Understanding Copper Activation and Xanthate Adsorption on Sphalerite by Time-of-Flight Secondary Ion Mass Spectrometry, X-ray Photoelectron Spectroscopy, and in Situ Scanning Electrochemical Microscopy
Bibliographic record
Abstract
In situ scanning electrochemical microscopy (SECM) was applied for the first time to study the copper activation and subsequent xanthate adsorption on sphalerite. The corresponding surface compositions were analyzed by time-of-flight secondary ion mass spectrometry (ToF-SIMS) and X-ray photoelectron spectroscopy (XPS). The probe approach curve (PAC) using SECM shows that unactivated and activated sphalerite surfaces have negative current feedback and partially positive current feedback, respectively, suggesting that Cu x S is formed on the sphalerite after copper activation. The copper activation of sphalerite strongly depends on the surface heterogeneity (e.g., presence of polishing defects, chemical composition), impacting the subsequent xanthate adsorption process. The SECM, ToF-SIMS, and XPS analyses show that during the copper activation the polishing defects, which have high excess surface energy, tend to consume more copper ions, resulting in Cu-rich regions by forming CuS-like species, while Fe oxide/hydroxide forms at Fe-rich regions. The XPS spectra further confirm that the CuS-like species involve Cu(I) and S(−I). The SECM imaging shows that after xanthate adsorption the current response at the Cu-rich regions decreases because of the formation of cuprous xanthate (CuX) and dixanthogen (X 2 ) while increases at the Fe-rich regions mainly due to the chemisorption of xanthate on Fe oxide/hydroxide. Our results shed light on the fundamental understanding of the electrochemical processes on sphalerite surface associated with its copper activation and subsequent xanthate adsorption in flotation.
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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".