Development of highly active internal steam methane reforming catalysts for intermediate temperature solid oxide fuel cells
Bibliographic record
Abstract
Fuel processing is one of the essential parts for development of intermediate solid oxide fuel cells (IT-SOFC). Natural gas (methane) is considered as the most abundant and cost effective fuel for the production of hydrogen for IT-SOFC. The primary aim of this thesis is to use a novel precursor material—layered double hydroxide (LDH) – for developing a new type of cost effective, highly active and long lasting catalyst which can reform natural gas in IT-SOFC anode environment. Small amount of noble metals Pd, Rh and Pt are used as promoters to enhance the catalyst’s performance as while maintaining the cost relatively low. The research objectives are achieved by a series of studies including catalysts synthesis, characterisation and the catalytic activities. The thesis initially gives a comprehensive review on fuel cell and SOFC technology, steam methane reforming and reforming catalyst to provide better understanding of the research. Experimental studies include the effects of the synthetic conditions of the LDH precursors and thermal treatments on the physical, chemical behaviours and catalytic activities of the catalysts and promotional effects by noble metals. The LDH derived catalysts compositions, promoter quantities and operating conditions are optimised for the best performance in the IT-SOFC anode environment. A new method for the development of precursor sol for easy coating of the anode is developed and studied. The sol preparation is achieved by acid attack. The sol developed is found to produce better coating and has very high catalytic properties after activation. The catalysts developed were tested for their stability and self-activation ability to ensure its use in the commercial cells. The findings of the present study indicate that the catalysts developed show excellent catalytic performance and these catalysts have very high potential for further commercialisation in IT-SOFC.
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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 | 0.001 |
| 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".