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Record W3035155389 · doi:10.1063/5.0009984

Ion-gated transistors based on porous and compact TiO2 films: Effect of Li ions in the gating medium

2020· article· en· W3035155389 on OpenAlexafffund
Arunprabaharan Subramanian, Ben George, Sanyasi Rao Bobbara, Irina Valitova, Irene Ruggeri, Francesca Borghi, Alessandro Podestà, Paolo Milani, Francesca Soavi, Clara Santato, Fabio Cicoira

Bibliographic record

VenueAIP Advances · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Économie, de la Science et de l'Innovation - QuébecFonds de recherche du Québec – Nature et technologiesUniversità di BolognaCMC Microsystems
KeywordsMaterials scienceGatingTransistorIonOptoelectronicsNanotechnologyBioelectronicsVoltageChemistryElectrical engineeringOrganic chemistryBiosensor

Abstract

fetched live from OpenAlex

Ion-gated transistors (IGTs) are attractive for chemo- and bio-sensing, wearable electronics, and bioelectronics, because of their ability to act as ion/electron converters and their low operating voltages (e.g., below 1 V). Metal oxides are of special interest as transistor channel materials in IGTs due to their high mobility, chemical stability, and the ease of processing in air at relatively low temperatures (<350 °C). Titanium dioxide is an abundant material that can be used as a channel material in n-type IGTs. In this work, we investigate the role of the morphology of the TiO2 channel (porous vs compact films) and the size of the cations in the gating media ([EMIM][TFSI] and [Li][TFSI] dissolved in [EMIM][TFSI]) to study their role on the electrical characteristics of IGTs. We found that both the film morphology and the type of gating medium highly affect the electrical response of the devices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2020
Admission routes2
Has abstractyes

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