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Record W2346979809 · doi:10.1002/anie.201601476

Fast Quantitation of Target Analytes in Small Volumes of Complex Samples by Matrix‐Compatible Solid‐Phase Microextraction Devices

2016· article· en· W2346979809 on OpenAlexafffund
Hamed Piri‐Moghadam, Fardin Ahmadi, Germán Augusto Gómez‐Ríos, Ezel Boyacı, Nathaly Reyes‐Garcés, Ali Aghakhani, Barbara Bojko, Janusz Pawliszyn

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolid-phase microextractionAnalyteComplex matrixChromatographyMatrix (chemical analysis)Phase (matter)Materials scienceChemistryAnalytical Chemistry (journal)Mass spectrometryGas chromatography–mass spectrometry

Abstract

fetched live from OpenAlex

Herein we report the development of solid-phase microextraction (SPME) devices designed to perform fast extraction/enrichment of target analytes present in small volumes of complex matrices (i.e. V≤10 μL). Micro-sampling was performed with the use of etched metal tips coated with a thin layer of biocompatible nano-structured polypyrrole (PPy), or by using coated blade spray (CBS) devices. These devices can be coupled either to liquid chromatography (LC), or directly to mass spectrometry (MS) via dedicated interfaces. The reported results demonstrated that the whole analytical procedure can be carried out within a few minutes with high sensitivity and quantitation precision, and can be used to sample from various biological matrices such as blood, urine, or Allium cepa L single-cells.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.356
Teacher spread0.309 · 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
GenreMethods

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

Citations110
Published2016
Admission routes2
Has abstractyes

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