Light Emitting Diode Based Photocatalytic Treatment of Sulfolane Contaminated Water using Nanomaterials
Bibliographic record
Abstract
Sulfolane is a highly water soluble and stable compound that is used in many industries due to its excellent performance as an industrial solvent. Elevated levels of sulfolane have been detected in groundwater in Alberta due to accidental release from gas treatment plants. Stringent environmental regulatory requirements regarding sulfolane have generated a need for effective water treatment technologies for removal of sulfolane. This research investigates a photocatalytic water treatment system aimed at removing sulfolane from groundwater. In particular, degradation of sulfolane using a photoreactor fitted with light emitting diodes (LEDs) was studied. The performances of commercial TiO2 powder (P25) and reduced graphene oxide TiO2 composite (RGO-TiO2) were compared. The impact of matrix effects and type of irradiation were investigated for photocatalytic degradation of sulfolane. In addition, a reusability test was conducted for the photocatalyst to examine the degradation of sulfolane in consecutive cycles with new batches of sulfolane contaminated water. The results demonstrated that the combination of UVA-LED and P25 yields better performance than UVA-LED and RGO-TiO2 for the degradation of sulfolane. UVA-LEDs displayed more efficient use of photon energy when compared with the mercury lamps. A significant decrease in sulfolane degradation was observed in the presence of anions and co-contaminants. LED based TiO2 photocatalysis was effective in degrading sulfolane even after three photocatalytic cycles. Oxidants and nanomaterials were used to improve TiO2 based photocatalytic degradation of sulfolane. Hydrogen peroxide (H2O2), sodium persulfate (PS) and ozone (O3) were the oxidants studied and carbon nanotubes (CNT) and nanosized zero valent iron (nZVI) were used as the nanomaterials. The impact of these oxidants and nanomaterials at various dosages were evaluated in both Milli-Q water and groundwater. The results indicate that with a suitable dose of oxidants or nanomaterials, photocatalytic degradation of sulfolane in Milli-Q water can be enhanced. The addition of ozone contributed to a significant increase in sulfolane degradation rate in Milli-Q water. The experiments conducted in groundwater showed that oxidants (H2O2, PS and O3) increased the degradation of sulfolane while the nanomaterials (CNT and nZVI) impeded sulfolane degradation in groundwater.
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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".