The influence of surface morphology of TiO2 coating on the performance of dye-sensitized solar cells

The optimization of solar energy conversion efficiency of dye-sensitized solar cells (DSSCs) was investigated by the tuning of TiO₂ photoelectrode's surface morphology. Double-layered TiO₂ photoelectrodes with four different structures were designed by the coating of TiO₂ suspension, incorporated with low and high molecular weight poly(ethylene glycol) as a binder. Among these four systems, P2P1, where P1 and P2 correspond to the molecular weight of 20,000 and 200,000, respectively, showed the highest efficiency under the conditions of identical film thickness and constant irradiation. This can be explained by the larger pore size and higher surface area of P2P1 TiO₂ electrode than the other materials as revealed by scanning electron microscopic (SEM) and Brunauer–Emmett–Teller (BET) analyses. Electrochemical Impedance Spectroscopy (EIS) analysis shows that P2P1 formulation displayed a smaller resistance than the others at the TiO₂/electrolyte interface. The best efficiency (η) of 9.04% with the short-circuit photocurrent density (J_sc) and open-circuit voltage (V_oc) of 18.9 mA/cm² and 0.74 V, respectively, was obtained for a solar cell by introducing the light-scattering particles to the TiO₂ nanoparticles matrix coated on FTO electrode having the sheet resistivity of 8 Ω/sq.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

Aerogel/epoxy thermal coatings for carbon fibre reinforced plastic substrates

The present work studies an aerogel/epoxy composite that was dip coated onto a carbon fibre substrate by adding the aerogel at the 1?h and the 1.5 mark of the epoxy cure. Both coatings show decrease in thermal conductivity values (39% and 47% respectively) when compared to a pure epoxy coating. The coatings’ reflectance spectra also provided further evidence for the existence of the nano-pores within the aerogel particles. The aerogel coating was modelled using material properties from literature and solved using finite element methods. The model, which validated using experimental data, was then used to predict the coating’s performance in cyclic thermal loads. Additionally, coatings on a single surface- top and bottom; were also modelled and compared with the double coating system wherein it was seen that the double coating system had the lowest rate of temperature change and fluctuations at steady-state in contrast to the bottom coating which, showed the fastest drop in temperature as well as the highest fluctuations at steady state conditions. The performance of the top coating was in the middle.     

Electrochemistry of metals and semiconductors in fluoride media

The electrochemical surface transformations and diverse applications of a variety of metals and semiconductors in a wide range of fluoride media such as aqueous, non-aqueous media, liquid HF media, room temperature fluoride melts and molten fluoride media with a melting range covering 50–1000°C are reviewed. Nickel shows excellent corrosion resistance in the absence of water. The anodic performance of this metal in electrochemical perfluorination and NF₃ production is discussed. Compact carbon materials serve as anodes in fluorine generators. In high temperature melts, they perform as consumable anodes. Graphitic carbon undergoes intercalation/de-intercalation process and related battery applications. Cu/CuF₂ couple is a good reference electrode. Pt and vitreous carbon materials are the inert electrodes of choice for electro analytical applications. Electrodeposition of Lithium as a non-dendritic uniform phase is important in Lithium metal based secondary batteries. High temperature fluoride melts are used in electro-deposition of valve metals such as Nb, Ta, and Ti. The stability and decomposition of fluoride complexes in these media are of interest.     

A compact multi-source multi-substrate evaporator for thin film studies of rare earth sulphides

To facilitate materials research by thin film techniques, we have built a compact multi-source multi-substrate evaporator. We have successfully prepared crystalline binary EuS (T_c = 16 K), SmS and Sm₂S₃ as well as ternary Sm_1?xLn_xS (Ln = Tm, Yb) compounds by tenchnique of co-evaporation. Special precautions were taken to avoid sulphur contamination. The X-ray, optical and magnetic properties of these films confirmed the good quality of the samples. Optical data obtained on Sm_1?xYb_xS indicate a stable valency of 2 for Sm and Yb for all values of x.     

Voltammetric investigations on the transition between dissolution, passivation and deposition characteristics of Ni, Cu and their alloys in fluorine based ionic liquid

Multisweep cyclic voltammetric (CV) responses of nickel, copper, Monel and nickel–copper alloy had been extensively studied and compared in a variety of non-aqueous solvents such as acetonitrile (AN), propylene carbonate (PC) and sulfolane containing triethylamine trishydrogen fluoride (TEA·3HF) ionic liquid. The quantity of dissolution as well as surface morphological transformation on the electrode surfaces as a result of anodic polarization were investigated using atomic absorption spectroscopy (AAS) and scanning electron microscopy (SEM) respectively. The nature of crystallites formed on the polarized electrode was characterized using X-ray diffraction (XRD). The voltammetric study clearly indicates that Ni, Monel and Ni–Cu alloy are passive and stable in neat TEA·3HF medium in the recorded potential region of CV. Surface morphology of Ni after polarization, reveals the generation of pits, whereas the evolution of small crystallites of CuF₂ are noted on the polarized alloy material, as evidenced by SEM pictures. Copper electrode shows reversible voltammetric characteristics with high charge recovery ratio (q_c/q_a) suggesting that in this medium, Cu can certainly serve as reference electrode. Addition of water in TEA·3HF medium increases the solubility and stability of these metal fluoride film. In solvents such as PC, AN and sulfolane containing TEA·3HF, Ni and their alloys exhibit remarkable passivity and the charge recovery ratio decreases to some extent for Cu. In TEA·3HF/AN medium, the dissolution of Cu is very high. The present investigation suggests that the relative stability of all the four electrodes in neat TEA·3HF and solvents containing 0.1 M TEA·3HF decreases in the order: Ni > Monel > Ni–Cu alloy > Cu and relative solubility of metal fluoride films in the three solvents increases in the order: PC < sulfolane < AN.     

Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.