A CuCl-catalyzed Ullmann-type C–N cross-coupling reaction of carbazoles and 2-bromopyridine derivatives has been developed for the synthesis of N-heteroarylcarbazole derivatives employing 1-methyl-imidazole and t-BuOLi as ligand and base, respectively, both of which are found to significantly promote the reaction. Low cost and low loading of both catalyst and ligand, together with high reaction yields, render this practical reaction to be suitable for large-scale preparations and could be useful in material science.

An efficient and practical CuCl-catalyzed hydroxylation of N-heteroarylcarbazole bromide for the preparation of N-heteroarylcarbazolyl phenols with a broad functional group scope and yield up to 98% was developed. It was found that both the ligand and base played critical roles in the functional group transformation and that different products could be generated by changing the base for some substrates. t-BuONa was demonstrated to be a better base for the catalytic system to avoid the formation of the ether byproduct. In addition, this approach was suitable for large-scale preparation and was successfully applied in the gram-scale synthesis of phosphorescent emitters PtNON and PdNON, demonstrating its practicability in organic synthesis methodology and materials science. Furthermore, the X-ray crystal diffraction, DFT calculations, and photophysical properties were also investigated for the metal complexes.

•LCNC would bring new challenges to traditional classification methods.•An excellent fluorescent probe QDs are designed for sensing various green teas.•The fluorescent data array sensor is firstly developed to tackle LCNC.•The new detection mode is superior to the conventional fluorescent method for LCNC.Fluorescent “turn-off” sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence “turn-off” model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the “turn-off” fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established “turn-off” fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the “turn-off” model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.Download high-res image (224KB)Download full-size image

•A new model based on double QDs is established for pesticide residues detection.•The fluorescent data array sensor is coupled with chmometrics methods.•The sensor can be highly sensitive and selective detection in actual samples.As a popular detection model, the fluorescence “turn-off” sensor based on quantum dots (QDs) has already been successfully employed in the detections of many materials, especially in the researches on the interactions between pesticides. However, the previous studies are mainly focused on simple single track or the comparison based on similar concentration of drugs. In this work, a new detection method based on the fluorescence “turn-off” model with water-soluble ZnCdSe and CdSe QDs simultaneously as the fluorescent probes is established to detect various pesticides. The fluorescence of the two QDs can be quenched by different pesticides with varying degrees, which leads to the differences in positions and intensities of two peaks. By combining with chemometrics methods, all the pesticides can be qualitative and quantitative respectively even in real samples with the limit of detection was 2 × 10−8 mol L−1 and a recognition rate of 100%. This work is, to the best of our knowledge, the first report on the detection of pesticides based on the fluorescence quenching phenomenon of double quantum dots combined with chemometrics methods. What's more, the excellent selectivity of the system has been verified in different mediums such as mixed ion disruption, waste water, tea and water extraction liquid drugs.

•QSSR of a biomimetic catalytic system was studied.•Interpretable linear and nonlinear modeling was proposed by a sparse regression.•A set of 44 nonlinear transforms of single descriptors were developed.•Particle swarm optimization was used to select the optimal sparse variables.•The proposed method was shown useful in modeling the complex QSSR.A particle swarm optimization (PSO) based sparse regression (PSO-SR) strategy was proposed to study the quantitative structure-selectivity relationship (QSSR) of a biomimetic catalytic system, where the selectivity in the mild oxidation of o-nitrotoluene to o-nitrobenzaldehyde was related to the molecular descriptors of 48 metalloporphyrin catalysts. PSO was used to obtain an optimal variable combination for linear or nonlinear models. For nonlinear modeling, a set of 44 nonlinear transforms were developed for each single descriptor. To enable model interpretability and reduce the risk of overfitting, the total descriptors were divided into subclasses and the selected variables were forced to be sparsely distributed in each subclass. Model complexity was controlled by adjusting the maximum total number of variables included. Accurate linear and nonlinear PSO-SR models were developed using multiple linear regression (MLR) and partial least squares (PLS) and validated by randomly and repeatedly splitting the data into training and test objects for 500 times. The best predictions were obtained with 10 variables with linear (Q2=0.9460) and nonlinear (Q2=0.9505) models. The results indicate PSO-SR could provide an effective and useful strategy for modeling and interpreting complex QSSR problems. The proposed nonlinear modeling method could provide more information for model interpretation by probing and catching the unknown nonlinear relationship between a descriptor and the observed selectivity.

•This paper deals with large-class-number classification (LCNC) problems.•LCNC and related solutions have been rarely discussed in chemometrics.•A new method for LCNC was proposed using an ensemble strategy (ES).•ES was shown to be superior to two traditional methods in discriminating 25 teas.Large-class-number classification (LCNC) would bring new challenges to pattern recognition due to increased data complexity and class overlapping. In this study, a novel ensemble strategy (ES) was proposed to tackle LCNC problems. By combining the One-Versus-Rest (OVR) and One-Versus-One (OVO) strategies to design a set of classifiers with reduced class numbers, ES assigns a new object to the class receiving the most votes. When two or more classes obtain the most votes, an additional OVR model is developed to discriminate them. ES, OVR, OVO and the softmax function were investigated to discriminate the geographical origins of 25 green tea samples using near-infrared (NIR) spectroscopy and Partial Least Squares Discriminant Analysis (PLSDA). Using the Standard Normal Variate (SNV) as a spectral scatter correction technique, the total accuracy was 0.6468 for OVR-PLSDA, 0.8494 for OVO-PLSDA, 0.9299 for PLSDA-softmax, and 0.9377 for ES-PLSDA, respectively. Using other preprocessing methods and multiple random splitting of the data sets obtained the similar results. The poor performance of OVR can be attributed to the increased possibility of class overlapping and high sub-model complexity. OVO was less influenced by LCNC because it is based on a set of relatively simpler two-class classifiers. PLSDA-softmax could overcome the class overlapping by nonlinear transformations. ES was demonstrated to be capable of extracting more useful information from sub-models and achieved improved performance in LCNC.

Both multi-class and one-class discrimination analyses (DAs) have been widely used for tracing the geographical origins of Protected Designation of Origin (PDO) foods. However, due to the complexity of potential non-PDO frauds, both methods have encountered some problems. Because multi-class DA tries to classify two or more predefined classes, its classification results will be unreliable when it is used to predict a new object from an untrained class. One-class DA is developed using only the information concerning one-class objects, so they cannot necessarily ensure the model specificity for detection of various food frauds. In this work, a new chemometric strategy was proposed by fusion of multi-class and one-class DA to trace the geographical origin of a Chinese dried shiitake mushroom with PDO. The PDO shiitake objects (n = 161) and non-PDO objects (n = 264) from five other main producing areas were analyzed using near-infrared spectroscopy. The classification performance of multi-class DA, one-class DA, and model fusion was compared. With second-order derivative (D2) spectra, model fusion obtained a high sensitivity (0.944) and specificity (0.968). Model comparison indicates that fusion of multi-class and one-class DA can enhance the specificity for detecting various non-PDO foods with little loss of model sensitivity.

•Untargeted detection of maleic acid (MA) in cassava starch (CS) was developed.•One-class partial least squares detected 0.6% (w/w) or more MA in CS.•Taking second-order derivatives improved the specificity for untargeted detection.•Accurate calibration of MA was achieved by least-squares support vector machines.Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6% (w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192 (w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.