Upscalling Graphene Production through Atmospheric Pressure Microwave Plasma Technology for Ultra-Long-Life Li-S Batteries
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».