Optimising Catalyst Design for Hydrogen Fuel Cells through Structure to Performance Correlations
Bibliographic record
Abstract
Electrocatalytic reactions are of particular importance for their application to energy conversion technologies, including energy storage, fuel generation, and consumption of these sources of energy. The efficiency of these processes relies on the balance of many factors including the transport of reagents and by-products at rates applicable for energy demands of the sought-after applications, while also creating a material design that is both economic to prepare and operate. A goal of our research is to seek correlations between the structure of electrocatalysts and their performance. This presentation will discuss a couple of strategies to prepare and evaluate electrocatalysts for hydrogen fuel cells. These strategies include the use of pore forming materials that we utilize for both adjusting the loading, dimensions, and composition of nanocatalylsts, but also enable fine tuning of the ability of these materials to manage transport of gases and fluids. The latter are tuned through adjusting the pore size, connectivity between the pores, and thickness of the desired materials. Tuning these and similar properties of the electrocatalysts and their support materials lends itself to a systematic approach to identifying optimal parameters desired within the catalyst materials, and to guiding the future work in preparing materials on a large scale for incorporation into fuel cell stacks. To evaluate the performance of these electrocatalysts, these materials are incorporated into the cathode catalyst layers of proton exchange membrane fuel cells using a combination of techniques. The techniques include methods that are scalable to larger scales and that are compatible with current manufacturing practices. These catalysts are compared to catalyst films prepared from industry standards for their physical, electrochemical and fuel cell performance characteristics. Some of these methods demonstrate an improved mass activity of the electrocatalyst under fuel cell operating conditions, while others are guiding the design and optimization of future electrocatalysts. These techniques demonstrate a series of approaches to preparing electrocatalysts by design, and to tuning their properties with implications in improving the performance of electrocatalysts for use in hydrogen and possibly other types of fuel cells.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".