Surface charge, effective charge and dispersion/aggregation properties of nanoparticles
Bibliographic record
Abstract
Abstract A careful investigation of the relationship between surface properties and colloidal behaviour of nanometric particles in concentrated media has shed some light on the important parameters that must be controlled in order to improve the dispersion of mineral particles. Experimental methods such as rheology and osmometry reveal that the aggregation/dispersion process is not only a matter of electrostatics as stated by classical theories. In practice, the relationship between the surface charge and the state of dispersion is probably much less straightforward than generally assumed by the classical argument stating that the higher the surface charge, the higher the electrostatic repulsion between particles and the more dispersed the particles. Our results on model hematite and industrial TiO 2 systems suggest that the state of dispersion is not always correlated to the magnitude of the surface charge, but seems to depend on the surface density of condensed monovalent counter‐ions. Accordingly, it is believed that the monovalent species present at the interface of mineral particles are deeply involved in the dispersion process of highly concentrated slurries. Much attention should be addressed to the presence of condensed species onto the surface and to their influence on the structure at the interface, in order to prepare better formulations involving (hydr)oxide particles in aqueous media. © 2003 Society of Chemical Industry
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".