Esterquat EQ-90 as a green novel collector for effective desilication in magnesite flotation: Adsorption mechanisms and selectivity
Bibliographic record
Abstract
This study investigates the selective adsorption behavior and flotation efficacy of the eco-friendly esterquat EQ-90 on quartz and magnesite, leveraging an array of advanced analytical techniques, including micro-flotation tests, Zeta potential analysis, contact angle measurement, FTIR, SEM-EDS, XPS, and TOF-SIMS. Micro-flotation tests demonstrated that EQ-90 achieved a 93.15 % recovery for quartz, while maintaining the magnesite recovery at only 5.26 %. Zeta potential and contact angle analyses confirmed the robust adsorption of EQ-90 on quartz, rendering it hydrophobic, while magnesite exhibited negligible interaction. FTIR, SEM-EDS, and XPS analyses revealed substantial increases in C and N content and significant shifts in binding energies on quartz surfaces post EQ-90 treatment, corroborating the selective adsorption mechanism. TOF-SIMS imagery further validated these findings, showing pronounced EQ-90 concentrations on quartz. This comprehensive analysis underscores EQ-90′s efficacy in selectively adsorbing onto quartz, thereby optimizing its flotation efficiency. The study offers significant insights and a robust foundation for employing EQ-90 in the selective separation of quartz from magnesite, advancing flotation processes in mineral processing.
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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) | 0.000 | 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".