Biomimetic routes for photosplitting of water

Natural photosynthesis involves splitting of water into O₂ and H₂ by thermodynamically uphill electron transfer using photon energy absorbed by green chlorophylls and other light-absorbing molecules in leaf chloroplast. It is a multiphotonmultielectron-photosensitized reaction. The artificial photocatalytic system must mimic both the function and the scheme of natural photosynthesis, to bring about this uphill electron transfer. The key concept is organization to prevent back electron transfer. A preliminary report of the attempts made in our laboratory to promote vectorial electron transfer by using various techniques such as PEC cells and liposomes, using chlorophylls isolated from spinach leaves as well as synthesis of their analogues porphyrins synthesized in the laboratory is presented.     

Surface Pyroelectricity in Cubic SrTiO3

Symmetry‐imposed restrictions on the number of available pyroelectric and piezoelectric materials remain a major limitation as 22 out of 32 crystallographic material classes exhibit neither pyroelectricity nor piezoelectricity. Yet, by breaking the lattice symmetry it is possible to circumvent this limitation. Here, using a unique technique for measuring transient currents upon rapid heating, direct experimental evidence is provided that despite the fact that bulk SrTiO₃ is not pyroelectric, the (100) surface of TiO₂‐terminated SrTiO₃ is intrinsically pyroelectric at room temperature. The pyroelectric layer is found to be ≈1 nm thick and, surprisingly, its polarization is comparable with that of strongly polar materials such as BaTiO₃. The pyroelectric effect can be tuned ON/OFF by the formation or removal of a nanometric SiO₂ layer. Using density functional theory, the pyroelectricity is found to be a result of polar surface relaxation, which can be suppressed by varying the lattice symmetry breaking using a SiO₂ capping layer. The observation of pyroelectricity emerging at the SrTiO₃ surface also implies that it is intrinsically piezoelectric. These findings may pave the way for observing and tailoring piezo‐ and pyroelectricity in any material through appropriate breaking of symmetry at surfaces and artificial nanostructures such as heterointerfaces and superlattices.     

Design and Analysis of 150° Bend SIW and Corrugated SIW Bandpass Filter with Multiple Transmission Zeroes Using Reactive Periodic Structures Suitable for Microwave Integrated Circuits (MICs)

Keller SYMplus CNC which is developed by Germany’s Keller company is a software which used for NC programming and simulation. This paper described and analyzed the characteristics of Keller SYMplus CNC software in NC programming and simulation from four aspects, model design, rapid manufacturing, machining simulation and post processing. Then compares Keller SYMplus CNC with commonly used NC programming and simulation software UG, MasterCAM, and CAXA from the profile milling and teaching function.     

On‐Line Detection of Contamination in a Bioprocess Using Artificial Neural Networks

Fermentation is a very important bioprocess for the production of drugs, food products, beverage and for animal cell line culture etc. The substrates used in these processes are expensive. So on‐line monitoring of contamination in a fermentation process is very essential in industries. A new method for contamination detection is being proposed in this paper. This is a software‐based method. In this new method, the state variables of the process are used to detect contamination of the process.     

Effect of SiC Content on Mechanical and Tribological Properties of Al2024-SiC Composites

Silicon - Aluminium matrix composites are scientifically engineered materials possessing higher potential in automotive, aerospace and defence applications. Therefore this study focuses on the...     

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients (MFCC), Fundamental Frequency (F₀), Shimmer (%), Jitter (%), and Harmonic to Noise Ratio (HNR). The experiments were performed using Saarbrucken Voice Database (SVD) and a privately collected dataset. The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models. According to the experimental results, our proposed approach has a 70% increase in processing speed over Principal Component Analysis (PCA) and performs remarkably well with a recognition accuracy of 95.24% on the SVD dataset surpassing the previous best accuracy of 82.37%. In the case of the private dataset, our proposed method achieved an accuracy rate of 93.37%. It can be an effective non-invasive method to detect dysphonia.     

Characteristics Study of Physical,Mechanical and Tribological Behaviour of SiC/TiB2 Dispersed Aluminium Matrix Composite

Silicon - SiC and TiB2 particle reinforced Al7075 matrix composite has numerous applications such as aircraft structures, mould tool manufacturing, and structural application due to less weight to...