Densification and Microstructural Evolution of Hierarchically Porous Ceramics During Sintering
Bibliographic record
Abstract
Several technologically important ceramics are porous. One common approach to make high porosity ceramics is to use fugitive pore formers followed by partial densification. The resultant ceramic has hierarchical porosity: extrinsic large pores from the burnout of the pore former; and intrinsic small inter‐particle pores. In this study, the effect of sintering conditions and pore former on porosity and microstructure of hierarchically porous ceramics has been investigated. Pore size and pore volume fraction evolution of intrinsic and extrinsic pores were quantified as a function of sintering conditions. The effect of porosity and sintering conditions on average grain size is also investigated. It is shown that during sintering both the extrinsic and the intrinsic porosity decrease but along different trajectories. It is shown that the intrinsic pores control the grain growth. The results are discussed in terms of standard mechanisms and models available in the literature.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".