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Spyridon Koutsoukos Frederik Philippi Francisco Malaret Tom Welton 《Chemical science》2021,12(20):6820
There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids. 相似文献
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Karen Beatriz Méndez-Rodríguez Nicté Figueroa-Vega César Arturo Ilizaliturri-Hernandez Mónica Cardona-Alvarado Jaime Antonio Borjas-García Carlos Kornhauser Juan Manuel Malacara Rogelio Flores-Ramírez Francisco Javier Pérez-Vázquez 《Biomedical chromatography : BMC》2020,34(12):e4956
Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice. 相似文献
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Brandão Pedro Francisco Ramos Rui Miguel Rodrigues José António 《Analytical and bioanalytical chemistry》2018,410(26):6873-6880
Analytical and Bioanalytical Chemistry - Formaldehyde is often applied in the industrial production of different products, such as textiles, insulation materials, or cosmetics, due to its... 相似文献
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Vladimir A. Borovikov Francisco Javier Mendoza 《Journal of Fourier Analysis and Applications》2002,8(4):399-406
We study the pointwise convergence problem for the inverse Fourier transform of piecewise smooth functions, i.e., whether SrD f (\bx) ? f (\bx)S_{\rho D} f (\bx) \to f (\bx) as r? ¥\rho \to \infty . r? ¥\rho \to \infty . Here for \bx,\bxi ? \Rn\bx,\bxi \in \Rn SrDf(\bmx)=\dsf1(2p)n/2\intlirD [^(f)](\bxi) e\dst iá\bmx,\bxi? d\bxi . S_{\rho D}f(\bm{x})=\dsf1{(2\pi)^{n/2}}\intli_{\rho D} \widehat{f}(\bxi) e^{\dst i\langle\bm{x},\bxi\rangle} d\bxi~. is the partial sum operator using a convex and open set DD containing the origin, and rD={ r\bxi:\bxi ? D }\rho D=\left\{ \rho \bxi:\bxi\in D \right\}. 相似文献
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A.F. Mesquita A.O. Porto G.M. de Lima J.M. Delgado F.S. Lameiras 《Solid State Communications》2005,136(3):133-137
Hexagonal and cubic cadmium selenide were prepared from a chemical route by using cadmium chloride and potassium selenium hydride obtained from reaction of selenium powder and potassium boron hydride. The product obtained was thermally treated under argon flux at 300, 500 and 600 °C for 2 h and characterized by X-ray photoelectron spectroscopy and X-ray diffraction. The X-ray diffraction data were refined by Rietveld method and the structural parameters were determined for the phases of each annealed samples. At 300 °C five phases were identified: Cubic and hexagonal cadmium selenides and the contaminants: Potassium chloride, boron oxide and cadmium boron oxide. At 500 and 600 °C only the hexagonal cadmium selenide phase was identified besides the other above mentioned contaminant. 相似文献
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