Performance Benefits of Multiwall Carbon Nanotubes in the Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layer
Bibliographic record
Abstract
In a polymer electrolyte membrane (PEM) fuel cell, the effective removal of product liquid water is necessary for achieving high power output. Liquid water produced at the cathode catalyst layer (CL) accumulates in the pores of the gas diffusion layer (GDL) and impedes oxygen diffusion from gas channels to the catalytic reaction sites. To reduce liquid water build up in the GDL, dual-layer GDLs comprised of a carbon fiber macro-porous substrate and a micro-porous layer (MPL) are typically used. The MPL covers the large surface pores of the substrate yielding a smooth interfacial contact region between the CL and the GDL. This smooth interface results in superior thermal and electrical conductivities as well as reduced water accumulation in the region between the CL and GDL. The MPL is typically a mixture of carbon black particles and hydrophobic agents. The structural properties of the MPL, such as surface crack size and porosity distribution, are dependent on its composition. An MPL containing 20-40 wt. % of hydrophobic agent was shown to facilitate product water removal at high current density operation1. Recently, it was found that the addition of carbon nanotubes (CNT) led to the strong adhesion between carbon black particles and the reduction of ohmic and mass transport losses 2-4. In this work, the physical properties of the SGL 25 series GDLs (SGL Group) were characterized by various imaging methods. Three types of commercially available GDLs were studied: SGL 25BC, 25BI and 25BN. SGL 25BC is the standard MPL with 23 wt.% PTFE, while SGL 25BI contains a reduced PTFE content of 10 wt.%. In SGL 25BN, CNTs are added to the standard MPL (i.e. 23 wt.% PTFE with CNT). Scanning electron microscopy (SEM) images of each GDL are shown in Figure 1. High intensity X-rays generated at the BMIT-BM beamline of the Canadian Light Source were utilized to image the fuel cell in operando, and the liquid water distribution in the resulting radiographs was identified with in-house post-processing algorithms. Images were obtained with a pixel resolution of 6.5 µm at a frame rate of 0.33 frames per second. In this work, the performance of these materials will be discussed, in particular, within the context of their varying microstructure. Reference 1. Qi Z, Kaufman A. Improvement of water management by a microporous sublayer for PEM fuel cells. J Power Sources. 2002;109(1):38-46. 2. Lin S, Chang M. Effect of microporous layer composed of carbon nanotube and acetylene black on polymer electrolyte membrane fuel cell performance. Int J Hydrogen Energy. 2015;40(24):7879-7885. 3. Fan C, Chang M. Improving proton exchange membrane fuel cell performance with carbon nanotubes as the material of cathode microporous layer. Int J Energy Res. 2016;40(2):181-188. 4. Schweiss R, Steeb M, Wilde PM, Schubert T. Enhancement of proton exchange membrane fuel cell performance by doping microporous layers of gas diffusion layers with multiwall carbon nanotubes. J Power Sources. 2012;220:79-83. 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".