A multiscale model for quantifying helium diffusion in porous unsintered glass
Bibliographic record
Abstract
Abstract Helium‐aided sintering of porous unsintered glass is a complex multiscale process, characterised by three different timescales, namely, that of helium diffusion, heat conduction, and radial shrinkage of the glass core. This work presents a multiscale model for quantifying heat and helium diffusion in a shrinking core system by decoupling the timescales based on their orders of magnitude. We obtain analytical solutions of our model, which allow us to quantify the spatio‐temporal profiles of temperature and helium concentration in the glass during the sintering process. Our results show that the introduction of helium increases the sintering rate of glass, and we conclude that pre‐sintering heating followed by helium‐aided sintering is better than simultaneous heating and helium diffusion. We also show that the pre‐sintering heating process for a standard glass sample should not be longer than an hour for the sake of heat economy, following which we may switch to the helium‐aided sintering process, where the sintering should occur under isothermal conditions for approximately 6 h. We perform dynamic simulations using glass porosity as a parameter, and find the sintering rate to be directly proportional to the initial porosity of the glass sample.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".