Photochemical degradation of aqueous artificial sweeteners by <scp>UV</scp>/<scp>H<sub>2</sub>O<sub>2</sub></scp> and their biodegradability studies
Bibliographic record
Abstract
Abstract BACKGROUND Photochemical degradations of three commonly used artificial sweeteners, namely aspartame (ASP), acesulfame K (ACE), and sucralose (SUC), are studied in multicomponent aqueous systems, and treated through UV/H2O2 process in a recirculating batch reactor. The biodegradation characteristics of the three sweeteners are also investigated both in single and multicomponent aqueous systems through respirometry. The results are used to draw conclusions and recommendations for onsite treatment of industrial wastewaters containing artificial sweeteners. RESULTS The effects of the operating temperature and the applied H2O2 dosage are found to be significant on the overall degradation efficiency. An interaction effect between aspartame and sucralose is identified, resulting in a temporary improvement in total organic carbon (TOC) removal in some cases. Respirometric tests confirm that acesulfame K and sucralose are non‐biodegradable, whereas aspartame is readily biodegradable with a 6‐day carbonaceous biochemical oxygen demand to theoretical oxygen demand (cBOD6/ThOD) ratio of 0.63 ± 0.02. CONCLUSIONS It is concluded that activated sludge processes can remove ASP even in the presence of ACE and SUC. The latter two compounds cannot be degraded by activated sludge. Hence, the UV/H2O2 process is a suitable treatment technique for simultaneous removal when all three sweeteners are present in an aqueous matrix. A higher temperature of the wastewater stream may be used as a process variable to reduce oxidant dosing when applicable. © 2020 Society of Chemical Industry
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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".