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Record W2772206214 · doi:10.1002/9783527698646.ch4

Carbon‐Based Nanobiomaterials

2017· other· en· W2772206214 on OpenAlexaff
Samad Ahadian, Farhad Batmanghelich, Raquel Obregón, Deepti Rana, Javier Ramón‐Azcón, Ramin Banan Sadeghian, Murugan Ramalingam

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCarbon nanotubeNanotechnologyGrapheneMaterials scienceBiosensorBiomoleculeTissue engineeringRegenerative medicineMicrofabricationBiocompatible materialBiomedical engineeringChemistryCellMedicine

Abstract

fetched live from OpenAlex

Nanobiomaterials and microfabrication technologies have recently found many applications in biology and medicine resembling micro- and nanofeatures of extracellular matrix (ECM), cell–ECM interaction, and biological processes. In particular, carbon-based nanomaterials (carbon nanotubes (CNTs) and graphene) have received much attention and applications as functional biomaterials in tissue regeneration, delivery of biomolecules, biosensing, and bioimaging because of their significant properties such as high mechanical properties, high electrical conductivity, visibility in near-infrared frequencies, and high surface area. Here, we summarize some applications of CNTs and graphene as scaffolds and cell culture substrates in tissue engineering and stem cell differentiation. Other biomedical applications of CNTs and graphene as carriers in the delivery of biomolecules, as biosensors, and as bioimaging agents are also described. Future research direction may be applications of CNTs and graphene as biocompatible, multifaceted, and functional biomaterials in preclinical and clinical studies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.997

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)0.0040.000

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 head0.009
GPT teacher head0.211
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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