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Record W2082042127 · doi:10.1021/ie061666q

Pretreatment of Liquid Silicone Rubbers to Remove Volatile Siloxanes

2007· article· en· W2082042127 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2007
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsMcMaster University
FundersOntario Centres of Excellence
KeywordsSiliconeElastomerMaterials scienceSilicone ElastomersComposite materialSilicone oilCuring (chemistry)Thermal stabilityChemical engineering

Abstract

fetched live from OpenAlex

Liquid silicone rubbers (LSR) are widely used to create devices with complex shapes for various commercial and consumer applications, because of their many beneficial properties including lubricity, thermal and electrical stability, and aesthetic feel. Regulatory bodies require postcure thermal treatment of silicone elastomers to remove volatile materials: the rate and efficiency of these processes depends on the specific elastomer properties (e.g., cross-link density). We examine in this paper the ability to remove volatiles before curing in the mold, a process that should be much less dependent on specific elastomer formulation. The thermal devolatilization efficiency, optionally under vacuum, of silicone elastomers prior to cure, was compared to different convection heating techniques postcure. Parts A (olefin-functional silicone and the catalyst) and B (Si−H functional silicone) were treated separately or mixed, and the ability to create parts and the requirement for postcure thermal devolatization (200 °C for 4 h) were determined. Themolysis precure permitted the removal of volatile species, but with several key caveats: (i) Loss of volatiles from part B, in particular, was accompanied (especially in moist atmospheres) by premature cure, likely due to cure mechanisms other than hydrosilylation and the thermal loss of inhibitors. Even without part A, the part B samples skinned over after a few hours. (ii) The pot life significantly decreased, particularly as volatiles were removed from part B. (iii) The efficiency of devolatilization can be detrimentally affected by transpiration the migration of volatiles from one silicone elastomer object to another via contact or gas-phase transfer. Thinner objects both lost and absorbed volatiles by contact and evapotranspiration more effectively than thicker objects. Precure treatment had little effect on the resulting elastomer properties. To establish if precure thermolysis is a viable route to devolatilization, it was determined that the surface/volume ratio of the object to be prepared should be considered, as this takes into account the relative proportion of both thin and thick sections of the complex object to be molded. In the case that the object consists primarily of thick objects, precure devolatilization of part A can be an effective way to mitigate the need for postcure thermal treatment.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.338
Teacher spread0.265 · 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