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Record W4308719908 · doi:10.3390/s22218492

Molecularly Imprinted Polymer-Modified Microneedle Sensor for the Detection of Imidacloprid Pesticides in Food Samples

2022· article· en· W4308719908 on OpenAlexafffund
Samuel M. Mugo, Weihao Lu, Scott Robertson

Bibliographic record

VenueSensors · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsMacEwan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImidaclopridMolecularly imprinted polymerPesticideChromatographyEnvironmental chemistryNanotechnologyMaterials scienceChemistryOrganic chemistrySelectivityBiology

Abstract

fetched live from OpenAlex

A portable, molecularly imprinted polymer (MIP)-based microneedle (MN) sensor for the electrochemical detection of imidacloprid (IDP) has been demonstrated. The MN sensor was fabricated via layer-by-layer (LbL) in-tube coating using a carbon nanotube (CNT)/cellulose nanocrystal (CNC) composite, and an IDP-imprinted polyaniline layer co-polymerized with imidazole-functionalized CNCs (PANI-co-CNC-Im) as the biomimetic receptor film. The sensor, termed MIP@CNT/CNC MN, was analyzed using both cyclic voltammetry (CV) and differential pulse voltammetry (DPV) and showed excellent electrochemical performance for the detection of IDP. The CV detection range for IDP was 2.0-99 µM, with limits of detection (LOD) of 0.35 µM, while the DPV detection range was 0.20-92 µM with an LOD of 0.06 µM. Additionally, the MIP@CNT/CNC MN sensor showed excellent reusability and could be used up to nine times with a 1.4 % relative standard deviation (% RSD) between uses. Lastly, the MIP@CNT/CNC MN sensor successfully demonstrated the quantification of IDP in a honey sample.

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)0.000
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 score0.095
Threshold uncertainty score0.376

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 head0.038
GPT teacher head0.250
Teacher spread0.213 · 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

Citations28
Published2022
Admission routes2
Has abstractyes

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