Exploration of Vapor Electrolysis for Chemical Transformations
Bibliographic record
Abstract
Electrolytic devices provide a carbon-free option to producing hydrogen, with many advantages such as modular, scalable design and flexibility of operating parameters (temperature, pressure, etc.).1 Typically, electrolyzers are run under conditions that require a significant amount of clean, liquid water and are run at several A/cm2. However, the solar flux limits achievable current densities to ~100 mA/cm2 and furthermore the strong solar resources are oftentimes located in regions away from a ready supply of clean water. Finally, multiphase flow in electrolyzers also complicate the transport physics and reaction kinetics. These above challenges can be surmounted through the use of vapor electrolyzer, where water vapor is split to oxygen and hydrogen directly. Vapor electrolysis has not been extensively studied due to a limitation in how much water can be supplied to the system – water electrolysis is never mass-transport limited. Some studies have shown such a device2,3 but are more proof of concept. In this talk, we discuss an experimental diagnostic study of various parameters to increase the performance of such cells that can reach 1 A/cm2. In addition, using mathematical modeling we explore the various losses endemic to this system as well as its natural advantages to utilize impure water in arid or possibly marine climates. Finally, we will discuss how the understanding gained on vapor electrolysis and gas-diffusion electrodes can be transferred to the electrochemical conversion of other gases (e.g., methane, carbon dioxide, etc.). Preliminary findings of fuel electrochemical oxidation to partial oxidation products will be discussed. Acknowledgements: J.C.F. would like to thank Dr. Michael Gerhardt, Dr. Nemanja Danilovic, and Dr. Yagya Regmi for the helpful discussions. J.C.F. also acknowledges support from National Science Foundation Graduate Research Fellowship under Grant No. DGE 1106400. This work was partially funded by the Energy & Biosciences Institute through the EBI-Shell program. References: Carmo M., et. al. International Journal of Hydrogen Energy,Volume 38, Issue 12, 2013 . Kistler T.A., et. al. Electrochem. Soc.volume 166, issue 5, 2019 Spurgeon J.M. , et. al. Energy Environ. Sci., 2011, 4, 2993-2998
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".