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Flexible Chemiresistive pH Sensor Based on Polyaniline / Carbon Nanotube Nanocomposite for IoT Applications

2021· article· en· W4200263296 on OpenAlexaff
Homa Emami, Shirin Mahinnezhad, Ahmad Al Shboul, Mohsen Ketabi, Andy Shih, Ricardo Izquierdo

Bibliographic record

Venue2021 IEEE Sensors · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsNanocompositePolyanilineCarbon nanotubeMaterials sciencePolyethylene terephthalateElectrodeAnnealing (glass)NanotechnologyChemiresistorChemical engineeringPolymerComposite materialChemistryPolymerization

Abstract

fetched live from OpenAlex

This study presents a screen-printed and flexible chemiresistive pH sensor based on a nanocomposite of polyaniline emeraldine salt (PANI(ES)) and single-walled carbon nanotubes (SWCNTs). An optimized solution of SWCNTs/PANI(ES) (60/40 wt%) solution was drop-casted on top of flexible silver electrodes screen-printed on polyethylene terephthalate substrate. The sensor was annealed at an optimized temperature of 90 °C for 1 hour with a subsequent PANI(ES) drop_cast and annealing step. The developed chemiresistive pH sensor achieved high signal stability, sensitivity of 2.72 Ω/pH, linearity in the pH range of 2 – 10, and response times of 70 seconds. The pH’s sensitivity of the SWCNTs/PANI nanocomposite depends on the protonation/deprotonation process. The proposed sensor is an excellent candidate for smart medical bandage and wound monitoring applications.

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 science0.0000.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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
Published2021
Admission routes1
Has abstractyes

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Same venue2021 IEEE SensorsSame topicAnalytical Chemistry and SensorsFrench-language works237,207