Composite Nafion-Functionalized PDMS Electrospun Fibers for Direct Methanol Fuel Cells
Bibliographic record
Abstract
Direct methanol fuel cells (DMF) have received great attention as a promising portable device due to its high power density, low overall emission and longtime power supply. As the key component of the DMFC system, proton exchange membranes (PEM) are extensively studied since the conception of of proton electrolyte membrane fuel cells in early 1960s. An efficient membrane essentially provides two main functionalities: (1) physically separating the anode from the cathode, and (2) providing proton pathways for inter-electrode proton transport. Nafion is the most widely used PEMs due to its high proton conductivity. Water is of an important element during proton transfer mechanism; however it causes membrane swelling, resulting in serious methanol permeation. To overcome swelling and excessive methanol crossover, a novel PDMS reinforced Nafion membrane is developed in this paper. PDMS has good liquidity before crosslinking due to its flexible molecular structure. Its hydrophobicity also effectively limits membrane swelling in a high relative humidity environment. In this study, Nafion solution is electrospun into mats and the morphological structures are observed via a scanning electron microscopy. Different ratios of Nafion are also mechanically dispersed into PDMS and mixture is successfully infiltrated into the void space among fibers to improve the connectivity of proton transport. The performances of different fabricated composite membranes are compared against a simple Nafion membrane in terms of water swelling and uptake, and the fuel cell performance.
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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".