Abstract
Theoretical modelling plays a crucial role in the characterization of nanostructure-based solar cells, semiconductor devices that directly convert sunlight into electricity. Photovoltaic technology is environmentally friendly and a popular means of generating electric power, becoming the third most used renewable energy source in the world. Yet, the downscaling of device dimensions poses considerable challenges for device simulation. If atomistic models are desirable at the nanoscale, they must connect to continuous media models in order to extract the current–voltage characteristics of the material. Nevertheless, the extension beyond the mesoscopic regime is excessively expensive from a computational point of view. In addition, products of technology are systems that often operate far from equilibrium, as they exchange matter with their environment. As a result of nonunitary dynamics, the environment ‘measures’ the system but the observer does not know the result. In a semiconductor, the environment may be represented by the load/electrode interface which resists particle flow through the device. Parametrization of the effective model Hamiltonians based on the tight-binding approach represents a solution to bridge the micro- and macroscale pictures in a comprehensive multiscale simulation framework.
Original language | English (US) |
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Title of host publication | Green Chemistry and Computational Chemistry |
Subtitle of host publication | Shared Lessons in Sustainability |
Publisher | Elsevier |
Pages | 147-181 |
Number of pages | 35 |
ISBN (Electronic) | 9780128198797 |
ISBN (Print) | 9780323851824 |
DOIs | |
State | Published - Jan 1 2021 |
All Science Journal Classification (ASJC) codes
- General Chemistry
Keywords
- Atomistic representation of a material
- Conduction band
- Photocurrent generation
- Photovoltaics and sustainability
- Power conversion efficiency
- Semiclassical scenarios
- Semiconductors
- Site-based simulations
- Spread of site density in a lattice
- Transport characteristics of a material