Comprehensive insights into the application of graphene-based aerogels for metals removal from aqueous media: Surface chemistry, mechanisms, and key features
Notice bibliographique
Résumé
Efficient removal of heavy metals and other toxic metal pollutants from wastewater is essential to protect human health and the surrounding vulnerable ecosystems. Therefore, significant efforts have been invested in developing practical and sustainable tools to address this issue, including high-performance adsorbents. In this respect, within the last few years, graphene-based aerogels/xerogels/cryogels (GBAs) have emerged and drawn significant attention as excellent materials for removing and recovering harmful and valuable metals from different aqueous media. Such an upward trend is mainly due to the features of the aerogel materials combined with the properties of the graphene derivatives within the aerogel's network, including the GBAs' unique three-dimensional (3D) porous structure, high porosity, low density, large specific surface area, exceptional electron mobility, adjustable and rich surface chemistry, remarkable mechanical features, and tremendous stability. This review offers a comprehensive analysis of the fundamental and practical aspects and phenomena related to the application of GBAs for metals removal. Herein, we cover all types of (bottom-up) synthesized GBAs, including true microporous graphene-based aerogels as well as other 3D graphene-based open-cell interconnected mesoporous and macroporous aerogels, foams, and sponges. Indeed, we provide insights into the fundamental understanding of the GBAs' suitability for such an important application by revealing the mechanisms involved in metals removal and the factors inducing and controlling the highly selective behavior of these distinctive adsorbents. Besides conventional adsorptive pathways, we critically analyzed the ability of GBAs to electrochemically capture metal pollutants (i.e., electrosorption) as well as their efficiency in metals detoxification through reductive mechanisms (i.e., adsorption-reduction-readsorption). We also covered the reusability aspect of graphene aerogels (GAs)-based adsorbents, which is strongly linked to the GBAs' outstanding stability and efficient desorption of captured metals. Furthermore, in view of their numerous practical and environmental benefits, the development and application of magnetically recoverable GAs for metals removal is also highlighted. Moreover, we shed light on the potential practical and scalable implementation of GBAs by evaluating their performance in continuous metals removal processes while highlighting the GBAs' versatility demonstrated by their ability to remove multiple contaminants along with metal pollutants from wastewater media. Finally, this review provides readers with an accessible overview and critical discussion of major recent achievements regarding the development and applications of GAs-based adsorbents for metal ions removal. Along with our recommendations and suggestions for potential future work and new research directions and opportunities, this review aims to serve as a valuable resource for researchers in the field of wastewater treatment and inspire further progress towards developing next-generation high-performance GBAs and expanding their application.
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 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) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| 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) | 0,001 | 0,001 |
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 ».