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Record W4411066502 · doi:10.1007/s44411-025-00216-3

An Innovative Direction of Graphene and Its Derivatives in Clinical Applications for Oral Health and Dentistry: A Narrative Review

2025· review· en· W4411066502 on OpenAlexaff
Fateme Eskandari, Seyedeh Sara Aghili, Hussein Rahimi, Rozhina Hamidi, Alireza Razavian, Dorara Dortaj, Seyed Ali Mosaddad, Seyed Ali Mosaddad, Pirihiym Fitehizydih, Saeide Rahimi, Reza Sayyad Soufdoost, Ahmed Hussain, Hamid Tebyanian

Bibliographic record

VenueBratislavské lekárske listy/Bratislava medical journal · 2025
Typereview
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGrapheneNarrativeNarrative reviewDentistryOral healthMedicineEngineering ethicsMedical physicsOrthodonticsNanotechnologyMaterials scienceEngineeringArtIntensive care medicineLiterature

Abstract

fetched live from OpenAlex

Graphene is a single layer of carbon atoms arranged in a honeycomb pattern. In numerous research fields, graphene has attracted significant attention since its discovery. In terms of Young's modulus, graphene has one of the highest values on record. Graphene-based nanomaterials demonstrate remarkable antimicrobial, physicochemical, thermal, electrical, mechanical, optical, and biological properties as potential agents. Nanomaterials based on graphene have been investigated recently in dentistry in restorative dentistry, prosthodontics, endodontics, and cancer detection and treatment. This article is a review of the application of graphene nanoparticles in dentistry. The article reviews many experiments that highlight the potential application of graphene-based materials in dentistry and discusses their properties and promise for enhancing dental materials.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.050
GPT teacher head0.422
Teacher spread0.372 · 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.

Study designOther design
Domainnot available
GenreReview

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

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