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Record W2921649411 · doi:10.1039/c8an02448k

Fast screening of illicit drugs in beverages and biological fluids by direct coupling of thin film microextraction to dielectric barrier discharge ionization-mass spectrometry

2019· article· en· W2921649411 on OpenAlexafffund
Mario F. Mirabelli, Emanuela Gionfriddo, Janusz Pawliszyn, Renato Zenobi

Bibliographic record

VenueThe Analyst · 2019
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersUniversity of WaterlooNovartis
KeywordsMass spectrometryChromatographyMaterials scienceIonizationDielectric barrier dischargeAnalytical Chemistry (journal)DielectricChemistryOptoelectronicsIonOrganic chemistry

Abstract

fetched live from OpenAlex

A direct and fast method for screening and quantification of illicit drugs in beverages and biological fluids was developed by using dielectric barrier discharge (DBD) as ionization technique, in combination with high-resolution mass spectrometry. Extraction of the targeted analytes was carried out with thin film microextraction (TFME) using ultrasound as agitation method. The targeted analytes were then thermally desorbed and introduced into the source without the need for any cryofocusing apparatus. The performance of this new analytical set up were compared to conventional TFME-TDU-GC/MS, showing enhanced linear dynamic range and lower limits of quantitation (low pg ml-1) achievable at the same extraction conditions (5 min extraction time). The performance of the method was tested in different beverages and body fluids, confirming its applicability for quantitative analysis of the targeted drugs in complex samples.

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 categoriesInsufficient payload (model declined to judge)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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.

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

Citations49
Published2019
Admission routes2
Has abstractyes

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