RESUMO
PbS quantum dots (QDs), among the most mature nanocrystals obtained by colloidal chemistry, are promising candidates in optoelectronic applications at various operational frequencies. QD device performances are often determined by charge transport, either carrier injection before photoemission or charge detection after photoabsorption, which is significantly influenced by the dielectric environment. Here, we present the electronic structure and the optical gap of PbS QDs versus size for various solvents calculated using ab initio methods including the many-body perturbation approaches. This study highlights the importance of the dielectric environment, pointing out (1) the non-negligible shift of the electronic structure due to the ground state polarization and (2) a substantial impact on the electronic bandgap. The electron-hole binding energy, which varies largely with the QD size and solvent, is well-described by an electrostatic model. This study reveals the fundamental physics of size and solvation effects, which could be useful to design PbS QD-based optoelectronic devices.
RESUMO
As nanocrystals (NCs) gain maturity, they become central building blocks for optoelectronics in devices such as solar cells and, more recently, infrared focal plane arrays. Now that the proof of concept of these devices has been established, their optimization requires a deeper understanding of their electronic and optical features to engineer their optoelectronic properties accurately. Though PbS NCs have been extensively investigated, the complex optical index of PbS NC thin films remains mostly unknown. Some previous works have unveiled the optical index for this type of material optimized for solar cells (excitonic peak at 940 nm), but longer wavelengths remain scarce and surface chemistry effects, which are known to be of central importance for layer doping, are simply unexplored. Here, we conduct a systematic investigation of the complex optical index of PbS NC thin films using broadband spectrally resolved ellipsometry. The obtained results are then compared with simulations combining tight-binding (TB) modeling at the NC level and the Bruggeman model to expand the results to the film scale. While TB calculation gives the NC optical indices, we extract the key NC film parameters such as the NC volume fraction and ligand indices by fitting the Bruggeman formula to ellipsometry measurements. We also bring evidence that this joint modeling method can be conducted without the need for ellipsometry data while preserving the main feature of the experimental results. Finally, the unveiled optical indices are used to model the absorption of short-wave infrared diode stacks based on PbS NCs and are relevant for state-of-the-art devices. Our electromagnetic modeling shows that the absorption within the contact is now a major limitation of the current device operated at the telecom wavelength.
RESUMO
Correlated quantum-chemical methods for condensed matter systems, such as the random phase approximation (RPA), hold the promise of reaching a level of accuracy much higher than that of conventional density functional theory approaches. However, the high computational cost of such methods hinders their broad applicability, in particular for finite-temperature molecular dynamics simulations. We propose a method that couples machine learning techniques with thermodynamic perturbation theory to estimate finite-temperature properties using correlated approximations. We apply this approach to compute the enthalpies of adsorption in zeolites and show that reliable estimates can be obtained by training a machine learning model with as few as 10 RPA energies. This approach paves the way to the broader use of computationally expensive quantum-chemical methods to predict the finite-temperature properties of condensed matter systems.
