An Investigation on Advanced Functional Carbonaceous Materials for High Performance Composites in Fuel Cell Applications
Bibliographic record
Abstract
Hydrogen fuel cell, one of the main power sources in environmentally benign hydrogen economy, has its own cost and performance limitations which is a large drawback for moving forward with this demanding technology to reduce GHG emissions particularly in the transportation sector from mid-to-heavy duty vehicles. Research on the bipolar plates, a vital component, can be a solution to address this on-going issue which needs improvement in electrical and mechanical attributes. In this dissertation, investigation was carried out on anisotropically distributed advanced functional carbonaceous materials along with the introduction of novel biocarbons which can facilitate to improve the performance of carbon polymer composites by increasing the movement of charged particles through decoupling of repulsion, increased conjugated carbon-carbon aromatic structure and through the spin non-conservation in the crystal lattice structure. The highest electrical conductivity was achieved 221 S/cm and flexural strength 64 MPa, well above the US DOE target of 100 S/cm and 25 MPa respectively. It was observed that the non-destructive porous structure measurement can determine the extent of electrical conductivity. A novel continuous carbon fiber process showed crucial potential in improved mechanical properties with excellent electrical conductivity. The use of multi-fillers and intercalated carbonaceous materials can help to reduce the density, thickness and weight to a large extent which is important for the fabrication of a highly conductive, mechanically flexible and light-weight composite. In this research, a holistic approach of effective intrinsic properties, optimized process parameters and novel functional materials proved to facilitate achieving a high-performance composite for bipolar plates.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".