New Approach to Improve the DMFC Performance By Nafion-Functionalized Graphene Oxide Matrix Membranes Using the Electrochemical Exfoliation of Graphite As a Source of the Graphene Oxide
Bibliographic record
Abstract
Direct Methanol Fuel Cell (DMFC) is one of the most attractive and effective source of energy for portable applications. Methanol, which is used as fuel, is a manageable liquid with excellent energy storage densities. Beside the advantages of the DMFC, some main issues are still present in the performance of this fuel cell. One of the major disadvantages is the methanol crossover, which is attributed to the transport along with water molecules. Nafion is the most common materials used as electrolyte membrane in fuel cells. It is a perfluorosulfonic acid polyetectrolyte polymer which has an excellent chemical stability and high proton conductivity. However, Nafion allows the methanol permeation from anode to cathode leading to a mixed potential and the reduction of methanol concentration at the anode side with the consequent decreasing of the fuel cell performance. Therefore, finding a membrane able to hinder the methanol permeation while maintaining the proton conductivity has been heavily researched [1, 2]. To minimize the methanol crossover problems in Nafion membranes, many researches focused on fillers to modify the Nafion membrane and create a barrier for the methanol or improve the water/methanol selectivity. Zeolites, zirconium, SiO2 or polypyrrole are some of the materials which were tested. Another approach to modify the Nafion membrane is developing mixed matrix membranes where the filler materials are incorporated in situ in the membrane along with the polymer[2]. Graphene oxide (GO) has unique properties for membranes applications because of its 2D structure and interesting mechanical and thermal stability. Moreover, abundant oxygen-containing functional groups provide amphiphilic nature and allow convenient chemical functionalization, which makes this material and its derivatives promising inorganic filler for composite membranes. Different studies have been carried out for The University of Manchester to prove that incorporating CVD Graphene and GO into the different layers of the PEM fuel cell MEAs, improves the overall performance of the fuel cell [3,4]. This work presents a new functionalized GO-Nafion mixed matrix membrane in order to enhance the DMFC performance by decreasing the methanol crossover. The GO is produced by Electrochemical Exfoliation of Graphite (EGO) as an alternative to the traditional Hummer’s method. EGO has been presented as a green and cost-effective approach for producing high quality of graphene in high yield using simple equipment [5-7]. Further functionalization of GO will improve the compatibility of the graphene-based material with the Nafion and allowing less methanol crossover and higher proton conductivity. References: [1] Wei Jia, Beibei Tang, and Peiyi Wu. Novel Slighthly Reduced Graphene Oxide Based Proton Exchange Membrane with Constructed Long-Range Ionic Nanochennels via Self-Assembling of Nafion. Applied Materials & Interfaces, 9, 22620-22627 (2017). [2] Hung-Chung Chien, Li-Duan Tsai, Chiu-Ping Huang, Chi-yun Kang, Jiunn-Nan Lin, Feng-Chih Chang. Sulfonated Graphene Oxide/Nafion Composite Membranes for High-Performance Direct Methanol Fuel Cells. International Journal of Hydrogen Energy, 28, 13792-13801 (2013) [3] Stuart M. Holmes, Orabhuraj Balakrishnan, Vasu. S. Kalangi, Xiang Zhang, Marcelo Lozada-Hidalgo, Pulickel M Ajayan, Rahul R. Nair. 2D Crystals Significantly Enhance the Performance of a Working Fuel Cell. Advance Energy Materials, 7, (2017). [4] S. Al-Batty, C. Dawson, S. P. Shanmukham, E. P. L. Roberts and S. M. Holmes. Improvement of direct methanol fuel cell performance using a novel mordenite barrier layer. J Mater. Chem. A, 2016, 4, pp. 10850-10857. [5] Richard Gondosiswanto, Xunyu Lu, and Chuan Zhao. Preparation of Metal-Free Nitrogen-Doped Graphene via direct electrochemical exfoliation of graphite in ammonium nitrate. Australian Journal of Chemistry, 68 (2015) 830-835. [6] Xunyu Lu and Zhao. Controlled electrochemical intercalation, exfoliation and in-situ nitrogen doping of graphite in nitrate-based proton ionic liquids. Physical Chemistry Chemical Physics,15 (2013), 30005-200009. [7] Khaled Parvez, Ahong-shuai Wu, Tongjin Li, Xianjie Liu, Robert Graft, Xinliang Feng, and Klaus Mullen. Exfoliation of Graphite into Graphene in Aqueous solution of Inorganic Salts. Journal of the American Chemical Society, 136, (2014), 6038-6091.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".