Modelling of Evaporative Cooling of Porous Medium Filled with Evaporative Liquid

The cooling effect by evaporative liquid is modeled by considering that heat is transferred from the system to the surrounding due to evaporation of liquid through the pores present in the medium. The variation of cooling rate with cell size, volume fraction of pores and physical conditions has been analyzed. The model demonstrates that it increases with increase in thickness of the foam slab and with increase in velocity of air. It is also observed that cooling effect decreases with decrease in volume fraction of porosity and with increase in relative density, cell size, thermal conductivity and relative humidity.     

Poly(dimethylsiloxane)‐containing thermoplastic elastomer/gold–silver alloy nanocomposites for thermally/oxidatively stable and antimicrobial coating

Polymer materials with embedded silver (Ag) nanoparticle (NP) are of considerable interest owing to their enhanced antimicrobial activity and physical properties compared to host polymer. Antimicrobial and thermally/oxidatively stable coating not only enhances the durability of the coated material but also reduces the growth of bacteria/fungus and thus reduces the chance of infection. For this purpose, we have prepared polydimethylsiloxane‐containing predominantly poly(meth)acrylates‐based pentablock thermoplastic elastomer (TPE)/gold (Au)–Ag alloy nanocomposites (NCs) with antimicrobial activity and enhanced physical properties. In situ simultaneous reduction of appropriate amount of metal salts in the presence of block copolymer produced Au–Ag NPs of size 5–10 nm. Such embedded 5–10 nm sized particles (loading, 0.1–0.2 wt%) improved the mechanical property, thermal/oxidative stability, and antimicrobial activity of the NCs. The NC films also exhibited tunable surface wetting behavior and optical properties. The NC films showed low level of Ag leaching as confirmed by inductively coupled plasma spectrometer and UV–visible spectroscopy. Improved thermal/oxidative resistance of the TPE/Au–Ag alloy NCs enhanced antimicrobial activity, together with low level of leaching characteristic of the embedded NPs deemed suitable for further use of these NCs material for antimicrobial and oxidatively stable coating applications. POLYM. COMPOS., 36:2103–2112, 2015. © 2014 Society of Plastics Engineer     

Ultrathin and Switchable Nanoporous Catalytic Membranes of Polystyrene‐b‐poly‐4‐Vinyl Pyridine Block Copolymer Spherical Micelles

An easy fabrication of close‐packed and block copolymer micelles‐based ultrathin membranes for water purification, separation, catalytic, and dye degradation applications is reported. Nanoporous membranes based on the self‐assembly of 2‐(4′‐hydroxybenzeneazo) benzoic acid (HABA)‐polystyrene‐b‐poly(4‐vinylpyridine) (PS‐b‐P4VP) diblock copolymers supramolecular complexes are prepared by simple spin coating on pore‐filled polyethylene terephthalate (PET) track‐etched membranes. The prepared membranes are characterized by scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and water permeation studies. The separation performance is studied by lysozyme protein rejection. The prepared membranes are also used to in situ synthesize gold nanoparticles in the corona of PS‐b‐P4VP spheres for catalytic activity towards the reduction of p‐nitrophenol and degradation of congo red dye in flow through operation mode in a stirred cell membrane reactor. More than 95% reduction for p‐nitrophenol and >98% degradation of Congo red at a sufficiently high flux indicates its suitability for catalytic transformation and environmental remediation applications.     

Structural,dielectric, magnetic,and optical properties of Ni0.75Zn0.25Fe2O4–BiFeO3 composites

The magnetoelectric composites of spinel ferrite Ni_0.75Zn_0.25Fe₂O₄ (NZF) and BiFeO₃ (BFO) with general formula xNi_0.75Zn_0.25Fe₂O₄ + (1 ? x)BiFeO₃ (x = 0, 0.10, 0.20, and 0.30) have been prepared via hybrid processing route. Subsequently, the effects of addition of NZF on structural, dielectric, magnetic, magnetoelectric, and optical properties of BFO have been investigated, and significant enhancements have been observed in physical observables such as grain size, dielectric constant, magnetization, and polarization in ferroelectric hysteresis loops. The variation of magnetization with temperature indicates the presence of spin glass behavior along with the ferromagnetic component. The magnetoelectric coupling is found with a value of magnetocapacitance to be 4.6 % for 30 mol% addition of NZF. The optical properties of the composites are also studied using UV–Vis diffuse reflectance spectroscopy, Photoluminescence spectroscopy, and FTIR spectroscopy.     

A novel amperometric biosensor for oxalate determination using multi-walled carbon nanotube-gold nanoparticle composite

An amperometric oxalate biosensor using nanohybrid film of multi-walled carbon nanotubes (MWCNTs) and gold colloidal nanoparticles (GNPs) via carbodiimide chemistry by forming amide linkages between carboxylic acid groups on the CNTs and amine residues of cysteamine self-assembled monolayer (SAM) has been prepared. The c-MWCNTs were immobilized on the gold (Au) electrode and characterized by FTIR. The morphologies of the c-MWCNT/Au and GNPs/MWCNT/Au electrodes were investigated by scanning electron microscopy (SEM) and the electrochemical performance of the Au, c-MWCNT/Au and GNPs/c-MWCNT/Au electrodes were also studied amperometrically. The Cl⁻ and NO₃⁻ insensitive oxalate oxidase from grain sorghum was finally immobilized on this electrode. The influence of pH, temperature and oxalate concentration on electrode activity was studied. The electrode showed optimum response within 7 s. The electrocatalytic response showed a linear dependence on the oxalic acid concentration ranging from 1 to 800 μM with a detection limit of 1 μM. The K_m value for the oxalic acid sensor was 444.44 μM. The enzyme electrode retained 30% of its initial activity after 5 months, when stored at 4 °C. The electrode was employed for measurement of oxalic acid in serum, urine and foodstuffs.     

A Lesson learned from PMF based approach for Semantic Recommender System

Linked Open Data cloud is being conceived and published to improve the usability and performance of various applications including Recommender System. While most of the existing works incorporate semantic web information into recommendation system by exploiting content based method, we introduced a collaborative filtering based semantic dual probabilistic matrix factorization approach. We have used semantic item features, generated in an unsupervised manner and incorporated them into user-item preference matrix for recommendation by co-factoring two matrices. To mitigate the difficulty of high dimensionality, sparsity and possible noise in the semantic item-property matrix, Singular Value Decomposition was used as an unsupervised preprocessing step. To evaluate our new approach, RMSE are compared with 10 state-of-art algorithms especially Probabilistic Matrix Factorization which our method is based on, Precision is also compared between our method and PMF. Although similar dual matrix factorization approaches exist but most of them deal with very small item property matrix with abundant entries while our approach introduced a high dimensional and sparse item property matrix through an unsupervised automatic way which also alleviate the efforts to create those item property matrices.     

Machine Learning Assisted Analysis,Prediction, and Fabrication of High-Efficiency CZTSSe Thin Film Solar Cells

The Earth-abundant element-based Cu₂ZnSn(S,Se)₄ (CZTSSe) absorber is considered as a promising material for thin-film solar cells (TFSCs). The current record power conversion efficiency (PCE) of CZTSSe TFSCs is ≈13%, and it's still lower than CdTe and CIGS-based TFSCs. A further breakthrough in its PCE mainly relies on deep insights into the various device fabrication conditions; accordingly, the experimental–oriented machine learning (ML) approach can be an effective way to discover key governing factors in improving PCE. The present work aims to identify the key governing factors throughout the device fabrication processes and apply them to break the saturated PCE for CZTSSe TFSCs. For realization, over 25,000 data points were broadly collected by fabricating more than 1300 CZTSSe TFSC devices and analyzed them using various ML techniques. Through extensive ML analysis, the i-ZnO thickness is found to be the first, while Zn/Sn compositional ratio and sulfo-selenization temperature are other key governing factors under thin or thick i-ZnO thickness to achieve over 11% PCE. Based on these key governing factors, the applied random forest ML prediction model for PCE showed Adj. R² = >0.96. Finally, the best-predicted ML conditions considered for experimental validation showed well-matched experimental outcomes with different ML models.     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.