Ultra-low-temperature sintering of TiO2 via grain boundary diffusion enabled by nanosecond laser irradiation
Bibliographic record
Abstract
Sintering of metal oxide ceramics typically requires high temperatures to achieve densification; however, excessive heat often leads to grain coarsening and phase instability. In this study, nanosecond (ns) laser irradiation is employed for the first time as a pre-treatment step of TiO 2 nanoparticle to lower the sintering temperature by tailoring the microstructure at the nanoscale. During ns laser exposure, the localized high-energy input lowers the activation energy for dislocation nucleation, thereby increasing dislocation density. Subsequently, with optimized exposure duration, heat accumulation induces localized annealing, which facilitates dislocation annihilation and initiates in situ recrystallization during irradiation. This process leads to the formation of new nanoscale grains within individual nanoparticles prior to sintering. During subsequent furnace sintering at low temperature (750 °C), these laser-induced nanograins remain stable and serve as diffusion-active pathways, promoting a transition from surface diffusion to grain boundary diffusion, as confirmed by diffusion coefficient analysis. This mechanism enhances densification, reduces porosity, and improves relative density. At elevated temperatures (∼1050 °C), extreme annealing destabilizes the laser-induced nanoscale grains, effectively suppressing grain boundary-mediated diffusion. Overall, the findings demonstrate that grain boundary diffusion can drive densification at low temperatures, bypassing the conventional grain growth typically associated with ceramic sintering.
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".