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Fully Printed pH Sensor Based in Carbon Black/Polyaniline Nanocomposite

2021· article· en· W4200281261 on OpenAlexaff
Shirin Mahinnezhad, Homa Emami, Mohsen Ketabi, Ahmad Al Shboul, Najet Belkhamssa, 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
KeywordsPolyanilineNanocompositeCarbon blackMaterials scienceScreen printingElectrodePotentiometric titrationChemical engineeringCarbon paste electrodePotentiometric sensorElectrochemistryWorking electrodeElectrochemical gas sensorPolymerizationNanotechnologyPolymerCyclic voltammetryChemistryComposite material

Abstract

fetched live from OpenAlex

In this work, a fully screen-printed and flexible potentiometric pH sensor was designed and fabricated by incorporating a carbon black (CB) paste/polyaniline emeraldine salt (PANI-ES) nanocomposite as the working electrode and Ag/AgCl as the quasi-reference electrode. Rather than the PANI electrochemical polymerization deposition method, the PANI-ES was blended with a commercial CB paste for screen-printing, enabling a fully printed and scalable process. As a result, a nanocomposite mixture of 99.1% CB and 0.9% PANI-ES emerged as a promising nanocomposite candidate to develop high-performance pH sensors. The sensor exhibited a near Nernstian sensitivity of 50 mV/pH, response time of 15 s at room temperature, high linearity in the pH range between 3 and 11 and reversible pH sensing performance. The sensing mechanism depends mainly on the degree of the oxidation states transition of PANI-ES at different pH levels. The proposed flexible pH sensor can be used to monitor a patient’s health and water quality.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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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