The modelling of molecular structure and ion transport in sulfonic acid based ionomer membranes
Bibliographic record
Abstract
One of the areas in which progress is to be made if the polymer electrolyte membrane fuel cell (PEMFC) is to become the replacement for the internal combustion engine is the development of new materials (catalysts and membranes) that demonstrate improved performance characteristics accompanied by acceptable manufacturing costs. Many of these newer materials are sulfonated polymers, however, membranes with sulfonimide functionals are faced with limitations similar to those of Nafion(R) a perfluorinated sulfonic acid ionomer which is the central component of the proton exchange membrane. Nafion is costly, has a low maximum operating temperature and various problems associated with the transport of water and fuel. Another group of advanced membranes include (1) and (4) the complexation of basic polymers with oxo-acids aromatic backbone polymers such as polyetherketones (PEEKK and PEEK), (2) the inclusion of small inorganic particles like silica or zirconium phosphates within the membrane (3) acid-base blending of covalent crosslinking of polymers, which offer definite cost and stability advantages over Nafion membranes, but exhibit substantially lower conductivity at the lower water contents. The membranes in (2) and (3) exhibit increased thermal stability, up to 140 degrees C, reduced swelling and methanol and water crossover, but at a penalty in terms of conductivity and mechanical stability. At the same time, membranes with immobilized acid demonstrate conductivities as high as those seen in the hydrated systems, but with drastically reduced methanol crossover. This paper represents the second stage of a modelling effort to overcome some of these problems. The work is based on computation of the proton friction and diffusion coefficients within the PEM pore using a nonequilibrium statistical mechanical framework. Taken together, the molecular and transport modeling studies provide the means of connecting the molecular scale information of the polymer with the macroscopic transport properties of the membrane. It is important to note the bridging of the different length and time scales was accomplished without resorting to any 'fitting' or adjustable parameters. 37 refs., 5 tabs., 6 figs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".