MétaCan
Menu
Back to cohort
Record W2322547025 · doi:10.1109/nano.2014.6967998

Flexible temperature sensor with laser scribed graphene oxide

2014· article· en· W2322547025 on OpenAlexaff
Rouzbeh Kazemzadeh, Woo Soo Kim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGrapheneMaterials scienceOxideOptoelectronicsLaserTemperature measurementNanotechnologyEngineering physicsOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Fabrication of flexible temperature sensor array using reduced graphene oxide (rGO) by laser scribing is presented. This unique type of sensor employs graphene as sensing material in order to generate temperature distribution map of human skin, which is important for distinguishing some physiological changes. This device is flexible and can be attached easily on wrist or other body parts. CO2laser is used to reduce the graphene oxide into highly conductive rGO with less than 3×10-3S/m in resistivity. Laser-induced reduction is a very fast, facile, chemical-free, room temperature process that uses the photo-thermal reactivity of GO.

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 science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.245
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

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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicGraphene research and applicationsFrench-language works237,207