Characterizing Frothers through Critical Coalescence Concentration (CCC)95-Hydrophile-Lipophile Balance (HLB) Relationship
Bibliographic record
Abstract
Frothers are surfactants commonly used to reduce bubble size in mineral flotation. This paper describes a methodology to characterize frothers by relating impact on bubble size reduction represented by CCC (critical coalescence concentration) to frother structure represented by HLB (hydrophile-lipophile balance). Thirty-six surfactants were tested from three frother families: Aliphatic Alcohols, Polypropylene Glycol Alkyl Ethers and Polypropylene Glycols, covering a range in alkyl groups (represented by n, the number of carbon atoms) and number of Propylene Oxide groups (represented by m). The Sauter mean size (D32) was derived from bubble size distribution measured in a 0.8 m3 mechanical flotation cell. The D32 vs. concentration data were fitted to a 3-parameter model to determine CCC95, the concentration giving 95% reduction in bubble size compared to water only. It was shown that each family exhibits a unique CCC95-HLB relationship dependent on n and m. Empirical models were developed to predict CCC95 either from HLB or directly from n and m. Commercial frothers of known family were shown to fit the relationships. Use of the model to predict D32 is illustrated.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".