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Record W2312964882 · doi:10.1021/jp4074653

Insights into Desorption Ionization on Silicon (DIOS)

2013· article· en· W2312964882 on OpenAlexaff
Jin Li, R. H. Lipson

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2013
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSoft laser desorptionMass spectrometryThermal ionizationIonizationChemistrySubstrate (aquarium)Electron ionizationDesorptionAnalytical Chemistry (journal)SiliconAtmospheric-pressure laser ionizationProtonationMatrix-assisted laser desorption electrospray ionizationAnalyteMatrix-assisted laser desorption/ionizationIonPhysical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Desorption ionization on silicon (DIOS), a variant of surface-assisted laser desorption ionization (SALDI) which uses porous silicon (pSi) as a substrate, is a well-established and effective soft-ionization technique in mass spectrometry. In this work DIOS experiments found that the caffeine analyte could be detected by mass spectrometry as both a radical cation and a protonated species with relative intensities that depend critically on the position of the incident laser focus relative to substrate surface. In both cases analyte desorption appears to be driven by a thermal mechanism. Radical cation formation is attributed to electron transfer reactions between the desorbed neutral analyte and pSi due to the large electron affinity of the substrate. Preliminary experiments where different incident laser wavelengths were used suggest that the ionization mechanism leading to the detection of protonated peptide Dalargin involves in part electrons and holes formed when photoexciting pSi above its electronic band gap.

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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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