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Record W2331084549 · doi:10.1021/ac300743k

High-Pressure Gas Phase Femtosecond Laser Ionization Mass Spectrometry

2012· article· en· W2331084549 on OpenAlexafffund
Jiahui Peng, Noah Puskas, P. B. Corkum, D. M. Rayner, Alexandre V. Loboda

Bibliographic record

VenueAnalytical Chemistry · 2012
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersGovernment of Ontario
KeywordsChemistryAmbient ionizationAtmospheric-pressure laser ionizationElectron ionizationIonizationIon sourceMass spectrometryChemical ionizationAtmospheric-pressure chemical ionizationThermal ionizationFemtosecondIonDirect electron ionization liquid chromatography–mass spectrometry interfaceDesorption electrospray ionizationLaserAtomic physicsFragmentation (computing)Analytical Chemistry (journal)OpticsPhysics

Abstract

fetched live from OpenAlex

We describe a novel ion source for analytical mass spectrometry based on femtosecond laser ionization at pressures at and above atmospheric and characterize its performance when coupled to a tandem quadrupole/time-of-flight mass spectrometer. We assess source saturation limits, ionization and sampling efficiencies, the effective ionization volume, and limits of detection. We demonstrate 100% efficient ionization for a set of organic compounds and show that the degree of ion fragmentation over a range of laser powers is favorable compared to electron impact ionization, especially in that a substantial parent ion signal is always observed. We show how collisional cooling plays a role in controlling fragmentation at high pressures and address how ion-molecule chemistry can be controlled or exploited. High-pressure femtosecond laser ionization will allow "universal" and efficient ionization, presenting a research direction that will broaden the options for gas phase analysis beyond the capabilities of electron impact ionization.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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