Tuning Transport Mechanisms in Fuel-Assisted Solid Oxide Electrolyzers for Enhanced Performance and Product Selectivity
Bibliographic record
Abstract
The demand for renewable energy storage systems and CO2-utilization technologies has become apparent in recent decades due to concerns related to the continuous emission of anthropogenic greenhouse gases. A promising technology that can help alleviate some of these concerns are solid oxide electrolyzers, since they have the ability to convert CO2 and steam into valuable chemicals and fuels from the electricity generated by renewable sources, including wind and solar energy. Furthermore, these devices can operate in fuel-assisted mode, wherein a reactant, such as methane, hydrogen, and/or carbon monoxide, is delivered to the anode in order to reduce its operating potential. This simple modification has been shown to decrease the operating potential by nearly one order of magnitude [1-3], and, in some instances, has enabled the cell to generate power and synthesis gas simultaneously [4-6]. Fuel-assisted solid oxide electrolyzers have therefore emerged as a compelling option for renewable energy storage and large-scale chemical production, and have begun to draw significant attention from researchers and designers. But despite previous efforts, an understanding of how to attain a desired product composition and efficiency remains unclear. The objective of the presented work, then, is to perform a scaling analysis of the transport phenomena pertinent to fuel-assisted cells, in order to develop a set of dimensionless ratios that characterize the interplay between chemical and electrochemical reactions, and advective and diffusive transport. These dimensionless ratios provide crucial information on how to control the contribution of chemical and electrochemical reactions, as well as strategies to mitigate concentration and temperature gradients within the electrodes and flow channels. This approach is intended to provide an enhanced understanding of how to increase the product selectivity and performance of fuel-assisted solid oxide electrolyzers, which will hopefully lead to enhanced electrode and cell designs. [1] A. Q. Pham, P. H. Wallman and R. S. Glass, US Patent 6,051,125 (2000). [2] F. Chen and Y. Wang, US Patent 9,574,274 (2017). [3] Y. Wang, T. Liu, S. Fang, G. Xiao, H. Wang and F. Chen, J. Power Sources, 277, 261 (2015). [4] H. Xu, B. Chen and M. Ni, J. Electrochem. Soc., 163, F3029 (2016). [5] Y. Patcharavorachot, S. Thongdee, D. Saebea, S. Authayanun and A. Arpornwichanop, Energy Convers. Manage., 120, 274 (2016). [6] H. Xu, B. Chen, J. Irvine and M. Ni, Int. J. Hydrogen Energy, 41, 21839 (2016).
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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 | 0.001 | 0.001 |
| 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".