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Record W2089009396 · doi:10.1002/biot.200600070

The potential of differential mobility analysis coupled to MS for the study of very large singly and multiply charged proteins and protein complexes in the gas phase

2006· article· en· W2089009396 on OpenAlexaff
Juan Fernández de la Mora, Sven Ude, Bruce A. Thomson

Bibliographic record

VenueBiotechnology Journal · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsSciex (Canada)
Fundersnot available
KeywordsDifferential mobility analyzerChemistryElectrophoresisIonGas phaseCharge (physics)Ion-mobility spectrometryPhase (matter)Range (aeronautics)Analytical Chemistry (journal)Mass spectrometryChemical physicsChromatographyMaterials sciencePhysical chemistryPhysics

Abstract

fetched live from OpenAlex

As previously demonstrated by the technique of gas-phase electrophoretic mobility molecular analyzer (GEMMA) introduced by Kaufman and colleagues, differential mobility analysis (DMA) of charge-reduced electrospray ions in the gas phase is a useful complement to MS for studying large proteins and their weakly bound complexes. Several limitations of GEMMA, the solutions for which have the potential to greatly improve its performance, are discussed here, including DMA resolution and transmission. A quantitative theory of charge reduction kinetics for dried multiply charged globular proteins at atmospheric pressures is also presented, showing that the charge reduction time must be carefully chosen to maximize a singly charged ion signal, while avoiding survival of contaminating multiply charged species. Because charge reduction limits the range of masses analyzable by MS, we also consider the potential of a parallel-plate DMA coupled in series to an MS for DMA-MS studies without charge reduction.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.011
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

Citations52
Published2006
Admission routes1
Has abstractyes

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