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Record W4406918716 · doi:10.1021/accountsmr.4c00334

Why and How to Investigate Biological Materials Processing: A Cross-Disciplinary Approach for Inspiring Sustainable Materials Fabrication

2025· article· en· W4406918716 on OpenAlexafffund
Matthew J. Harrington

Bibliographic record

VenueAccounts of Materials Research · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsCross disciplinaryFabricationNanotechnologyDisciplineEngineeringMaterials scienceBiochemical engineeringComputer scienceEngineering physicsData scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Conspectus Enhancing the performance and sustainability of materials is a major challenge facing humanity. With nearly 400 million tons of plastics manufactured per year and plastic waste accumulation of 12 billion tons expected by 2050, the production and buildup of anthropogenic petroleum-based waste is a major threat to our global ecosystem. This impending environmental catastrophe demands alternative sustainable and circular routes for material production. Additionally, there is a need for new polymeric materials that possess properties not currently found in synthetic materials for various applications in biomedical engineering, soft robotics, flexible electronics, and more. Nature offers inspiration for solving both of these environmentally, economically, and socially impactful global issues. Indeed, living organisms, such as spiders and mussels, rapidly fabricate polymeric biological materials from biomolecular building blocks (e.g., proteins) under green, environmentally benign processing conditions. These materials exhibit properties that surpass many synthetic plastics (e.g., high toughness, self-healing, “smart” adaptability, underwater adhesion), providing a blueprint for how humans can develop sustainable fabrication practices for producing next-generation materials. There is now a solid understanding of the structure–function relationships defining the performance of many biological materials, with control of structural hierarchy from nanoscale to centimeter scale emerging as a common design feature. Yet, it has been extremely challenging to replicate this hierarchical structure and, thus, the relevant properties in synthetic materials. This is largely due to a poor understanding of how these materials are fabricated by living organisms. Indeed, elucidation of the physicochemical principles underlying the fabrication of these and similar materials is significantly hampered due to experimental challenges in following these dynamic processes at the relevant spatiotemporal scales. Here, I outline a cross-disciplinary experimental approach spanning organismal biology, molecular biology, biochemistry, physical chemistry, and materials science for extracting design principles from biofabrication processes. As a model system, I focus on the fabrication of the mussel byssus–a biopolymeric fibrous holdfast with outstanding properties (underwater adhesion, high toughness, self-healing capacity) that is an established archetype for sustainable bioinspired fibers, glues, composites, and coatings. Careful analysis combining traditional histology and biochemical approaches with advanced spectroscopic imaging (e.g, confocal Raman spectroscopy, FTIR spectroscopy, and micro X-ray fluorescence), tomographic approaches (e.g., micro-CT), and advanced electron microscopy (e.g., focused ion beam scanning electron microscopy (FIB-SEM)) have yielded deep insights into the byssus assembly process, highlighting the key role of fluid protein condensates (liquid crystals and coacervates), microfluidic-like mixing, and protein–metal coordination bonds, as well as various physicochemical triggers (e.g., pH, redox, mechanical shear) that promote the self-organization and cross-linking of stimuli-responsive protein building blocks. Extracted concepts are already being applied for enhancing the sustainable fabrication of bioinspired materials for technical and biomedical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0040.008
Open science0.0020.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0120.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.122
GPT teacher head0.427
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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