Nanoscale Investigation of Nafion Membranes after Artificial Degradation
Bibliographic record
Abstract
In this contribution we report on the nanostructure and conductivity of freshly prepared as well as artificially degraded Nafion membranes investigated by contact atomic force microscopy (AFM), conductive AFM, and pulsed force-mode (PFM)-AFM. The different techniques can provide complementary information on structure and conductivity. Conductive AFM is used to investigate the conductivity of Nafion membrane surfaces. The images show a quite inhomogeneous distribution of current at the surface. The percentage of conductive surface increases with humi¬dity, but regions without any current are still present up to 80% relative humidity [1,2]. Comparison with PFM-AFM images, where differences in adhesion forces are measu¬red, indicates that hydrophobic regions, which are attribu¬ted to PTFE-like polymer back bone without conductivity, are present at the surface with comparable dimensions. Conductive AFM gives information about the internal structure of ionic clusters during current flow. High resolution current images of the membrane were used to directly compare the measured nanostructure of the single conductive channels with model predictions from the literature (Fig. 3). The influence of H2O2 treatment as a method for artificial degradation is investigated. The analysis of adhesion forces demonstrates a significant change of the surface properties with different membrane treatment. Topography and adhesion measurements clearly show materials changes with high resolution and correlate with changes in conductivity distributions. In particular, small protrusions in the topography correlating with strongly diminished conductivity in the current images are observed. These areas are interpreted as crystalline polymer backbone which higher stability against radical decomposition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".