Investigating the Influence of Inlet Relative Humidity on Polymer Electrolyte Membrane Fuel Cell Performance by Visualizing 4-D Water Distributions in Gas Diffusion Layers
Bibliographic record
Abstract
The catastrophic effects of atmospheric greenhouse gases and the depletion of non-renewable resources has led to the urgency to develop clean, sustainable energy technologies to meet increasing energy demands. However, the intermittent nature of current renewable energy technologies warrants the acquisition of on-demand renewable energy through either energy production or storage methods. Polymer electrolyte membrane fuel cells (PEMFCs) are promising candidates for this task, as they utilize the most abundant element on Earth, hydrogen, to produce high amounts of power under rapid changes in load with little to no greenhouse gas emissions (1). Therefore, PEMFCs have great potential to help offset the negative impact of atmospheric pollution due to excessive carbon emissions. However, liquid water management issues associated with high power output of the fuel cell typically leads to reduced performance and durability of the fuel cell, and thereby hinders global implementation of these devices (2). To minimize these losses and improve GDL material designs, an understanding of the relationship between product liquid water distributions in the cathode GDL and transport properties of PEMFCs under varying operating conditions is highly valuable. Previous works have characterized the effect of operating temperature on liquid water pathways and distributions in GDLs by visualizing operando PEMFCs with 3D imaging techniques such as X-ray computed tomography (CT) (3). The high-speed, high-resolution capabilities of these imaging techniques enable the visualization of dynamic pore-scale activity to elucidate transport mechanisms in the GDL. In this work, the effect of inlet relative humidity on the formation and distribution of liquid water pathways in cathode GDLs is investigated by imaging a PEMFC operando with synchrotron X-ray CT at high spatial resolution, enabling the resolution of water in the individual pores of the GDL. The contribution of a microporous layer (MPL) is also explored by imaging a cell with an MPL and without. Imaging is conducted on a specialized cell designed to facilitate continuous rotation about the CT stage, enabling fast acquisition of consecutive scans to achieve high temporal resolution useful for visualizing the dynamic development of preferential water pathways. Additionally, electrochemical impedance spectroscopy was performed to quantify mass transport losses. The sequence of CT images was utilized to capture the dynamics of liquid water development as well as stabilized water distributions. Visualizing the reconstructed images shows that as current increases, water production increases, and the development of water pathways to breakthrough at the flow field interface is observed. Additionally, results show that an increase in relative humidity led to a significant increase in cathode GDL water saturation. The contribution of this study to understanding transport mechanisms in GDL materials is significant to the characterization and optimal design of materials for improved PEMFC performance. Ultimately, the goal of this work is to accelerate the worldwide adoption of PEMFCs as a sustainable, reliable solution to replace conventional carbon-emitting energy sources. 1. Alaswad et al., J. Hydrog. Energy, 41, (2016) 2. Nagai et al., J. Power Sources, 435, (2019) 3. D. Shum et al., Electrochem. Acta, 256, (2017)
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".