Sulphur Induced Degradation of Nickel-Based Solid Oxide Fuel Cell Anodes
Bibliographic record
Abstract
Solid Oxide Fuel Cells (SOFCs) are high temperature solid-state electrochemical devices that convert fuel into electricity and heat with high efficiency. Many fuels suitable for SOFCs derive from hydrocarbon sources, such as natural gas or biogas; however, these contain significant impurities, most notably compounds containing sulphur, which can poison the nickel electrocatalyst in the anode of the fuel cell. Sulphur removal is usually carried out but it is complicated and expensive to achieve levels below 1 part per million (ppm). An enhanced scientific understanding of chemical interactions on the surface of SOFC electrodes is critical to the development of robust nickel-based anodes, but the mechanisms and effects of sulphur poisoning are not fully understood. The scope of this thesis is to advance the field of sulphur-poisoning research by studying the effect of current density on the sulphur-induced degradation, and focuses on intermediate-temperature (IT) conditions with nickel-gadolinia doped ceria (Ni-CGO), which is the most promising anode material for IT-SOFCs, operating between 600–800 °C. The work of this thesis is aimed at (i) investigating the kinetics of sulphur poisoning, and the effect of current density, by use of a specially built three-electrode electrochemical test rig; (ii) analysis of structural modifications to the anode microstructure as a result of exposure to fuel cell conditions and (iii) development and prototype testing of a miniature SOFC test rig with optical access for in situ Raman spectroscopy. Fuel cell operation at higher current density was found to partially mitigate the sulphur poisoning of up to 3 ppm H2S in H2 fuel, while microstructural analysis found that the presence of as little as 0.5 ppm H2S accelerated restructuring of nickel grain surfaces. Finally, preliminary proof-of-concept results were obtained for the in situ Raman rig, and suggestions for a future design are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".