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Papers
61,005 resultsShowing papers similar to Polymer engineering at the nexus of additive manufacturing, artificial intelligence, and responsible innovation
ClearAn integrated chemical engineering approach to understanding microplastics
Researchers proposed an integrated chemical engineering approach combining artificial intelligence, theoretical methods, and experimental techniques to better understand microplastic properties and behavior. The study suggests that the broad scope of chemical engineering makes it well-suited for characterizing microplastics and addressing the complexity of their environmental and health effects.
Leveraging 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.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Artificial Intelligence – Source of Inspiration or a Problem?
Not relevant to microplastics — this paper reviews the history and challenges of defining artificial intelligence as a field of computer science.
Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design
This review explores how artificial intelligence can help design sustainable bio-inspired materials that could replace conventional plastics. By learning from nature's degradable materials, AI tools could help develop alternatives that do not persist in the environment as microplastics. While not a direct health study, this research addresses a root cause of microplastic pollution by working toward materials that break down safely in natural ecosystems.
Impact of Machine/Deep Learning on Additive Manufacturing: Publication Trends, Bibliometric Analysis, and Literature Review (2013-2022).
This bibliometric review analyzes a decade of publications on the intersection of machine and deep learning with additive manufacturing (3D printing). The study is focused on manufacturing technology trends and is unrelated to microplastic research.
Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review
This review covers how artificial intelligence and machine learning are being applied to nanomanufacturing for medicine, robotics, and electronics. While not about microplastics directly, the AI-powered nanoscale detection and characterization methods discussed could be applied to identifying and quantifying nanoplastics in the environment and human tissue. Advances in nano-scale imaging and analysis driven by AI may eventually help researchers better understand human exposure to nanoplastics.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
Sustainable Materials and Technologies for Biomedical Applications
This review covers sustainable biomaterials for medical implants, including 3D-printed devices made from biopolymers, ceramics, and composites. While not directly about microplastics, it is relevant because developing biodegradable alternatives to traditional plastics in medical devices could reduce the amount of plastic waste that eventually breaks down into microplastics. The research highlights how sustainable manufacturing could help address plastic pollution at its source.
Innovative and Hybrid Post Processes for Additively Manufactured Parts
This paper examines post-processing techniques for parts made by additive manufacturing (3D printing) using thermoplastic polymers, noting that plastic parts and their waste contribute to microplastic contamination of soil and groundwater. Recycling engineered plastics from additive manufacturing is proposed as a mitigation strategy.
Microplastics in the environment: The role of polymer science
This paper highlights why understanding polymer science is essential for addressing the microplastics problem. Researchers argue that microplastics behave differently from other microparticles because of their unique polymer-specific interactions with the environment and living organisms. The study calls for interdisciplinary collaboration between polymer scientists and environmental researchers to develop better identification methods, risk assessments, and remediation strategies.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
3D printer waste, a new source of nanoplastic pollutants
This study identified 3D resin printers as a previously overlooked source of nanoplastic pollution, showing that the alcohol-based cleaning process generates plastic nanodebris that contaminates wastewater.
Design and Fabrication of Material Separation Machine for Sustainable Development
This paper is not relevant to microplastics research — it describes the design and fabrication of a robotic material separation machine intended to sort recyclable waste more efficiently using AI-inspired engineering principles.
An Examination of Microplastics: Environmental Impact, Sustainability, and Recyclability Innovation
This paper examined the environmental impact of microplastics, sustainability implications of current plastic use, and recycling options to address the plastic pollution crisis. It called for a transition toward circular economy approaches that reduce primary plastic production and increase recycled content.
Developing Eco-Friendly 3D-Printing Composite Filament: Utilizing Palm Midrib to Reinforce High-Density Polyethylene Matrix in Design Applications
This paper is not about microplastics. It describes the development of 3D-printing filaments made from high-density polyethylene reinforced with palm midrib nanoparticles for use in furniture and interior design. While the study uses a plastic polymer, it focuses on materials engineering and sustainable filament production rather than microplastic contamination or health effects.
Enzyme-Assisted Circular Additive Manufacturing as an Enabling Technology for a Circular Bioeconomy—A Conceptual Review
Not relevant to microplastics — this conceptual review explores enzyme-assisted circular additive manufacturing (3D printing with biological and biodegradable materials) as a sustainable manufacturing concept, without addressing microplastic pollution.
Advancing microplastic pollution management in aquatic environments through artificial intelligence
This review examines how artificial intelligence and robotics are being applied to tackle microplastic pollution in aquatic environments, covering waste collection, particle identification, and degradation monitoring. Researchers highlight several successful AI-driven projects deployed by countries and organizations around the world. The study suggests that integrating AI with traditional environmental methods holds significant promise for improving both the speed and accuracy of microplastic management.
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.
Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation
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: Challenges and Innovative Solutions
This review examines the multifaceted challenges of plastic pollution and surveys innovative solutions spanning material design, waste management, and remediation technologies. The paper synthesises current research on microplastic sources, environmental persistence, and emerging approaches to reducing plastic releases into ecosystems.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.
Three-Dimensional Printing of Multifunctional Composites: Fabrication, Applications, and Biodegradability Assessment
This paper is not about microplastics; it is a materials science review of polymer composites used in 3D printing, examining additive types, biodegradation pathways, and the environmental safety of biodegradable biocomposites.