Invited: Advanced Solid-State NMR Spectroscopy Studies for Improved Performance in PEM-FCs
Bibliographic record
Abstract
The membrane electrode assembly (MEA) installed in proton exchange membrane fuel cells (PEM-FCs) is critical to the power density and lifetime of these devices. Catalyst-coated membranes (CCMs), essential components of state-of-the-art MEA designs, provide an optimal structure for catalyst function, a wide range of operational temperatures, and optimal electronic performance.1 Furthermore, efforts have been made to increase the ionic conductivity and mechanical performance of these membranes through composition with various solid acids.2 , 3Unfortunately, due to the complexity of PEM-FC devices, there are several operational challenges to overcome, including catalyst poisoning, low proton conductivity at ambient humidities, and ionomer or membrane degradation. This work aims, in part, to investigate the incorporation of functionalized graphene oxide (GO) into PEM-FCs. The membrane within these devices is most commonly made of Nafion®, a proprietary fluoropolymer, due to its high ionic conductivity and chemical stability. However, the material is expensive and not ideal for every fuel cell application. GO, a derivative of the super-material graphene, has intrinsic proton conductivity which is comparable to Nafion®. This makes GO and GO/composite membranes viable candidates for use in PEM-FCs.4-6Little work has been published on structural relationships or the mechanisms of ion conductivity within these composite materials. To date, novel functionalized GO samples have been reliably synthesized. Sulfonic acid groups have been chemically grafted onto GO sheets, connected by organic linker groups of varying length and rigidity. These materials have been studied using various 1H and 13C solid state NMR techniques. 13C NMR spectra show clearly the functional group modification of GO samples after the grafting of alkylsulfonic acid groups. Dehydration of these samples allows the collection of 1H spectra with resolved acid proton / water peaks, despite the rapid exchange between these two proton populations. This lecture will describe current results in studying the synthesis and modification of sulfonated GO samples, as well as the proton dynamics and electrochemical performance in polymer electrolyte membranes either composed of or infused with functionalized GO. Additionally, 1H Double Quantum NMR is a well-established probe of local dynamics.7 Here for the first time, we extend this concept to characterize the fluorinated ionomer backbone. 19F double quantum SSNMR methods are applied to industrially-relevant ionomer materials to quantify and compare local dynamics of the ionomer side chains and backbones. 19F Double Quantum Filtered NMR experiments have been performed to investigate proton dynamics as well as local dynamics of perfluorinated polymer backbone and side chains. It has been shown that the backbone 19F has a steeper dipolar coupling build-up curve compared to the side chain, indicating a noticeable difference in rigidity. Future studies on the effect of relative humidity on 19F dynamics will provide a measure of the trends in local mobility at the molecular level, distinguishing side-chain from backbone contributions. References: 1) Bessarabov, D.; Hitchcock, A. Membrane Technology 2009, 12, 6-12. 2) Yan, Z. B.; De Almeida, N. E.; Traer, J. W.; Goward, G. R. Phys. Chem. Chem. Phys. 2013, 15(41) 17983-17992. 3) Lee, Y. J.; Bingöl, B.; Murakhtina, T.; Sebastiani, D.; Meyer, W. H.; Wegner, G.; Spiess, H. W. J. Phys. Chem. B 2007, 111(33) 9711-9721. 4) Kumar, R.; Scott, K. Chem. Commun. 2012, 48(45) 5584-5586. 5) Tseng, C. Y.; Ye, Y. S.; Cheng, M. Y.; Kao, K. Y.; Shen, W. C.; Rick, J.; Chen, J. C.; Hwang, B. J. Adv. Energy Mater. 2011, 1(6) 1220-1224. 6) Zarrin, H.; Higgins, D.; Jun, Y.; Chen, Z.; Fowler, M. J. Phys. Chem. C 2011, 115(42) 20774-20781. 7) Ghassemzadeh, L.; Kreuer, K.; Maier, J.; Müller, K. J. Power Sources 2011, 196 (5) 2490-2497.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".