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...     

Visible-light-induced,autopromoted nickel-catalyzed three-component arylsulfonation of 1,3-enynes and mechanistic insights

Science China Chemistry - A light-induced, nickel-catalyzed three-component arylsulfonation of 1,3-enynes in the absence of photocatalyst is reported. This methodology exhibited mild conditions,...     

Finite-sample properties of estimators for first and second order autoregressive processes

Statistical Inference for Stochastic Processes - The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily...     

Mechanical Characteristics of Diamond-Like Moiré Films

JETP Letters - The mechanical characteristics of diamond-like films, such as Dn21.8, Dn27.8, and Dn29.4 moiré diamanes, formed by the hydrogenation of graphene layers twisted at an angle of...     

Infrared Reflection Spectra of (FeIn2S4)x∙(In2S3)1–x Solid Solutions

Journal of Applied Spectroscopy - IR reflectance spectroscopy at 50–450 cm–1 was used to study homogeneous monocrystals of (FeIn2S3)x?(In2S3)1–x grown by the method of...     

The comparable structural,elastic anisotropic and thermophysical properties of advanced U–Si fuel to baseline UO2: a DTF method

The European Physical Journal B - Molecular dynamics and Monte Carlo methods are common measurements to study the diffusion coefficients of the fluid particles under restricted conditions. Here,...     

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%.

     

Quantum Key Distribution with Untrusted Detectors Accessible to an Eavesdropper

JETP Letters - A simple fundamental modification of quantum key distribution protocols has been proposed: it is not required to protect the results of avalanche detectors from an eavesdropper, but...     

MWCNTs/hydroxypropyl cellulose/polyethylene glycol-based shape-stabilized phase change materials

Journal of Thermal Analysis and Calorimetry - Polyethylene glycol (PEG) is widely used as phase change materials (PCMs). However, the leakage and low thermal conductivity issues restrict its...     

Rapid qualitative profiling and quantitative analysis of Juglandis Mandshuricae Cortex and seven flavonoids by ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry

Juglandis Mandshuricae Cortex is the bark of Juglans mandshurica Maxim., which has been used as a folk medicine plant in China and India. In this study, an ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry method was developed to clarify and quantify the chemical profiling of Juglandis Mandshuricae Cortex rapidly. A total of 113 compounds were characterized. Among them, seven flavonoids were simultaneously quantified in 15 min, including myricetin, myricetrin, taxifolin, kaempferol, quercetin, quercitrin, and naringenin. The method was validated for accuracy, precision, and the limits of detection and quantification. All calibration curves showed a good linear relationship (r > 0.9990) within test ranges. The intra- and inter-day relative standard deviations were less than 2.16%. Accuracy validation showed that the recovery was between 95.6 and 101.3% with relative standard deviation values below 2.85%. The validated method was successfully applied to determine the contents of seven flavones in Juglandis Mandshuricae Cortex from seven sources and the contents of these places were calculated respectively. This method provides a theoretical basis for further developing the medicinal value of Juglandis Mandshuricae Cortex.