Enhancing the selectivity of carboxylic acid surfactants in fluorite flotation via cation–π interaction coupling
Bibliographic record
Abstract
Abstract The rapid depletion of high‐grade fluorite deposits has made the purification of fluorite from low‐grade ores increasingly critical. Traditional carboxylate surfactants face significant challenges in fluorite/calcite separation due to their similar affinity for both minerals. To address this limitation, we designed a novel surfactant 1‐(2‐octyldodecyl)‐1,2‐phenyl dicarboxylate (ODBD), containing phenyl groups, which exhibited superior flotation separation performance for fluorite and calcite compared to sodium oleate. X‐ray photoelectron spectroscopy, surface force measurements, and first‐principles calculations confirm that ODBD's carboxyl groups adsorbed onto fluorite and calcite in deprotonated forms, while its phenyl groups engaged in cation–π interactions with markedly higher affinity for fluorite surfaces. This dual adsorption mechanism created a pronounced synergistic effect, leading to superior selective adsorption of ODBD on fluorite. These findings demonstrate that coupling cation–π interactions with chemical interactions represents a viable strategy to significantly enhance surfactant selectivity, providing valuable insights for the optimization of traditional surfactants.
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 | 0.000 |
| 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 | 0.000 | 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".