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Record W4407204336 · doi:10.1080/25740881.2025.2460206

Biocompatible Graphene Polymer Composites: New Frontiers in Biomedical and Environmental Applications

2025· article· en· W4407204336 on OpenAlexaff
Maziyar Sabet

Bibliographic record

VenuePolymer-Plastics Technology and Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsBiocompatible materialGrapheneMaterials scienceComposite materialPolymerNanotechnologyEngineeringBiomedical engineering

Abstract

fetched live from OpenAlex

Graphene-polymer composites (GPCs) have emerged as transformative materials due to their exceptional mechanical, thermal, and electrical properties, enabling advanced applications across diverse fields. This review bridges biomedicine and environmental sustainability, providing an interdisciplinary perspective that addresses critical challenges such as scalability, biocompatibility, and environmental impact. Key highlights include advancements in scalable fabrication techniques like 3D printing, electrospinning, and melt mixing, alongside functionalization strategies to optimize performance. Novel applications in tissue engineering, water purification, and air filtration are critically analyzed, offering actionable insights for researchers and industry professionals. This comprehensive synthesis positions GPCs as next-generation materials poised to revolutionize multiple sectors.

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 score0.052
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.003
GPT teacher head0.185
Teacher spread0.183 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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