Phase‐Field Modeling of Microstructural Evolution by Freeze‐Casting
Bibliographic record
Abstract
Freeze‐casting has attracted great attention as a potential method for manufacturing bioinspired materials with excellent flexibility in microstructure control. The solidification of ice crystals in ceramic colloidal suspensions plays an important role during the dynamic process of freeze‐casting. During solidification, the formation of a microstructure results in a dendritic pattern within the ice‐template crystals, which determines the macroscopic properties of materials. In this paper, the authors propose a phase‐field model that describes the crystallization in an ice template and the evolution of particles during anisotropic solidification. Under the assumption that ceramic particles represent mass flow, namely a concentration field, the authors derive a sharp‐interface model and then transform the model into a continuous initial boundary value problem via the phase‐field method. The adaptive finite‐element technique and generalized single‐step single‐solve (GSSSS) time‐integration method are employed to reduce computational cost and reconstruct microstructure details. The numerical results are compared with experimental results, which demonstrate good agreement. Finally, a microstructural morphology map is constructed to demonstrate the effect of different concentration fields and input cooling rates. The authors observe that at particle concentrations ranging between 25 and 30% and cooling rate lower than −5° min−1 generates the optimal dendrite structure in freeze casting process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".