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Record W2943139992 · doi:10.1088/1361-6463/ab1dbb

Tungsten oxide ion-gated phototransistors using ionic liquid and aqueous gating media

2019· article· en· W2943139992 on OpenAlexafffund
Gabriel Vinicius De Oliveira Silva, Arunprabaharan Subramanian, Xiang Meng, Shiming Zhang, Martin S. Barbosa, Bill Baloukas, Daniel Chartrand, Juan C Gonzáles, Marcelo O. Orlandi, Francesca Soavi, Fabio Cicoira, Clara Santato

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTungstenIonic liquidTungsten oxideIonAqueous solutionGatingIonic bondingAqueous mediumChemistryOxideMaterials scienceInorganic chemistryPhysical chemistryOrganic chemistryBiophysicsCatalysis

Abstract

fetched live from OpenAlex

Abstract Ion-gated transistors employ ionic gating media (e.g. ionic liquids, polymer electrolytes, aqueous saline solutions) to modulate the density of the charge carriers in the transistor channel. Not only they operate at low voltages (ca 0.5–1 V) but they can also feature printability, flexibility and easy integration with chemo- and bio-sensing platforms. Metal oxides are transistor channel materials interesting for their processability in air, at low temperature. Among metal oxides, tungsten oxide (band gap ca 2.5–2.7 eV) stands out for its electrochromic, gas sensing and photocatalytic properties. Here we demonstrate ion-gated tungsten oxide transistors and phototransistors working in different ion gating media, such as one hydrophobic ionic liquid and an aqueous electrolyte, fabricated both on rigid and flexible substrates. Ion-gated tungsten oxide phototransistors operating in aqueous media could be used as photocatalytic sensors in portable applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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 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

Citations15
Published2019
Admission routes2
Has abstractyes

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