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Record W3123304413

Development and Application of Solid-Phase Microextraction Probe Electrospray Ionization

2020· dissertation· en· W3123304413 on OpenAlexfundno aff
Milaan Thirukumaran

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolid-phase microextractionElectrospray ionizationChromatographyExtractive electrospray ionizationAnalytical Chemistry (journal)ChemistryMass spectrometryMaterials scienceGas chromatography–mass spectrometryProtein mass spectrometry
DOInot available

Abstract

fetched live from OpenAlex

Ambient ionization mass spectrometry (AIMS) is a category of mass spectrometry (MS) techniques originally characterized as using ambient ionization sources to analyze samples with little to no sample preparation and no chromatography step. This set of techniques have quickly gained popularity due to fast workflows and the ability to perform high throughput analysis. However, AIMS is prone to high matrix effects and reduced sensitivities. Solid-phase microextraction (SPME) is commonly used to mitigate these effects due to easy integration into pre-existing AIMS workflows, enabling preconcentration and extraction. Probe electrospray ionization (PESI) is a technique developed by Hiraoka and colleagues in 2007, then commercialized by Shimadzu Corporation years later. In PESI a small metal probe is dipped into the sample and immediately moved upwards, close to the inlet of a mass spectrometer to facilitate electrospray ionization (ESI). In recent years there has been a shift from using PESI for qualitative studies towards quantitative studies. With this shift in intentions, sample preparation has been incorporated into PESI workflows. The objective of this work is to incorporate SPME as a sample preparation method for PESI and develop applications for this technique. The first objective was to see if the PESI probes could be coated and to see if SPME-PESI-MS/MS could give reliable MS data. To ensure reproducibility of such small probes, intra- and inter-probe reproducibility tests by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) were conducted using drugs of abuse. These results show reproducibility of the probes with almost all relative standard deviations being ≤ 15%. Afterward, the optimal desorption solution for SPME-PESI-MS/MS was determined. It was also found that a coated PESI probe used for SPME-PESI-MS/MS could not be used for a subsequent LC-MS/MS run without extracting the sample again due to significant desorption by SPME-PESI-MS/MS. Furthermore, an application of SPME-PESI-MS/MS to quantitate drugs of abuse from 30µL of plasma was developed. The intra-day precision of said method was under 15% for all compounds. The inter-day precision of all compounds was under 15% except for lorazepam at the 30 ng mL-1 validation point and oxazepam at the 90 ng mL-1 validation point. The accuracy of all compounds for this method was within 80-120% except for lorazepam at the 30 ng mL-1 validation point. The small dimensions of the coated PESI probes were then leveraged to determine the free concentration and plasma protein binding of diazepam from human plasma by SPME-PESI-MS/MS. The plasma protein binding determined by SPME-PESI-MS/MS was 99.3% which falls within literature values of 97-99% from human plasma samples spiked with 25 ng mL-1 of diazepam. Finally, the development of a screening method for aminoglycosides was explored with SPME-PESI-MS/MS. This was to explore the use of AIMS technologies as an alternative screening method for compounds that require conditions that are highly detrimental to MS systems (i.e. high salt concentrations or ion-pairing reagents).

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.242
Teacher spread0.235 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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