Ultrasound and air‐disturbance‐based enhancement of spiral microchannel extraction of <scp>Cu<sup>2+</sup></scp>
Bibliographic record
Abstract
Abstract In this work, we evaluated the enhancement effect on the extraction of Cu2+ from water by introducing ultrasound and air disturbance in a spiral microchannel device. We used 2‐ethylhexyl phosphonic acid mono‐2‐ethylhexyl ester (P507) as an extractant and used kerosene as a solvent. The effects of flow rate, temperature, initial pH of water, organic/aqueous phase ratio (O/A), the concentration of extractant, and the inner diameter of the pipe on the extraction of Cu2+ were investigated in liquid‐liquid micro‐extraction (LLME), ultrasonic micro‐extraction (UME), and liquid‐liquid‐gas micro‐extraction (LLGME). We found that, at a similar extraction rate, the extraction capacity per unit of time of UME was five times that of LLME, and the extraction capacity per unit of time of LLGME was 10 times that of LLME. By comparing the effects of three methods on extracting Cu2+, we followed the following priority order: LLGME > UME > LLME. The results of three‐stage extraction under optimum conditions were also studied. The results revealed that the highest extraction rate of the three methods could reach about 92% with the processing time of 72 seconds under optimal conditions.
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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) | 0.000 | 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".