Stability assessment of sulfided NiMo-based catalysts for continuous flow supercritical water hydrodeoxygenation applications
Bibliographic record
Abstract
Sulfided NiMo-based catalysts are widely used in hydroprocessing, including hydrotreating and hydrocracking in petroleum refining and renewable fuel production via biomass conversion. The development of hydrothermal liquefaction (HTL) for biocrude has increased interest in direct hydrothermal upgrading processes, such as (hydro)deoxygenation and desulfurisation. This study evaluated the stability of NiMo- and NiW-based catalysts under continuous-flow conditions to assess their suitability for hydrothermal upgrading. After just 2 hours in supercritical water (SCW), significant metal leaching was observed: Mo losses ranged from 47–97%, and Ni losses from 35–80%. Sulfidation provided only modest improvement, with losses still at 56–80% for Mo and 52–75% for Ni. Tungsten loss reached 90% in the sulfided NiW catalyst. Mo leaching was particularly severe during the heating phase, with over 60% of Mo and around 20% of Ni lost during heating and cooling, peaking at 200 °C. At 400 °C, leaching rates from unreduced NiMo/AC were 0.4 mg h⁻¹ for Ni and 10 mg h⁻¹ for Mo. Sulfided NiMo showed better Mo stability, with leaching rates of 0.24 mg h⁻¹ for Ni and 3.6 mg h⁻¹ for Mo. These findings highlight the importance of assessing catalyst stability under realistic flow conditions. NiMo-, NiW-, and by extension, CoMo- and CoW-based catalysts are unsuitable for hydrothermal upgrading due to poor stability in SCW, particularly during heat-up and cool-down phases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".