Publication

Disentangling Thermal from Electronic Contributions in the Spectral Response of Photoexcited Perovskite Materials

Résumé

Disentangling electronic and thermal effects in photoexcited perovskite materials is crucial for photovoltaic and optoelectronic applications but remains a challenge due to their intertwined nature in both the time and energy domains. In this study, we employed temperature-dependent variable-angle spectroscopic ellipsometry, density functional theory calculations, and broadband transient absorption spectroscopy spanning the visible to mid-to-deep-ultraviolet (UV) ranges on MAPbBr(3) thin films. The use of deep-UV detection opens a new spectral window that enables the exploration of high-energy excitations at various symmetry points within the Brillouin zone, facilitating an understanding of the ultrafast responses of the UV bands and the underlying mechanisms governing them. Our investigation reveals that the photoinduced spectral features remarkably resemble those generated by pure lattice heating, and we disentangle the relative thermal and electronic contributions and their evolutions at different delay times using combinations of decay-associated spectra and temperature-induced differential absorption. The results demonstrate that the photoinduced transients possess a significant thermal origin and cannot be attributed solely to electronic effects. Following photoexcitation, as carriers (electrons and holes) transfer their energy to the lattice, the thermal contribution increases from similar to 15% at 1 ps to similar to 55% at 500 ps and subsequently decreases to similar to 35-50% at 1 ns. These findings elucidate the intricate energy exchange between charge carriers and the lattice in photoexcited perovskite materials and provide insights into the limited utilization efficiency of photogenerated charge carriers.

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Concepts associés (28)
Estimation spectrale
L'estimation spectrale regroupe toutes les techniques d'estimation de la densité spectrale de puissance (DSP). Les méthodes d'estimation spectrale paramétriques utilisent un modèle pour obtenir une estimation du spectre. Ces modèles reposent sur une connaissance a priori du processus et peuvent être classées en trois grandes catégories : Modèles autorégressif (AR) Modèles à moyenne ajustée (MA) Modèles autorégressif à moyenne ajustée (ARMA). L'approche paramétrique se décompose en trois étapes : Choisir un modèle décrivant le processus de manière appropriée.
Densité spectrale de puissance
On définit la densité spectrale de puissance (DSP en abrégé, Power Spectral Density ou PSD en anglais) comme étant le carré du module de la transformée de Fourier, divisé par le temps d'intégration, (ou, plus rigoureusement, la limite quand tend vers l'infini de l'espérance mathématique du carré du module de la transformée de Fourier du signal - on parle alors de densité spectrale de puissance moyenne).
Spectral leakage
The Fourier transform of a function of time, s(t), is a complex-valued function of frequency, S(f), often referred to as a frequency spectrum. Any linear time-invariant operation on s(t) produces a new spectrum of the form H(f)•S(f), which changes the relative magnitudes and/or angles (phase) of the non-zero values of S(f). Any other type of operation creates new frequency components that may be referred to as spectral leakage in the broadest sense. Sampling, for instance, produces leakage, which we call aliases of the original spectral component.
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MOOCs associés (4)
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can be used as a starting point or a basic refresher in elementary DSP
Digital Signal Processing II
Adaptive signal processing, A/D and D/A. This module provides the basic tools for adaptive filtering and a solid mathematical framework for sampling and quantization
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