Facile Room‐Temperature Anion Exchange Reactions of Inorganic Perovskite Quantum Dots Enabled by a Modular Microfluidic Platform

In an effort to produce the materials of next‐generation photoelectronic devices, postsynthesis halide exchange reactions of perovskite quantum dots are explored to achieve enhanced bandgap tunability. However, comprehensive understanding of the multifaceted halide exchange reactions is inhibited by their vast relevant parameter space and complex reaction network. In this work, a facile room‐temperature strategy is presented for rapid halide exchange of inorganic perovskite quantum dots. A comprehensive understanding of the halide exchange reactions is provided by isolating reaction kinetics from precursor mixing rates utilizing a modular microfluidic platform, Quantum Dot Exchanger (QDExer). The effects of ligand composition and halide salt source on the rate and extent of the halide exchange reactions are illustrated. This fluidic platform offers a unique time‐ and material‐efficient approach for studies of solution phase‐processed colloidal nanocrystals beyond those studied here and may accelerate the discovery and optimization of next‐generation materials for energy technologies.     

Compact ultra‐wideband bandpass filter with variable notch characteristics based on transversal signal‐interaction concepts

A novel technique is presented to design highly compact microstrip ultra‐wideband (UWB) bandpass filters that exhibit high selectivity quasi‐elliptical response. The design is based on transversal signal‐interaction concepts that enable the inclusion of single or dual notch‐bands within the filter's passband to eliminate interference from other services that coexist within the UWB spectrum. The filter configuration comprises of two transmission paths which include folded T‐shaped stepped impedance resonators (SIRs) that are capacitively coupled with the input/output lines to enable signal transmission. It is shown that by combining the filters of different passband centre frequencies an UWB filter can be realised with either a single‐ or dual‐notch function. The theoretical performance of the filter is corroborated via measurements to confirm that the proposed filter exhibits UWB passband of 123% for a 3 dB fractional bandwidth, a flat group‐delay with maximum variation of less than 0.3 ns, passband insertion loss less than 0.94 dB, high selectivity, a sharp rejection notch‐band with attenuation of ?23 dB, and a good overall out‐of‐band performance. Furthermore, the filter occupies a significantly small area of 94 mm² compared with its classical counterparts. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:549–559, 2014.     

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.     

Establishment of a calibration curve for an isocentric cobalt unit using Monte Carlo simulation

Measurement of dose distribution in patients during radiotherapy is impossible. The Monte Carlo simulation is an alternative method for dose calculations. In routine radiotherapy, the source-to-surface distance(SSD)method is not practical for an isocentric unit because it requires numerous values of tissue–air ratios and inverse square law. Therefore, this method is time consuming. In this paper, the curves of relative depth doses were obtained for three different SSDs using the MCNP4C Monte Carlo simulation and approximated with a single curve called calibration curve. This curve was compared to the curve obtained by published data, differing in approximately 5% in the worst case. It was also observed that the obtained results were more accurate for distances between-5 and 10 cm from source-to-axis distance.     

Unit commitment-based load uncertainties based on improved particle swarm optimisation

This paper presents an intelligent method based on Improved Particle Swarm Optimisation to solve a unit commitment problem that takes into account the uncertainty in the demand. This uncertainty is included in the optimisation problem as a joint chance constraint that bounds the minimum value of the probability to jointly meet the deterministic power balance constraints. The demand is modelled as a multivariate, normally distributed, random variable and the correlation among different time periods is also considered. To demonstrate the effectiveness and robustness of the proposed algorithm, a system with 10 thermal and wind units with various conditions is simulated. The results and numerical experiments are compared with other methods to provide valuable information for both operational and planning problems.     

New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach

This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalization capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.     

Rheology of fluids measured by correlation force spectroscopy

We describe a method, correlation force spectrometry (CFS), which characterizes fluids through measurement of the correlations between the thermally stimulated vibrations of two closely spaced micrometer-scale cantilevers in fluid. We discuss a major application: measurement of the rheological properties of fluids at high frequency and high spatial resolution. Use of CFS as a rheometer is validated by comparison between experimental data and finite element modeling of the deterministic ring-down of cantilevers using the known viscosity of fluids. The data can also be accurately fitted using a harmonic oscillator model, which can be used for rapid rheometric measurements after calibration. The method is non-invasive, uses a very small amount of fluid, and has no actively moving parts. It can also be used to analyze the rheology of complex fluids. We use CFS to show that (non-Newtonian) aqueous polyethylene oxide solution can be modeled approximately by incorporating an elastic spring between the cantilevers.     

Real-time separation of aerosolized Staphylococcus epidermidis and polystyrene latex particles with similar size distributions

For rapid and effective detection of airborne microorganisms, it is preferable to remove dust particles during the air sampling process because they can reduce the detection accuracy of measurements. In this study, a methodology of real-time separation ofaerosolized Staphylococcus epidermidis (S. epidermidis) andpolystyrene latex (PSL) particles of similar size was investigated. These two species represent biological and non-biological particles, respectively. Due to their different relative permittivities, they grasp different numbers of air ions under corona discharge. After these charged particles enter a mobility analyzer with airflow, in which an electric field is applied perpendicular to the airflow, the S. epidermidis and PSL particles separate, due to the difference in their electric mobilities, and exit through two different outlets. Purities and recoveries for S. epidermidis and PSLat their respective outlets were determined with measurements of aerosol number concentrations and ATP bioluminescence intensities at the inlet and two outlets. The results were that purities for PSL and S. epidermidis were 70% and 80%, respectively. This methodology provides a rapid and simple way to increase the detection accuracy of bacterial agents in air.

Copyright © 2017 American Association for Aerosol Research     

Sn-adopted fullerene $$(\hbox {C}_{60})$$ nanocage as acceptable catalyst for silicon monoxide oxidation

In recent years, the discovery of metal catalysts for the oxidation of silicon monoxide (SiO) has become extremely important. In first step, the Sn adoption of fullerene (\(\hbox {C}_{60})\) was investigated and then activation of surface of \(\hbox {Sn-C}_{60}\) via \(\hbox {O}_{2}\) molecule was examined. In second step, the SiO oxidation on surface of \(\hbox {Sn-C}_{60}\) via Langmuir Hinshelwood (LH) and Eley Rideal (ER) mechanisms was investigated. Results show that \(\hbox {O}_{2}\hbox {-Sn-C}_{60}\) can oxidize the SiO molecule via \(\hbox {Sn-C}_{60}\hbox {-O-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60}\hbox {-O-O}^{*}\hbox {-SiO} \rightarrow \hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}_{2}\) and \(\hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60} + \hbox {SiO}_{2}\) reactions. Results show that SiO oxidation via the LH mechanism has lower energy barrier than ER mechanism. Finally, \(\hbox {Sn-C}_{60}\) is an acceptable catalyst with high performance for SiO oxidation in normal temperature.