An Injection Method for the Fast Preparation of Graphene Oxide Membranes with Tunable Microstructures and Controllable Mechanical Properties
Bibliographic record
Abstract
Objectives Graphene oxide (GO) membranes are promising materials in many fields such as supercapacitors1, gas and liquid separation2, sensing3, and acoustic transduction4. For these applications, a high efficiency, scalable method for preparing GO membranes without compromising mechanical properties are required. To date, the most commonly used technique to manufacture GO membranes is vacuum filtration. By filtering the GO suspension, GO sheets are stacked and assembled into membranes. However, this technique has several shortcomings. Firstly, this method cannot be used to control the ordering of GO sheets – a property that determines the microstructures and directly influences interactions between sheets. Secondly, this technique is time- and energy-consuming, and the size of the GO membranes produced is limited by the size of the filter5. New Results We report a new method for preparing large scale GO membranes with tunable microstructures and controllable mechanical properties. This method involves injecting the GO suspension through a thin nozzle into a coagulation bath for promoting the gelation of the GO into a membrane. The nozzle moves at a speed coordinated with the injection flow rate to prepare uniformly thick membranes (Fig. 1(a)). The flow rate in the nozzle induces shear stress, which in turn induces the alignment of GO sheets. Therefore, by adjusting the flow rate, the alignment of GO sheets is tunable, and the mechanical property is controllable. By using this technique, we achieved a long continuous GO membrane with high ultimate tensile strength (UTS) of 67-160 MPa at preparation speeds of up to 160 cm/min. As a comparison, the vacuum filtration method takes a day to prepare a single 47 mm diameter GO membrane with a UTS of 70-125 MPa6. Conclusions In this study, we designed a highly efficient flow injection method that allows us to prepare large-scale GO membranes with tunable microstructures rapidly. This in turn allows us to influence interactions between sheets and, most importantly, manipulate the mechanical properties. In this paper, we show the vast improvement in UTS measurements for GO membranes prepared using the flow injection method as opposed to the vacuum-filtration. This technique will enable fast and controllable production of GO membranes for various fields which call for membranes with good mechanical performance. Fig. 1. (a) Schematically drawing of the process of the injection method for GO membranes preparation; (b) UTS of GO membranes prepared under different flow rates Acknowledgments This research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the McGill Engineering Doctoral Awards (MEDA). References 1. Wang, Y.; Shi, Z.; Huang, Y.; Ma, Y.; Wang, C.; Chen, M.; Chen, Y., Supercapacitor devices based on graphene materials. The Journal of Physical Chemistry C 2009, 113 (30), 13103-13107. 2. Jiang, D.-e.; Cooper, V. R.; Dai, S., Porous graphene as the ultimate membrane for gas separation. Nano letters 2009, 9 (12), 4019-4024. 3. Zhou, Q.; Zheng, J.; Onishi, S.; Crommie, M.; Zettl, A. K., Graphene electrostatic microphone and ultrasonic radio. Proceedings of the National Academy of Sciences 2015, 112 (29), 8942-8946. 4. Wu, Y.; Yu, C.; Wu, F.; Li, C.; Zhou, J.; Gong, Y.; Rao, Y.; Chen, Y., A highly sensitive fiber-optic microphone based on graphene oxide membrane. Journal of Lightwave Technology 2017, 35 (19), 4344-4349. 5. Liu, Z.; Li, Z.; Xu, Z.; Xia, Z.; Hu, X.; Kou, L.; Peng, L.; Wei, Y.; Gao, C., Wet-spun continuous graphene films. Chemistry of Materials 2014, 26 (23), 6786-6795. 6. Park, S.; Lee, K.-S.; Bozoklu, G.; Cai, W.; Nguyen, S. T.; Ruoff, R. S., Graphene oxide papers modified by divalent ions—enhancing mechanical properties via chemical cross-linking. ACS nano 2008, 2 (3), 572-578. Figure 1
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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 | 0.001 |
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".