Highly Ordered rGO for Advanced Energy Storage Applications: Scalable and Cost-Effective Production
Source abstract
Abstract
Reduced graphene oxide (rGO) is emerging as a versatile material with outstanding potential in energy storage, sensors, and electronics due to its excellent conductivity and mechanical properties. For optimal stability and performance, rGO must be highly ordered to ensure efficient electron and ion transport. Despite the emergence of various reduction protocols for graphene oxide, their commercial viability is limited by high costs and low scalability for industrial applications. Here, we present a scalable thermal plasma spraying technique to reduce graphene oxide with a high production rate of 52 g/h at a very low cost of 4.36 USD/g. We achieved highly ordered rGO with a high carbon-to-oxygen ratio (C/O = 11.4), high specific surface area (198 m 2 g –1 ), better electrical conductivity of 3288 S m –1, thermal stability (600 °C), and low defect density ( I D / I G = 0.18). The rGO showcased enhanced electrochemical properties, with a specific capacitance of 275 F g –1 with 96% retention after 10,000 cycles for supercapacitors and a charge capacity of 182 mA h g –1 for sodium-ion battery applications. This work introduces plasma spray as a cost-effective, scalable method for synthesizing highly ordered rGO, advancing energy storage technologies.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Energy Harvesting in Wireless Networks · Advancements in Battery Materials · Parallel Computing and Optimization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Plasma Spray Coating Laboratory, Department of Metallurgical and Materials Engineering
Indian Institute of Technology Patna
Funding
Aeronautics Research and Development Board · Indian Institute of Technology, Patna
References (31)
Show source-provided references
1. 10.1039/D2EE00771A10.1039/D2EE00771A ↗
2. 10.1038/nenergy.2017.8910.1038/nenergy.2017.89 ↗
3. 10.1002/adma.20140091010.1002/adma.201400910 ↗
4. 10.1016/j.enss.2022.07.00210.1016/j.enss.2022.07.002 ↗
5. 10.1021/acsnano.9b0677810.1021/acsnano.9b06778 ↗
6. 10.1126/science.ado411310.1126/science.ado4113 ↗
7. 10.1016/j.xinn.2021.10011510.1016/j.xinn.2021.100115 ↗
8. 10.1016/j.rser.2020.11002610.1016/j.rser.2020.110026 ↗
9. 10.1039/C9TC04916A10.1039/C9TC04916A ↗
10. 10.1007/s11696-020-01196-010.1007/s11696-020-01196-0 ↗
11. 10.1016/j.compositesa.2022.10737310.1016/j.compositesa.2022.107373 ↗
12. 10.1016/j.cej.2020.12701810.1016/j.cej.2020.127018 ↗
13. 10.1016/j.xcrp.2021.10062710.1016/j.xcrp.2021.100627 ↗
14. 10.1039/C7NR02943H10.1039/C7NR02943H ↗
15. 10.1016/j.matchemphys.2019.12232510.1016/j.matchemphys.2019.122325 ↗
16. 10.1016/j.carbon.2022.11.05110.1016/j.carbon.2022.11.051 ↗
17. 10.4236/graphene.2017.6100110.4236/graphene.2017.61001 ↗
18. 10.1002/smll.20230154610.1002/smll.202301546 ↗
19. 10.1039/D2CP04836A10.1039/D2CP04836A ↗
20. 10.1021/acs.chemrev.6b0003910.1021/acs.chemrev.6b00039 ↗
21. 10.1021/acsnano.1c0217810.1021/acsnano.1c02178 ↗
22. 10.1002/adma.20100073210.1002/adma.201000732 ↗
23. 10.1021/acs.chemmater.9b0039410.1021/acs.chemmater.9b00394 ↗
24. 10.1038/ncomms255510.1038/ncomms2555 ↗
25. 10.1021/acsnano.0c0945110.1021/acsnano.0c09451 ↗
26. 10.1002/adfm.20231626610.1002/adfm.202316266 ↗
27. 10.1088/2053-1583/ad167510.1088/2053-1583/ad1675 ↗
28. 10.1039/C4TA02693D10.1039/C4TA02693D ↗
29. 10.1002/adma.20140095110.1002/adma.201400951 ↗
30. 10.1021/acsnano.8b0407510.1021/acsnano.8b04075 ↗
31. 10.1016/j.electacta.2016.08.05810.1016/j.electacta.2016.08.058 ↗