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Record W4411861852 · doi:10.1080/00202967.2025.2520667

Antibiofilm potential of graphene-dispersed alkoxysilane coatings: a materials science perspective

2025· article· en· W4411861852 on OpenAlexaff
Hideyuki Kanematsu, Song‐Zhu Kure‐Chu, Risa Kawai, Hidekazu Mimura, Takayoshi Nakano

Bibliographic record

VenueTransactions of the IMF · 2025
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsGrapheneMaterials scienceNanotechnologyPerspective (graphical)Computer science

Abstract

fetched live from OpenAlex

This study investigates the antibiofilm properties of alkoxysilane-based coatings incorporating dispersed multilayer graphene powder. Graphene, synthesised via a proprietary method, was incorporated into a resin matrix at various concentrations (0.1%, 0.5%, and 1.0% by weight) and applied to glass substrates. Raman spectroscopy confirmed the presence of defect-rich, multilayer graphene, which is known to enhance antimicrobial surface properties. Biofilm formation by Staphylococcus epidermidis was evaluated using ISO 4768-standardised crystal violet staining. Results indicated that biofilm development was effectively suppressed at concentrations up to 0.5%, whereas an unexpected increase was observed at 1.0%, possibly due to graphene aggregation and reduced surface exposure. The findings suggest that both graphene concentration and dispersion quality critically influence antibiofilm efficacy. The study highlights the dual role of graphene, both as a physical and chemical antibacterial agent, and its potential application in medical, industrial, and hygienic materials. Further investigation is warranted to optimise dispersion and explore microbial-material interactions in real-world conditions.

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 score0.017
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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