Optimization of paste formulation for TiO2 nanoparticles with wide range of size distribution for its application in dye sensitized solar cells

Dye sensitized solar cell (DSSC) can be an economically viable and technically simpler alternate to the silicon based solar cells. Films of nanocrystalline titanium dioxide (TiO₂) are considered as the most suitable photoelectrode for DSSC. For this study, TiO₂ powder of anatase phase, synthesized in acidic environment was used. The average diameter of the nanoparticles was ～20 nm and BET surface area was 64.68 m²/g. Different TiO₂ pastes were prepared by varying the proportion of TiO₂ powder, α-terpineol, and ethyl cellulose (EC) in their composition. The TiO₂ paste was cast on fluorine doped tin oxide (FTO) coated glass surface using doctor blade to prepare photoelectrode of TiO₂ film. Composition of the paste ingredients was optimized by comparing the conversion efficiencies of the DSSCs fabricated with the photoelectrode of thickness ～18 μm. The outcome of this study can be crucial for the preparation of reliable TiO₂ paste in a simple way for its application in DSSC.     

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.     

Economic feasibility of energy efficiency measures in residential buildings

A study has been performed to develop cost benefit data to be used by a local electric utility in defining a rebate program to encourage energy efficient construction in the Desert Southwest region of the USA. Several potential efficiency upgrades for production homes in the region were identified and an evaluation of each building component was performed using building energy simulation software. A model home developed in Las Vegas was used to calibrate the numerical model and was considered as a basis for further study. The validated building models were then used to predict the annual energy savings and payback periods for various upgrades. In all cases, comparisons were made to code-built houses with the same general overall characteristics. Useful life of most of these upgrades far exceeds their payback periods. It is found that a typical house, with the cost-effective upgrades installed, consumed 42.5% less annual energy compared to a home built to code. A 3.19 kWp PV system installed on the south-facing roof of the home can generate 5982 kWh annually, making it a net-zero (electrical) energy home. PV systems in Las Vegas have encouraging benefit cost ratios when federal and state rebates are considered.     

A novel elephant herd optimization model with a deep extreme Learning machine for solar radiation prediction using weather forecasts

The Journal of Supercomputing - In recent times, the exploitation of solar resources has remained a major issue due to increasing energy utilization globally. For effective solar resource...     

Biopolymer-based trimetallic nanocomposite synthesis,characterization and its application in the catalytic degradation of 4-nitrophenol

Journal of Materials Science: Materials in Electronics - Gum kondagogu (GK), a natural biopolymer was successfully employed in the synthesis of trimetallic (AgAuPd) nanocomposites and characterized...     

Hydrothermal Synthesis and Photocatalytic Activity of Mn3O4 Nanoparticles

Topics in Catalysis - In this work, Mn3O4 nanoparticles (NPs) were synthesized by hydrothermal method using the precursor manganese acetate and polyvinyl alcohol (PVA) as...     

Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.     

Dechlorination of chlorophenols using magnesium-palladium bimetallic system

Ninety-four percent removal of 10 mg L(-1) of pentachlorophenol (PCP) was achieved by treatment with 154.5mM Mg(0) and 0.063 mM K(2)PdCl(6) in the presence of 175 mM acetic acid in 1h reaction time. Dechlorination of PCP was found to be sequential and phenol was identified as the end product along with accumulation of trace concentrations of tetra- and trichlorophenols. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) revealed that palladium in its metallic form (Pd(0)) produced by reduction of Pd4+, was spatially separated from magnesium granules when acid was included in the reaction. These colloidal palladium particles generated active reductive species of hydrogen and dechlorinated chlorophenols. In the absence of acid, the efficiency of dechlorination of PCP by Pd/Mg(0) system was very low and chief mechanism of removal of the compound was through sorption onto solid surfaces. Thus, it was important to include acid in the system to: (a) facilitate corrosion of Mg(0) and reduction of Pd4+ to Pd(0), (b) provision of protons to produce H2, (c) retard formation of insoluble oxides and hydroxides that may deposit on the magnesium granules and sorb PCP and its partially dechlorinated products and. Application of 154.5 mM Mg(0)/0.063 mM K(2)PdCl(6) on PCP, 2,4,5-trichlorophenol (TCP) and 2-chlorophenol (MCP) with organic chloride equivalence showed that the rate and extent of removal increased with decrease in number of chlorine atoms on phenol.     

Average-Cost Optimal Policies for an Unreliable Flexible Multiproduct Machine

This paper is concerned with optimal production planning on a single failure-prone flexible machine that produces N distinct part types. The machine is flexible in the sense that no setup is required for switching from production of one part type to another. We consider the problem of controlling production rates to minimize the expected long-run average cost of product surpluses over time. We assume constant unit holding and shortage costs and constant demand rates for the part types. Moreover, the costs are assumed to be the same for all products. We provide an explicit optimal solution for the problem.