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Record W3159348283 · doi:10.1002/elsa.202100039

Wearable microneedle dual electrochemical sensor for simultaneous pH and cortisol detection in sweat

2021· article· en· W3159348283 on OpenAlexaff
Samuel M. Mugo, Weihao Lu, Marika Wood, Stéphane Lemieux

Bibliographic record

VenueElectrochemical Science Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMacEwan University
Fundersnot available
KeywordsDetection limitMaterials sciencePolydimethylsiloxaneAnalyteElectrochemical gas sensorReproducibilityLinear rangeChromatographyPolyanilineWearable computerBiomedical engineeringNanotechnologyChemistryElectrochemistryComputer scienceElectrodeEmbedded systemComposite materialMedicinePolymer

Abstract

fetched live from OpenAlex

Abstract We report herewith an inexpensive flexible dual target electrochemical sensor for simultaneous detection of pH and cortisol in human sweat. The sensor was fabricated by printing layer by layer (LbL) on a conductive microneedle polydimethylsiloxane (PDMS) flexible substrate. The dual sensor integrates two detection chambers comprising polyaniline (PANi) and cortisol imprinted poly (glycidylmethacrylate‐co ethylene glycol dimethacrylate) (poly (GMA‐co‐EGDMA)). The dual wearable sensor rapidly (< 1 min) responded linearly to pH in the range of 3–9, while the cortisol sensor chamber had a linear range of 0–100 ng/mL. The cortisol sensing region had an excellent limit of detection (LOD) of 1.4 ± 0.3 ng/mL, with intra‐batch reproducibility of 2.4% relative standard deviation (%RSD). The inter‐batch precision (%RSD for three different sensors) was determined to be 4.7%. Demonstrating excellent stability and reusability, a single patch of cortisol sensor was used for 15 times over a 30‐day period, with minimal change in response. The dual analyte wearable sensors were effective for detection of pH and cortisol in real human sweat.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.228
Teacher spread0.222 · 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.

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

Citations39
Published2021
Admission routes1
Has abstractyes

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