Grant: Postgraduate Scholarships - Doctoral
Concordia University
$40,000.00
Thin-film Interface Materials for Lithium-Sulfur Batteries: From Machine Learning to Practical Application
FY 2024-25
Summary
Lithium-Sulfur Battery, Cathode, Nanomaterial, Atomic Layer Deposition, Nanodepositing, Synthesis, Density Functional Theory, Machine Learning, Energy Storage, Computational Chemistry
Program
Postgraduate Scholarships - Doctoral
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
579533-2023
Name nom
Nizami, Ameer AT
Department département
Chemical and Materials Engineering (CME)
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
40000
Program id
PGSD
Program name en
Postgraduate Scholarships - Doctoral
Program name fr
Bourses d'études supérieures - doctorat
Group en
RESEARCH TRAINING AND TALENT DEVELOPMENT
Group fr
FORMATION EN RECHERCHE ET PERFECTIONNEMENT DES COM
Committee code
194
Committee name en
S&F Selection Committee for Engineering B
Committee name fr
Comité de sélection des bourses en génie B
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Thin-film Interface Materials for Lithium-Sulfur Batteries: From Machine Learning to Practical Application
Application summary
Lithium-Sulfur Battery, Cathode, Nanomaterial, Atomic Layer Deposition, Nanodepositing, Synthesis, Density Functional Theory, Machine Learning, Energy Storage, Computational Chemistry
