examines AI-enabled advances in polymer informatics, focusing on machine learning and deep learning approaches for accelerating the design of application-specific polymeric materials across energy storage, production, and sustainable economy applications including recyclable and biodegradable polymers. The review highlights how AI-powered workflows are shortening the design-to-discovery cycle for next-generation polymer materials.
Bioplastic design using multitask deep neural networks
Researchers used deep neural networks trained on nearly 23,000 polymer chemistries to identify 14 biodegradable bioplastics — made from biological sources rather than petroleum — that could replace the seven most commonly used synthetic plastics, which account for 75% of global plastic production. This AI-driven approach accelerates the search for eco-friendly plastic alternatives that naturally break down rather than fragmenting into persistent microplastics.
Feasibility of Utilizing Machine Learning to Identify a More Sustainable Alternative to Polyester in Textiles
Researchers used machine learning trained on the PI1M polymer dataset of over one million compounds to identify sustainable alternatives to polyester in textiles, predicting key material properties including glass transition temperature, density, melting temperature, oxygen permeability, and bulk modulus. The study found that artificial neural networks could screen polymer candidates for environmental compatibility while maintaining performance characteristics comparable to polyester.
Revealing factors influencing polymer degradation with rank-based machine learning
Researchers developed a machine learning platform using a ranking-based algorithm to predict and compare how easily different polymer materials biodegrade, integrating three different experimental datasets with varying conditions. Analysis revealed key molecular factors that control degradability, offering guidance for designing more environmentally friendly plastics.
Machine intelligence-accelerated discovery of all-natural plastic substitutes
Researchers combined robotics and machine learning to rapidly discover biodegradable plastic substitutes made entirely from natural ingredients, using an automated system to test 286 material combinations and build a predictive model that can design new materials to order. This approach dramatically speeds up the search for alternatives to petroleum-based plastics that contribute to microplastic pollution.
Strategic selection tool for thermoplastic materials in a renewable circular economy: Identifying future circular polymers
Researchers developed a strategic material selection tool to guide the transition toward a renewable circular economy for thermoplastics, helping identify which polymers can meet performance requirements while being decoupled from fossil feedstocks and compatible with biodegradation or closed-loop recycling.
Recent Advances in Polymer Design through Machine Learning: A Short Review
This review examines recent advances in applying machine learning — including supervised learning, unsupervised learning, and artificial neural networks — to polymer informatics, covering property prediction, synthesis optimisation, and polymer classification across diverse applications from medicine to aerospace. The authors highlight how growing datasets and improving ML techniques are enabling more systematic and effective polymer design compared to traditional trial-and-error approaches.
Applied machine learning as a driver for polymeric biomaterials design
Researchers reviewed how machine learning could accelerate the design of new medical-grade polymers by predicting properties like biodegradability and biocompatibility, bypassing slow trial-and-error lab work. The main obstacle identified is the lack of standardized, publicly available data on medically relevant polymer characteristics needed to train reliable AI models.
Bio-based plastics in a circular economy: A review of recovery pathways and implications for product design
Researchers reviewed how bio-based plastics — made from renewable plant sources — can be recovered and recycled at end-of-life, finding that the feasibility of eight different recovery methods depends heavily not just on plastic chemistry but on how products are designed, and offering guidance for designers to improve recyclability.
Design of new biopolymers for biomedicine and food-packaging
Researchers review new biopolymer designs intended for biomedical and food packaging applications, aiming to replace fossil-fuel-based plastics with biodegradable alternatives from renewable sources. Widespread adoption of such materials could significantly reduce long-term microplastic pollution.
Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning
Researchers developed machine learning models using molecular descriptors to predict the adsorption capacity of microplastics for organic pollutants in aqueous environments, achieving high accuracy across multiple polymer types and enabling faster environmental risk assessment.
Machine Learning-Driven Multi-Objective Optimization of Enzyme Combinations for Plastic Degradation: An Ensemble Framework Integrating Sequence Features and Network Topology
Researchers developed a machine learning framework to identify optimal enzyme combinations for breaking down polyester plastics. The study integrated kinetic data, protein sequence features, and network analysis to predict effective enzyme-substrate relationships, offering a computational approach to accelerating the discovery of enzymatic solutions for plastic waste degradation.
A machine learning approach to designing tough and degradable polyamides based on multiblock structures
Researchers applied Gaussian process regression and multi-objective machine learning optimization to design multiblock polyamides composed of Nylon6 and alpha-amino acid segments that simultaneously achieve high toughness and biodegradability. The approach successfully identified amino acid sequences yielding polymers with both desirable mechanical properties and degradability, demonstrating a data-driven pathway for overcoming the trade-off between performance and environmental sustainability in biodegradable plastics.
Advances in the Modeling of Synthesis, Design and Properties of Polymers
This computational chemistry dissertation used atomistic simulations to study the synthesis and properties of emerging polymer materials. Computational approaches to polymer design could accelerate development of biodegradable plastics that break down quickly rather than persisting as microplastics.
Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries
Researchers reviewed how dynamic covalent chemistry can be applied to polymeric materials to enable closed-loop recyclability, addressing the waste accumulation caused by current plastics. The study examines how reversible chemical bonds can be tailored for specific reprocessing conditions and evaluates the potential economic and environmental impacts of these recyclable polymer systems.
In silico COSMO-RS predictive screening of ionic liquids for the dissolution of plastic
Researchers screened 9,405 ionic liquids for plastic dissolution capability using computational modeling (COSMO-RS), then validated the most promising candidates experimentally, identifying potential green solvents for plastic waste recycling.
AI-driven rational design of promiscuous and selective plastic-binding peptides
Researchers used AI-driven computational design to develop both promiscuous and selective plastic-binding peptides capable of either broadly capturing heterogeneous microplastic mixtures or specifically targeting individual polymer types, providing new tools for microplastic quantitation, capture, and degradation applications.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach
A deep learning analysis of gastrointestinal bioaccessibility data for 18 microplastic types found that polymer structural rigidity and surface area were the key physicochemical properties controlling desorption of pyrene and 4-nonylphenol under digestive conditions, covering a bioaccessibility range of 16–83% across polymer types.
Polymer‐Based Recycling Strategies for Plastic Waste: A Comprehensive Review
This comprehensive review evaluates mechanical and chemical recycling strategies for plastic waste, noting that mechanical recycling is widely used but limited by polymer degradation, while chemical recycling offers higher quality recovery but at greater energy and financial cost. The study highlights emerging technologies including AI-assisted sorting, nanotechnology, and biodegradable polymer development as promising approaches for building a more circular plastics economy.
Design framework for circular and sustainable packaging design
Researchers developed a novel packaging design framework integrating circularity and sustainability (C&S) criteria using literature review, expert brainstorming, and field visits. The framework addresses conflicts between sustainability and functional requirements and provides practical iterative strategies for packaging designers.
Ranking environmental degradation trends of plastic marine debris based on physical properties and molecular structure
Researchers used a data-driven approach to rank how quickly different types of plastic marine debris degrade in the ocean, identifying glass transition temperature and water repellency as the key molecular features that determine whether a plastic breaks down fast, slowly, or barely at all. The findings provide a predictive framework for understanding which ocean plastics pose the longest-lasting pollution threat.
Deep transfer learning benchmark for plastic waste classification
Researchers benchmarked six deep transfer learning models for classifying plastic waste types, achieving high accuracy in automated sorting that could help address plastic pollution by improving recycling efficiency.
Deep learning for chemometric analysis of plastic spectral data from infrared and Raman databases
A novel deep learning architecture called PolymerSpectraDecisionNet was trained to identify common recyclable plastics from infrared and Raman spectral databases. The model outperformed conventional chemometric methods for polymer classification and was designed to handle real-world spectral variability relevant to the plastics recycling industry.