Promotion of the Inactive Iron Sulfide to an Efficient Hydrodesulfurization Catalyst
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Bibliographic record
Abstract
Extensive efforts have been devoted to developing desulfurization catalysts to effectively remove sulfur from fuel. Active phase metals including cobalt, nickel, molybdenum, and tungsten have been extensively used in industry for hydrotreating/hydrodesulfurization catalysts for over 50 years. However, while it is desirable to use inexpensive materials to do the same job, it is a grand challenge. Herein, we report a Fe-based sulfide catalyst that is tuned by zinc with high activity for HDS, which shows an industrial application potential to replace industrial Mo-based catalysts. With an optimal configuration that has a Fe:Zn ratio close to 1:1, the reaction rate constants of the dibenzothiophene (DBT) and 4,6-dimethydibenzothiophene (4,6-DMDBT) HDS are increased by 9.2 and 17.4 times, respectively, in comparison with the sums of those on the monoiron and zinc sulfides. HDS activity for the sterically hindered 4,6-DMDBT on the FeZn sulfide catalyst is even close to that of Co-MoS 2 . The experimental results indicate that the addition of Zn greatly modifies the electronic properties of iron sulfide by transferring electrons from Zn to Fe, which tunes the d band center to modulate the adsorption behavior of DBT and 4,6-DMDBT. In combination with theoretical calculations, our experiments show that the addition of Zn dramatically tunes the formation of sulfur vacancies. We propose that the formation of sulfur vacancies is the critical factor for designing highly efficient Fe-based sulfide catalysts. This study provides the design principle of low-cost desulfurization catalysts for industrial refinery applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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