Silicon quantum dots: surface matters

Silicon quantum dots (SiQDs) hold great promise for many future technologies. Silicon is already at the core of photovoltaics and microelectronics, and SiQDs are capable of efficient light emission and amplification. This is crucial for the development of the next technological frontiers—silicon photonics and optoelectronics. Unlike any other quantum dots (QDs), SiQDs are made of non-toxic and abundant material, offering one of the spectrally broadest emission tunabilities accessible with semiconductor QDs and allowing for tailored radiative rates over many orders of magnitude. This extraordinary flexibility of optical properties is achieved via a combination of the spatial confinement of carriers and the strong influence of surface chemistry. The complex physics of this material, which is still being unraveled, leads to new effects, opening up new opportunities for applications. In this review we summarize the latest progress in this fascinating research field, with special attention given to surface-induced effects, such as the emergence of direct bandgap transitions, and collective effects in densely packed QDs, such as space separated quantum cutting

Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements

This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA