MétaCan
Menu
Back to cohort
Record W2037972111 · doi:10.1021/nl103015w

Probing Charge Transfer at Surfaces Using Graphene Transistors

2010· article· en· W2037972111 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNano Letters · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversité du Québec à MontréalPolytechnique MontréalMcGill UniversityUniversité de MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsGrapheneMaterials scienceNanotechnologyDopingSubstrate (aquarium)Field-effect transistorElectrochemistryTransistorGraphene nanoribbonsElectrodeChemical physicsOptoelectronicsChemistryVoltagePhysics

Abstract

fetched live from OpenAlex

Graphene field effect transistors (FETs) are extremely sensitive to gas exposure. Charge transfer doping of graphene FETs by atmospheric gas is ubiquitous but not yet understood. We have used graphene FETs to probe minute changes in electrochemical potential during high-purity gas exposure experiments. Our study shows quantitatively that electrochemistry involving adsorbed water, graphene, and the substrate is responsible for doping. We not only identify the water/oxygen redox couple as the underlying mechanism but also capture the kinetics of this reaction. The graphene FET is highlighted here as an extremely sensitive potentiometer for probing electrochemical reactions at interfaces, arising from the unique density of states of graphene. This work establishes a fundamental basis on which new electrochemical nanoprobes and gas sensors can be developed with graphene.

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.

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)0.000
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 score0.004
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

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 head0.021
GPT teacher head0.256
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