Ethanol-Derived Graphene By Microwave Plasma Torch: A Suitable Material for Manufacturing High-Performance Metal-Sulfur Batteries
Bibliographic record
Abstract
Since its discovery, graphene has been acclaimed as " the material of the future " for its outstanding properties and promising applications, especially in energy storage and high-efficiency battery design [1,2]. Among the various synthesis methods, atmospheric-pressure microwave plasma technology stands out as an innovative approach that is gradually gaining prominence in the scientific and technological fields.While none of the traditional methods offers a single-step, low-cost approach for obtaining high-quality graphene powder, plasma technology represents a significant advance. More concretely, microwave plasma torches (Fig. 1) exhibit high reactivity, being capable of inducing decomposition reactions with a high efficiency (~100%), surpassing traditional chemical processes. Organic molecules are broken into atomic components, leading to the formation of compounds different from the original ones when the atoms recombine at the plasma outlet. At atmospheric pressure, the numerous collisions between electrons and heavy particles favor the nucleation process of materials, such as graphene, by injecting carbon precursors. It is an eco-friendly, cost-effective and scalable method for high-quality graphene generation in a single-step process. The generation of graphene through ethanol decomposition in a particular type of torch, the TIAGO torch (Torche à Injection Axiale sur Guide d’Ondes), device designed by Prof. Michel Moisan, has been extensively studied [3-6]. Throughout these studies, the synthesis process of graphene using atmospheric-pressure plasma technology has been gradually optimized, reaching production rates of 140 mg/h in the most recent research [6]. There, nitrogen adsorption/desorption tests as well as pore size distribution measurements of graphene were carried out (Fig. 2). In this characterization, isotherm curves show a type IV behavior, related to mesoporous materials, and a similar pore size distribution. Their porosity fits a bi-modal distribution, with a first zone of small mesoporosity (3-18 nm), and a second zone with large mesopores and some macropores (30-70 nm). The absence of microporosity is confirmed by t-plot method. Moreover, graphene synthesized through microwave plasma technology is found to be of particular interest for applications in the energy storage field, specifically in the design of high-performance Lithium-Sulfur (Li-S) batteries. During discharge, the lithium metal anode undergoes oxidation, releasing lithium ions and electrons that migrate to the sulfur cathode. However, during the electrochemical reactions at the positive electrode, the cyclic sulfur molecule (S 8 ) is reduced, leading to the formation of a series of polysulfides (Li 2 S n ), some of them in solution. Here lies one of the main drawbacks of these batteries: the shuttle effect, which involves the migration of polysulfides during charge and discharge cycles. This causes the formation of polysulfide deposits on the lithium electrode, which results in a loss of battery capacity and faster degradation of performance over time, diminishing energy efficiency and reducing lifespan. However, the porosity properties of graphene can fight the shuttle effect, capturing polysulfides in solution as shown in Fig. 3. The first picture depicts the dissolution of Li 2 S 6 polysulfide in a dioxolane (DOL) and dimethoxyethane (DME) mixture to mimic the electrolyte environment used in batteries. The yellow color characteristic of high-order polysulfides is clearly visible. In the subsequent image, graphene powder is incorporated. After two hours, it is observed that the solution has become colorless, indicating the adsorption of these polysulfides by the graphene matrix. Recently, the integration of ethanol-derived graphene obtained through plasma technology in sulfur-graphene composites acting as a highly-efficient positive electrode has been proven to be a suitable strategy for the design of Li-S batteries, (Fig. 3) with ultralong cycle life [2]. References [1] F.J. Soler-Piña et al, J Colloid Interface Sci, 640 (2023) 990-1004. [2] J.M. Blázquez-Moreno et al, J. Power Sources, 630 (2025) 236173. [3] C. Melero et al, Plasma Phys Control Fusion , 60 (2018) 014009. [4] A. Casanova et al, Fuel Processing Technology , 212 (2021) 106630. [5] J. Toman et al, Fuel Processing Technology , 239 (2023) 107534. [6] F. J. Morales-Calero et al, Chem. Eng. J., 498 (2024) 155088 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-I00y 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".