Photocatalytic Degradation of Sulfolane Using a LED-Based Photocatalytic Treatment System
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
Sulfolane is an emerging industrial pollutant detected in the environments near many oil and gas plants in North America. So far, numerous advanced oxidation processes have been investigated to treat sulfolane in aqueous media. However, there is only a few papers that discuss the degradation of sulfolane using photocatalysis. In this study, photocatalytic degradation of sulfolane using titanium dioxide (TiO2) and reduced graphene oxide TiO2 composite (RGO-TiO2) in a light-emitting diode (LED) photoreactor was investigated. The impact of different waters (ultrapure water, tap water, and groundwater) and type of irradiation (UVA-LED and mercury lamp) on photocatalytic degradation of sulfolane were also studied. In addition, a reusability test was conducted for the photocatalyst to examine the degradation of sulfolane in three consecutive cycles with new batches of sulfolane-contaminated water. The results show that LED-based photocatalysis was effective in degrading sulfolane in waters even after three photocatalytic cycles. UVA-LEDs displayed more efficient use of photon energy when compared with the mercury lamps as they have a narrow emission spectrum coinciding with the absorption of TiO2. The combination of UVA-LED and TiO2 yielded better performance than UVA-LED and RGO-TiO2 for the degradation of sulfolane. Much lower sulfolane degradation rates were observed in tap water and groundwater than ultrapure water.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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