Electrochemical Oxidation of an Organic Dye Adsorbed on Tin Oxide and Antimony Doped Tin Oxide Graphene Composites
Bibliographic record
Abstract
Electrochemical regeneration suffers from low regeneration efficiency due to side reactions like oxygen evolution, as well as oxidation of the adsorbent. In this study, electrically conducting nanocomposites, including graphene/SnO2 (G/SnO2) and graphene/Sb-SnO2 (G/Sb-SnO2) were successfully synthesized and characterized using nitrogen adsorption, scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy. Thereafter, the adsorption and electrochemical regeneration performance of the nanocomposites were tested using methylene blue as a model contaminant. Compared to bare graphene, the adsorption capacity of the new composites was ≥40% higher, with similar isotherm behavior. The adsorption capacity of G/SnO2 and G/Sb-SnO2 were effectively regenerated in both NaCl and Na2SO4 electrolytes, requiring as little charge as 21 C mg−1 of adsorbate for complete regeneration, compared to >35 C mg−1 for bare graphene. Consecutive loading and anodic electrochemical regeneration cycles of the nanocomposites were carried out in both NaCl and Na2SO4 electrolytes without loss of the nanocomposite, attaining high regeneration efficiencies (ca. 100%).
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.
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".