The effect of morphology and doping on photoluminescence of ZnO nanostructures

In this work, optical properties of ZnO nanostructures prepared by chemical vapor deposition under different conditions were investigated. ZnO nanostructures were characterized by electron microscopy and photoluminescence. A high intensity green emission and a narrow UV emission band are observed in photoluminescence spectra of ZnO nanostructures related to the below-band-gap and band-edge that their intensities depend on the morphology of the nanostructures. It is considered that the green emission is originated from structural defects. In addition, the influence of thermal treatment and dopants such as iron and copper, on the photoluminescence (PL) properties of the ZnO nanostructures was investigated.     

Performance evaluation of fast Fourier-transform continuous cyclic-voltammetry pesticide biosensor

In this work, a method for the fast monitoring of OPs in flow-injection systems was evaluated. The fast Fourier transform continuous cyclic-voltammetry (FFTCCV) at the carbon-paste electrode in a flowing solution system was used for determination of OPs. In this method the S/N ratio is enhanced by using of fast Fourier transform of the analyte and signal integration. FFTCCV can be considered as a new sensitive, accurate and fast method for determination of drugs and some pesticides. However, in order to obtain better sensitivity for a specific target, experimental parameters should be optimized. Response surface methodology (RSM) was applied to optimize three effective parameters (enzyme activity, multiwall carbon nanotube quantity and acidic sol–gel quantity). The optimum values for the tested parameters were enzyme amount H0.169 U cm⁻², multiwall carbon nanotube (MWCNT) 0.607 mL and acidic sol–gel 1.012 mL. The optimum feed pH, feed flow rate, ATChCl concentration and sweeping-rate were found to be 7.4, 0.34 mL min⁻¹, 0.750 mM and 10 V s⁻¹, respectively. The long-term stability of this flow-through system was 80% of its initial response after 120 days. Based on an incubation time of 12 min, it was found that the detection limit for paraoxon was equal to 1.7 × 10⁻⁷ mg L⁻¹ (6.2 × 10⁻¹³ M). The developed biosensor exhibited good repeatability and reproducibility. This study provides a new, modern, sensitive tool for the analysis of organophosphate pesticides.     

Hybrid-DFT Study and NBO Interpretation of the Configurational Behavior of 2-Halotetrahydrothiopyran S-Oxides

Abstract

Natural bond orbital (NBO) interpretation and hybrid density functional theory (hybrid-DFT: B3LYP/Def2-TZVPP)-based methods were used to investigate the impacts of the generalized anomeric effects (GAE), electrostatic, and steric interactions on the conformational properties of cis and trans isomers of 2-fluoro-, 2-chloro-, and 2-bromotetrahydrothiopyran S-oxide (1–3). The results obtained showed that the trans-axial configurations are the most stable forms of compounds 1–3. Based on the results obtained, the instability of the second lowest energy-minimum (cis-equatorial configuration, with axial S?O and equatorial C?X bonds, X = halogen atoms) increases from compound 1 to compound 3. This trend is also observed for the third lowest energy-minimum (i.e., the trans-equatorial configuration). Contrary to the trend observed for the cis- and trans-equatorial forms, the instability of the cis-axial form compared to the trans-axial form, increases from 1 to 2 but decreases slightly from 2 to 3. The correlations between the GAE, bond orders, steric effects, ΔG, Δμ, structural parameters, and conformational and configurational behaviors of compounds 1–3 have been investigated.

Supplemental materials are available for this article. Go to the publisher's online edition of Phosphorus, Sulfur, and Silicon and the Related Elements to view the free supplemental file.     

Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g., through denoising) or to estimate the relation between latent causes and measurements in medical data. In the latter case, LVMs in the form of noisy-OR Bayes nets represent the standard approach to relate binary latents (which represent diseases) to binary observables (which represent symptoms). Bayes nets with binary representation for symptoms may be perceived as a coarse approximation, however. In practice, real disease symptoms can range from absent over mild and intermediate to very severe. Therefore, using diseases/symptoms relations as motivation, we here ask how standard noisy-OR Bayes nets can be generalized to incorporate continuous observables, e.g., variables that model symptom severity in an interval from healthy to pathological. This transition from binary to interval data poses a number of challenges including a transition from a Bernoulli to a Beta distribution to model symptom statistics. While noisy-OR-like approaches are constrained to model how causes determine the observables’ mean values, the use of Beta distributions additionally provides (and also requires) that the causes determine the observables’ variances. To meet the challenges emerging when generalizing from Bernoulli to Beta distributed observables, we investigate a novel LVM that uses a maximum non-linearity to model how the latents determine means and variances of the observables. Given the model and the goal of likelihood maximization, we then leverage recent theoretical results to derive an Expectation Maximization (EM) algorithm for the suggested LVM. We further show how variational EM can be used to efficiently scale the approach to large networks. Experimental results finally illustrate the efficacy of the proposed model using both synthetic and real data sets. Importantly, we show that the model produces reliable results in estimating causes using proofs of concepts and first tests based on real medical data and on images.     

Genetic algorithm optimization for finned channel performance

Compared to a smooth channel,a finned channel provides a higher heat transfer coefficient;increasing the fin height enhances the heat transfer.However,this heat transfer enhancement is associated with an increase in the pressure drop.This leads to an increased pumping power requirement so that one may seek an optimum design for such systems.The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm.Each fin arrangement is considered a solution to the problem (an individual for genetic algorithm).An initial population is generated randomly at the first step.Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.     

Entirely green protocol for the synthesis of β-aminoketones using saccharose as a homogenous catalyst

Saccharose was applied as an efficient and homogenous catalyst for a one-pot, three-component Mannich reaction for the formation of β-aminoketones from aromatic aldehydes, anilines, and acetophenone at ambient temperature in excellent yields. This protocol has the following advantages: mild conditions, high yields, clean reaction profiles, operational simplicity, and environmentally benign and simple work-up procedures.     

Improvement of GC‐MS Analysis of Shahrbabak Ziziphora tenuior Essential Oil by Using Multivariate Curve Resolution Approaches

Essential oil of aerial parts of Ziziphora tenuior growing in Shahrbabak in central Iran are isolated by hydrodistillation. Due to complexity of essential oils, there are fundamental problems such as co‐elution in their direct gas chromatography‐mass spectrometry analysis. These problems can result in low similarity matches in MS library search, so that true identification and determination of individual components may fail. In the present work, each component was identified and determined using GC‐MS coupled with multivariate curve resolution (MCR) techniques. In this way, more information along with higher accuracy and precision can be extracted from pure experimental GC‐MS data. The number of identified components found increased from 37 in direct similarity search to 80 in GC‐MS/MCR method. To identify each individual component, similarity search and Kovat's retention index comparison were implemented. The results found showed that pulegone (38.3%), 3′,5′‐dihydroxyacetophenone (22.83%), isomenthone (7.06%), 2‐methyl‐5‐(1‐methylethyl)‐phenol (3.41%), limonene (2.59%) and 2‐acetyl‐4,4‐dimethyl‐cyclopent‐2‐enone (2.49%) were the most abundant components. The reported compounds accounted for 94.39% of total content of the essential oil. A characteristic feature of the Iranian Ziziphora tenuior is the absence of piperitenone in its constituents compared with the oil of other Ziziphora species from Turkey.     

Optimization of Walnut Oil Nanoemulsions Prepared Using Ultrasonic Emulsification: A Response Surface Method

Response surface methodology-central composite rotatable design (RSM-CCRD) was applied to determine effects of ultrasonic time (UT, 5–15 minutes), walnut oil (WO, 4–10% w/w) content, and concentration ratio of Span 80 to Tween 80 (K ₀, 0.55–0.80), on the some physicochemical characteristics of WO/water nanoemulsion including average particle size, Span and loss of antioxidant activity (LAA). Analysis of variance (ANOVA) showed that second-order polynomial models with high R ² (0.944–0.983) were well adjusted to predict response variables. The linear effect of UT was found to be most significant in all response surface models. The optimal conditions were: UT of 5.0 minutes, WO content of 7.35% w/w, and K ₀ of 0.8. Under these conditions, the average particle size, Span, and LAA were 356.08 nm, 0.548%, and 10.96. The adequacy of the models was confirmed by production this nanoemulsion under optimum values given by the model.