Synthesis, characterization and activity of an immobilized photocatalyst : natural porous diatomite supported titania nanoparticles

Diatomite, a porous non-metal mineral, was used as support to prepare TiO2/diatomite composites by a modified sol–gel method. The as-prepared composites were calcined at temperatures ranging from 450 to 950 _C. The characterization tests included X-ray powder diffraction (XRD), scanning electron microscopy (SEM) with an energy-dispersive X-ray spectrometer (EDS), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption measurements. The XRD analysis indicated that the binary mixtures of anatase and rutile exist in the composites. The morphology analysis confirmed the TiO2 particles were uniformly immobilized on the surface of diatom with a strong interfacial anchoring strength, which leads to few drain of photocatalytic components during practical applications. In further XPS studies of hybrid catalyst, we found the evidence of the presence of Ti–O–Si bond and increased percentage of surface hydroxyl. In addition, the adsorption capacity and photocatalytic activity of synthesized TiO2/diatomite composites were evaluated by studying the degradation kinetics of aqueous Rhodamine B under UV-light irradiation. The photocatalytic degradation was found to follow pseudo-first order kinetics according to the Langmuir–Hinshelwood model. The preferable removal efficiency was observed in composites by 750 _C calcination, which is attributed to a relatively appropriate anatase/rutile mixing ratio of 90/10.

A constitutive model for mechanical response characterization of pumpkin peel and flesh tissues under tensile and compressive loadings

Enhancing quality of food products and reducing volume of waste during mechanical operations of food industry requires a comprehensive knowledge of material response under loadings. While research has focused on mechanical response of food material, the volume of waste after harvesting and during processing stages is still considerably high in both developing and developed countries. This research aims to develop and evaluate a constitutive model of mechanical response of tough skinned vegetables under postharvest and processing operations. The model focuses on both tensile and compressive properties of pumpkin flesh and peel tissues where the behaviours of these tissues vary depending on various factors such as rheological response and cellular structure. Both elastic and plastic response of tissue were considered in the modelling process and finite elasticity combined with pseudo elasticity theory was applied to generate the model. The outcomes were then validated using the published results of experimental work on pumpkin flesh and peel under uniaxial tensile and compression. The constitutive coefficients for peel under tensile test was α = 25.66 and β = −18.48 Mpa and for flesh α = −5.29 and β = 5.27 Mpa. under compression the constitutive coefficients were α = 4.74 and β = −1.71 Mpa for peel and α = 0.76 and β = −1.86 Mpa for flesh samples. Constitutive curves predicted the values of force precisely and close to the experimental values. The curves were fit for whole stress versus strain curve as well as a section of curve up to bio yield point. The modelling outputs had presented good agreement with the empirical values and the constructive curves exhibited a very similar pattern to the experimental curves. The presented constitutive model can be applied next to other agricultural materials under loading in future.

Uncertainty quantification applied to the performance analysis of a conical diffuser

This paper offers an uncertainty quantification (UQ) study applied to the performance analysis of the ERCOFTAC conical diffuser. A deterministic CFD solver is coupled with a non-statistical generalised Polynomial Chaos(gPC)representation based on a pseudo-spectral projection method. Such approach has the advantage to not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic field. The stochactic results highlihgt the importance of the inlet velocity uncertainties on the pressure recovery both alone and when coupled with a second uncertain variable. From a theoretical point of view, we investigate the possibility to build our gPC representation on arbitray grid, thus increasing the flexibility of the stochastic framework.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

An efficient forward-backward algorithm to MSDEPP including batteries and voltage control devices

Electric distribution networks are now in the era of transition from passive to active distribution networks with the integration of energy storage devices. Optimal usage of batteries and voltage control devices along with other upgrades in network needs a distribution expansion planning (DEP) considering inter-temporal dependencies of stages. This paper presents an efficient approach for solving multi-stage distribution expansion planning problems (MSDEPP) based on a forward-backward approach considering energy storage devices such as batteries and voltage control devices such as voltage regulators and capacitors. The proposed algorithm is compared with three other techniques including full dynamic, forward fill-in, backward pull-out from the point of view of their precision and their computational efficiency. The simulation results for the IEEE 13 bus network show the proposed pseudo-dynamic forward-backward approach presents good efficiency in precision and time of optimization.

Competitive interactions of anti-carcinogens with serum albumin: A spectroscopic study of bendamustine and dexamethasone with the aid of chemometrics

Interactions between the anti-carcinogens, bendamustine (BDM) and dexamethasone (DXM), with bovine serum albumin (BSA) were investigated with the use of fluorescence and UV–vis spectroscopies under pseudo-physiological conditions (Tris–HCl buffer, pH 7.4). The static mechanism was responsible for the fluorescence quenching during the interactions; the binding formation constant of the BSA–BDM complex and the binding number were 5.14 × 105 L mol−1 and 1.0, respectively. Spectroscopic studies for the formation of BDM–BSA complex were interpreted with the use of multivariate curve resolution – alternating least squares (MCR–ALS), which supported the complex formation. The BSA samples treated with site markers (warfarin – site I and ibuprofen – site II) were reacted separately with BDM and DXM; while both anti-carcinogens bound to site I, the binding constants suggested that DXM formed a more stable complex. Relative concentration profiles and the fluorescence spectra associated with BDM, DXM and BSA, were recovered simultaneously from the full fluorescence excitation–emission data with the use of the parallel factor analysis (PARAFAC) method. The results confirmed that on addition of DXM to the BDM–BSA complex, the BDM was replaced and the DXM–BSA complex formed; free BDM was released. This finding may have consequences for the transport of these drugs during any anti-cancer treatment.

Application of organo-beidellites for the adsorption of atrazine

The surfaces of natural beidellite were modified with cationic surfactant octadecyl trimethylammonium bromide at different concentrations. The organo-beidellite adsorbent materials were then used for the removal of atrazine with the goal of investigating the mechanism for the adsorption of organic triazine herbicide from contaminated water. Changes on the surfaces and structure of beidellite were characterised by X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and BET surface analysis. Kinetics of the adsorption studies were also carried out which show that the adsorption capacity of the organoclays increases with increasing surfactant concentration up until 1.0 CEC surfactant loading, after which the adsorption capacity greatly decreases. TG analysis reveals that although the 2.0 CEC sample has the greatest percentage of surfactant by mass, most of it is present on external sites. The 0.5 CEC sample has the highest proportion of surfactant exchanged into the internal active sites and the 1.0 CEC sample accounts for the highest adsorption capacity. The goodness of fit of the pseudo-second order kinetic confirms that chemical adsorption, rather than physical adsorption, controls the adsorption rate of atrazine.

Liquid metal/metal oxide frameworks with incorporated Ga2O3 for photocatalysis

Solvothermally synthesized Ga2O3 nanoparticles are incorporated into liquid metal/metal oxide (LM/MO) frameworks in order to form enhanced photocatalytic systems. The LM/MO frameworks, both with and without incorporated Ga2O3 nanoparticles, show photocatalytic activitydue to a plasmonic effect where performance is related to the loading of Ga2O3 nanoparticles. Optimum photocatalytic efficiency is obtained with 1 wt% incorporation of Ga2O3 nanoparticles. This can be attributed to the sub-bandgap states of LM/MO frameworks, contributing to pseudo-ohmic contacts which reduce the free carrier injection barrier to Ga2O3.

Crystal structures and hydrogen bonding in the proton transfer salts of nicotine with 3,5-dinitrosalicylic acid and 5-sulfosalicylic acid

The structures of the 1:1 anhydrous salts of nicotine (NIC) with 3,5-dinitrosalicylic acid (DNSA) and 5-sulfosalicylic acid (5-SSA), namely (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 2-carboxy-4,6-dinitrophenolate, C10H15N2+ C7H3N2O7-, (I) and (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 3-carboxy-4-hydroxybenzenesulfonate, C10H15N2+ C7H5O6S-, (II) are reported. The asymmetric units of both (I) and (II) comprise two independent nicotinium cations (C and D) and either two DNSA or two 5-SSA anions (A and B), respectively. One of the DNSA anions shows a 25% rotational disorder in the benzene ring system. In the crystal of (I), inter-unit pyrrolidinium N-H...N(pyridine) hydrogen bonds generate zigzag NIC cation chains which extend along a while the DNSA anions are not involved in any formal inter-species hydrogen bonding but instead form pi--pi associated stacks which parallel the NIC chains along a [ring centroid separation, 3.857(2)A]. Weak C-H...O interactions between chain substructures give an overall three-dimensional structure. With (II), A and B anions form independent zigzag chains with C and D cations, respectively, through carboxylic acid O-H...N(pyridine) hydrogen bonds. These chains, which extend along b are pseudo-centrosymmetrically related and give pi--pi interactions between the benzene rings of anions A and B and the pyridine rings of the NIC cations C and D, respectively [ring centroid separations, 3.6422(19) and 3.7117(19)A]. Present also are weak intermolecular C-H...O hydrogen-bonding interactions between the chains, giving an overall three-dimensional structure.

Bisphenol A sorption by organo-montmorillonite: Implications for the removal of organic contaminants from water

Remediation of bisphenol A (BPA) from aqueous solutions by adsorption using organoclays synthesized from montmorillonite (MMT) with different types of organic surfactant molecules was demonstrated. High adsorption capacities of the organoclays for the uptake of BPA were observed and these demonstrated their potential application as strong adsorbents for noxious organic water contaminants. The adsorption of BPA was significantly influenced by pH, with increased adsorption of BPA in acidic pH range. However, the organoclays intercalated with highly loaded surfactants and/or large surfactant molecules were less influenced by the pH of the environment and this was thought to be due to the shielding the negative charge from surfactant molecules and the development of more positive charge on the clay surface, which leads to the attraction of anionic BPA even at alkaline pH. The hydrophobic phase created by loaded surfactant molecules contributed to a partitioning phase, interacting with BPA molecules strongly through hydrophobic interaction. Pseudo-second order kinetic model and Langmuir isotherm provided the best fit for the adsorption of BPA onto the organoclays. In addition, the adsorption process was spontaneous and exothermic with lower temperature facilitating the adsorption of BPA onto the organoclays. The described process provides a potential pathway for the removal of BPA from contaminated waters.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Effect of constitutive material models on seismic response of two-story reinforced concrete frame

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

Stochastic analysis of a radial-inflow turbine in the presence of parametric uncertainties

This paper presents an uncertainty quantification study of the performance analysis of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multi-purpose Small Power Unit. A deterministic 3D volume-averaged Computational Fluid Dynamics (CFD) solver is coupled with a non-statistical generalized Polynomial Chaos (gPC) representation based on a pseudo-spectral projection method. One of the advantages of this approach is that it does not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic and geometric fields. The stochastic results highlight the importance of the blade thickness and trailing edge tip radius on the total-to-static efficiency of the turbine compared to the angular velocity and trailing edge tip length. From a theoretical point of view, the use of the gPC representation on an arbitrary grid also allows the investigation of the sensitivity of the blade thickness profiles on the turbine efficiency. The gPC approach is also applied to coupled random parameters. The results show that the most influential coupled random variables are trailing edge tip radius coupled with the angular velocity.

Scalable Bayesian Computation for intractable likelihoods in image analysis

The inverse temperature hyperparameter of the hidden Potts model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.