Pyrazine-Functionalized Donor-Acceptor Covalent Organic Frameworks for Enhanced Photocatalytic H2 Evolution with High Proton Transport.

The well-defined 2D or 3D structure of covalent organic frameworks (COFs) makes it have great potential in photoelectric conversion and ions conduction fields. Herein, a new donor-accepter (D-A) COF material, named PyPz-COF, constructed from electron donor 4,4',4″,4'″-(pyrene-1,3,6,8-tetrayl)tetraaniline and electron accepter 4,4'-(pyrazine-2,5-diyl)dibenzaldehyde with an ordered and stable π-conjugated structure is reported. Interestingly, the introduction of pyrazine ring endows the PyPz-COF a distinct optical, electrochemical, charge-transfer properties, and also brings plentiful CN groups that enrich the proton by hydrogen bonds to enhance the photocatalysis performance. Thus, PyPz-COF exhibits a significantly improved photocatalytic hydrogen generation performance up to 7542 µmol g-1 h-1 with Pt as cocatalyst, also in clear contrast to that of PyTp-COF without pyrazine introduction (1714 µmol g-1 h-1 ). Moreover, the abundant nitrogen sites of the pyrazine ring and the well-defined 1D nanochannels enable the as-prepared COFs to immobilize H3 PO4 proton carriers in COFs through hydrogen bond confinement. The resulting material has an impressive proton conduction up to 8.10 × 10-2 S cm-1 at 353 K, 98% RH. This work will inspire the design and synthesis of COF-based materials with both efficient photocatalysis and proton conduction performance in the future.

Ring Repeating Unit: An Upgraded Structure Representation of Linear Condensation Polymers for Property Prediction.

Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure-property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (Tg) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.

A QSTR model for toxicity prediction of pesticides towards Daphnia magna.

Because of the large amount of pesticides discharged into rivers, adverse effects could be induced to aquatic organisms. Daphnia magna is often used as an indicator organism to evaluate the toxicity of pesticides. In this study, a quantitative structure-toxicity relationship (QSTR) model was established based on norm descriptors for predicting the acute toxicity of pesticides to Daphnia magna. The model results showed the good predictability (Rtraining2 = 0.8045, Rtesting2 = 0.8224). The validation results of internal validation, external validation, Y-randomization test and application domain analysis demonstrated the model's stability, reliability and robustness. Therefore, the above results indicate that norm descriptors might be universal for describing the relationship between the toxicity and structures of pesticides compounds. Moreover, some pesticides' toxicities without experimental data were also predicted by this model.

Simultaneous determination of nitrophenol isomers based on reduced graphene oxide modified with sulfobutylether-ß-cyclodextrin.

The present study reports the development of an electrochemical sensor based on sulfobutylether-ß-cyclodextrin modified reduced graphene oxide hybrid (SBCD-rGO) for simultaneous detection of nitrophenol isomers. First, SBCD-rGO hybrid was synthesized and detailed characterized. Afterwards, a sensor was fabricated via the modification of glassy carbon electrode (GCE) with SBCD-rGO, and its electrochemical detection performances were also investigated. Then, the constructed electrochemical sensor was applied to detect nitrophenol isomers by voltammetry analysis. The results suggested that the sensitivities were 389.26, 280.88 and 217.19 µA/mM for p-nitrophenol (p-NP), m-nitrophenol (m-NP), and o-nitrophenol (o-NP), respectively, and their corresponding detection limits were all about 0.05 µM. Significantly, the combination of voltammetry analysis with the constructed sensor and data analysis by multiple linear regression realized the simultaneous detection of nitrophenol isomers.

Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo.

Zebrafish embryos are highly sensitive to toxicant exposure and have been used to evaluate the potential eco-toxicity caused by organic pollutants in the aquatic environment. This study was to develop four quantitative structure-activity relationship (QSAR) models based on norm descriptors for acute toxicity of different exposure times toward zebrafish embryo of organic compounds with various structures. Norm descriptors were obtained by calculating the norm index of the atomic distribution matrix, which was composed of atomic spatial distribution and atomic properties. These norm index-based QSAR models presented satisfactory results with R2 of 0.8549, 0.9162, 0.8335 and 0.8119 for 48, 96, 120 and 132 h, respectively. Validation results including cross validation, external validation, Y-randomized test and applicability domain analysis indicated that the proposed models were stable, robust and reliable. Accordingly, these norm descriptors might be effective in predicting the acute toxicity of various organics to zebrafish embryos, which might be useful for evaluating the potential hazards of organic pollutants to aquatic environment.

Norm index in QSTR work for predicting toxicity of ionic liquids on Vibrio fischeri.

Ionic liquids have been becoming new 'green solvent' because of the low saturation vapor pressure, less volatilization and more recycling utilization. Since most ILs are soluble in water, it should be indispensable to evaluate the ecotoxicology effect of ILs on aquatic environment before using them widely. Based on the concept of norm index, a set of norm descriptors were proposed for anions, cations and ILs. The whole IL structure optimization method has been used to build a predictive norm index-based quantitative structure-toxicity relationship model for the toxicity of ILs on Vibrio fischeri. Statistical results indicated that norm descriptors were reliable and robust in expressing the relationship between structural information and toxicity of ILs. Meanwhile, a series of ILs without experimental values were predicted based on this stable QSTR model. The results indicated that for imidazole-based ILs, an increase in the length of substituent in the branch could enhance the toxicity of ILs on Vibrio fischeri, and the branch contains hydroxyl group, double bond or triple bonds might reduce the toxicity of ILs. Results obtained in this present work would be valuable for the molecular design and the toxicity evaluation toward aquatic organism of ILs.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics.

The n-octanol/water partition coefficient (logKow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R2 = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logKow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logKow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logKow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logKow value of a compound.

Norm Index-Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout.

The aquatic ecological environment is being threatened from overuse of pesticides, and the aquatic toxicity toward rainbow trout (Oncorhynchus mykiss) plays a significant role in environmental risk assessment of agrochemicals. In the present study, 2 norm index formulas were developed, from which several norm descriptors were derived. A quantitative structure-activity relationship (QSAR) model was established for the prediction of acute toxicity (median lethal concentration) toward rainbow trout of various pesticides. Results indicated that the present QSAR model presented an R2 of 0.8053. Meanwhile, internal validation (QLOO2 = 0.7606), external validation (Rtraining2 = 0.8011, Rtesting2 = 0.8108), Y-randomization test, and applicability domain analysis further demonstrated the stability, reliability, and wide application domain of the present QSAR model. Accordingly, these norm descriptors might be applicable to the structures of pesticides for predicting the acute toxicity to aquatic organism. Environ Toxicol Chem 2020;39:352-358. © 2019 SETAC.

Norm index for predicting the rate constants of organic contaminants oxygenated with sulfate radical.

The degradation of organic contaminants in aquatic systems has raised immense attention worldwide, and the second-order rate constant ([Formula: see text]) of water pollutants oxidized by sulfate radical anion is an important index for assessing the degradation efficiency of organics. Herein, a new norm mathematical formula is defined. Based on this, four new descriptors are proposed and a QSPR model is developed for predicting [Formula: see text] using 30 families of emerging organic pollutants in water. The statistical results fully prove that this model has good fitting effect and stability with R2 of 0.8862, Q2LOO of 0.8466, and Q25-fold of 0.8329, respectively. The validation results including cross validation, applicability domain analysis, and model comparison show that this model has good robustness, predictive performance, and reliability. These decent results indicate that the new norm mathematical formula is effective in calculating descriptors and the norm indexes have a great application for evaluating the transformation fate of organic pollutants by sulfate radical in aquatic systems.

Norm index-based QSTR model to predict the eco-toxicity of ionic liquids towards Leukemia rat cell line.

The evaluation of eco-toxicity of ionic liquids (ILs) in the aquatic environment is essential for their safe utilization and QSTR approach plays an important role in obtaining the eco-toxicity data of ILs with diverse structures. Usually, the descriptors used to build QSTR model were made up of anion and cation descriptors, and their interactions were often neglected to some extent. In this work, based on the optimization of the ILs structure, a new set of descriptors were proposed to describe the interaction between anions and cations, and some new atomic distribution matrices were constructed to calculate norm descriptors of ILs, anion and cation. A norm index-based QSTR model was built to predict the eco-toxicity of ILs toward Leukemia rat cell line (IPC-81). This model has satisfactory statistical results with the R2 of 0.954 and RMSE of 0.241, respectively. Furthermore, leave-one-out cross-validation and applicability domain results showed good stability and predictability of this model. This approach showed that the interaction between cations and anions could be reflected by optimizing the whole structure of ILs which might play an important role for describing the eco-toxicity of ILs. Therefore, it is further suggested that the norm descriptors would be applicable to predict the eco-toxicity of ILs towards IPC-81.

Multiple toxicity endpoint-structure relationships for substituted phenols and anilines.

Quantitative structure-toxicity relationship (QSTR) models with the same mathematical structure were proposed for predicting the multiple toxicity endpoints of substituted phenols and anilines towards Chlorella vulgaris (C. vulgaris) based on the norm indexes. Four aquatic toxicity endpoints including growth inhibition concentrations of IC50, IC20, LOEC and NOEC towards C. vulgaris were involved in the modeling work. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R2). Leave-one-out cross validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly, the results obtained in this work suggested that it might be possible to develop QSTR models with the same mathematical structure for predicting multiple toxicity endpoints successfully via norm index descriptors.

QSAR model for predicting the toxicity of organic compounds to fathead minnow.

In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R2) and squared relation coefficient of the cross validation (Q2) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.

QSAR models for describing the toxicological effects of ILs against Candida albicans based on norm indexes.

The quantitative structure-activity relationship (QSAR) model is an effective alternative to traditional experimental toxicity testing, which is undoubtedly important for modern environmental risk assessment and property prediction. Based on this background, the toxicological effects of ionic liquids (ILs) against Candida albicans (C. albicans) were studied via the QSAR method. A large diverse group of 141 and 85 ILs that have a minimal inhibitory concentration (MIC) and a minimum fungicidal concentration (MBC) against C. albicans were used to obtain multiple linear regression models. These two models were developed based on matrix norm indexes and proposed based on the atomic character and position. Matrix norm indexes proposed in our research group were used to calculate the toxicity of these ILs towards C. albicans for the first time. These two models precisely estimated the toxicity of these ILs towards C. albicans with a square of correlation coefficient (R2) of = 0.930 and a standard error of estimate (SE) of = 0.254 for pMIC, and for pMBC, R2 = 0.873 and SE = 0.243.

QSAR models for describing the toxicological effects of ILs against Staphylococcus aureus based on norm indexes.

The hazardous potential of ionic liquids (ILs) is becoming an issue of great concern due to their important role in many industrial fields as green agents. The mathematical model for the toxicological effects of ILs is useful for the risk assessment and design of environmentally benign ILs. The objective of this work is to develop QSAR models to describe the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of ILs against Staphylococcus aureus (S. aureus). A total of 169 and 101 ILs with MICs and MBCs, respectively, are used to obtain multiple linear regression models based on matrix norm indexes. The norm indexes used in this work are proposed by our research group and they are first applied to estimate the antibacterial toxicity of these ILs against S. aureus. These two models precisely and reliably calculated the IL toxicities with a square of correlation coefficient (R2) of 0.919 and a standard error of estimate (SE) of 0.341 (in log unit of mM) for pMIC, and an R2 of 0.913 and SE of 0.282 for pMBC.

Topological study on the toxicity of ionic liquids on Vibrio fischeri by the quantitative structure-activity relationship method.

As environmentally friendly solvents, ionic liquids (ILs) are unlikely to act as air contaminants or inhalation toxins resulting from their negligible vapor pressure and excellent thermal stability. However, they can be potential water contaminants because of their considerable solubility in water; therefore, a proper toxicological assessment of ILs is essential. The environmental fate of ILs is studied by quantitative structure-activity relationship (QSAR) method. A multiple linear regression (MLR) model is obtained by topological method using toxicity data of 157 ILs on Vibrio fischeri, which are composed of 74 cations and 22 anions. The topological index developed in our research group is used for predicting the V. fischeri toxicity for the first time. The MLR model is precise for estimating LogEC50 of ILs on V. fischeri with square of correlation coefficient (R(2)) = 0.908 and the average absolute error (AAE) = 0.278.