Two Phase Flow Modeling and Characterization of Oxygen Bubbles in PEM Water Electrolysis Cells
Bibliographic record
Abstract
The formation, growth, and stability of oxygen bubbles inside the porous transport layer (PTL) pores of a polymer electrolyte membrane water electrolysis (PEMWE) cell have been modeled. In a PEMWE cell, O2 bubbles form and grow inside the anode catalyst layer (ACL) and PTL pores. The most critical impact of the O2 bubbles on the overall performance is the screening of the ACL surface. Semi-empirical relations for the current density-surface coverage for gas evolving electrodes have previously been developed [1] and were used for our PEMWE modeling [2]. This screening effect has been evaluated as an overpotential term, and the results were used to predict the effects of the catalyst and PTL properties on the electrolysis cell performance. The fluctuations in the cell current density were used to identify different types of bubbles and their detachment frequency from the anode electrode. In this work, the anode catalyst surface has been described as a smooth, planar and horizontal plane. The PTL pores are described as identical straight hydrophilic cylinders with 1-mm height. Such a structure resembles tunable PTL structures such as those synthesized by Kang et al. [3]. Stable O2 bubble nuclei form mainly at the intersection of the anode catalyst layer and the PTL pore wall [3,4]. Our modeling results show the effects of three different types of bubbles in a PEMWE cell: i) nucleation-driven, ii) drag-driven, and iii) buoyancy-driven bubbles. Figure 1 shows the relative lifetime and overpotential effects of the O2 bubbles in a PEMWE with a PTL consisting of 11 μm dia. pores operated at a fixed temperature of 80°C and a balanced pressure of 1 bar [5]. Comparing the current density fluctuations in chronoamperometry experiments with the bubble detachment frequencies obtained through modeling provides a distribution of different types of bubbles in the electrolysis cell. These frequencies vary according to the PTL structure and the PEMWE cell operating conditions. Understanding of bubble growth and detachment mechanisms are of significant importance for reduction of mass transport-related losses and enhancement of PEMWE performance. References: H. Vogt and K. Stephan, Electrochim. Acta, 155, 348–356 (2015). E. Tabu Ojong et al., Int. J. Hydrogen Energy, 42 , 25831-25847 (2017). Z. Kang et al., Energy Environ. Sci., 10, 166-175 (2017). O. F. Selamet et al., Int. J. Hydrogen Energy, 38, 5823–5835 (2013). A. Nouri-Khorasani, E. Tabu Ojong, T. Smolinka, and D. P. Wilkinson, Int. J. Hydrogen Energy, 42, 28665–28680 (2017). Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".