Nano-Iron Carbide-Catalyzed Fischer-Tropsch Synthesis of Green Fuel: Surface Reaction Kinetics-controlled Regimes in a 3-φ Slurry-Continuous Stirred Tank Reactor
Bibliographic record
Abstract
Liquid fuel derived from renewable resources is one of the technologies under development as part of "biorefining" platforms. Fisher-Tropsch synthesis (FTS) is a commercial technology producing alternative fuels from coal (CTL-Coal-To-Liquid) and natural gas (GTL; Gas-To-Liquid). FTS Biomass-To-Liquid (BTL), although not yet at the market level, is a continuously-growing field, and its successful commercialization depends on improving techno-economic sustainability.In a previous study by the authors' research team, a plasma-synthesized nano-iron carbide catalyst (PS-Nano-FeC) demonstrated direct relationship to the presence of iron carbide high-FTS and water-gas-shift (WGS) activity, with high-catalyst stability and regenerability in a 3-phase slurry, continuouslystirred tank reactor (S-CSTR).Although these results, along with a recently-published phenomenological model, are indicators of the industrial potential of this catalyst, transport phenomena, chemical mechanisms and their intrinsic kinetics are needed as reactor scale-up tools.In the work reported here, the PS-Nano-FeC catalyst was tested in a S-CSTR with hexadecane as liquid carrier.We evaluated the optimal operating conditions for a surfacereaction, kinetics-controlled regime.The results include: (1) Reactant conversion and product yields; (2) Fresh and used catalyst instrumental analyses; (3) A model considering all transfer and surface kinetics, accompanied by proof of the rate-controlling step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".