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Record W3174780439 · doi:10.1007/s00216-021-03452-y

Observation of charged droplets from electrospray ionization (ESI) plumes in API mass spectrometers

2021· article· en· W3174780439 on OpenAlexaff
Clara Markert, Marco Thinius, Laura C. Lehmann, Chris Heintz, Florian Stappert, Walter Wißdorf, Hendrik Kersten, Thorsten Benter, Bradley B. Schneider, Thomas R. Covey

Bibliographic record

VenueAnalytical and Bioanalytical Chemistry · 2021
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsSpinal Cord Injury BC
FundersBergische Universität Wuppertal
KeywordsMass spectrometryIonizationElectrosprayChemistryElectrospray ionizationIon sourceAmbient ionizationAnalytical Chemistry (journal)SpectrometerIonHighly charged ionExtractive electrospray ionizationAtmospheric pressureChemical ionizationChromatographySample preparation in mass spectrometryOpticsPhysics

Abstract

fetched live from OpenAlex

Electrospray ionization (ESI) generates bare analyte ions from charged droplets, which result from spraying a liquid in a strong electric field. Experimental observations available in the literature suggest that at least a significant fraction of the initially generated droplets remain large, have long lifetimes, and can thus aspirate into the inlet system of an atmospheric pressure ionization mass spectrometer (API-MS). We report on the observation of fragment signatures from charged droplets penetrating deeply the vacuum stages of three commercial mass spectrometer systems with largely different ion source and spray configurations. Charged droplets can pass through the ion source and pressure reduction stages and even into the mass analyzer region. Since droplet signatures were found in all investigated instruments, the incorporation of charged droplets is considered a general phenomenon occurring with common spray conditions in ESI sources.

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 score0.034
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

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