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Record W3021407457 · doi:10.1039/d0cc02310h

Reduced graphene oxide–silicon interface involving direct Si–O bonding as a conductive and mechanical stable ohmic contact

2020· article· en· W3021407457 on OpenAlexaff
Soraya Rahpeima, Essam M. Dief, Chandramalika R. Peiris, Stuart Ferrie, Xiaofei Duan, Simone Ciampi, Colin L. Raston, Nadim Darwish

Bibliographic record

VenueChemical Communications · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsBentley (Canada)
FundersAustralian Research Council
KeywordsOhmic contactGrapheneMaterials scienceOxideSiliconElectrical conductorInterface (matter)Silicon oxideNanotechnologyComposite materialOptoelectronicsMetallurgyContact angleLayer (electronics)Silicon nitride

Abstract

fetched live from OpenAlex

Metal-semiconductor junctions are essential contacts for semiconductor devices, but high contact junction resistance is a limiting operational factor. Here, we establish an ohmic contact of low resistance of <4 × 10-6 Ω cm2 between platinum and n-type Si (111)-H surfaces. This involved Si-O covalent bonding a monolayer of graphene oxide (GO) to the Si surface followed by electrochemical reduction to form reduced graphene oxide (rGO). Current-voltage plots demonstrate that the GO/rGO transformation is associated with a change from a rectifying to an ohmic contact. The process is a viable method for constructing semiconductor-rGO interfaces and demonstrates that GO/rGO monolayers can be used as active components in tuning the contact resistance of metal-semiconductor junctions.

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 communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.326
Teacher spread0.261 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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