Interfacial Design of Advanced 2D Nanomaterials for Sustainable Electrochemical Energy Storage
Notice bibliographique
Résumé
Background: Advanced 2D nanomaterials are of great interest in electrochemical energy storage as they exhibit outstanding conductivity, high surface area, and tunable interfacial properties. Graphene, MXenes, transition metal dichalcogenides (TMDs), layered double hydroxides (LDHs), and other similar materials serve as X which is important in electrochemical energy storage (EES) devices. However, their ability to enhance charge transport properties, increase electrode stability, and facilitate high energy density storage makes them excellent candidates for next-generation batteries and supercapacitors. Nonetheless, major hurdles including interfacial instability, limited scalability, high manufacturer costs as well as environmental protection limit their utilization. Resolving these problems is crucial for realizing the full of 2D nanomaterials for commercial applications. Aim: The present review takes an intensive overview of the existing progress, issues that still need to be overcome, and the exploration priorities that could transfigure interfacial engineering of 2D nanomaterials towards sustainable electrochemical energy storage. Understanding the effectiveness of various nanomaterials in nanocomposite storage devices relies on knowledge of their interfacial cross-correlation and their role in energy storage capacity; therefore, this study compiles the most widely used nanomaterials, their interfacial properties, energy storage performance, and identifies critical gaps in the research that need to be overcome to make nanocomposite storage devices more ubiquitous. Methods: A systematic review methodology was followed by a structured literature search on some databases (PubMed, Scopus, Web of Science, Science Direct, and Google Scholar). A study selection was performed according to predefined inclusion and exclusion criteria to obtain relevant and quality studies. Only articles published in the last five years (2019–present) with a focus on 2D nanomaterials in electrochemical energy storage, and that were peer-reviewed, were included. The overall credibility of the chosen studies was established using quality evaluation tools like AMSTAR, Cochrane Risk of Bias Assessment, and Newcastle-Ottawa Scale. Conclusion: The data present in this study suggest that the most common 2D nanomaterials used in energy storage applications are graphene (40%), MXenes (30%), TMDs (20%), and LDHs (10%). These materials are highly promising candidates for applications in lithium-ion batteries, supercapacitors, and sodium-ion batteries, due to their various advantages including high specific capacity (35%), fast charge/discharge rates (30%), and long cycle life (25%). However, significant challenges still exist, with major barriers being interfacial instability (35%), scalability issues (30%), and high production costs (10%). Our study also suggests some important research directions, such as the development of interfacial modification strategies (40%), cost reduction techniques (30%) and green synthesis approaches (20%) for optimization of 2D nanomaterials. Takeaway: The interfacial engineering of 2D nanomaterials offers great opportunities for improving the performance and sustainability of electrochemical energy storage systems. Despite the exciting electrochemical properties of these materials, successful commercialization will need to solve hurdles in stability, cost, and scalability. Conclusively, the present research could pave the way for future studies on the development of hybrid nanostructures, effective manufacturing processes, and sustainable fabrication techniques for practical applications. This work illuminates both the promise and challenge of 2D nanomaterials and guides future energy storage LI-Ion initiatives.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».