MétaCan
Menu
Back to cohort
Record W2903629456 · doi:10.1002/aelm.201800381

Dual‐Gate Organic Field‐Effect Transistor for pH Sensors with Tunable Sensitivity

2018· article· en· W2903629456 on OpenAlexfundno aff
Raphael Pfattner, Amir M. Foudeh, Shucheng Chen, Weijun Niu, James R. Matthews, Mingqian He, Zhenan Bao

Bibliographic record

VenueAdvanced Electronic Materials · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
FundersAgència de Gestió d'Ajuts Universitaris i de RecercaNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsSensitivity (control systems)Field-effect transistorTransistorOrganic semiconductorSemiconductorCapacitive couplingField effectNanotechnologyElectrical engineeringElectronic engineeringVoltage

Abstract

fetched live from OpenAlex

Abstract Dual‐gate field‐effect transistors (FETs) based on organic semiconductor polymer and SiOx as the topmost active sensing layer permit monitoring of pH in physiologically relevant conditions in a fast and reversible fashion. Beyond that, due to the bottom gate‐induced field effect, such sensors exhibit tunable sensitivity and provide faster continuous measurements compared to conventional bulky glass bulb pH sensors. pH response of bare SiOx is evaluated independently by means of voltmeter measurements. When assembled in dual‐gate architecture, the pH response of FET devices scales in agreement with the theoretical model, which assumes capacitive coupling, exhibiting an amplification of up to 10. This opens up the possibility for reversible and reliable sensing based on organic semiconductors well beyond pH sensors.

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 score0.004

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 communication0.0000.000
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 head0.003
GPT teacher head0.218
Teacher spread0.215 · 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

Citations50
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Electronic MaterialsSame topicAnalytical Chemistry and SensorsFrench-language works237,207