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Record W2915525719 · doi:10.1149/ma2018-01/21/1365

Two Phase Flow Modeling and Characterization of Oxygen Bubbles in PEM Water Electrolysis Cells

2018· article· en· W2915525719 on OpenAlexaff
Amin Nouri-Khorasani, Jason Tai Hong Kwan, Arman Bonakdarpour, David P. Wilkinson

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverpotentialAnodeElectrolysisElectrolyteMaterials scienceNucleationBubbleBuoyancyChemical engineeringElectrolysis of waterCurrent densityElectrodeChemistryThermodynamicsMechanicsElectrochemistry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.210
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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