The recycling of sulphur in waste and conversion to manganese sulphate: Process optimization, kinetic study, mechanistic insights
Bibliographic record
Abstract
Abstract Waste sulphur produced in industrial processes is often exposed to the air, and its diffusion and combustion in the environment can produce harmful substances, posing a significant risk to ecological safety and human health. Herein, a novel method for the direct treatment of solid sulphur with manganese dioxide based on a simple sealed reactor is proposed. Significantly, the developed sulphur treatment technology could prepare high purity manganese sulphate at the same time. Under the optimum conditions of the reaction temperature of 187.2°C, reaction time of 21.6 h, sulphuric acid loading of 1.00 mol ∙ L −1 , and manganese dioxide dosage of 4.00 mol ∙ mol‐S −1 derived by the response surface method (RSM), the removal efficiency of sulphur could reach near 100%, and the purity of the manganese sulphate product obtained was more than 98%. Kinetic model fitting analysis and several characterization methods were adopted to confirm that the reaction process between sulphur and manganese dioxide follows the unreacted nuclear model controlled by surface chemical reaction, and the transformation paths of Mn and S elements were proposed. This study provides a novel method for the resource treatment of waste sulphur and the preparation of high purity manganese sulphate.
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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 | 0.001 |
| 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".