MétaCan
Menu
Back to cohort
Record W4404627806 · doi:10.1109/jsen.2024.3496295

Multilayered Single-Walled Carbon Nanotube-Based Flexible Temperature Sensor

2024· article· en· W4404627806 on OpenAlexafffund
Zifan Li, Fangyan Sun, Lina Rose, Gnanesh Nagesh, Nalin Kumar Shekar, Prithvi Raj Pedapati, Asif Ansar, C. Ramesh Kumar, Daniella Skaf, Simon Rondeau‐Gagné, Mohammed Jalal Ahamed

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeMaterials scienceTemperature measurementComposite materialOptoelectronicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

This article presents the development of a multilayered single-walled carbon nanotube (SWNT)-based temperature sensor fabricated using a spray coating process on a flexible polydimethylsiloxane (PDMS) substrate. The carbon nanotube (CNT)-based sensor showed negative temperature coefficient (NTC) behavior, with a decrease in resistance change as the temperature increased from 293 to 383 K. It exhibited a high temperature coefficient of resistance (TCR) at −1.99/K (295–323 K) and a response time of 2.8 s. The sensor’s flexibility was tested by varying the bending radius from 21.8 to 10.9 mm and the sensor displayed consistent performance under mechanical deformation. The uniformity, roughness, and surface morphology of the coated CNTs were measured. The sensors’ accuracy and repeatability were assessed through controlled heating and cooling cycles. The stability and reliability of the sensor are improved by optimizing the coating parameters, fabrication method, and device design. The sensor showed consistency and long-term stability demonstrating its reliability for practical applications in conformal settings. The flexible CNT sensors presented in this article offer the potential to be applied for temperature sensing in conformal and flexible fitting applications, for example, temperature detection in electric vehicle (EV) battery cells.

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 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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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 teacher head, not a consensus.

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

Citations6
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicCarbon Nanotubes in CompositesFrench-language works237,207