MétaCan
Menu
Back to cohort
Record W2595618223 · doi:10.1039/c6nr09795b

Direct visualization of carbon nanotube degradation in primary cells by photothermal imaging

2017· article· en· W2595618223 on OpenAlexfundno aff
Julie Russier, Laura Oudjedi, Martin Piponnier, Cyrill Bussy, Kostas Kostarelos, Brahim Lounis, Alberto Bianco, Laurent Cognet

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilCentre National de la Recherche ScientifiqueOttawa Hospital Research InstituteEuropean CommissionAgence Nationale de la RechercheCentre International de Recherche aux Frontières de la Chimie
KeywordsCarbon nanotubePhotothermal therapyNanotechnologyMaterials scienceDegradation (telecommunications)FluorescenceNanostructureComputer scienceOptics

Abstract

fetched live from OpenAlex

Assessment of biodegradability of carbon nanotubes (CNTs) is a critically important aspect that needs to be solved before their translation into new biomedical tools. CNT biodegradation has been shown both in vitro and in vivo, but we are limited by the number of analytical techniques that can be used to follow the entire process. Photothermal imaging (PhI) is an innovative technique that enables the quantitative detection of nanometer-sized absorptive objects. In this study, we demonstrate that PhI allows the observation of the degradation process of functionalized multi-walled carbon nanotubes (MWCNTs) following their internalization by primary glial cells. The absence of interference from the biological matrix components, together with the possibility to combine PhI with other detection techniques (e.g. fluorescence, light or electron microscopy) validate the potential of this method to follow the fate and behavior of carbon nanostructures in a biological environment.

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 score0.006
Threshold uncertainty score0.313

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)0.0000.000

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueNanoscaleSame topicNanoparticles: synthesis and applicationsFrench-language works237,207