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Record W3097151607 · doi:10.1002/slct.202002865

Disposable Capacitive Biosensor for Dopamine Sensing

2020· article· en· W3097151607 on OpenAlexaff
Dhanjai Dhanjai, Nancy Yu, Samuel M. Mugo

Bibliographic record

VenueChemistrySelect · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsMacEwan UniversityConcordia University of Edmonton
Fundersnot available
KeywordsBiosensorPolyanilineCyclic voltammetryDetection limitMaterials scienceDopamineNanotechnologyScanning electron microscopePolythiopheneChemical engineeringChemistryNuclear chemistryChromatographyConductive polymerPolymerElectrochemistryElectrodeComposite materialPolymerization

Abstract

fetched live from OpenAlex

Abstract The present paper reports fabrication of a disposable tyrosinase (Tyr) biosensor for rapid detection of dopamine. Tyr immobilized polyaniline/carbon nanotubes/cellulose nanocrystals (Tyr@PANI/CNTs/CNC) conductive film was fabricated on polyvinyl acetate (PVA) transparency and characterized by scanning electron microscopy (SEM) and cyclic voltammetry (CV). Tyr catalyzed dopamine oxidation to o ‐dopaquinone was analysed by CV and capacitance was recorded. PANI/CNTs/CNC film acted as a suitable enzyme support which also showed its synergistic effect in accelerating the biocatalytic oxidation reaction. Tyr biosensor exhibited excellent reproducibility, and specificity towards dopamine with correlation coefficient ( R 2 ) of 0.9508 and limit of detection (LOD) of 1.57 nM within linear concentration range of 7–1000 mM. The study suggested practical utilization of disposable biosensor towards dopamine detection in biological fluids.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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