Remediation of heavy-metal-contaminated sediments in USA using ultrasound and ozone nanobubbles
Bibliographic record
Abstract
The lower 12·875 km of the Passaic River (NJ, USA) is heavily contaminated due to industrial activities – specifically heavy metal extraction from chromium (Cr)-ore-processing plants and production of pesticides and herbicides. Conventional methods for remediating contaminated sediments have limited application due to the tidal action and urban development of the contaminated section of the Passaic River. Hence, this study proposes an in situ technology using ultrasound and ozone (O3) nanobubbles to remediate the sediments. Ultrasound is capable of desorbing heavy metals from soil, and ozone can oxidise the released heavy metals to a form that is mobile for ease of extraction. Nanobubbles are used as an effective ozone delivery method for the oxidation of heavy metals. Bench-scale tests were performed to evaluate the feasibility of the proposed technology. Ozone nanobubbles increased the solubility of ozone in water and reduced wastage. Also, due to the high ozone concentrations in water, chromium oxidation increased. A synthetic soil with a grain size distribution similar to that of actual river sediments was artificially contaminated with chromium and used in this research. Test results showed a 97·54% chromium removal efficiency, suggesting the feasibility of the proposed technology for pilot-scale studies.
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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".