Advancements in the application and mechanism of fine-grained mineral flotation collectors
Bibliographic record
Abstract
The poor floatability of fine- and ultrafine-grained minerals, including sulfide and oxide minerals, is a huge issue confronting the mineral industry. Collectors are critical to the flotation of fine-grained minerals; therefore, developing high efficiency collectors has always been a hot research topic for industries and academia. This work has systematically reviewed the advancements in the development of collectors for fine mineral flotation in the last decades as well as provides an outlook for prospective studies. Collectors can be divided into sulfide and oxide mineral collectors, which can be further divided into ionic collectors, nonionic collectors, nonpolar oily collectors, nanocollectors, and biologic collectors based on their chemical composition. For sulfide minerals, ionic collectors mainly include xanthate, phosphate, and diethyldithiocarbamate, which are soluble in water and are able to dissociate sulfur-containing anions to interact with sulfide minerals. Some oily collectors and nanocollectors can also be used for the flotation of ultrafine sulfide minerals. For oxide minerals, the commonly used anionic collectors are hydroxamate, phosphate, arsenate, and fatty acid, while cationic collectors mainly comprise amine collectors. Nonionic and biologic collectors are used in oxide mineral flotation. Mechanisms underpinning the adsorption of collectors on the mineral surface include electrostatic interactions, chelation interactions, hydrogen bonding, chemical bonding, and metal-ion coordination. In addition, composite collectors, such as anionic/anionic collectors, anionic/cationic collectors, cationic/cationic collectors, and ionic/nonionic collectors, exhibit high collection capability for fine-grained minerals compared to single collectors. This is because they can promote collector adsorption on mineral surfaces through a series of synergistic interactions, such as co-adsorption, charge compensation, function complementarity, and variations in the critical micelle concentration. The rapid development of computational chemistry and artificial intelligence can help in investigating the quantitative relationship between the molecular structures of collectors and their collecting capability for fine minerals, thereby promoting the development of highly efficient novel collectors that uses shorter time for flotation. Increased effort is required for the development and utilization of harmless green collectors due to the rigid environmental requirement, and they are vital to the development of the mineral industry. In addition, nanocollectors will also gain increasing attention due to their unique physical and chemical properties and advantages over conventional collectors. Therefore, this paper is of great guiding significance for the development and application of fine-grained mineral flotation collectors.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".