Interfacial Characteristics of Graphene Containing Novel Microporous Layers for PEM FCs
Bibliographic record
Abstract
The cathodic microporous layer (MPL) provides many beneficial properties for performance improvement of the H2 PEM FC, particularly with respect to water management and oxygen transport to the catalyst layer [1,2]. The MPL performance is highly dependent on its in-operando material properties such as morphology, porosity and electrical conductivity (in-plane and through-plane, respectively). Furthermore, the interfacial interconnectivity of the MPL material properties with the nano-scale features of the catalyst layer (CL) and the macro-scale characteristics of the gas diffusion layer (GDL) are essential, yet difficult to study, elements in the optimal operation of the fuel cell cathode. In this study diverse graphene based MPLs were investigated by comparison with conventional carbon black and graphite materials. The graphene MPLs were composed of: stand-alone graphene foam [3], electrochemically exfoliated graphene micro-flakes [4] and reduced graphene oxide [5]. The general performance trends revealed lower overpotentials for the graphene MPLs in the electrode kinetic and ohmic controlled regions. In the electrode kinetic region, the improvement was attributed to the unique stacked flake morphology of the graphene assuring excellent interfacial adhesion with the catalyst layer combined with graphene’s high electronic conductivity. The lower ohmic polarization resistance on the other hand, is due to the compact and tortuous morphology of graphene MPLs exhibiting a higher tendency for water retention and inducing, thereby, superior membrane humidification. The enhanced water retention of the graphene MPL, however, can cause catalyst layer flooding at current densities greater than 1000 mA cm-2. To overcome this limitation, composite MPLs containing graphene flakes and carbon black (e.g., Vulcan XC-72) in a 1:1 weight ratio were manufactured and investigated. The graphene – carbon black composite MPLs, demonstrated lower overpotentials throughout the entire polarization curve with maximum power densities 81% and 28% higher compared with the carbon black only MPL at 20% and 100% cathode relative humidity, respectively. In addition, the graphene composite MPLs showed vastly superior durability at 20% cathode humidity in 5 h galvanostatic polarization experiments at 1000 mA cm-2. References: M. Blanco, D.P. Wilkinson, Int. J. Hydrogen Energy, 39, 16390-16404 (2014). T. Swamy, E.C. Kumbur, M.M. Mench, J. Electrochem. Soc., 157, B77-B85 (2010). M.J. Leeuwner, D.P. Wilkinson, E. Gyenge, Fuel Cells, 6, 790-801 (2015). A.T. Najafabadi, M.J. Leeuwner, D.P. Wilkinson, E. Gyenge, ChemSusChem, 9, 1689-1697 (2016). M.J. Leeuwner, D.P. Wilkinson, E. Gyenge, manuscript in preparation (2017).
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".