Preparation and characterization of doped WO3 photocatalyst powders

WO₃ semiconductor particles, useful in solar energy conversion processes, were doped with transition metal ions, Ti(III), V(IV), Cr(III), Mn(II), Fe(III), Co(II), Ni(II), Cu(II), Zn(II) and Ru(III) by a high-temperature sintering technique. The method of preparation of these photocatalysts is described in detail. The structural changes effected during sintering were investigated by X-ray powder diffraction (XRD) and scanning electron microscopy (SEM). The XRD analysis indicated that the monoclinic crystal structure of WO₃ was not altered during sintering. SEM studies showed that the sizes of the particles ranged from 1 to 10 μm and the crystallinity was increased due to doping. The dopants were found to be mostly distributed on the surface of WO₃ particles.     

Influence of 2,6 (N-pyrazolyl)isonicotinic acid on the photovoltaic properties of a dye-sensitized solar cell fabricated using poly(vinylidene fluoride) blended with poly(ethylene oxide) polymer electrolyte

A novel method of introducing a synthesized organic nitrogenous compound 2,6 (N-pyrazolyl)isonicotinic acid (BNIN) and its effect on the conduction behavior of poly(vinylidene fluoride) (PVdF)–poly(ethylene oxide) (PEO) polymer-blend electrolyte with potassium iodide (KI) and iodine (I₂) and the corresponding performance of the dye-sensitized solar cells (DSSCs) were studied. A systematic investigation of the blends using FTIR provides evidence of interaction of BNIN with the polymer. Differential scanning calorimetry (DSC) study proves the miscibility of these polymers. Due to the coordinating and plasticizing effects of BNIN, the ionic conductivity of polymer blend electrolytes is enhanced. The efficiency of DSSC using BNIN doped polymer blend electrolyte was 7.3% under an illumination of 60 mW cm⁻² were observed for the best performance of a solar cell in this work.     

Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

Ozone: An Advanced Oxidation Technology for Starch Modification

ABSTRACT

Ozone processing is one of the encouraging non-thermal and bio-friendly techniques in the food processing sector. The applicability of ozone technology in food industry is increasing due to its antimicrobial action and modification of functional properties of the foods. The structural modifications of starches have major applications in the food and bakery industry for producing products with increased shelf life, improved texture, and retention of moisture content. The positive response of ozonation in carboxyl and carbonyl group alters the viscosity of starch molecules. Rheological characteristics like low viscosity even at an increased concentration, desirable binding properties, and film-forming ability have increased its use in the food processing industry. The influence of ozonation in the physicochemical properties is mainly retrogradation and cross-linking of amylose and amylopectin molecules and enzymatic modifications. Ozonation cause change in crystallinity, viscosity, expansion ratio, and gelatinization temperatures. Finally, ozonation induces many possible changes in native starches for the effective utilization in the processing sectors. In this review, starch modifications utilizing ozone and various research achievements and scientific reports focusing on the effect of ozonation in terms of physical, chemical, and thermal properties of native starches and on the possible modifications have been summarized and discussed. In conclusion, ozone is a green technology that can be effectively used as an alternative oxidation technique for starch modification.     

β-glucosidase assisted biosynthesis of gold nanoparticles: A green chemistry approach

Biogenic gold nanoparticles using medically important phytochemical (β-glucosidase) are demonstrated herein. The reduction capabilities of phytochemical and their ability to stabilize the new genre gold nanoparticles, were characterized using UV-visible, FT-IR, HR-TEM and XRD measurements. This report also focuses the newly formed gold nanoparticles application on promoting the defensive mechanism of silkworm Bombyx mori. The results highlight the possibility of green pathways to produce functionalized gold nanoparticles of medicinal significance.     

Evaluating non-stick properties of different surface materials for contact frying

The paper describes, characterises and validates the construction of an experimental rig for making contact frying experiments under controlled conditions. The construction enables a controlled fouling of different coatings on steel and aluminium plate under realistic frying conditions, in order to evaluate non-stick and cleaning properties of the coatings. In accordance with industry standards pancake was selected as the food model for the non-stick properties. The performance of different frying surfaces (stainless steel, aluminium, PTFE (polytetrafluoroethylene) and three ceramic coatings with two different levels of smoothness) were tested at different frying temperatures and rated by a standardised rating procedure. The subjective rating assessment was validated by measuring the force of adhesion. The performances of the surfaces were reproducible and significantly different to be used for screening of new surface coatings for contact frying tested in frying experiments at the same temperature. In contrast, conventional testing in a convection oven could not distinguish between these surfaces. Comparative tests of the ceramic coatings showed that surface roughness had a distinct effect on their non-stick properties, so that the smoother surfaces gave a higher force of adhesion between pancake and surface.     

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

Development of novel,nutritive antacid tablets based on milk proteins

Antacid tablets based on milk proteins were developed by a wet granulation method, with the aim of controlling the problem of hyperacidity in its early stages. Optimisation of the tablet formulation was carried out using milk protein ingredients, viz. rennet casein (RC) and whey protein concentrate (WPC), with mannitol used as a functional filler. An RC‐WPC ratio and a mannitol level of 1.96% and 10.75% w/w, respectively, were found to be optimum. The RC‐WPC ratio had a significant impact on buffering capacity (BC), tablet friability and density. The filler material, mannitol, also has an impact on BC and tablet density.     

Cu-doping effect on the structural, optical and photoluminescence properties of Sn0.98Cr0.02O2 nanoparticles by co-precipitation method

Sn_0.98-xCr_0.02Cu_xO₂ nanoparticles were prepared by co-precipitation method with different Cu concentrations from x = 0 to 0.05 and annealed at 600°C for 2 h in air atmosphere. The prepared particle were characterized by X-ray diffraction (XRD), energy dispersive X-ray spectra, UV–visible spectrophotometer, and Fourier transform infrared spectroscopy. The XRD measurement reveals that the prepared nanoparticles have different microstructure without changing a tetragonal structure. The calculated average crystalline size decreased from 12.2 to 10.5 nm for x = 0–0.02 then gradually increased up to 16.8 nm for x = 0.05 which were confirmed by Scanning electron microscope. The optical studies were done by UV–visible spectrometer and the energy band gap values were calculated from absorption spectra. A small red shift from 3.52 eV (Cu = 0) to 3.49 eV (Cu = 0.02, ΔE_g = 0.03 eV) at lower Cu concentrations and a remarkable blue shift from 3.49 eV (Cu = 0.02) to 3.71 eV (Cu = 0.05, ΔE_g = 0.22 eV) at higher Cu concentrations is due to Cu-doping in Cr–SnO₂ matrix. The presence of functional groups and the chemical bonding is confirmed by Fourier transforms infrared spectra. PL spectra of Sn_0.98-xCr_0.02Cu_xO₂ nanoparticle described the shift in UV emission from 366 to 381 nm (red shift) and a shift in blue band emission from 458 to 482 nm (red shift) which confirms the Cu-doping in Cr–SnO₂ matrix. The doping of Cu in the present system is useful to tune the emission wavelength and hence is appreciable for the fabrication of nano-optoelectronic devices and photo-catalytic applications.