Sulfonated poly(arylene ether nitrile)-based composite membranes enhanced with Ca2+ bridged carbon nanotube-graphene oxide networks
Bibliographic record
Abstract
Abstract As the core component of proton exchange membrane fuel cell, proton exchange membranes (PEM) have attracted much attention of researchers. To trade-off the proton conduction, dimensional stability and anti-oxidation ability, graphene oxide (GO) and acidized multi-walled carbon nanotubes (MWCNT) using calcium ion as coordination bridge (GO-Ca2+-MWCNT) was synthesized, and then incorporating into sulfonated poly(arylene ether nitrile) (SPEN) to fabricate SPEN/GO-Ca2+-MWCNT organic-inorganic composite membranes by solution-casting method and explore the influence of varying loading on performances as PEM. It was found that the proton conductivity of the composite membranes was higher than that of SPEN, while maintaining better dimensional stability, excellent anti-oxidation ability and good mechanical properties. All of these were attributed to the formation of three-dimensional structure between GO and MWCNT bridged by Ca2+. Particularly, the SPEN/GO-Ca2+-MWCNT-1 composite membrane exhibited excellent tensile strength of 71.45 MPa, better thermal stability as well as high proton conductivity (0.054 S/cm at 30 ℃, and 0.193 S/cm at 90 ℃), above 10-2 S/cm, satisfying the requirement of fuel cells. All in all, the results indicate that the filler with three-dimensional network structure can effectively improve the performances of SPEN, and the prepared composite membranes show potential applications in many fields.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".