Ca(1)(-)(x)Na(2)(x)Al(2)B(2)O(7): a structure with tunable density of Na(+) vacancies

A series of samples with the composition Ca(1)(-)(x)Na(2)(x)Al(2)B(2)O(7) (0 < x < or = 1) was investigated and a hexagonal structure with unusually large range of homogeneity (at least from x = 0.01 to 0.95) was revealed. The hexagonal phase consists of [Al(2)B(2)O(7)](infinity)(2)(-) lamellae stacked along the c axis, as in CaAl(2)B(2)O(7) and Na(2)Al(2)B(2)O(7). Nevertheless, the configuration and stacking sequence of the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae are different in these three structures. In the hexagonal structure of Ca(1)(-)(x)()Na(2)(x)()Al(2)B(2)O(7), Ca and half Na cations (Na1) statistically occupy the same crystallographic site which is located between the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae, the other half Na cations (Na2) distribute in the planes bisecting the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae. Depending on the composition, the site occupation factor of Na2 site can vary in the same range as x, leading to a tunable density of Na(+) vacancies in the structure. The AlO(4) tetrahedra and BO(3) triangles in the structure tilt in appropriate ways to improve the bond valence sum of Na2 cations which are not sufficiently bonded to the anions.     

中子成像技术在元素分析中的应用

中子成像作为一种快速、直观的无损检测技术,在核工业、航空航天、新能源、地质、考古、先进制造等多个领域得到广泛应用。中子成像利用中子不带电、穿透能力强、对轻元素敏感、可区分同位素和近邻元素等特性,非常适合开展含氢元素、近邻元素和同位素等材料的无损检测。本文概述了中子成像技术的基本原理及特点,并结合中国先进研究堆(CARR)中子成像装置上的应用案例, 重点介绍了国内外中子成像技术在储氢材料、燃料电池、岩石、核燃料元件、古代文物等领域的典型应用。随着中子成像技术的不断发展和广泛应用,有望为我国更多领域研究提供更强有力的技术支撑。     

锗元素测定方法综述

锗在国防工业、航空航天和通信等领域中的战略性,锗含量的测定对于保证材料质量和满足国际标准至关重要。本文综述了锗含量测定方法的多种技术,包括分光光度法、原子荧光光谱法、原子吸收光谱法、电感耦合等离子体原子发射光谱法、电感耦合等离子体质谱法以及滴定法。在每个检测方法的介绍中,详细探讨了方法的原理、前处理步骤以及应用范围,并分别总结了各个方法的优势和不足。最后,强调了锗含量测定方法的意义,特别是在满足出口监管和促进科学研究方面的作用。同时对锗元素的测定方法进行了展望,为未来的发展提供了参考方向。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Distinguishable RGE Running Effects Between Dirac Neutrinos and Majorana Neutrinos with Vanishing Majorana CP-Violating Phases

In a novel parametrization of neutrino mixing and in the approximation of т-lepton dominance, we show that the one-loop renormalization-group equations (RGEs) of Dirac neutrinos are different from those of Majorana neutrinos even if two Majorana CP-violating phases vanish. As the latter can keep vanishing from the electroweak scale to the typical seesaw scale, it makes sense to distinguish between the RGE running effects of neutrino mixing parameters in Dirac and Majorana cases. The differences are found to be quite large in the minimal supersymmetric standard model with sizable tanβ, provided the masses of three neutrinos are nearly degenerate or have an inverted hierarchy.     

A molecular mechanics approach for analyzing tensile nonlinear deformation behavior of single-walled carbon nanotubes

In this paper, by capturing the atomic information and reflecting the behaviour governed by the nonlinear potential function, an analytical molecular mechanics approach is proposed. A constitutive relation for single-walled carbon nanotubes (SWCNT’s) is established to describe the nonlinear stress-strain curve of SWCNT’s and to predict both the elastic properties and breaking strain of SWCNT’s during tensile deformation. An analysis based on the virtual internal bond (VIB) model proposed by P. Zhang et al. is also presented for comparison. The results indicate that the proposed molecular mechanics approach is indeed an acceptable analytical method for analyzing the mechanical behavior of SWCNT’s. The project supported by the National Natural Science Foundation of China (10121202, 90305015 and 10328203), the Key Grant Project of Chinese Ministry of Education (0306) and the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU 7195/04E).     

Interpretation of Strengthening Mechanism of Densified Wood from Supramolecular Structures

In this study, densified wood was prepared by hot pressing after partial lignin and hemicellulose were removed through alkaline solution cooking. The tensile strength and elastic modulus of densified wood were improved up to 398.5 MPa and 22.5 GPa as compared with the original wood, and the characterization of its supramolecular structures showed that the crystal plane spacing of the densified wood decreased, the crystallite size increased, and the maximum crystallinity (CI) of cellulose increased by 15.05%; outstandingly, the content of O(6)H⋯O(3′) intermolecular H-bonds increased by approximately one-fold at most. It was found that the intermolecular H-bond content was significantly positively correlated with the tensile strength and elastic modulus, and accordingly, their Pearson correlation coefficients were 0.952 (p < 0.01) and 0.822 (p < 0.05), respectively. This work provides a supramolecular explanation for the enhancement of tensile strength of densified wood.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Compositions and Biological Activities of Pomegranate Peel Polyphenols Extracted by Different Solvents

Pomegranate peel extract (PPE), which is abundant in polyphenols, holds immerse prospects for the treatment of airway infection. In this study, water and ethanol of 30%, 50%, and 80% were used to prepare PPE. A total of 18 phenols belonging to 8 categories of polyphenols were identified in PPE by HPLC-MS/MS. The PPE from the four extraction solvents possessed different antioxidant, antibacterial, and anti-inflammatory activities. Principal component analysis revealed that though total flavonoids (TFs), total polyphenols (TPs), and total tannins (TTs) were responsible for the reducing power of PPE, only TFs contributed to the effect of PPE in inhibiting lipid membrane peroxidation. TPs, TTs, and punicalagin were positively correlated with the antibacterial strength against S. aureus while TTs alone contributed to the inhibition of methicillin-resistant S. aureus, implying the crucial role of TT in suppressing bacteria. Meanwhile, TTs was associated with the prevention of IL-6 release. The PPE with higher contents of TPs, TTs, and punicalagin had a weaker capacity to decrease nitric oxide secretion. PPE of 30% ethanol gained the highest integrated score due to its stronger antioxidant, antibacterial, and anti-inflammatory activities. It is a suitable candidate for the therapy of respiratory tract infection.     

Synthesis of Halopyrazole Matrine Derivatives and Their Insecticidal and Fungicidal Activities

Matrine is a traditional botanical pesticide with a broad-spectrum biological activity that is widely applied in agriculture. Halopyrazole groups are successfully introduced to the C13 of matrine to synthesize eight new derivatives with a yield of 78–87%. The insecticidal activity results show that the introduction of halopyrazole groups can significantly improve the insecticidal activity of matrine on Plutella xylostella, Mythimna separata and Spodoptera frugiperda with a corrected mortality rate of 100%, which is 25–65% higher than matrine. The fungicidal activity results indicate that derivatives have a high inhibitory effect on Ceratobasidium cornigerum, Cibberella sanbinetti, Gibberrlla zeae and Collectot tichum gloeosporioides. Thereinto, 4-Cl-Pyr-Mat has the best result, with an inhibition rate of 23–33% higher than that of matrine. Therefore, the introduction of halogenated pyrazole groups can improve the agricultural activity of matrine.