An overview of the temperature dependence of the zeta potential of aqueous suspensions
Bibliographic record
Abstract
Zeta potential is a crucial parameter in colloid and surface science which reflects the electrokinetic potential at the slipping plane of particles in suspensions. Despite the broad range of interests and applications of the temperature dependence of zeta potential, the relationship between temperature and zeta potential is not entirely understood. Understanding the temperature dependence of zeta potential is essential for applications in various fields, from colloidal stability in drug delivery to flotation recovery in mineral processing. Although the concept has been around for nearly two centuries, dedicated high-temperature zeta potential measurements are a relatively recent development. Challenges have been arising due to the limitations of traditional measurement techniques at elevated temperatures and the influence of temperature on other factors affecting zeta potential. As a result, the zeta potential values of many materials at various conditions relevant to natural or desired settings are not known accurately. This review comprehensively explores the influence of temperature on zeta potential, detailing how thermal variations affect the electrokinetic properties of suspensions. The present knowledge of the temperature dependence of zeta potential and its relationship with the physicochemical characteristics of suspensions, such as pH, type and concentration of the background electrolyte, dissolved ions, surface composition, and dissolution of the particles as key points in understanding and predicting the behavior of colloidal particles in processes are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".