Alkaline modified A-site deficient perovskite catalyst surface with exsolved nanoparticles and functionality in biomass valorisation
Bibliographic record
Abstract
Environmental problems associated with the use of fossil fuels and increase in energy demands due to rise in population and rapid industrialisation, are the driving forces for energy. Catalytic conversion of biomass to renewable energies is among the promising approaches to materialize the above. This requires development of robust catalysts to suppress deactivation due to carbon deposition and agglomeration. In this work, surface properties and chemistry such as exsolution of B-site metal catalyst nanoparticles, particle size and distribution, as well as catalyst-support interactions were tailored through the use of alkaline dopants to enhance catalytic behaviour in valorisation of glycerol. The incorporation of alkaline metals into the lattice of an A-site deficient perovskite modified the surface basic properties and morphology with a consequent robust catalyst-support interaction. This resulted in promising catalytic behaviour of the materials where hydrogen selectivity of over 30% and CO selectivity of over 60% were observed. The catalyst ability to reduce fouling of the catalyst surface as a result of carbon deposition during operation was also profound due to the robust catalyst-support interaction occurring at the exsolved nanoparticles due to their socketing and the synergy between the dopant metals in the alloy in perovskite catalyst systems. In particular, one of the designed systems, La0.4Sr0.2Ca0.3Ni0.1Ti0.9O3±δ, displayed almost 100% resistance to carbon deposition. Therefore, lattice rearrangement using exsolution and choice of suitable dopant could be tailored to improve catalytic performance.
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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".