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Enregistrement W4412512115 · doi:10.1149/ma2025-01193174mtgabs

Ethanol-Derived Graphene By Microwave Plasma Torch: A Suitable Material for Manufacturing High-Performance Metal-Sulfur Batteries

2025· article· en· W4412512115 sur OpenAlexaboutno aff
Francisco Javier Morales‐Calero, Jesús M. Blázquez‐Moreno, Antonio Cobos‐Luque, Andrés M. Raya, J. Muñoz, Almudena Benítez, Norma Y. Mendoza-González, J. A. Alcusón, M. D. Calzada, Álvaro Caballero, Rocío Rincón

Notice bibliographique

RevueECS Meeting Abstracts · 2025
Typearticle
Langueen
DomaineMaterials Science
ThématiqueMXene and MAX Phase Materials
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTorchSulfurGrapheneMicrowavePlasma torchMaterials sciencePlasmaMetalMetallurgyNanotechnologyChemical engineeringEngineeringPhysicsTelecommunications

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,240
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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