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Record W4389222210 · doi:10.3390/jox13040047

Carbon Nanotubes and Graphene Materials as Xenobiotics in Living Systems: Is There a Consensus on Their Safety?

2023· article· en· W4389222210 on OpenAlex
David Gendron, Grzegorz Bubak

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Xenobiotics · 2023
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsCegep de Thetford
Fundersnot available
KeywordsNanotechnologyCarbon nanotubeBiomedicineGrapheneContext (archaeology)Impact of nanotechnologyBiocompatibilityHuman healthRisk analysis (engineering)Materials scienceSocietal impact of nanotechnologyBusinessMedicineBioinformaticsEnvironmental health

Abstract

fetched live from OpenAlex

Carbon nanotubes and graphene are two types of nanomaterials that have unique properties and potential applications in various fields, including biomedicine, energy storage, and gas sensing. However, there is still a debate about the safety of these materials, and there is yet to be a complete consensus on their potential risks to human health and the environment. While some studies have provided recommendations for occupational exposure limits, more research is needed to fully understand the potential risks of these materials to human health and the environment. In this review, we will try to summarize the advantages and disadvantages of using carbon nanotubes and graphene as well as composites containing them in the context of their biocompatibility and toxicity to living systems. In addition, we overview current policy guidelines and technical regulations regarding the safety of carbon-based nanomaterials.

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.

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)0.001
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 score0.106
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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