Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes

Champa-Bujaico, Elizabeth; Díez-Pascual, Ana M.; Garcia-Diaz, Pilar; Sessini, Valentina; Mosquera, Marta E.G.

doi:10.1016/j.indcrop.2024.119018

Industrial Crops and Products

2024

DOI: 10.1016/j.indcrop.2024.119018

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Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes

Elizabeth Champa-Bujaico,

Ana M. Díez-Pascual,

Pilar Garcia-Diaz

et al.

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Cited by 3 publications

References 78 publications

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Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers

Lalonde,

Pilania,

Marrone

2025

Polym. Chem.

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Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers

Lalonde,

Pilania,

Marrone

2025

Polym. Chem.

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Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

Chen,

Tang

2024

Nanomaterials

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The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.

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Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Malashin,

Martysyuk,

Tynchenko

et al. 2024

Polymers

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The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes.

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Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes

Cited by 3 publications

References 78 publications

Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers

Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers

Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

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