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Record W1996837894 · doi:10.1002/mas.20302

APCI/APPI for synthetic polymer analysis

2011· article· en· W1996837894 on OpenAlexaff
Péran Terrier, Bernard Desmazières, Jeanine Tortajada, William Buchmann

Bibliographic record

VenueMass Spectrometry Reviews · 2011
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAtmospheric-pressure chemical ionizationChemistryMass spectrometryDesorption electrospray ionizationPhotoionizationElectrospray ionizationIonizationPolymerChromatographyDirect electron ionization liquid chromatography–mass spectrometry interfaceAnalytical Chemistry (journal)Chemical ionizationOrganic chemistryIon

Abstract

fetched live from OpenAlex

Modern mass spectrometry of synthetic polymers involves soft ionization techniques. Whereas matrix-assisted laser desorption/ionization (MALDI) and electrospray (ESI) are employed routinely, atmospheric pressure chemical ionization (APCI) and more recently atmospheric pressure photoionization (APPI) are used to a lesser extent. However, these latter ionization methods coupled to liquid-phase separation techniques create new opportunities for the characterization of polymers, especially for low molecular weight compounds or for the polymers that are poorly ionizable by the usual methods. After a part devoted to the description of classical MS methods employed for polymer analysis (MALDI, ESI, and their use with chromatography), APCI and APPI techniques will be described, discussed, and selected examples will present the interest of these ionization sources (or interfaces for LC/MS) in the field of polymer analysis.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0560.075

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 head0.046
GPT teacher head0.288
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2011
Admission routes1
Has abstractyes

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