Studies on Growth and Various Properties of Triglycine Sulfate (TGS) Crystals Doped with Copper Sulphate

Pure and copper sulpahte-doped Triglycine Sulfate(TGS) salts were synthesized and single crystals  of the synthesized salts were grown from aqueous solutions by slow evaporation technique.  The  structural studies on the grown crystals were carried out by  single crystal XRD analysis and found that the grown crystals crystallize in monoclinic structure. The FTIR spectra have been recorded in the range 500-4000 cm-1  and   the functional groups  of the grown crystals have been identified.  UV-Visible   spectra show that the grown crystals have wide optical transparency in the entire visible region. Atomic absorption study reveals the presence of copper  in  the doped TGS crystals.   Density of the samples have been measured by floatation method. The dielectric  studies  for the pure and copper sulphate-doped TGS crystals  have been performed by measuring the dielectric parameters like dielectric constant  and dielectric loss  at a constant frequency of 1000 Hz  with the  temperatures ranging from 30  to  75 oC  and this study reveals an increase of dielectric constant and loss when TGS crystal  is doped with copper sulphate.  Microhardness  studies have been carried out  to analyse  the mechanical strength of the samples.ÂÂ

Spectroscopic, thermal, second order and third order NLO studies of N, N’ -dimethyl urea crystal

Nonlinear optical (NLO) crystals are classified into organic, inorganic and semi organic crystals and these crystals are used in the fields of optical communication, optical computing, frequency doubling, optical data processing and opto electronics. In this work an organic NLO crystal namely DMU crystal was prepared. Slow evaporation technique was adopted to grow the single crystals of DMU after the growth period of 35 days. The harvested crystals have been subjected to various characterization techniques like XRD, FTIR, FT-Raman, TG/DTA, SHG, EDAX, impedance, optical and Z-scan studies. From the studies, is observed that DMU crystal has orthorhombic structure and it has the melting point at 105 oC and has the decomposition point at 275 oC. The relative SHG efficiency of DMU crystal was found to be more than one and third order NLO parameters were evaluated. The optical band gap of DMU crystal was found to be 5.008 eV.The results from various studies were analyzed.

Synthesis, Growth and Spectroscopic Studies of L-Alanine Hydrogen Chloride(Lahc) Crystals

L-alanine Hydrogen Chloride (LAHC) salt was synthesized by taking L-alanine and hydrochloric acid in 1:1 molar ratio and the solubility of the synthesized salt in deionized water was determined at different temperatures. Single crystals of  L-alanine  Hydrogen Chloride (LAHC) were grown by solution method with slow evaporation technique. The grown crystals were characterized by single crystal X-ray diffraction (XRD) analysis, FTIR studies and UV-visible transmittance studies and the NLO activity of the grown crystal has been checked by Second Harmonic Generation (SHG) test.ÂÂ

Studies on growth and Characterization of urea-doped diglycine picrate (DGP) single crystals

Urea-doped diglycine picrate (DGP) single crystals were grown by solution method with slow evaporation technique. The solubility of the urea-doped DGP was carried out at various temperatures and it is observed that the solubility of urea-doped DGP is higher than that of pure DGP crystal. When urea was added as dopant, morphological alterations were noticed in DGP crystal. Cell parameter values of the grown urea-doped DGP crystal were obtained from the XRD analysis and the presence of functional groups was identified from FTIR study. Dlectric studies were performed for the grown crystals and it is found that the values of dielectric constant and loss for urea-doped DGP crystal are more than those of pure DGP crystal. The Vickers microhardness values were measured for the grown crystals. From the microhardness study, it is observed that urea- doped DGP crystal is harder than pure DGP crystal

GROWTH, SPECTRAL, NLO AND IMPEDANCE STUDIES OF POTASSIUM AMMONIUM SULPHATE CRYSTALS GROWN BY AQUEOUS SOLUTION TECHNIQUE

Single crystals of Potassium Ammonium Sulphate (PAS) have been grown by the free evaporation method and characterized structurally, electrically, optically and mechanically. X-ray diffraction analysis indicates the crystal system as orthorhombic .The functional groups have been identified using Fourier transform infrared spectral analysis. UV-visible transmittance spectra showed wide transparency window in visible and near IR region. The hardness values of the grown sample have been found by Vickers microhardness test. Studies of electrical properties of PAS crystal using a complex impedance spectroscopy (CIS) technique show the decrease of bulk resistance with rise in temperature indicating a typical negative temperature coefficient of resistance (NTCR) type behavior similar to that of an insulator. The nature of Nyquist plots reveals the presence of bulk and grain boundary effects in PAS crystal. Moreover, the variation in dc conductivity is found to increase with temperature

Surface acoustic wave distribution and acousto-optic interaction in proton exchanged LiNbO<SUB>3</SUB> waveguides

The efficiency of acoustooptic (AO) interaction in YZ-cut proton exchanged (PE) LiNbO3 waveguides is theoretically analysed by determining the overlap between the optical and acoustic field distributions. The present analysis takes into account the perturbed SAW field distribution due to the presence of the PE layer on the LiNbO3 substrate determined by the rigorous layered medium approach. The overlap is found to be significant upto very high acoustic frequencies of the order of 5 GHz, whereas in the earlier analysis by vonHelmolt and Schaffer [6] for diffused waveguides, it was shown that the overlap integral rolls down to nearly zero at this high frequency range

Optimized machine learning model for air quality index prediction in major cities in India

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city

Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used