Sulfided Homogeneous Iron Precatalyst for Partial Hydrogenation and Hydrodesulfurization of Polycyclic Aromatic Model Asphaltenes
Bibliographic record
Abstract
Here, we report the first study to use Fe2S2(CO)6 as a well-defined petroleum-soluble precatalyst for partial hydrogenation of polycyclic aromatic and heteroaromatic compounds, including pyrene, phenanthrene, naphthalene, and benzothiophene. The in situ-generated heterogeneous catalyst was characterized using a combination of thermogravimetric analysis (TGA), combustion analysis (CHNS), Fourier transform infrared (FT-IR) spectroscopy, scanning transmission electron microscopy (STEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy-energy dispersive X-ray (SEM-EDX). Catalytic performance of Fe2S2(CO)6 was evaluated in a batch microreactor coupled with an agitator, varying reaction temperature, pressure, and precatalyst loading. The active phase, consisting of iron sulfide nanoparticles, was prepared from purified Fe2S2(CO)6 in toluene; no sulfur additive is required. The results demonstrate that a “presulfided” iron catalyst leads to partial hydrogenation of polycondensed aromatics under moderate conditions, with naphthalene showing only low conversion. Activated carbon, γ-alumina, and activated silica are all effective dispersants for the Fe precursor. The reactivity shows self-consistent substrate dependence, varying with the resonance energy stabilization of the starting compounds and partially saturated intermediates coupled with the surface adsorption enthalpy of the aromatic ring system. The active catalyst derived from this precursor exhibits higher catalytic activity than commercial iron sulfide precatalysts. Importantly, the catalyst is active for hydrodesulfurization (HDS) of benzothiophene, albeit modestly so.
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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 | 0.000 | 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 | 0.000 | 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 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".