MétaCan
Menu
Back to cohort
Record W4285397696 · doi:10.1149/ma2022-019726mtgabs

(Invited) Kinetics and Thermodynamics of Swcnts and Bnnts Encapsulation with α-Sexithiophene in Liquid Phase

2022· article· en· W4285397696 on OpenAlexaff
Charlotte Allard, P. Desjardins, Étienne Gaufrès, Richard Martel

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsKineticsMaterials scienceLuminescenceCarbon nanotubeRaman spectroscopyPhotobleachingNanotechnologyEncapsulation (networking)NanotubeRaman scatteringIntermolecular forceChemical physicsFluorescenceChemistryOptoelectronicsMoleculeOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Single-Walled Carbon Nanotubes (SWNTs) and Boron Nitride Nanotubes (BNNTs) come with hollow 1D centers, which can serve to define narrow spaces for encapsulating small organic dyes. The encapsulation process is of scientific interest because it can be used to tailor the optical properties of the resulting dyes@NTs nanohybrids. Past works have shown that the dyes@SWCNT exhibits a strongly enhanced Raman scattering cross section, while the dyes@BNNTs do emit robust, generally red-shifted, luminescence at wavelengths down to the near-IR. In both cases, the nanotube protects the encapsulated dyes from photobleaching, provides high confinement, and reinforces intermolecular interactions between dyes into specific aggregation states. Here, we compare the liquid phase encapsulation process of α-sexithiophene (6T), which is a conjugated rod-like dye, inside SWCNTs and BNNTs. Raman and luminescence imaging experiments are used to monitor the 6T encapsulation process of a large ensemble of individual nanotubes in liquid-phase. This method probes statistically the encapsulation kinetics using hundreds of individual nanotubes according to various parameters (dye concentration and temperature). The results highlight a kinetic model in which single and double aggregates is formed sequentially. This kinetics and the associated thermodynamic parameters for the 6T encapsulation will be presented and discussed so as to gain a better control over the emission properties of the nanohybrids.

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)0.001
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 score0.019
Threshold uncertainty score0.498

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 head0.245
Teacher spread0.234 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicCarbon Nanotubes in CompositesFrench-language works237,207