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Record W2171880625 · doi:10.1002/jrs.4722

SERS‐based assays for sensitive detection of modafinil

2015· article· en· W2171880625 on OpenAlexfundno aff
Mehmet Gökhan Çağlayan, Hilal Torul, Feyyaz Onur, Uğur Tamer

Bibliographic record

VenueJournal of Raman Spectroscopy · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuWorld Anti-Doping Agency
KeywordsDetection limitAnalyteAptamerModafinilChromatographyChemistryMolecularly imprinted polymerSurface-enhanced Raman spectroscopyExtraction (chemistry)Colloidal goldRaman spectroscopySilver nanoparticleSolid phase extractionNanoparticleNanotechnologyMaterials scienceRaman scatteringPharmacologyPhysicsMolecular biologyBiochemistrySelectivityBiology

Abstract

fetched live from OpenAlex

In this paper, we proposed two sensitive surface‐enhanced Raman spectroscopy assays for the determination of modafinil in urine matrix. In the first assay, modafinil was extracted by solid phase extraction and determined after sandwiching with silver nanoparticles. In the second assay, modafinil was extracted by non‐specific interaction with magnetic gold nanoparticles and determined by sandwiching with magnetic gold and silver nanoparticles. The non‐specific magnetic extraction does not require any analyte specific agent like antibody, aptamer, or molecularly imprinted polymer, therefore, the cost and complexity of the assay is very low. Both assays are capable for application to urine samples with the detection limits under the minimum required performance limit of modafinil. The assays were validated in terms of accuracy, precision, detection limits, and ranges. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.096
GPT teacher head0.431
Teacher spread0.335 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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