Enhancing Ceramic Structural and Interfacial Properties via Micro‐Patterning and Macro‐Architectural Integration
Bibliographic record
Abstract
Abstract This study investigates a dual‐scale strategy combining micro‐patterning with macro‐architectural features to enhance the mechanical and interfacial properties of bioinspired ceramic composites. High‐tolerance alumina tiles are precisely laser‐micromachined with unidirectional (0°, 90°, and 45°) and bidirectional ((0°, 90°) and ±45°) patterns, then laminated with thermoplastic Surlyn layers through vacuum bagging and heat treatment to achieve optimal bonding. Double lap joint (DLJ) and three‐point bending (3PB) tests are performed to evaluate interfacial bonding, flexural behavior, and energy absorption. DLJ results indicated significant enhancement in interfacial bonding, with the ±45° pattern achieving a 107% increase in interfacial damage dissipation energy compared to plain specimens, attributed to mechanical interlocking and crack deflection. In 3PB tests, unidirectional patterns showed minimal impact on flexural properties, whereas bidirectional patterns reduced stiffness and strength. However, integrating micro‐patterns with hexagonal macro‐architectures notably improved energy absorption by 60% in laminated ceramic beams. This synergistic dual‐scale approach represents a substantial advancement over conventional ceramics, enabling superior post‐failure performance and energy absorption, with the potential for resilient materials in aerospace and other high‐performance applications.
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 | 0.000 |
| 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) | 0.001 | 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".