Pretreatment with fine bubbles for effective zinc ion precipitation and removal from zinc electroplating wastewater
Bibliographic record
Abstract
Abstract Heavy metals such as zinc are widely used in plating plants for the processing of material surfaces; therefore, an adequate wastewater treatment is required afterwards to comply with wastewater regulations. However, the presence of chemicals such as degreasing agents in the wastewater generated from the plating process inhibits the formation of hydroxides, thereby making treatment of wastewater and compliance with the stipulated regulations difficult. Herein, the effects of fine bubbles on oil cleaning and wastewater treatment were investigated for developing an alternative cleaning technology to degreasing agents. The concentration of fine bubbles significantly affected the oil cleaning. The fine bubbles demonstrated remarkable cleaning of highly viscous oil, which was difficult to remove using pure water. The high cleaning effect of fine bubbles on oil was achieved because of the hydrophobic interaction of the bubbles. Furthermore, a decrease in cleaning effect was observed for samples with uneven shapes, which was attributed to the difficulty in penetration of fine bubbles in materials with irregularities. Furthermore, fine bubble cleaning exhibited better efficiency for wastewater treatment than degreaser cleaning. Therefore, fine bubble cleaning is speculated to be an excellent cleaning technology with high oil cleaning effectiveness and improved wastewater treatability.
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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".