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Record W1995772204 · doi:10.1039/b402607c

Time-of-flight secondary ion mass spectrometry (TOF-SIMS):—versatility in chemical and imaging surface analysis

2004· article· en· W1995772204 on OpenAlexaff
R. N. S. Sodhi

Bibliographic record

VenueThe Analyst · 2004
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSecondary ion mass spectrometryTime of flightMass spectrometryResolution (logic)ChemistryIonAnalytical Chemistry (journal)NanotechnologyCharacterization (materials science)Static secondary-ion mass spectrometryImage resolutionAerospace engineeringMaterials scienceComputer scienceChromatographyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has emerged as one of the most important and versatile surface analytical techniques available for advanced materials research. This arises from its excellent mass resolution, sensitivity and high spatial resolution providing both chemical and distributional (laterally and depth) information for a wide variety of materials and applications. Understanding the various modes of operation and the information that each provides is crucial to the analyst in order to optimise the type of data that is obtained. New developments in primary ion sources and the application of multivariate analysis techniques, which can only extend the versatility and applicability of the technique, are also discussed.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.003
GPT teacher head0.201
Teacher spread0.198 · 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

Citations319
Published2004
Admission routes1
Has abstractyes

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