(Invited) Carbon Dots – from Imaging to Green Energy Applications
Notice bibliographique
Résumé
In recent years, nanomaterials, defined as materials < 100 nm in a single dimension, have garnered significant interest for the development of novel applications in the physical and life sciences. This is especially true for luminescent nanoparticles, which have been investigated for the development of sensors, imaging/diagnostic probes, display and solar cell applications. Recently, a relatively new class of luminescent nanomaterials, namely carbon dots, has come to light. Carbon dots, sometimes known as carbongenic dots, are carbon, oxygen, nitrogen and hydrogen containing materials with the first two elements typically accounting for ~90% of their elemental composition. Moreover, they are typically water dispersible and can be prepared from an abundant number of inexpensive sources including small molecules such as citric acid, amino acids and sugars. While they are small in size (typically 1-10 nm), they can offer a high quantum yield of emission, a process that is controlled through passivation of the surface with an organic reagent. Of particular interest are their optical properties, which can be tailored via careful selection of the starting precursors and the desired synthesis route resulting in the ability to generate fluorescence from the blue to the near infrared regions of the spectrum. In addition to their versatile optical properties, these carbon dots are generally known to have low cytotoxicity and good biocompatibility. Combined with their small size and versatile optical properties, developing carbon dots as a nanoplatform can be achieved as they lend themselves for integration in a myriad of applications most notably in bio-imaging and sensing. Our work focuses on achieving a fundamental understanding of the synthesis of carbon dots in order to control their size and achieve homogenous surface chemistry and optical properties. As they are fluorescent, we have investigated their development as bimodal imaging probes and exploited their temperature and pH dependent optical properties in order develop novel intracellular temperature and pH sensors. We can also endow them with chiral properties during synthesis opening up novel avenues for chiral sensing and antimicrobial applications. Finally, in recent developments, we have exploited their physico-chemical properties in green energy applications focusing on the production of biofuels.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».