Designing Partially Coated Ultrathin Hydrophobic Silica Layer for Highly Conductive and Durable Carbon Nanofiber as Cathode Carbon Support in Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
For the last decade, the polymer electrolyte fuel cells (PEFCs) have been recognized as a strong alternative power source instead of the gasoline-powered internal combustion engine due to their high energy conversion efficiency and environmental benignity. Among a number of composition factors, the development of cathode electrode with affordable durability is arguably the most important consideration for maintaining long-term automotive applications because their performances are typically limited by permanently damaged carbon supports and platinum (Pt) loss during abnormal and transient conditions such as very fast potential transient condition and frequent start-up/shutdown procedures. In the carbon corrosion procedures, it is important to have an appropriate strategy that provides means to significantly reduce the carbon corrosion as follows: i) there are fewer water molecules in the vicinity of carbon supports to decrease the carbon corrosion and Pt oxidation formation according to mechanism of electrochemical carbon corrosion; and ii) the spill-over of ∙OH radical from Pt particles should be minimized by avoiding the direct contact between oxidized Pt and carbon surface. Herein, we reported a new strategy to functionalize the graphitized platelet carbon nanofiber (PCNF) surface, which was developed to make an unevenly-coated ultrathin hydrophobic silica layer on PCNF. Subsequently, Pt nanoparticles would be deposited into an empty space between an unevenly-coated silica layers. According to the experimental observation related to the electrochemical reactions, the initial performance of membrane electrode assembly (MEA) based on PCNF composite with 5 wt% silica layer exhibited similar to that for virgin PCNF due to similar HFR values. To the best of our knowledge, its initial cell performance is one of the best among the PCNF composites with silica layer reported in the literature. In addition, the PCNF composite with silica layer showed superior long-term durability compared to the virgin PCNF due to enhanced durability of carbon support and Pt nanoparticles.
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 | 0.000 | 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) | 0.000 | 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".