A new direction in design and manufacture of co-sensitized dye solar cells: Toward concurrent optimization of power conversion efficiency and durability

A novel methodology was implemented in the present study to concurrently control power conversion efficiency (η) and durability (D) of co-sensitized dye solar cells. Applying response surface methodology (RSM) and Desirability Function (DF), the main influential assembling (dye volume ratio and anti-aggregation agent concentration) and operational (performance temperature) parameters were systematically changed to probe their main and interactive effects on the η and D responses. Individual optimization based on RSM elucidated that D can be solely controlled by changing the ratio of vat-based organic photosensitizers, whereas η takes both effects of dye volume ratio and anti-aggregation concentration into account. Among the studied factors, the performance temperature played the most vital role in η and D regulation. In particular, however, multi-objective optimization by DF explored the degree to which one should be careful about manipulation of assembling and operational parameters in the way maximization of performance of a co-sensitized dye solar cell.     

Mild and Efficient Synthesis of Benzo-Fused Seven- and Eight-membered Ring Lactams: A Convenient Approach to Biologically Interesting Chemotypes

A general and efficient method for the synthesis of benzo-fused 7- and 8-membered ring lactams via the Beckmann rearrangement of cyclic oximes is presented.

Supplemental materials are available for this article. Go to the publisher's online edition of Synthetic Communications® to view the free supplemental file.     

Efficient computation of operator‐type response sensitivities for uncertainty quantification and predictive modeling: illustrative application to a spent nuclear fuel dissolver model

This work honors the 75th birthday of Professor Ionel Michael Navon by presenting original results highlighting the computational efficiency of the adjoint sensitivity analysis methodology for function‐valued operator responses by means of an illustrative paradigm dissolver model. The dissolver model analyzed in this work has been selected because of its applicability to material separations and its potential role in diversion activities associated with proliferation and international safeguards. This dissolver model comprises eight active compartments in which the 16 time‐dependent nonlinear differential equations modeling the physical and chemical processes comprise 619 scalar and time‐dependent model parameters, related to the model's equation of state and inflow conditions. The most important response for the dissolver model is the time‐dependent nitric acid in the compartment furthest away from the inlet, where measurements are available at 307 time instances over the transient's duration of 10.5 h. The sensitivities to all model parameters of the acid concentrations at each of these instances in time are computed efficiently by applying the adjoint sensitivity analysis methodology for operator‐valued responses. The uncertainties in the model parameters are propagated using the above‐mentioned sensitivities to compute the uncertainties in the computed responses. A predictive modeling formalism is subsequently used to combine the computational results with the experimental information measured in the compartment furthest from the inlet and then predict optimal values and uncertainties throughout the dissolver. This predictive modeling methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution for the a priori known mean values and uncertainties characterizing the model parameters and the computed and experimentally measured model responses. This approximate a priori distribution is subsequently combined using Bayes' theorem with the “likelihood” provided by the multi‐physics computational models. Finally, the posterior distribution is evaluated using the saddle‐point method to obtain analytical expressions for the optimally predicted values for the parameters and responses of both multi‐physics models, along with corresponding reduced uncertainties. This work shows that even though the experimental data pertains solely to the compartment furthest from the inlet (where the data were measured), the predictive modeling procedure used herein actually improves the predictions and reduces the predicted uncertainties for the entire dissolver, including the compartment furthest from the measurements, because this predictive modeling methodology combines and transmits information simultaneously over the entire phase‐space, comprising all time steps and spatial locations. Copyright © 2016 John Wiley & Sons, Ltd.     

Computational-Based Approach for Predicting Porosity of Electrospun Nanofiber Mats Using Response Surface Methodology and Artificial Neural Network Methods

Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats.     

Optimization of cadmium adsorption from aqueous solutions by functionalized graphene and the reversible magnetic recovery of the adsorbent using response surface methodology

Novel functionalized graphene adsorbent was prepared and characterized using different techniques. The prepared adsorbent was applied for the removal of cadmium ions from aqueous solution. A response surface methodology was used to evaluate the simple and combined effects of the various parameters, including adsorbent dosage, pH, and initial concentration. Under the optimal conditions, the cadmium removal performance of 70% was achieved. A good agreement between experimental and predicted data in this study was observed. The experimental results revealed of cadmium adsorption with high linearity follow Langmuir isotherm model with maximum adsorption capacity of 502 mg g^?1, and the adsorption data fitted well into pseudo‐second order model. Thermodynamic studies showed that adsorption process has exothermic and spontaneous nature. The recommended optimum conditions are: cadmium concentration of 970 mg L^?1, adsorbent dosage of 1 g L^?1, pH of 6.18, and T = 25 °C. The magnetic recovery of the adsorbent was performed using a magnetic surfactant to form a noncovalent magnetic functionalized graphene. After magnetic recovery of the adsorbent both components (adsorbent and magnetic surfactant) were recycled by tuning the surface charges through changing the pH of the solution. Desorption behavior studied using HNO₃ solution indicated that the adsorbent had the potential for reusability.     

Detection of individual insulating entities by electrochemical blocking

Electrochemical blocking is a type of single-entity electrochemical measurement particularly well adapted to the detection of insulating particles. The digital detection of ultralow concentrations of artificial entities such as polymer particles or biotargets such as proteins and bacteria represents an exceptional opportunity for sensing applications. In this review, we explore the latest development in the field of electrochemical blocking and propose some perspectives.     

Organoboron Derivatives of Stereoregular Phenylcyclosilsesquioxanes

This study presents the synthesis of organoboron derivatives of stereoregular 4-, 6-, and 12-unit phenylcyclosilsesquioxanes. All compounds obtained were isolated in good yields (70–80 %) and were fully characterized by ¹H, ¹³C, ²⁹Si, ¹¹B NMR, IR spectroscopy, HRMS ESI, and elemental microanalysis. The structure of the key modifier, obtained for the first time, 4-(tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl) dimethylvinylsilane, was also confirmed by single-crystal XRD.     

Orthogonal Wavelets Analysis of Electroanalytical Signals

ABSTRACT

A signal processing technique based on orthogonal wavelet analysis is applied to process various simulated electroanalytical signals. The results indicate that if the scale parameters are selected, the orthogonal wavelet processing method (OWPM) can remove high-frequency noise. Experimental signal was recorded by computer and used to test the OWPM procedure. After processing with OWPM, the processed data was used to analyze the mechanism of the electrode reactions. Processed results of the experimental data are also satisfactory.