Fuel Cell Modeling Strategic Roadmap: A Systematic Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Polymer electrolyte fuel cells (PEFCs) are promising electrochemical devices for the direct conversion of chemical energy of a fuel into useful electrical work with vast applications in automotive, stationary, and autonomous power. It is widely recognized that progress in PEFC technology is a multi‐disciplinary challenge and hinges on Research and Development (R&D) breakthroughs in design, fabrication, and implementation of innovative materials, processes, and system optimization. Fuel cell modeling, in particular, has been the subject of intense research in the past two decades, as it is of great importance to design and process optimizations. Building upon the insights obtained in a European Collaborative Research Program, we present an analysis of fuel cell modeling R&D roadmap by focusing on technical and market attributes and the inter‐relations therein. The roadmap is driven by three distinct outcomes – alpha, beta and commercial versions, reflecting the maturity of the multi‐scale software. All roadmap entries are organized in layers, namely Market and Business; Services; Products; High Level Targets; Technology; Science; and Enablers and Resources. This study contributes to a much needed foundation for further planning of potential R&D and demonstration projects of fuel cells for automotive and other emerging sectors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it