Methyl position affect the fluorescence performance of HBT derivatives for the detection of hypochlorite under alkaline condition

Machine Learning Algorithms for Intelligent Decision Recognition and Quantification of Cr(III) in Chromium Speciation

Cr(III) is a common oxidation state of chromium, and its presence in the environment can occur naturally or as a result of human activities, such as industrial processes, mining, and waste disposal. This article explores the application of machine learning algorithms for the intelligent decision recognition and quantification of Cr(III) in chromium speciation. Three different machine learning models, namely, the Decision Tree (DT) model, the PCA-SVM (Principal Component Analysis-Support Vector Machine) model, and the LDA (Linear Discriminant Analysis) model, were employed and evaluated for accurate and efficient classification of chromium concentrations based on their fluorescence responses. Furthermore, stepwise multiple linear regression analysis was utilized to achieve a more precise quantification of trivalent chromium concentrations through fluorescence visualization. The results demonstrate the potential of machine learning algorithms in accurately detecting and quantifying Cr(III) in chromium speciation with implications for environmental and industrial applications in chromium detection and quantification. The findings from this research pave the way for further exploration and implementation of these models in real-world scenarios, offering valuable insights into various environmental and industrial contexts.

Design, Synthesis and Evaluation of Fluorescent Properties of Benzothiazole Derivatives

Fluorescence imaging is conducive to establish a bridge between molecular biology and clinical medicine, and provides new tools for disease process research, early diagnosis, and efficacy evaluation, because of the advantages of rapid imaging and nondestructive detection. Herein, a series of fluorescent molecules with thiadiazole, or thiazole, or benzothiazole cores were designed and synthesized to develop more excellent fluorescent molecules in bio-imaging. According to theoretical and experimental methods, we found that benzothiazole derivative 14 B with conjugate expansion by (4-aminophenyl) ethynyl group was the most excellent fluorescent molecule among all the investigated compounds and exhibited low cytotoxicity and strong blue and green fluorescence by confocal cell imaging.

Dangling Water Molecules Bridge for ESIPT in Aggregated TMP: A Theoretical Study

We present a theoretical study on the occurrence of excited-state proton transfer in an aggregated structure of 2-(benzo[d]thiazol-2-yl)-6-methoxyphenol (TMP) exclusively in water among polar solvents, as reported in a recent experiment (Bhattacharyya, A. New J. Chem. 2019, 43, 15087). Our extensive investigation of the TMP monomer and dimer implementing density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods, in three different solvents, namely, water, methanol, and dimethyl sulfoxide (DMSO), with explicit inclusion of solvent molecules demonstrated the existence of both enol and keto forms of the TMP dimer in the excited state, but only in water; this confirmed the experimental emission spectra completely and simultaneously validated the aggregation-induced emission phenomenon. Further analysis of various parameters such as potential energy scan (PES) of the hydroxyl (O-H) bond involved in hydrogen bonding, frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP), and infrared (IR) stretching frequencies of both the monomer and dimer forms of TMP in different solvents clearly indicated the geometry of the dimer, with the arrangement of the solvent molecules to be the sole reason for the excited-state charge transfer. The bridging alignment of water molecules in between the stacked units of the TMP dimer results in intermolecular interactions, ultimately leading to intermolecular proton transfer in the excited state.