Bibliographic record
Abstract
Titanium dioxide (TiO2) has found widespread use. Typically it is used in another matrix to impart certain properties. For example, it is widely used as a white pigment for paints and polymers. The aim of this research work was to achieve improvements in the sense of processability as well as the dispersion performance of alumina surface treated pigmentary TiO2in polymer matrix. Wet chemical method was used to modify the surface of the TiO2 pigment. Surface treatment included precipitation of hydrous oxides of aluminium on the surface of TiO2 particles. During controlled surface treatment, agglomeration has been avoided, which has been proved to improve applicative properties of TiO2 particles. In addition to that, organic additives were applied to enhance performance attributes of the pigmentary TiO2. The effectiveness of surface treatment was determined using scanning-transmission (STEM) and transmission (TEM) electron microscopy. Quantitative evaluation of quality and dispersion of the pigments has been performed using Filter pressure test. Lower pressure generated during filter pressure test when particles were well dispersed in a polymer matrix. Surface treatment also affected pigment processibility; i.e. filterability and settling, which is of high importance for process planning.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".