Improvement of Liquid Hydrocarbon Yield in CO<sub>2</sub> Fischer–Tropsch Synthesis over Potassium-added Iron Carbide Catalyst
Bibliographic record
Abstract
フィッシャー · トロプシュ合成（Fischer–Tropsch synthesis; FTS）を経由したCO2水素化反応（CO2-FTS）による液体炭化水素（C5＋）の効率的な合成を目指し，炭化鉄触媒（FeCx）へのカリウム添加効果を調べた。シュウ酸鉄二水和物に硝酸カリウムを加えCOガス流通下で熱分解することで，カリウム含有量の異なる炭化鉄触媒（K–FeCx）を調製した。K(z)–FeCx触媒（K/Fe＝z/100，z＝0, 1, 5, 10: モル比)上でのCO2-FTS試験によって得られたC5＋,others収率とCH4収率を比較した。K(1)–FeCx触媒を用いた場合は比較した触媒の中で最も高いC5＋,others収率が得られた。さらに，K(1)–FeCx触媒はカリウムを含まない触媒に比べて副生するCH4の収率が低くなった。K(1)–FeCx触媒はFTS反応の活性点であるχ-Fe5C2相を最も多く含んでいるとともに，過剰にカリウムを加えた触媒と比べてカリウム自身によるFTS活性点の被覆が少ないため，液体炭化水素収率が向上したと考えた。
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".