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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.005
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