Hydrogen Evolution Reaction by Platinum Coating

A thin layer of platinum coating (0.5 µgcm−2) on stainless steel surface was deposited by direct current (DC) and pulse current (PC) electrodeposition method for hydrogen evolution reaction (HER) application. Scanning electron microscopy, atomic force microscopy, and X-ray diffraction analysis were used to characterize the coatings. Linear sweep voltammetry and cyclic voltammetry studies were carried out to know the overpotential values for hydrogen evolution reaction (HER) on these coatings. The optimization of catalytic activity for hydrogen evolution using different coating methods helps in reducing the overall cost. Tafel polarization experiments were conducted for DC and PC platinum coating to know hydrogen generation trend. Cathodic slope and HER current values revealed that, coatings obtained at 75% duty cycle by PC method exhibit lower cathodic slope, high current density of 150 mA/cm2 and more corrosion current with highest hydrogen evolution. Chronopotentiometry experiments showed that 20 ml of hydrogen was collected from 75% sample

Dosimetry induced modifications in structural, magnetic and Mössbauer spectroscopy studies of 60Co γ-irradiated Co0.5Ni0.5Fe2O4

In the present work, we report structure, magnetic and Mössbauer spectroscopic studies of nanocrystalline Co0.5Ni0.5Fe2O4 ferrites irradiated with different doses (0, 50 and 100 kGy) of 60Co γ-irradiation. Samples are synthesized by solution combustion route. γ-Irradiation of all nanoparticles preserves the stable spinel cubic (Fd-3m) structure, as confirmed by X-ray diffractogram. XRD analysis reveals that the lattice parameter found in the range of 8.339–8.361 Å. The lattice parameter increased after gamma irradiation. FTIR spectra shows two absorption bands, which confirms the formation of spinel cubic structure. The magnetic behavior of the samples was further investigated using a Vibrating sample magnetometer and Mössbauer spectroscopy. The saturation magnetization found in the range of 55.39–47.81 emu/g. The saturation magnetization and remnant magnetizations of the pristine samples persist even after irradiation also. The magnetic coercivity found in the range of 931–892 Oe. The coercivity decreases with irradiation, indicating a reduction of magnetic anisotropy in the nanoparticles. After γ-irradiation, the redistribution of cations among the A-site and B-site with dose the magnetic coercivity in the samples brings down in the nanoparticles. At low temperature (14 K) and room temperature, Mössbauer spectroscopy has been done to thorough the hyperfine structure of unirradiated and irradiated nanoparticles. The spectral parameters related to the occupation of Fe3+ ions at the A-site and B-site. The variation in magnetic properties is due to the cation redistribution among A and B interstices. These emerge are cautious to comprehend the nature and stability of the magnetic strength of Co0.5Ni0.5 ferrite nanoparticles

Numerical-Solution-For-Nonlinear-Klein–Gordon Equation via Operational-Matrix by Clique Polynomial of Complete Graphs

This study introduced a generalized operational matrix using Clique polynomials of a complete graph and proposed the latest approach to solve the non-linear Klein–Gordon (KG) equation. KG equations describe many real physical phenomena in fluid dynamics, electrical engineering, biogenetics, tribology. By using the properties of the operational-matrix, we transform-the non-linear KG equation into a system-of algebraic-equations. Unknown coefficients to be determined by Newton's method. The present-technique is applied-to four problems, and the obtained-results are-compared with-another-method in the literature. Also, we discussed some theorems on convergence analysis and continuous property

Defects Induced Persistent Photoconductivity in Monolayer MoS2

Understanding the relaxation mechanisms of photoexcited charge carriers in two-dimensional materials is indispensable from the fundamental point of view and for future optoelectronic applications. Through the photoconductivity and electronic transport experiments, we probe the mechanisms behind the persistent photoconductivity (PPC) in monolayer molybdenum disulfide (MoS2). The temperature (T) and power-dependent photoresponse studies reveal that the relaxation of excited charge carriers is strongly affected by the random fluctuations of local potentials. The relaxation time (τ) increases from τ 12 s at T = 16.5 K to τ 1235 s at T = 297 K, indicating PPC is a high T phenomenon in monolayer MoS2. The transport measurements demonstrate that the defect states with the density 4.43 × 1014 eV-1 cm–2 in a low gate voltage regime, originating from the sulfur vacancies, are responsible for these fluctuations. With a rise in temperature, the defect states undergo a transition from localization to extended states at T ≥ 100 K and thereby form the percolation network, which profoundly influences the relaxation mechanism. Our meticulous experiments and quantitative analysis provide newer insight into the origin of PPC in monolayer MoS2

Spark-Based Sentiment Analysis of Tweets Using Machine Learning Algorithms

Analysis of sentiment is an important aspect of machine learning and has found its vivid application in marketing and sales, political campaigns, etc. analysis of product reviews, company reviews have become an integral part of modern-day marketing, but it is difficult to process so many product reviews and ratings hence to cumulate all these we can use a neutral platform, social media. This paper addresses these problems of sentiment analysis of tweets; using machine learning techniques and big data analytics. We implement spark framework to achieve the map-reduced model. The result of the analysis shows that Support Vector Machine outperforms the other algorithms while considering both time consumption and accuracy

Unsupervised Sentiment Classification of Twitter Data using Emoticons

Twitter is a powerful social media where people share their opinion on various topics. Sentiment Analysis on twitter data gives the classification of opinion on a topic as positive, negative or neutral. Twitter messages are written informally and tweets are short. Hence, the classification of tweets by only considering the text part of the message does not give accurate results. To improve the classification accuracy we use Emotion Tokens like Emoticons or Emojis. Emotion Tokens are independent of language, grammar or size of the tweet. Considering Emotion tokens while classifying tweets will improve the accuracy of classification. In this paper, we propose unsupervised Sentiment classification on Twitter Data using Emoticons to improve the performance of classification

TLC directed isolation and in silico analysis of antimicrobial metabolite from Nigrospora sphaerica inhabiting Croton bonplandianus Baill

TLC-bioautography amalgamated with hyphenated spectroscopy aid in precise in situ detection of secondary metabolites with pharmaceutical significance. TLC bioautography offers efficient and economical strategy in identifying compounds of interest from crude extracts. The present investigation has been focused on detection of antimicrobial metabolite from the culture broth of Nigrospora sphaerica inhabiting Croton bonplandianus Baill. The antimicrobial profiling confirmed the bioactive nigrosporalactone to possess broad-spectrum activity against test human pathogens with minimum inhibitory concentration values in the range 6.25 µg to 100 µg. The in silico studies revealed protein targets 1I01 (E. coli beta-ketoacyl reductase), 1IYL (C. albicans N-myristoyl transferase) had the highest binding score of -6.1 Kcal/mol. © 2021 SAA

Post classification change detection based on feature-based ensemble classifiers

Change detection is a challenging task in the field of remote sensing. Mainly, the change map is used for disaster assessment, monitoring deforestation and urban studies. In this paper, we present a novel method for post classification change detection. Google Earth images of 2011 and 2016 of Bangalore East are used for the study. Multiple features such as texture features, morphological features are extracted using grey level co-occurrence matrix (GLCM) and morphological operations respectively. Linear discriminant analysis (LDA) is used to reduce the dimension of the selected features for the training set. The proposed ensemble classifier system (ECS) exploits K-nearest neighbour (KNN), support vector machine (SVM) and maximum likelihood classifier (MLC). The proposed method adopts the subsample kernel-based