Quantitative Dispersion Analysis of Inclusions in Polymer Composites
Bibliographic record
Abstract
The state of dispersion plays an important role on the performance of polymer nanocomposites. Dispersion is usually assessed based on the qualitative evaluation of microscopy micrographs. In this paper, a quantitative algorithm is introduced for analyzing the dispersion of inclusions in polymer composites using image analysis. In a binary image, on-pixels are considered as particle elements while off-pixels stand for matrix elements. To quantify the dispersion, the mean distance value between any matrix elements to their corresponding nearest neighboring particle element is measured. A dispersion index, DI, is then defined by comparing the image of interest with the associated uniformly dispersed case. Synthetic models were utilized to examine the sensitivity of the algorithm to various dispersity scenarios such as the effect of particle size, clustering, and cluster distribution. Optical micrographs of carbon nanotube modified epoxy with different states of dispersion were also employed to assess the applicability and functionality of the algorithm to real micrographs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".