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Record W2322648425 · doi:10.1021/jp212231j

Light-Controlled Resistance Modulation in a Photochromic Diarylethene–Carbon Nanotube Blend

2012· article· en· W2322648425 on OpenAlexaff
Calogero Sciascia, Rossella Castagna, Maria Dekermenjian, Richard Martel, Ajay Ram Srimath Kandada, Fabio Di Fonzo, Andrea Bianco, Chiara Bertarelli, Moreno Meneghetti, Guglielmo Lanzani

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDiarylethenePhotochromismMaterials scienceElectrical conductorCarbon nanotubeElectrical resistance and conductanceElectrodeOptoelectronicsMoleculeVisible spectrumExcitationElectrical resistivity and conductivityPhotochemistryNanotechnologyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Photochromic molecules are part of a large class of materials in which light stimulus not only induces a color variation but also affects other physicochemical properties. However, the change of bulk electrical properties (e.g., electrical conductivity) via light excitation remains difficult to control because the intrinsically switchable molecules may lose their functionality when wired with conductive electrodes. In contrast with previous work based on single molecules, here we demonstrate a facile and accessible “wet-chemical” method to produce light-induced electrical switching. The electrical conductivity of a photochromic blend composed of diarylethene polymer and single-walled carbon nanotubes (SWNTs) is reversibly tuned according with UV–vis excitation. The devices present good thermal stability and remarkable fatigue resistance under ambient conditions. Supported by electrical and spectroscopic evidence, we show that the intertube electrical coupling, mediated by the light-induced electrocyclization of the diarylethene unit, is the mechanism responsible for the modulation.

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 score0.002
Threshold uncertainty score0.488

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 head0.008
GPT teacher head0.231
Teacher spread0.224 · 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

Citations34
Published2012
Admission routes1
Has abstractyes

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