Digital moiré interferometric analysis on the effect of nanoparticle conditioning on the mechanical deformation in dentin
Bibliographic record
Abstract
Dentin is a biological composite that forms the major bulk of tooth structure. Understanding the biomechanical response of dentin structure to forces is essential to restore the loss of mechanical integrity associated with dentin loss during disease or treatment procedures. Moiré interferometry is an optical interferometry based method, which allows wholefield, real-time analysis of dental structures with high-sensitivity. The aim of this study was to investigate the deformation gradients in dentin during function and subsequent to surface conditioning with bioactive biopolymeric nanoparticle. Slab shaped dentin specimens were prepared and a customized loading jig was used to compressively load the specimens from 10 N to 50 N. Specific regions of interest was chosen on the dentin specimens for strain analysis. The digital moiré interferometry experiments showed a distinct deformation pattern in dentin in the direction perpendicular to the dentinal tubules, which increased with increase in dentin loss. The dentin conditioned with nanoparticles did not display marked increase in strain gradients with loads. The current photomechanical experiment highlighted the impact of nanoparticle treatment to improve the mechanical integrity of dentin.
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".