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Record W3106888680 · doi:10.1002/sia.6909

Part I: Molecular weight characterization of linear polydimethyl siloxanes by secondary ion mass spectrometry

2020· article· en· W3106888680 on OpenAlexaff
Michaeleen L. Pacholski, Paul R. Vlasak

Bibliographic record

VenueSurface and Interface Analysis · 2020
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsSiliconeSecondary ion mass spectrometryPolymerMaterials scienceSiloxaneMolar mass distributionCharacterization (materials science)Thin filmAnalytical Chemistry (journal)PolydimethylsiloxanePolymer chemistryChemical engineeringIonChemistryComposite materialNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

A correlation of SIMS ion intensities to the molecular weight of linear, trimethyl‐terminated polydimethyl siloxane (PDMS) has been developed by normalizing PDMS endgroup‐related signal to backbone‐related signal for a set of well‐characterized polymers with narrow molecular weight distributions. These initial experiments on thick PDMS films illustrate that PDMS molecular weight can be estimated using SIMS. A second paper describes the challenges in analyzing thin PDMS films on metal and polymer substrates. PDMS molecular weight determination is important to understanding surface‐related performance of materials using silicone additives across many different platforms including coefficient of friction, slip, mar, mold release, and nonstick properties as well as providing more specific characterization of surface contamination.

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)0.001
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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