Pycnometric-Additive Determining of the Degree of Coating of High-Strength Synthetic Diamond Grinding Powders using the Actual 3D Morphology of their Grains
Bibliographic record
Abstract
The methodological components of direct and indirect analytical determining of the degree of coating of synthetic diamond grinding powders are analyzed. It has been established that the weight method most used in practice for determining this technological property of grinding powder is not universal for different methods of applying the coating. More universal in this regard, as the review of publications showed, is the well-known indirect-analytical method based on the pycnometric-additive approach. An improved variant of this method is proposed, aimed at application to high-strength synthetic diamond grinding powders. The method takes into account the peculiarities of the 3D morphology of the grains of such powders. Using the example of grinding powder AC300 500/400, the grains of which were coated with a solution of a mixture of boron oxide, sodium silicate, and titanium carbide, the advantages of using the proposed method are illustrated. The results of a comparison of determining the degree of coating by a known method and its improved variant are presented.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".