Particle Size Effect On Load Transfer In Single Particle Composite Samples Via X-Ray Difiraction
Bibliographic record
Abstract
Particulate composites are widely used in many aerospace applications including protective coatings, adhesives, or structures, and their mechanical properties and behavior have gained increasing significance. The addition of modifiers such as alumina generally leads to improved mechanical properties. In this work, samples with an isolated alumina particle embedded in an epoxy matrix were created to replicate the ideal assumptions for many particulate mechanics models. The effect of particle size on load transfer is determined here using a unique X-Ray Difiraction experimental set-up at the Canadian Light Source. At the Very Sensitive Elemental and Structural Probe Employing Radiation from a Synchrotron (VESPERS) beamline, a custom miniature mechanical load frame was used to apply compressive loads to each sample. At three different compressive loads, the alumina within each sample was exposed to a hard X-ray beam which created a difiraction pattern that was collected by a 2-D detector. A trend of increasing load transfer with increasing particle size was observed during the analysis of the difiraction rings. Results from this work provide experimental insight into the effect of particle size on load transfer in single particle composites and can serve to experimentally validate the theoretical load transfer models that currently exist.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".