Nanostructured Materials Engineering and Characterization for Battery Applications
Editat de Amadou Belal Gueye, Hanna J. Maria, Nandakumar Kalarikkal, Modou Fall, Arul Manuel Stephan, Sabu Thomasen Limba Engleză Paperback – 20 iun 2024
- Presents practical consideration for battery usage such as LCA, recycling and green batteries
- Covers battery characterization techniques including electrochemical methods, microscopy, spectroscopy and X-ray methods
- Explores battery models and computational materials design theories
Preț: 1248.24 lei
Preț vechi: 1371.69 lei
-9% Nou
Puncte Express: 1872
Preț estimativ în valută:
238.89€ • 248.14$ • 198.43£
238.89€ • 248.14$ • 198.43£
Carte tipărită la comandă
Livrare economică 27 ianuarie-10 februarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780323913041
ISBN-10: 0323913040
Pagini: 714
Ilustrații: 250 illustrations (150 in full color)
Dimensiuni: 152 x 229 mm
Greutate: 0.94 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323913040
Pagini: 714
Ilustrații: 250 illustrations (150 in full color)
Dimensiuni: 152 x 229 mm
Greutate: 0.94 kg
Editura: ELSEVIER SCIENCE
Public țintă
Materials scientists and engineers in academia and R&D; mechanical and chemical engineers.Cuprins
SECTION 1: Introduction to energy storage systems and fundamentals
1. Electrochemical energy storage technologies: state of the art, case studies, challenges, and opportunities
2. Battery modeling
SECTION 2: Engineering of battery materials
3. Nanostructured cathode materials
4. Nanostructured electrolyte materials
5. Nanostructured functionalized separators
6. Nanostructured anode materials
7. Computational materials design of nanostructured materials for battery applications
SECTION 3: Battery characterization
8. Characterization of battery materials by electrochemical method
9. Characterization of battery materials by microscopy techniques
10. Characterization of battery materials by neutron scattering methods
11. Characterization of battery materials by X-ray methods
12. Characterization of battery materials by mechanical
13. Characterization of battery materials by surface spectroscopy methods
SECTION 4: Applications, practical considerations, and perspectives on batteries
14. Battery manufacturing—from laboratory to industry—challenges
15. Life cycle assessment of batteries
16. Battery applications
17. A simplified model to improve the performance of repurposed electric vehicle batteries
18. Fully green batteries
19. Integrated technologies and novel nanostructured materials for energy storage
20. Future of lignocellulosic biomassderived activated carbon for battery application
21. Artificial intelligence and machine learning in battery materials and their applications
1. Electrochemical energy storage technologies: state of the art, case studies, challenges, and opportunities
2. Battery modeling
SECTION 2: Engineering of battery materials
3. Nanostructured cathode materials
4. Nanostructured electrolyte materials
5. Nanostructured functionalized separators
6. Nanostructured anode materials
7. Computational materials design of nanostructured materials for battery applications
SECTION 3: Battery characterization
8. Characterization of battery materials by electrochemical method
9. Characterization of battery materials by microscopy techniques
10. Characterization of battery materials by neutron scattering methods
11. Characterization of battery materials by X-ray methods
12. Characterization of battery materials by mechanical
13. Characterization of battery materials by surface spectroscopy methods
SECTION 4: Applications, practical considerations, and perspectives on batteries
14. Battery manufacturing—from laboratory to industry—challenges
15. Life cycle assessment of batteries
16. Battery applications
17. A simplified model to improve the performance of repurposed electric vehicle batteries
18. Fully green batteries
19. Integrated technologies and novel nanostructured materials for energy storage
20. Future of lignocellulosic biomassderived activated carbon for battery application
21. Artificial intelligence and machine learning in battery materials and their applications