Inicio
Explorar
nav.journalClub
Tendencias
Más
synapse
⌘+K
Idioma
Español
Español
Graphene-enhanced Cr-Ti-TiN broadband absorber for solar thermal systems: a computational and machine learning approach | Synapse
March 3, 2026
Graphene-enhanced Cr-Ti-TiN broadband absorber for solar thermal systems: a computational and machine learning approach
KA
Khaled Aliqab
Jouf University
BH
Bo Bo Han
AB
Ashish Baldania
Gujarat University
Ver todo
Puntos clave
Improvement in absorption efficiency significantly enhances solar thermal systems.
Using computational methods, the study shows a 25% increase in absorption performance.
Utilizing machine learning techniques, the analysis identifies optimal material configurations for absorbers.
The findings suggest that graphene usage could revolutionize energy-efficient thermal applications.
Mark Helpful
Me gusta
Save
Guardar
Relay
Compartir
Mark Helpful
Me gusta
Save
Guardar
Relay
Compartir
Cite This Study
Copy
Aliqab et al. (Tue,) studied this question.
synapsesocial.com/papers/69a761bac6e9836116a2fc8c
https://doi.org/https://doi.org/10.1007/s10825-026-02516-5