Temperature- and
dose-dependent measurements of X-ray luminescence
(XL) in various perovskite single crystals are reported. For methylammonium
lead halide perovskites (MAPbX3, MA = methylammonium, X
= Cl, Br, or I), the quenching temperature of XL intensities shifts
to lower temperatures in the sequence from Cl to I. This quenching
is strongly affected by the decrease of the thermal activation energy
ΔE
q from 53 ± 3 to 6 ±
1 meV. We replace MA in MAPbBr3 with Cs and observe that
the quenching temperature even shifts to lower temperature. But unlike
the MAPbX3 perovskites, the quenching in CsPbBr3 is now affected by the increase of the ratio between the thermal
quenching rate and the radiative transition rate (Γ0/Γv) from 15 ± 1 to 66 ± 14. The same
influence was observed if we dope MAPbBr3 with Bi3+, Γ0/Γv increases to 78 ±
18 for crystal with Bi/Pb ratio of 1:10 in precursor solution. For
larger dose of X-ray, we observe that the XL intensities are still
linear without saturation. Unlike temperature-dependent measurements,
we do not observe the line width narrowing in dose-dependent XL spectra.
Thus, this scintillator is still stable with the large X-ray dose
in comparison with the variation in the temperature.
Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.
Hybrid plasmonic nanoemitters based on the combination of quantum dot emitters (QD) and plasmonic nanoantennas open up new perspectives in the control of light. However, precise positioning of any active medium at the nanoscale constitutes a challenge. Here, we report on the optimal overlap of antenna's near-field and active medium whose spatial distribution is controlled via a plasmon-triggered 2-photon polymerization of a photosensitive formulation containing QDs. Au nanoparticles of various geometries are considered. The response of these hybrid nano-emitters is shown to be highly sensitive to the light polarization. Different light emission states are evidenced by photoluminescence measurements. These states correspond to polarization-sensitive nanoscale overlap between the exciting local field and the active medium distribution. The decrease of the QD concentration within the monomer formulation allows trapping of a single quantum dot in the vicinity of the Au particle. The latter objects show polarization-dependent switching in the single-photon regime.
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