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Effects of matrix‐assisted laser desorption/ionization experimental conditions on quantitative compositional analysis of ethylene oxide/propylene oxide copolymers

2000· article· en· W4247579635 on OpenAlexaff
Rui Chen, Nan Zhang, Amy M. Tseng, Liang Li

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2000
Typearticle
Languageen
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryEthylene oxidePropylene oxideCopolymerDesorptionOxideMatrix (chemical analysis)IonizationQuantitative analysis (chemistry)Soft laser desorptionChemical engineeringPolymer chemistryInorganic chemistryMatrix-assisted laser desorption/ionizationOrganic chemistryChromatographyPolymerIonAdsorption

Abstract

fetched live from OpenAlex

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry has the potential to become a valuable tool for the compositional analysis of copolymers. For a copolymer composed of structurally very similar building blocks with minor chain length changes, one would expect the relative peak intensities observed in the MALDI mass spectra to reflect its composition, at least within a narrow mass range. However, we show that variations in experimental conditions in MALDI can have a significant effect on the mass spectral appearance of a copolymer. The effects of concentration, laser power, type of matrices and solvents on mass spectra of an ethylene oxide/propylene oxide copolymer are illustrated. These somewhat surprising results show that great care needs to be exercised when interpreting copolymer spectra for compositional analysis, even for copolymers with structurally similar monomers. This work also points out that further studies are needed to better understand and optimize spectral acquisition conditions for reliable copolymer compositional analysis by MALDI. Copyright © 2000 John Wiley & Sons, Ltd.

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 score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.297
Teacher spread0.286 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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