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Principles Of Fluorescence Spectroscopy

A review on g-C3N4-based photocatalysts

Because of the high catalytic activities and substrate specificity, natural enzymes have been widely used in industrial, medical, and biological fields, etc. Although promising, they often suffer from intrinsic shortcomings such as high cost, low operational stability, and difficulties of recycling. To overcome these shortcomings, researchers have been devoted to the exploration of artificial enzyme mimics for a long time. Since the discovery of ferromagnetic nanoparticles with intrinsic horseradish peroxidase-like activity in 2007, a large amount of studies on nanozymes have been constantly emerging in the next decade. Nanozymes are one kind of nanomaterials with enzymatic catalytic properties. Compared with natural enzymes, nanozymes have the advantages such as low cost, high stability and durability, which have been widely used in industrial, medical, and biological fields. A thorough understanding of the possible catalytic mechanisms will contribute to the development of novel and high-efficient nanozymes, and the rational regulations of the activities of nanozymes are of great significance. In this review, we systematically introduce the classification, catalytic mechanism, activity regulation as well as recent research progress of nanozymes in the field of biosensing, environmental protection, and disease treatments, etc. in the past years. We also propose the current challenges of nanozymes as well as their future research focus. We anticipate this review may be of significance for the field to understand the properties of nanozymes and the development of novel nanomaterials with enzyme mimicking activities.

Nanozymes: Classification, Catalytic Mechanisms, Activity Regulation, and Applications

PROMETHEE: A comprehensive literature review on methodologies and applications

The present paper review the drug-DNA interactions, their types and applications of experimental techniques used to study interactions between DNA and small ligand molecules that are potentially of pharmaceutical interest. DNA has been known to be the cellular target for many cytotoxic anticancer agents for several decades. Understanding how drug molecules interact with DNA has become an active research area at the interface between chemistry, molecular biology and medicine. In this review article, we attempt to bring together topics that cover the breadth of this large area of research. The interaction of drugs with DNA is a significant feature in pharmacology and plays a vital role in the determination of the mechanisms of drug action and designing of more efficient and specifically targeted drugs with lesser side effects. Several instrumental techniques are used to study such interactions. In the present review, we will discuss UV-Visible spectroscopy, fluorescence spectroscopy and cyclic voltammetry. The applications of spectroscopic techniques are reviewed and we have discussed the type of information (qualitative or quantitative) that can be obtained from the use of each technique. Not only have novel techniques been applied to study drug-DNA interactions but such interactions may also be the basis for the development of new assays. The interaction between DNA and drugs can cause chemical and conformational modifications and, thus, variation of the electrochemical properties of nucleobases.

Drug-DNA interactions and their study by UV-Visible, fluorescence spectroscopies and cyclic voltametry.

Determination of mixed metal ions by complexometric titration and nonlinear partial least squares calibration

Raw and roasted Semen Cassiae seeds, a complex traditional Chinese medicine (TCM), are used as examples to research and develop a method of classification analysis based on measurements of Fourier transform infrared (FT-IR) spectral fingerprints. Eighty samples of the TCM were measured in the mid-infrared range, 400-2000 cm(-1) (KBr pellets), and the complex overlapping spectra were submitted for interpretation to a principal component analysis least squares support vector machine (PC-LS-SVM), kernel principal component analysis least squares support vector machine (KPC-LS-SVM), and radial basis function artificial neural networks (RBF-ANN). The LS-SVM models were developed with an RBF kernel function and a grid search technique. Training models were constructed with the use of raw and first-derivative spectra and these were then verified by another data set containing both raw and roasted spectral objects. It was demonstrated that the first-derivative data set produced the best separation of the spectral objects. In general, satisfactory analytical performance was obtained with the PC-LS-SVM, KPC-LS-SVM, and RBF-ANN training models and with the classification of the verification spectral objects. With regard to chemometrics modeling, the performance of KPC-LS-SVM was somewhat more economical than that of the PC-LS-SVM model. It would appear that the latter relatively simple model would be sufficient for application to most small to medium sized FT-IR fingerprint data sets, but with larger matrices the more complex models, such as the RBF-ANN and KPC-LS-SVM, may be more advantageous on a computational basis.

Classification of raw and roasted Semen Cassiae samples with the use of Fourier transform infrared fingerprints and least squares support vector machines.

graphical a bstract A rapid and effective sensor for the analysis of nitrophenol-based explosive substances, represented by trinitrophenol (TNP), has been developed with the use of the blue luminescent graphene quantum dots (GQDs), which are derived from citric acid by a pyrolysis procedure.

A sensor based on blue luminescent graphene quantum dots for analysis of a common explosive substance and an industrial intermediate

Lotus has been widely cultivated and consumed in Asia, Oceania and America, and it is not only used as an ornamental plant but also as a dietary staple. In this work, fingerprinting techniques based on NIR spectroscopy and HPLC chromatography were used to discriminate four important kinds of lotus seed cultivars, Xianglian, Jianlian, Ganlian and Hulian. By the use of the NIR spectra and the aid of genetic algorithm-partial least squares (GA-PLS) and principal component analysis (PCA), cored lotus seed samples were successfully discriminated into four cultivars. High performance liquid chromatographic (HPLC) characteristic peaks detected at different wavelengths can also be used for PCA analysis to differentiate the lotus seed cultivars. Furthermore, quantitative prediction analysis of total sugar, total polysaccharide, liensinine and rutin in cored lotus seed (liensinine and rutin are mainly in lotus plumule) can be successfully performed with the use of the combined data matrix of 30 common characteristic HPLC peaks and 37 GA-PLS selected NIR spectral variables (wavelengths). The results suggested that the combination of NIR and HPLC supported by chemometrics is suitable for the discrimination of the plant cultivars and quantitative prediction of the active constituents in lotus seed samples.

The combination of NIR spectroscopy and HPLC chromatography for differentiating lotus seed cultivars and quantitative prediction of four main constituents in lotus with the aid of chemometrics

Yongnian Ni

Papers

Determination of mixed metal ions by complexometric titration and nonlinear partial least squares calibration

Classification of raw and roasted Semen Cassiae samples with the use of Fourier transform infrared fingerprints and least squares support vector machines.

A sensor based on blue luminescent graphene quantum dots for analysis of a common explosive substance and an industrial intermediate

The combination of NIR spectroscopy and HPLC chromatography for differentiating lotus seed cultivars and quantitative prediction of four main constituents in lotus with the aid of chemometrics

Nonenzymatic Amperometric Sensor for Nitrite Detection Based on a Nanocomposite Consisting of Nickel Hydroxide and Reduced Graphene Oxide