Co-Cu Metal Alloys from LaCo <sub>1-x</sub> Cu <sub>x</sub> O <sub>3</sub> Perovskites as Catalysts for Higher Alcohol Synthesis from Syngas
Bibliographic record
Abstract
The specific surface area of ground perovskites is strongly dependent on the synthesis and pretreatment conditions. Calcining the ground LaCo1-xCuxO3-? perovskites at 5000C causes a decrease of their specific surface, but the further reductive pretreatment of the calcined sample in hydrogen leads to only a minor change in the specific surface area.The introduction of extra- and intra-perovskite lattice copper has different effects on the structure stability, reducibility and catalytic properties of LaCoO3 perovskites. Under the same pretreatment conditions the basic structure of both LaCoO3 and Cu2O/LaCoO3 samples is retained at the reduction temperature of 5000C (90 min) while that of Co-Cu based perovskites is completely collapsed producing a high dispersion of bimetallic metals and Co-Cu alloys supported on amorphous La2O3. For a series of LaCo1-xCuxO3 samples, increasing the copper content in the perovskite structure leads to an increase in the metal surface area of the reduced phase. The pretreated catalysts were tested for CO dissociation and for alcohol synthesis from syngas. The results indicated that the copper sites neighboring with cobalt atoms in the reduced perovskites decrease the activity in CO dissociation and increase the rate of higher alcohol synthesis while extra-perovkite lattice copper tends to produce methanol, methane and CO2. The overall activity in syngas conversion and higher alcohol productivity are proportional to the Co-Cu metal surface. The role of dual sites of Co and Cu for the synthesis of higher alcohols is discussed. The catalytic activity and alcohol productivity as well as product distribution depend strongly on reaction temperature and pretreatment conditions. The highest productivity of alcohols in the present study is about 73 mg/gcat/h for sample LaCo0.7Cu0.3O3.
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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".