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Record W4280544022 · doi:10.1039/d1ay02084f

A MIP-enabled stainless-steel hypodermic needle sensor for electrochemical detection of aflatoxin B1

2022· article· en· W4280544022 on OpenAlexafffund
Marika Wood, Samuel M. Mugo

Bibliographic record

VenueAnalytical Methods · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsMacEwan University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsAflatoxinHypodermic needleElectrochemistryMaterials scienceNanotechnologyChemistryElectrodeFood scienceMedicine

Abstract

fetched live from OpenAlex

, as established by the Codex Alimentarius Commission. The widespread occurrence of AFB1 in the food chain identifies them as significant agricultural contaminants of global concern. We herewith demonstrate a molecularly imprinted polymer (MIP)-enabled stainless steel hypodermic needle sensor for sensitive electrochemical detection of AFB1. The stainless-steel hypodermic needle sensor was fabricated using a layer by layer (LbL) film coating comprising multiwalled carbon nanotubes (MWCNTs), cellulose nanocrystals (CNC), and an AFB1 imprinted polyaniline (PANI) biomimetic receptor film. The PANI@MIP/CNC-CNT hypodermic needlesensor showed excellent electrochemical capacitance response (∼10 min) to AFB1 with a linear range of 0-25 nM and a limit of detection (LOD) of 3 nM. Demonstrating good reusability, a single PANI@MIP/CNC-CNT hypodermic needle AFB1 sensor could be reused up to 7 times with a 2.8% relative standard deviation (% RSD) in the sensor's capacitive response. The PANI@MIP/CNC-CNT hypodermic needle sensor was effective in the detection of AFB1 spiked in milk.

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 score0.002

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)0.0010.001

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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

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