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Record W2173739621 · doi:10.1049/iet-cds.2015.0259

Graphene electronic sensors – review of recent developments and future challenges

2015· article· en· W2173739621 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Circuits Devices & Systems · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcMaster University
FundersMinisterio de Economía y CompetitividadCanada Research Chairs
KeywordsGrapheneNanotechnologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

Electronic sensors based on graphene have a high potential in many applications, due to the unique properties of the graphene material. This study is a review where the authors discuss the properties of graphene which are useful to sensing applications and they report and describe different types of graphene electronic sensors: biological, mechanical, gas and chemical sensors. They also discuss the ways to functionalise graphene and the used device structures. They compare the performance of the main types of biological, mechanical and chemical sensors. Finally, they explain the future challenges of graphene‐based sensors, in order to make graphene sensing systems and smart sensors, which would be their main breakthrough application.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.055
GPT teacher head0.299
Teacher spread0.244 · 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