Static bending of granular beam: exact discrete and nonlocal solutions

Meccanica - This study is an attempt towards a better understanding of the length scale effects on the bending response of the granular beams. To this aim, a unidimensional discrete granular chain...     

Electronically Excited States in Model Complexes of Noble Metal Clusters with Carbon Nanodots

Russian Physics Journal - Theoretical calculations of excited states in the complexes of gold and silver three-atom nanoclusters with carbon quantum nanodots are performed using the M062X...     

Screening of Synthetic Cathinones by Potentiometric Sensor Array and Chemometrics

This work demonstrates the analytical applicability of single ion-selective membranes (ISMs) and potentiometric sensor array to distinguish and detect cathinone derivatives. Potentiometric data from ISMs based on cation exchanger and varying content of calix[4]arene derivative were processed by principal component analysis (PCA). Such a combination of methods allowed discriminating various individual synthetic cathinones and their recognition from the mixture comprising primary amines (substituted amphetamines+aminoindane). Analytical parameters of ISM containing 1wt % of calix[4]arene derivative were sufficient to detect 1.0×10⁻⁴ mol.l⁻¹ 1-(4-fluorophenyl)-2-(ethylamino)butan-1-one and 2-(methylamino)-1-phenylbutan-1-one (buphedrone) in both model and saliva samples.     

TXRF spectrometry in the bioanalytical sciences: A brief review

This work describes the basic principles of Total-reflection X-Ray Fluorescence (TXRF) spectrometry and some of its most outstanding applications for investigating metal traces, mainly in biological and biomedical samples. TXRF is an analytical technique that combines the versatility of elemental analysis by X-ray energy dispersion with a low detection limit in the order of picograms of absolute mass or a few ppb (μg L⁻¹) as concentration. The microanalytical capacity of TXRF allows the study of small sample quantities. The TXRF can perform qualitative and mass ratio analyses of the elements present in a sample of only a few hundred nanograms. These properties are of great importance in the research of metal traces in biological systems, as they allow fast, precise, and accurate characterisation of the elemental fingerprint at trace level. Its successful application in studies of the coordination kinetics of new platinum-based antitumor drugs with DNA, its application in the study profile of the metal in healthy and cancerous human tissues, or even its application in the study of the processes of metal diffusion through cell membranes are just a few examples of TXRF capabilities in the biomedical sciences. This brief review's main objective is to provide an overview of the physical principles and possibilities of TXRF spectrometry. It also highlights some of the most outstanding applications that have been developed so far, mainly in the biological field, so that the reader can evaluate their potential applications.     

Inherent electrochemical activity of TiO2 (anatase,rutile) enhances the charge capacity of cathodes of lithium-sulfur batteries

Journal of Solid State Electrochemistry - The addition of nanocrystalline titanium dioxide (P90) to a cathode of Li/S cell enhances its voltammetric charge capacity by 19%, from which only a small...     

Determination of Polycyclic Aromatic Hydrocarbons in Soils and Bottom Sediments by Gas Chromatography–Mass Spectrometry with QuEChERS Sample Preparation

Journal of Analytical Chemistry - The work is dedicated to the determination of polyaromatic hydrocarbons (PAHs) in soils and bottom sediments by gas chromatography–mass spectrometry using...     

Non-Hermitian electronics multipods of electromagnetically induced transparency (EIT) and absorption (EIA)

Optical and Quantum Electronics - In the past few decades, the academic research and industrial synergy is dramatically accelerating to conceptualize high data rate services. The congestion in the...     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.