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Record W4408085817 · doi:10.1021/acs.biomac.4c01564

Upcycling of Livestock Industry Blood Waste into Bioplastics: Plasma Protein-Based Amyloid Fibrils

2025· article· en· W4408085817 on OpenAlexaff
Shuang Ding, Xing Chen, Yixiang Wang, Nana Zhang, Tao Yuan, Huayu Yang, Bowen Yan, Daming Fan

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsMcGill University
FundersChina Postdoctoral Science Foundation
KeywordsBioplasticAmyloid fibrilAmyloid (mycology)ChemistryLivestockFibrilBlood plasmaBiochemistryBiophysicsAmyloid βWaste managementBiologyInternal medicineMedicineEcologyEngineering

Abstract

fetched live from OpenAlex

This study presents a novel approach to harnessing the underutilized resource of livestock blood plasma proteins to produce bioplastic films based on amyloid fibrils. Upon acidic heating, a 20-h incubation period resulted in mature, semiflexible fibrils with an average length of 0.65 μm and a persistence length of 261 nm. Characterization using Thioflavin T intensity, circular dichroism, and FTIR spectroscopy revealed a cross-β-sheet structure stabilized by hydrogen bonding. The integration of plasma protein amyloid fibrils with poly(vinyl alcohol) (PVA) or methyl cellulose (MC) yielded bioplastic films that exhibit smooth and homogeneous micromorphology, enhanced toughness, and water stability, with PVA-based films demonstrating an exceptional elongation of ∼300%, suitable for food packaging applications. Compared to petroleum-based plastics, plasma amyloid fibril-incorporated films demonstrated a superior sustainability footprint (∼92%). This work underscores the potential of plasma protein amyloid fibrils in bioplastic applications, aligning with the global imperative for eco-friendly waste management and a circular economy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.233
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations7
Published2025
Admission routes1
Has abstractyes

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