Methane-Assisted Catalytic Desulfurization in an Environmentally Benign Way
Bibliographic record
Abstract
Abstract Petroleum is one of the most important natural resources for human beings, while the contained sulfur heteroatoms lead to a series of problems1. Therefore, a desulfurization process to reduce sulfur content is mandatory for clean petroleum utilization. Hydrodesulfurization is currently mature in industry, while this process is costly, energy intensive and environmentally unfriendly due to CO2 emission and H2S production2,3. Alternative cost-effective desulfurization process with environmentally benign sulfur-containing products remains unreported. Here we demonstrate that the desulfurization of a heavy oil model compound dibenzothiophene can be successfully achieved under methane environment over creatively designed dual catalyst system, generating a new sulfur-containing product CS2. Control experiments indicate that the presence of methane as well as catalyst components for direct desulfurization and methane activation are all required. The reaction process is better understood by extensive evidences from isotope labeling experiments, catalyst and product characterizations, density functional theory calculations and verification experiments, based on which a reasonable catalytic mechanism is proposed. It is found that methane-assisted desulfurization requires more stringent conditions, where sulfur vacancy abundance, methane activation capability and surface sulfur transfer are all indispensable. This study pioneers a transformational desulfurization route, which is more economically and environmentally attractive for petroleum processing industry.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".