3D-Printed Gas Flow Layers As Alternative to Conventional Flow Fields and Gdls in Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
3D printed gas diffusion layers (GDL) have been proposed [1] and implemented [2,3] as model systems for innovative water management strategies addressing two-phase transport challenges within the GDL of polymer electrolyte fuel cells (PEFC). Digital light processing (DLP) resin 3D-printers can be used to fabricate polymer structures with a deterministic cubic lattice design, which are subsequently carbonized in a pyrolysis process. The carbonized samples feature minimum solid dimensions of ~50 μm and pore sizes as small as ~100 μm. These lattice structures show promising PEFC performance as GDL replacements, particularly due to the high convective flow velocities and reduced diffusive transport distances [3]. In this presentation we discuss how such lattice structures can be used as an alternative cell architecture in which the conventional channel-rib flow field is entirely replaced by the printed structure (see Figure 1 a-b), similar as with metal foam flow fields [4]. The new architecture shows comparable performance to a conventional channel-rib configuration with commercial GDL materials (SGL-35BC), even when no GDL but only an MPL is used as an intermediate layer to the CL (see Figure 1c). The fuel cell performance characterization is complemented by operando X-ray radiography and X-ray tomography experiments using a lab-CT to gain insights into the liquid water distribution within the 3D-printed GFLs. References [1] D. Niblett et. al., Journal of the Electrochemical Society, 2020, 167, 013520 [2] D. Niblett et. al., International Journal of Hydrogen Energy, 2022, 47, pp. 23393-23410 [3] T. Dörenkamp et. al., ACS Appl. Materials & Interfaces, 2025, DOI: 10.1021/acsami.5c00770 [4] Y. Zhang et. al., Journal of Power Sources, 2021, 492, 229664 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".