Final Project Report: Development of Micro-Structural Mitigation Strategies for PEM Fuel Cells: Morphological Simulations and Experimental Approaches
Bibliographic record
Abstract
The durability of PEM fuel cells is a primary requirement for large scale commercialization of these power systems in transportation and stationary market applications that target operational lifetimes of 5,000 hours and 40,000 hours by 2015, respectively. Key degradation modes contributing to fuel cell lifetime limitations have been largely associated with the platinum-based cathode catalyst layer. Furthermore, as fuel cells are driven to low cost materials and lower catalyst loadings in order to meet the cost targets for commercialization, the catalyst durability has become even more important. While over the past few years significant progress has been made in identifying the underlying causes of fuel cell degradation and key parameters that greatly influence the degradation rates, many gaps with respect to knowledge of the driving mechanisms still exist; in particular, the acceleration of the mechanisms due to different structural compositions and under different fuel cell conditions remains an area not well understood. The focus of this project was to address catalyst durability by using a dual path approach that coupled an extensive range of experimental analysis and testing with a multi-scale modeling approach. With this, the major technical areas/issues of catalyst and catalyst layer performance and durability that were addressed are: 1. Catalyst and catalyst layer degradation mechanisms (Pt dissolution, agglomeration, Pt loss, e.g. Pt in the membrane, carbon oxidation and/or corrosion). a. Driving force for the different degradation mechanisms. b. Relationships between MEA performance, catalyst and catalyst layer degradation and operational conditions, catalyst layer composition, and structure. 2. Materials properties a. Changes in catalyst, catalyst layer, and MEA materials properties due to degradation. 3. Catalyst performance a. Relationships between catalyst structural changes and performance. b. Stability of the three-phase boundary and its effect on performance/catalyst degradation. The key accomplishments of this project are: • The development of a molecular-dynamics based description of the carbon supported-Pt and ionomer system • The development of a composition-based, 1D-statistical Unit Cell Performance model • A modified and improved multi-pathway ORR model • An extension of the existing micro-structural catalyst model to transient operation • The coupling of a Pt Dissolution model to the modified ORR pathway model • The Development A Semi-empirical carbon corrosion model • The integration and release of an open-source forward predictive MEA performance and degradation model • Completion of correlations of BOT (beginning of test) and EOT (end of test) performance loss breakdown with cathode catalyst layer composition, morphology, material properties, and operational conditions • Catalyst layer durability windows and design curves • A design flow path of interactions from materials properties and catalyst layer effective properties to performance loss breakdown for virgin and degraded catalyst layers In order to ensure the best possible user experience we will perform a staged release of the software leading up to the webinar scheduled in October 2013. The release schedule will be as follows (please note that the manual will be released with the beta release as direct support is provided in Stage 1): • Stage 0 - Internal Ballard Release o Cross check of compilation and installation to ensure machine independence o Implement code on portable virtual machine to allow for non-UNIX use (pending) • Stage 1 - Alpha Release o The model code will be made available via a GIT, sourceforge, or other repository (under discussion at Ballard) for download and installation by a small pre-selected group of users o Users will be given three weeks to install, apply, and evaluate features of the code, providing feedback on issues or software bugs that require correction prior to beta release • Stage 2 - Beta Release o The model code repository is opened to the general public on a beta release concept, with a mechanism for bug tracking and feedback from a large user group o Code will be tracked and patched for any discovered bugs or relevant feedback from the user community, upon the completion of three months without a major bug submission the code will be moved to a full version release • Stage 3 - Full Version Release o Code is version to revision 1.0 and that version is frozen in development/patching
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".