Remediation of heavy-metal-contaminated sediments in USA using ultrasound and ozone nanobubbles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (O 3 ) 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it