Emergence of ferromagnetism through the metal-insulator transition in undoped indium tin oxide films
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Robust contact schemes that boost stability and simplify the production process are needed for perovskite solar cells (PSCs). We codeposited perovskite and hole-selective contact while protecting the perovskite to enable deposition of SnOx/Ag without the use of a fullerene. The SnOx, prepared through atomic layer deposition, serves as a durable inorganic electron transport layer. Tailoring the oxygen vacancy defects in the SnOx layer led to power conversion efficiencies (PCEs) of >25%. Our devices exhibit superior stability over conventional p-i-n PSCs, successfully meeting several benchmark stability tests. They retained >95% PCE after 2000 hours of continuous operation at their maximum power point under simulated AM1.5 illumination at 65degrees C. Additionally, they boast a certified T97 lifetime exceeding 1000 hours.
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In this write-up, we have outlined the general methodology for treating relativity in the FP-LMTO method. The Schrodinger equation is insufficient for describing the physics of heavy elements because it leaves out any relativistic effects. Therefore DFT based on the Schrodinger equation is not sufficient for studying heavy materials. It is, however, possible to incorporate all of the effects of relativity in DFT if starting with a reformulation of the Hohenberg-Kohn theorem that proceeds from the Dirac equation, rather than the Schrodinger equation. The corresponding theorem in relativistic density functional theory (RDFT) shows that the ground-state density of the system is a functional of not only the charge density as in Schrodinger-based DFT, but also of the current density. This leads to a more elaborate procedure in practice when solving the Dirac-Kohn-Sham equations, the relativistic analogs of the Kohn-Sham equations.
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Poster to be presented at NAMBE to share results of recent research
This is the seminar I will present at WCCM conference highlighting our latest research work on incorporating genetic programming to obtain data-driven strength models for complex materials.