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Record W4280535698 · doi:10.3390/app12104932

Time-of-Flight Secondary Ion Mass Spectrometry Analyses of Self-Assembled Monolayers of Octadecyltrimethoxysilane on SiO2 Substrate

2022· article· en· W4280535698 on OpenAlexaff
Heng‐Yong Nie, Hamid-Reza Jahangiri-Famenini

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsWestern University
Fundersnot available
KeywordsMonolayerSelf-assembled monolayerSecondary ion mass spectrometrySubstrate (aquarium)ChemistrySilaneMoleculeFragmentation (computing)PolymerizationMass spectrometryIonOrganic chemistryChromatographyPolymerComputer science

Abstract

fetched live from OpenAlex

The self-assembled monolayers (SAMs) of organosilanes formed on an oxide substrate are thought to have a polymerized –Si–O–Si– network due to the homocondensation of silanols of hydrolyzed silane headgroups, which is the most significant difference in the SAMs of organosilanes in comparison with those of alkanethoils and organophsosphonic acids. In order to explore the interface chemistry of organosilane SAMs, surface-sensitive time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to compare ion fragmentation differences between the SAMs of octadecyltrimethoxysilane (OTMS) formed on a SiO2 substrate and free OTMS molecules, as well as oxide substrate. The ability of ToF-SIMS to verify the hydrolysis of the methoxy groups of OTMS molecules and to assess the polymerized –Si–O–Si– network in their SAMs was demonstrated, which shows that ToF-SIMS provides unique information to help us understand the interface chemistry of OTMS SAMs formed on oxides.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.248
Teacher spread0.235 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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