Kinetics formulation for Two-Dimensional Growth Behavior of Water/Ice Interface on Si Substrate
Bibliographic record
Abstract
Sublimation drying is used in the drying process of semiconductor device manufacturing. However, the solidification behavior mechanics of sublimation agents on substrates has not been clarified. Therefore, the properties of solidified films within substrate surfaces can become nonuniform, leading to their collapse. This study aimed to analyze the interface growth behavior during the cooling and solidification of a water/ice system as a basic case and to clarify the dynamic mechanism of the solidification behavior of liquid films on Si substrates. The solidification behavior of a water/ice system on Si substrates was captured on a video at different cooling rates. The recorded video was subjected to a digital image analysis to examine the crystal morphology and quantify the interface growth rate. The least-squares method with kinetic formulas was used to evaluate the feasibility of fitting the temperature variation to the interface growth rate. A visual examination of the morphology of interfacial growth revealed that it can be classified into four morphologies. The proposed kinetic equation describes the experimental results regarding the temperature dependence of the interfacial growth rate. Through image analysis, the interface growth rate of water and ice was quantified, and an evaluation formula was proposed.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".