New Strategy of Using Stannic Oxide as Catalyst in a Three-dimension Electrode Reactor for the Electro-oxidation of Organic Matter
Bibliographic record
Abstract
An efficient organic-matter (OM) degradation strategy using synthetic electrocatalysis particles as fixed filler in a threedimension electrode reactor was developed. In our work, SnO2-granular active carbon (SnO2-GAC) was prepared by integrating GAC with nano-SnO2 via the sol-gel method, using SnCl4 as starting material and gelatin as a stabilizer. The phase composition and micromorphology of the particles were characterized by X-ray diffraction, X-ray photoelectron spectroscopy and scanning/transmission electron microscopy techniques. The results showed the incorporation of SnO2 crystallized in a tetragonal lattice with an average crystallite size of 10.6 nm, which was easily accessible on the GAC surface and mesopores. Electrochemical properties were tested with cyclic voltammetry and electrochemical impedance spectroscopy methods that disclosed an improved response current with a simultaneous increase in anodic area, while the charge-transfer and electrolyte resistance obviously decreased, in contrast to the virgin GAC filler in the three-dimension electrode system. Although the energy consumption of SnO2-GAC (11.71 kWh·kg−1 COD) presented slight superiority than that of GAC (13.62 kWh·kg−1 COD) at the same chemical oxygen demand (COD) conversion of 98 % when a current density of 20 A·m−2 was employed for phenolic wastewater treatment, the required degradation time of the former (47.22 h) is greatly decreased compared with that of the latter (54.24 h). These results obviously confirm the superiority of the prepared SnO2-GAC in electro-oxidation of organic matters.
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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) | 0.001 | 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".