Upscalling Graphene Production through Atmospheric Pressure Microwave Plasma Technology for Ultra-Long-Life Li-S Batteries
Bibliographic record
Abstract
Since it was discovered in 2004, graphene has been hailed as "the material of the future" due to its remarkable properties and fascinating applications, including energy storage and innovative battery design [1,2]. Different methods have emerged for graphene synthesis, being the Hummers method the most fundamental approach. This method produces a low-quality product through a harmful process to the environment. More recent methods have enabled the production of high-quality graphene from highly oriented pyrolytic graphite (HOPG) by techniques such as mechanical exfoliation or laser ablation. However, these techniques lack of scalability, limiting their potential for meeting industrial requirements. Nevertheless, several methods have been developed to generate high-quality graphene that allows scalability, although they show different drawbacks. Among them, liquid phase exfoliation (LPE) and chemical vapor deposition (CVD) stand out. Therefore, none of this methods offers a single-step, low-cost approach for obtaining high-quality graphene powder. Plasma technology represents a significant advance in this field. Microwave plasma torches as the Torche à Injéction Axiale sur Guide D’Ondes (TIAGO) [3], are known for their high reactivity and efficiency (~100%) in decomposition reactions. Under atmospheric pressure, frequent collisions between electrons and organic molecules promote their breakdown into atomic components. When these atoms recombine at the plasma exit, they form compounds distinct from the original ones, facilitating the nucleation process of materials like graphene powder, as demonstrated when ethanol is used as a carbon precursor [4]. This approach offers an eco-friendly, cost-effective and scalable method for high-quality graphene generation in a single-step process. To optimize graphene yield, the TIAGO torch-based microwave plasma process was refined by adjusting ethanol flow [5] and the applied power [6]. Recent improvements include adding a metallic electromagnetic shielding around the torch to minimize microwave energy losses, boosting graphene production by 22.8% without compromising quality [7,8]. Physicochemical properties of the graphene powder were validated through Raman spectroscopy, X-ray photoelectron spectroscopy, electron microscopy and thermogravimetry. Upscaled graphene produced through microwave plasma decomposition of ethanol (MP-G) offers significant potential for energy storage applications, particularly in high-performance Lithium-Sulfur (Li-S) batteries. During discharge, the lithium metal anode oxidizes, releasing lithium ions and electrons that migrate to the sulfur cathode. However, during the electrochemical reactions at the positive electrode, sulfur (S8) reduction at the cathode forms polysulfides (Li2Sn ), some of which dissolve into the electrolyte, leading to the "shuttle effect" [9]. This phenomenon involves polysulfide migration during charge and discharge cycles, causing deposits on the lithium anode, which reduces battery capacity, accelerates performance degradation, lowers energy efficiency, and reduces lifespan. However, the conductivity and porosity of graphene can alleviate the shuttle effect, capturing polysulfides in solution. Furthermore, MP-G cathodes have shown remarkable performance at high rates (3C and 5C), with minimal capacity loss per cycle, even during ultra-long-term cycling. Additionally, they achieve a specific capacity of 256 mAh/g at a high-rate of 10C. This study highlights ethanol-derived graphene synthesized via microwave plasma torch as a viable, scalable alternative for advancing Li-S battery technology (see Figure). [1] A. Dias, et al. Chem. Eng. J., 430 (2022) 133153. [2] F.J. Soler-Piña, et al. J. Colloid Interface Sci., 640 (2023) 990. [3] M. Moisan, et al. Plasma Sources Sci. Techno.l, 10 (2001) 387. [4] C. Melero, et al. Plasma Phys. Control Fusion, 60 (2018) 014009. [5] A. Casanova, et al. Fuel Process. Technol., 212 (2021) 106630. [6] J. Toman, et al. Fuel Process. Technol., 239 (2023) 107534. [7] F. J. Morales-Calero, et al. Plasma Sources Sci. Technol., 32 (2023) 065001. [8] F. J. Morales-Calero, et al. Chem. Eng. J., 498 (2024) 155088 [9] A. Benítez, et al. Renew.Sustain. Energy Rev., 154 (2022) 111783. Aknowledgments: This work was partially supported by MCIN/AEI/ 10.13039/501100011033 and by the European Union NextGenerationEU/PRTR (PID2023-147436OA-I00, PID2023-147080OB-I00, PID2020-113931RB-I00, PDC2021-120903-I00 y TED2021-129261A-I00). The predoctoral contract of F.J. Morales-Calero was granted by a MOD-2.2 from Plan Propio de la Universidad de Córdoba (2020). A. Benítez was granted by “Juan de la Cierva – Incorporación” fellowship [IJC2020-045041-I]. Finally, the authors of the present work are greatly thankful to Prof. Michel Moisan of the Groupe de Physique des Plasmas (University of Montreal) for the TIAGO torch donation. 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".