Sequential injection system with higher dimensional electrochemical sensor signals Part 2. Potentiometric e-tongue for the determination of alkaline ions

An intelligent, automatic system based on an array of non-specific-response chemical sensors was developed. As a great amount of information is required for its correct modelling, we propose a system generating it itself. The sequential injection analysis (SIA) technique was chosen as it enables the processes of training, calibration, validation and operation to be automated simply. Detection was carried out using an array of potentiometric sensors based on PVC membranes of different selectivity. The diluted standard solutions needed for system learning and response modelling are automatically prepared from more concentrated standards. The electrodes used were characterised with respect to one and two analytes, by means of high-dimensionality calibrations, and the response surface of each was represented; this characterisation enabled an interference study of great practical utility. The combined response was modelled by means of artificial neural networks (ANNs), and thus it was possible to obtain an automated electronic tongue based on SIA. In order to identify the ANN which provided the best model of the electrode responses, some of the network's parameters were optimised and its usefulness in determining NH₄⁺, K⁺ and Na⁺ ions in synthetic samples was then tested. Finally, it was used to determine these ions in commercial fertilisers, the obtained results being compared with reference methods.     

Modelling the soundscape quality of urban waterfronts by artificial neural networks

The renewal of the urban waterfronts has become a major focus of attention for politicians and decision makers in the city’s management programs. The recognition of the patterns that define the waterfronts’ identity is essential to select new strategies of intervention for the environmental recovery. In order to create adequate environments for everyday life within a sustainable development, new links between human senses, human perception and design need to be created. Within this wide approach, the landscape and the soundscape play a significant role and can become a key driving force in the implementation of the changes. New techniques have to be tested to identify the sonic and visual parameters capable to explain the specificity of a waterfront. With this purpose, an artificial neural network (ANN) was developed, and the relative importance of the input variables was evaluated. The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models. The urban waterfront of Naples (Italy) was chosen as case study. The results obtained show that the performance of the neural network is better than the one of the linear regression (rANN = 0.949, rMLR = 0.639). The interpretation of the relative importance method is also quite satisfactory in the ANN.     

Effective medium representation and complex modes in 3D periodic metamaterials made of cubic resonators with large permittivity at mid-infrared frequencies

We review some of the techniques that lead to the effective medium representation of a three-dimensional (3D) periodic metamaterial. We consider a 3D lattice of lead telluride cubic resonators at mid-infrared (MIR) frequencies. Each cubic resonator is modeled with both an electric and a magnetic dipole, through a method called the dual dipole approximation. The electric and magnetic polarizabilities of a cubic resonator are computed via full-wave simulations by mapping the resonator's scattered field under electric/magnetic excitation only to the field radiated by an equivalent electric/magnetic dipole. We then analyze the allowed modes in the lattice, with transverse polarization and complex wavenumber, highlighting the attenuation that each mode experiences after one free space wavelength. We observe the presence of two modes with low attenuation constant, dominant in different frequency ranges, able to propagate inside the lattice: this allows the treatment of the metamaterial as a homogeneous material with effective parameters, evaluated by using various techniques. We then show that the metamaterial under analysis allows for the generation of artificial magnetism (i.e., relative effective permeability different than unity, including negative permeability with low losses) at MIR frequencies.     

半导体纳米复合材料的制备及其光电催化CO2还原

本实验通过模拟植物光合作用,设计制备了新颖的光电联合催化池3D-ZnO/Ni BiVO4/FTO,用电化学沉积法制备了泡沫镍负载的ZnO纳米棒光电阴极和BiVO4光电阳极,以0.1 mol·L^−1 KHCO3水溶液作为电解质,1 mmol·L^−1曙红Y为光敏剂,在−0.6 V硅太阳电池的电压下光电催化还原CO2得到了乙醇、乙酸和甲醇,总产率22.5μmol·L^−1·h^−1·cm^−2。实现了将太阳能贮存为化学能并减少了空气中的CO2,加深了学生对绿色化学和植物Calvin循环机理的理解。     

The lifetime of CFC substitutes studied by a network trained with chaotic mapping modified genetic algorithm and DFT calculations

The hydrohaloalkanes have attracted much attention as potential substitutes of chlorofluorocarbons (CFCs) that deplete the ozone layer and lead to great high global warming. Having a short atmospheric lifetime is very important for the potential substitutes that may also induce ozone depletion and yield high global warming gases to be put in use. Quantitative structure–activity relationship (QSAR) studies were presented for their lifetimes aided by the quantum chemistry parameters including net charges, Mulliken overlaps, E _HOMO and E _LUMO based on the density functional theory (DFT) at B3PW91 level, and the C-H bond dissociation energy based on AM1 calculations. Outstanding features of the logistic mapping, a simple chaotic system, especially the inherent ability to search the space of interest exhaustively have been utilized. The chaotic mapping aided genetic algorithm artificial neural network training scheme (CGANN) showed better performance than the conventional genetic algorithm ANN training when the structure of the data set was not favorable. The lifetimes of HFCs and HCs appeared to be greatly dependent on their energies of the highest occupied molecular orbitals. The perference of the RMSRE comparing to RMSE as objective function of ANN training was better for the samples of interest with relatively short lifetimes. C₂H₆ and C₃H₈ as potential green substitutes of CFCs present relatively short lifetimes.     

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pK_a, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.     

Automatic Electronic Tongue for On-Line Detection and Quantification of Organophosphorus and Carbamate Pesticides Using Enzymatic Screen Printed Biosensors

This work presents an automatic system, based on an electronic tongue, for resolution of mixtures of three pesticides. Inhibition detections were performed during the steady state of biosensors response. Three biosensors were built using two enzymes, electric eel (EE), genetically-modified Drosophila melanogaster (B131), and electric eel co-immobilized with drosophila melanogaster (BH). Calibrations curves for paraoxon, dichorlvos, and carbofuran were performed in the ranges 0.4–50.4 µM, 0.01–1.01 µM, 0.01–0.41 µM with LOD of 3.91 × 10^?8, 6.30 × 10^?11, and 5.84 × 10^?10, respectively. An artificial neural network (ANN) was used to model the combined response of three pesticides. A set of 19 mixtures were prepared in order to train the artificial neural network, the modeling was validated with a set of 6 spiked samples of river water. The error and recovery yields were found in consistent with expected values.     

CATALASE MIMIC WITH Fe (II) METALLOMICELLE

The effect of Fe (II) metallomicelle as a model of catalase, which was formed by adding surfactants (CTAB, SDS, LSS, Brij35) in Fe (II) -trien complex of molar ratios 1: 500 on the decomposition of hydrogen peroxide was investigated at 20°C and 30°C in pH 10 using KI-color and UV Spectrophotometry. A kinetic model for metallomicellar catalysis was proposed. The association constant of the ternary complex K and the rate constant of the decomposition of hydrogen peroxide k3 were obtained. The results indicate that the metallomicelles making up of Fe (II) metal complex and cationic or nonionic surfactants have obvious catalysis on the decomposition of hydrogen peroxide, but the metallomicelles making up of Fe (II) metal complex and anionic or zwitterionic surfactants have inhibition on this reaction.     

Unified flexible total synthesis of chlorofusin and artificial Click mimics as antagonists against p53–HDM2 interactions

A practical HPLC-free total synthesis of chlorofusin has been successfully accomplished in this work. The new synthesis showed great flexibility and convenience in generating artificial natural product-like mimics through Click chemistry, and enabled us to further investigate the biological importance of the chromophore and the spiro-aminal functionality of the natural product. The entire skeleton is believed to be essential for satisfactory biological activities of both natural chlorofusin and unnatural mimics. Two artificial Click hybrids were found to exhibit improved inhibitory activity against p53–HDM2 bindings over the natural product.     

A Combined Spectroscopic and Theoretical Study on a Ruthenium Complex Featuring a π-Extended dppz Ligand for Light-Driven Accumulation of Multiple Reducing Equivalents

The design of photoactive systems capable of storing and relaying multiple electrons is highly demanded in the field of artificial photosynthesis, where transformations of interest rely on multielectronic redox processes. The photophysical properties of the ruthenium photosensitizer [(bpy)₂Ru( oxim-dppqp )]²⁺ ( Ru ), storing two electrons coupled to two protons on the π-extended oxim-dppqp ligand under light-driven conditions, are investigated by means of excitation wavelength-dependent resonance Raman and transient absorption spectroscopies, in combination with time-dependent density functional theory; the results are discussed in comparison to the parent [(bpy)₂Ru(dppz)]²⁺ and [(bpy)₂Ru( oxo-dppqp )]²⁺ complexes. In addition, this study provides in-depth insights on the impact of protonation or of accumulation of multiple reducing equivalents on the reactive excited states.