Degradation of TCE by TEOS Coated nZVI in the Presence of Cu(II) for Groundwater Remediation
Bibliographic record
Abstract
The removal of TCE by nanofer zero valent iron (nanofer ZVI) coated with tetraethyl orthosilicate (TEOS) in the presence of Cu(II) at different environmental conditions was studied. The kinetics of TCE degradation by nanofer ZVI was determined. At a dosage of 10 mg of nanofer ZVI, almost 63% of TCE was removed, when Cu(II) and TCE were present. It contrasts with 42% degradation of TCE in the absence of Cu(II). SEM/EDS images indicated that Cu(II) is reduced to form Cu0 and Cu2O. These formations are considered to be responsible for enhancing TCE degradation. Direct reduction involves hydrogenolysis and β‐elimination in the transformation of TCE, while indirect reduction involves atomic hydrogen and no direct electron transfer from the metal to reactants. The reduction of activation energy was also noted indicating that the rate limiting step for TCE degradation in the presence of Cu(II) is surface chemical reaction rather than diffusion. Most of iron present in nanofer ZVI get dissolved causing the generation of localized positive charge regions and form metal chlorides. Local accumulation of hydrochloric acid inside the pits regenerates new reactive surfaces to serve as sources of continuous electron generation. No significant effect of TCE was noticed for Cu(II) sequestration.
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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".