Comparison of Nitrilotriacetic Acid and [<i>S</i>,<i>S</i>]-Ethylenediamine-<i>N</i>,<i>N</i>′-disuccinic Acid in UV–Fenton for the Treatment of Oil Sands Process-Affected Water at Natural pH
Bibliographic record
Abstract
The application of UV–Fenton processes with two chelating agents, nitrilotriacetic acid (NTA) and [ S, S ]-ethylenediamine- N, N ′-disuccinic acid ([ S, S ]-EDDS), for the treatment of oil sands process-affected water (OSPW) at natural pH was investigated. The half-wave potentials of Fe(III/II)NTA and Fe(III/II)EDDS and the UV photolysis of the complexes in Milli-Q water and OSPW were compared. Under optimum conditions, UV–NTA–Fenton exhibited higher efficiency than UV–EDDS–Fenton in the removal of acid extractable organic fraction (66.8% for the former and 50.0% for the latter) and aromatics (93.5% for the former and 74.2% for the latter). Naphthenic acids (NAs) removals in the UV–NTA–Fenton process (98.4%, 86.0%, and 81.0% for classical NAs, NAs + O (oxidized NAs with one additional oxygen atom), and NAs + 2O (oxidized NAs with two additional oxygen atoms), respectively) under the experimental conditions were much higher than those in the UV–H 2 O 2 (88.9%, 48.7%, and 54.6%, correspondingly) and NTA–Fenton (69.6%, 35.3%, and 44.2%, correspondingly) processes. Both UV–NTA–Fenton and UV–EDDS–Fenton processes presented promoting effect on the acute toxicity of OSPW toward Vibrio fischeri . No significant change of the NTA toxicity occurred during the photolysis of Fe(III)NTA; however, the acute toxicity of EDDS increased as the photolysis of Fe(III)EDDS proceeded. NTA is a much better agent than EDDS for the application of UV–Fenton process in the treatment of OSPW.
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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".