Quantum Dot Transfer from the Organic Phase to Acrylic Monomers for the Controlled Integration of Single-Photon Sources by Photopolymerization.

This paper reports on a new strategy for obtaining homogeneous dispersion of grafted quantum dots (QDs) in a photopolymer matrix and their use for the integration of single-photon sources by two-photon polymerization (TPP) with nanoscale precision. The method is based on phase transfer of QDs from organic solvents to an acrylic matrix. The detailed protocol is described, and the corresponding mechanism is investigated and revealed. The phase transfer is done by ligand exchange through the introduction of mono-2-(methacryloyloxy) ethyl succinate (MES) that replaces oleic acid (OA). Infrared (IR) measurements show the replacement of OA on the QD surface by MES after ligand exchange. This allows QDs to move from the hexane phase to the pentaerythritol triacrylate (PETA) phase. The QDs that are homogeneously dispersed in the photopolymer without any clusterization do not show any significant broadening in their photoluminescence spectra even after more than 3 years. The ability of the hybrid photopolymer to create micro- and nanostructures by two-photon polymerization is demonstrated. The homogeneity of emission from 2D and 3D microstructures is confirmed by confocal photoluminescence microscopy. The fabrication and integration of a single-photon source in a spatially controlled manner by TPP is achieved and confirmed by auto-correlation measurements.

On the Performance of Underlay Device-to-Device Communications.

This paper comprehensively investigates the performance of the D2D underlaying cellular networks where D2D communications are operated concurrently with cellular networks provided that the aggregate interference measured on licensed users is strictly guaranteed. In particular, we derive the outage probability (OP), the average rate, and the amount of fading (AoF) of the D2D networks in closed-form expressions under three distinct power allocation schemes, i.e., the path-loss-based, equal, and random allocation schemes. It is noted that the considered networks take into consideration the impact of the intra-D2D networks, the inter-interference from the cellular networks and background noise, thus involving many random variables and leading to a complicated mathematical framework. Moreover, we also reveal the behavior of the OP with respect to the transmit power based on the rigorous mathematical frameworks rather than the computer-based simulation results. The derived framework shows that increasing the transmit power is beneficial for the OP of the D2D users. Regarding the cellular networks, the coverage probability (Pcov) of the cellular users is computed in closed-form expression too. Monte Carlo simulations are given to verify the accuracy of the proposed mathematical frameworks. Our findings illustrate that the power allocation method based on prior path-loss information outperforms the other methods in the average sum rate.

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

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 nano-emitters with controlled single quantum emitter positioning on the local excitation field.

Hybrid plasmonic nano-emitters 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.

Current Oscillations and Intermittent Emission Near an Electrode Interface in a Hybrid Organic-Inorganic Perovskite Single Crystal.

Hybrid organic-inorganic lead perovskites have a great potential in optoelectronic device applications because of their high stability, narrow band emission, and strong luminescence. Single crystals with few defects are the best candidates to disclose a variety of interesting and important properties for light-emitting devices. Here, we investigate a single-crystalline CH3NH3PbBr3 perovskite for its transport and electroluminescence properties. A simple fabrication method was used to obtain a 10 ± 2 µm channel between two gold wire electrodes, which showed bright intermittent electroluminescence near the interface of one wire after cooling down with a constant biasing voltage. The active region of the perovskite single crystal was pristine, well isolated from surroundings through fabrication to the characterization process. Our presented sample provided an ideal condition to study bulk ionic-electronic properties of hybrid halide perovskites. At constant 6 V bias, the current through the sample shows temperature-dependent oscillation with Arrhenius behavior, suggesting a thermally activated process. The light emission from the sample experiences an intermittent emission rate once every 26 ± 6 min. Here, we envisage that the current oscillations and intermittent emission are caused by ion-mediated negative differential resistance and conductive filament formation, respectively. The latter observation inspires future applications of the material from neuromorphic computing to the development of electroluminescence devices.

Multi Band Gap Electronic Structure in CH3NH3PbI3.

Organo-lead halide perovskite solar cells represent a revolutionary shift in solar photovoltaics, introducing relatively soft defect containing semiconductors as materials with excellent charge collection for both electrons and holes. Although they are based on the nominally simple cubic perovskite structure, these compounds are in fact very complex. For example, in (CH3NH3)PbI3 the dynamics and ensuing structural fluctuations associated with the (CH3NH3)+ ions and the interplay with the electronic properties are still not fully understood, despite extensive study. Here, using ab-initio calculations, we show that at room and higher temperature, the rotation of CH3NH3 molecules can be viewed as effectively giving local structures that are cubic and tetragonal like from the point of view of the PbI3 framework, though in fact having lower symmetry. Both of these structures are locally polar, with sizable polarization, ~10 µC/cm2 due to the dipoles on the organic. They become energetically degenerate in the volume range, V ~ 250 Å3/f.u-265 Å3/f.u. We also find very significant dependence of the band gap on the local structure. This type of transition is analogous to a transition between two ferroelectric structures, where in-spite of strong electron phonon coupling, there is strong screening of charged defects which can lead to enhanced mobility and charge collection. The results provide insights into the enhanced light absorption near the band edge and good charge collection in this material.

Amine-Appended Hierarchical Ca-A Zeolite for Enhancing CO2 /CH4 Selectivity of Mixed-Matrix Membranes.

An amine-appended hierarchical Ca-A zeolite that can selectively capture CO2 was synthesized and incorporated into inexpensive membrane polymers, in particular polyethylene oxide and Matrimid, to design mixed-matrix membranes with high CO2 /CH4 selectivities. Binary mixture permeation testing reveals that amine-appended mesoporous Ca-A is highly effective in improving CO2 /CH4 selectivity of polymeric membranes. In particular, the CO2 /CH4 selectivity of the polyethylene oxide membrane increases from 15 to 23 by incorporating 20 wt % amine-appended Ca-A zeolite. Furthermore, the formation of filler/polymer interfacial defects, which is typically found in glassy polymer-zeolite pairs, is inhibited owing to the interaction between the amine groups on the external surface of zeolites and polymer chains. Our results suggest that the amine-appended hierarchial Ca-A, which was utilized in membrane fabrication for the first time, is a good filler material for fabricating a CO2 -selective mixed-matrix membrane with defect-free morphology.

Hierarchical Zeolites with Amine-Functionalized Mesoporous Domains for Carbon Dioxide Capture.

To prepare materials with high CO2 adsorption, a series of hierarchical LTA zeolites possessing various mesopore spaces that are decorated with alkylamines was designed and synthesized. The highest CO2 uptake capacity was achieved when (3-aminopropyl)trimethoxysilane (APTMS) was grafted onto the hierarchical LTA zeolite having the largest mesopores. Owing to the contributions of both alkylamine groups grafted onto the mesopore surfaces and active sites in the LTA zeolites, the amount of CO2 that can be taken up on these materials is much higher than for conventional aminosilicas such SBA-15 and MCM-41. Furthermore, the adsorbent shows good CO2 uptake capacity and recyclability in dynamic flow conditions.

A 3D mesoporous polysulfone-carbon nanotube anode for enhanced bioelectricity output in microbial fuel cells.

A facile fabrication method was developed to construct a three-dimensional (3D) mesoporous anode by coating single-walled carbon nanotubes (SWCNTs) on a mesoporous polysulfone matrix (MPPS) for microbial fuel cells (MFCs). Owing to highly active surface areas and efficient extracellular electron transfer between Shewanella cells and the anode, the MFC achieved an electricity output of 1410 mW m(-2), being one of the highest among the reported Shewanella-based MFCs.