MétaCan
Menu
Back to cohort

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

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

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.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.013
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