Pretreatment of Liquid Silicone Rubbers to Remove Volatile Siloxanes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".