Enhancing performance of advanced fuel cell design with functional energy materials and process
Bibliographic record
Abstract
Efficiency enhancement of hydrogen based electric powertrain is becoming highly relevant for medium to heavy duty transportation due to advantages of eminent electrochemical cell design and advances in infrastructure accessibility. Herein, a facile and highly effective fabrication process has been reported for the first time to demonstrate an outstanding mechanical strength and electrical conductivity simultaneously in the carbon rich composite designed to enhance the fuel cell performance. Improvement of composites with different advanced reinforced materials such as carbon veil, recycled carbon fiber as well as functional additives such as carbon black, multiwalled nanotube, etc. was investigated through a holistic approach of optimized parameters. Advanced composite plates have been designed to be mechanically flexible, electrically conductive and cost effective; this newly designed composite for bipolar plate supersedes by far the US Department of Energy (DOE) target for fuel cell bipolar plate with a flexural strength of over 64 MPa and exceeding electrical conductivity of 200 S/cm. Notably, tuned process parameters as well as novel architecture of materials such as continuous carbon fiber and carbon veil can facilitate the fabrication of a light-weight high-performance carbon polymer composite for a wide range of applications including battery electrodes, supercapacitors, fuel cells and solar cell.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".