ReviewPolymers2024
Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review.
Review in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- An Integrated Case Study on Fruit By-Products/Waste Management: Lactic Acid Bacteria Development, Artificial Intelligence Smart Implementation, and Reintegration into Food Chain.Foods (Basel, Switzerland) · 2026Article
- Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Control of immunogenic responses of microorganism-synthesized biopolymers for 3D bioprinting tissue engineering and regenerative medicine.Bioactive materials · 2026Review
- Silk-Fibroin-Based Strategies for Myocardial Infarction Repair: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Extraction, Characterization and Applications of Biopolymers from Sustainable Sources.Polymers · 2026Review
- Recent Advances in Silk Fibroin Derived fromJournal of functional biomaterials · 2025Review
- Isolation of high yield polyhydroxyalkanoate producing bacteria from contaminated soils and biopolymer characterization.Scientific reports · 2025Article
- Green Processes for Chitin and Chitosan Production from Insects: Current State, Challenges, and Opportunities.Polymers · 2025Review
- Therapeutic Prospects ofMarine drugs · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.