Determination of Agglomeration Kinetics in Nanoparticle Dispersions
Bibliographic record
Abstract
The direct application of nanoparticles as nonsupported adsorbents and catalysts is of high interest since they offer high surface areas with reduced mass transfer limitations. However, the natural tendency of these materials to aggregate, even faster when at high temperatures, makes the agglomeration process an important phenomenon to be studied, understood and, eventually controlled. A method to obtain the kinetics of nanoparticle agglomeration processes is presented in this paper. This analysis was based on the change of particle diameter during aggregation. The kinetic expression was validated with a series of experiments where the growth of Fe 2 O 3 nanoparticles immersed in base oil was followed at different times, temperatures, and particle concentrations. Results revealed the nature of the particle agglomeration process in the ranges of the experimental conditions; they indicated that physical adhesion, more than chemical binding, is the determining mechanism for agglomeration of Fe 2 O 3 nanoparticles immersed in base oil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".