A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Relationship between the eating quality and the physico-chemical properties of high grain quality rice in China

Relationship between eating quality(EQ)andphysico-chemical properties of 78 high grainquality rice varieties,45 indica and 33 japoni-ca,were studied in 1995.The samples werecollected from different rice growing regions in20 provinces.The physico-chemical proper-ties,including grain length(GL),length towidth ratio(L/W),chalky grain rate(CG),chalkiness(CH),translucency(TR),gela-tinization temperature(GT,measured by alka-li spreading value),gel consistency(CA2),amylase content(AC),and protein content(PC)were measured according to the standard

The Relationship between Microtexture Types and Indices of Physico-Mechanical Properties of Loess in China

The paper deals mainly with the relationship between the microtexture types and the indices of physicomechanical properties of loess. The results of study demonstrate that the study of microtextures of loess is of importance in the prediction and preliminary evaluation of engineering geological properties of loess in a region.

Recent advances in spinel-type electrocatalysts for bifunctional oxygen reduction and oxygen evolution reactions

The demand for efficient and environmentally-benign electrocatalysts that help availably harness the renewable energy resources is growing rapidly. In recent years, increasing insights into the design of water electrolysers, fuel cells, and metal–air batteries emerge in response to the need for developing sustainable energy carriers, in which the oxygen evolution reaction and the oxygen reduction reaction play key roles. However, both reactions suffer from sluggish kinetics that restricts the reactivity. Therefore, it is vital to probe into the structure of the catalysts to exploit high-performance bifunctional oxygen electrocatalysts. Spinel-type catalysts are a class of materials with advantages of versatility, low toxicity, low expense, high abundance, flexible ion arrangement, and multivalence structure. In this review, we afford a basic overview of spinel-type materials and then introduce the relevant theoretical principles for electrocatalytic activity, following that we shed light on the structure–property relationship strategies for spinel-type catalysts including electronic structure, microstructure, phase and composition regulation,and coupling with electrically conductive supports. We elaborate the relationship between structure and property, in order to provide some insights into the design of spinel-type bifunctional oxygen electrocatalysts.

Noble-metal-based high-entropy-alloy nanoparticles for electrocatalysis

Since the two seminal papers were published independently in 2004, high-entropy-alloys(HEAs) have been applied to structural and functional materials due to the enhanced mechanical properties, thermal stability, and electrical conductivity. In recent years, HEA nanoparticles(HEA-NPs) were paid much attention to in the field of catalysis for the promoted catalytic activity. Furthermore, the various ratios among the metal components and tunable bulk and surface structures enable HEAs have big room to enhance the catalytic performance. Especially, noble-metal-based HEAs displayed significantly improved performance in electrocatalysis, where the ‘core effects’ were employed to explain the superior catalytic activity. However, it is insufficient to understand the essential mechanism or further guide the design of electrocatalysts. Structure–property relationship should be disclosed for the catalysis on HEA-NPs to accelerate the process of seeking high effective and efficient electrocatalysts. Therefore, we summarized the recent advances of noble-metal-based HEA-NPs applied to electrocatalysis, such as hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, methanol oxidation reaction, ethanol oxidation reaction, formic acid oxidation reaction, hydrogen oxidation reaction, carbon dioxide reduction reaction and nitrogen reduction reaction. For each electrocatalytic reaction, the reaction mechanism and catalyst structure were presented, and then the structure–property relationship was elaborated. The review begins with the development, concept, four ‘core effect’ and synthesis methods of HEAs. Next,the electrocatalytic reactions on noble-metal-based HEA-NPs are summarized and discussed independently. Lastly, the main views and difficulties pertaining to structure–property relationship for HEAs are discussed.

In situ TEM revealing the effects of dislocations on lithium-ion migration in transition metal dichalcogenides

The two-dimensional (2D) structure of layered transition metal dichalcogenides (TMDs) provides unusual physical properties [1,2]and chemical reactivity [3,4], which can be influenced by defects such as dislocations [5,6]. For example, dislocations can act as nucleation sites for the onset of deformation when subjected to stress [7].

Machine learning in materials design:Algorithm and application

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

New mesogenic Schiff base esters comprising benzothiazole moiety:Synthesis and mesomorphic properties

A homologous series of Schiff base esters, 6-methoxy-2-（2-hydroxy-4-alkanoyloxybenzylidenamino）benzothiazoles, compris- ing a benzothiazole moiety as the core was synthesized. All the members of this series exhibited an enantiotropic nematic phase. The azomethine linkage along with the lateral hydroxyl and terminal methoxyl groups were found to exert an effect on the mesomorphic properties. 2009 Sie Tiong Ha. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.

Prediction of Molar Absorptivities of Color Reagents and Their Color Reactions with Yttrium by Artificial Neural Networks

The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

QSPR Studies on the Octanol/water Partition Coefficient (lgK_(ow)) of Substituted Anilines with 2D and 3D Methods

Octanol/water partition coefficient （Kow） is a crucial property for evaluating the environmental behavior and fate of organic compound. Herein, some quantitative structure-property relationship （QSPR） studies were performed to estimate and predict the lgK ow of substituted anilines. 2D method （multiple linear regression, MLR） and 3D method （comparative molecular field analysis, CoMFA） were applied in this study. Successful 2D and 3D models yielded the correlation coefficient （R2） values of 0.981 and 0.966 and the Leave-One-Out （LOO） cross-validated correlation coefficient （q2） values of 0.933 and 0.820, respectively. The developed models have a highly predictive ability in both internal and external validation. In addition, the results were interpreted in terms of physical and chemical meanings of descriptors and field contribution maps. It showed that the steric and electrostatic properties are the primary factors that govern the lgK ow of substituted anilines. The information obtained from the QSPR models would be helpful to the interpretation of structural features pertinent to the lgK ow of substituted anilines, which may be helpful in estimating the organic compounds＇ potential harm to the environment.

Application of TLSER method in predicting the aqueous solubility and n-octanol/water partition coefficient of PCBs,PCDDs and PCDFs

The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.

Structural Characterization and Octanol/water Partition Coefficients(LogP) Prediction for Oxygen-containing Organic Compounds

New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression（MLR） and partial least squares regression（PLS） methods were employed to build two relationship models between the structures and octanol/water partition coefficients（LogP） of the compounds. The modeling correlation coefficients（R） were 0.976 and 0.922, and the ＂leave one out＂ cross validation correlation coefficients（R（CV）） were 0.973 and 0.909, respectively. The results showed that the structural descriptors could well characterize the molecular structures of the compounds; the stability and predictive power of the models were good.

Identification model of multi-layered neural network parameters and its applications in the petroleum production

This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.

Electron-transporting boron-doped polycyclic aromatic hydrocarbons:Facile synthesis and heteroatom doping positions-modulated optoelectronic properties

While heteroatom doping serves as a powerful strategy for devising novel polycyclic aromatic hydrocarbons(PAHs), the further fine-tuning of optoelectronic properties via the precisely altering of doping patterns remains a challenge. Herein, by changing the doping positions of heteroatoms in a diindenopyrene skeleton, we report two isomeric boron, sulfur-embedded PAHs, named Anti-B_(2)S_(2) and Syn-B_(2)S_(2), as electron transporting semiconductors. Detailed structure-property relationship studies revealed that the varied heteroatom positions not only change their physicochemical properties, but also largely affect their solid-state packing modes and Lewis base-triggered photophysical responses. With their low-lying frontier molecular orbital levels, n-type characteristics with electron mobilities up to 1.5 × 10^(-3)cm^(2)V^(-1)s^(-1)were achieved in solution-processed organic field-effect transistors. Our work revealed the critical role of controlling heteroatom doping patterns for designing advanced PAHs.

Heterogeneous Fenton degradation of azodyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.

Theoretical relationship between sound velocity and the physical properties of submarine sediment

Density and elastic modulus change ratios are introduced to describe the sound velocity of submarine sediment. The density change ratio is a composite parameter describing the sound velocity. It is expressed by three physical parameters： porosity, solid phase density and seawater density. The elastic modulus change ratio is also a composite parameter of sound velocity. It is expressed by three physical parameters, including porosity, solid phase modulus and seawater bulk modulus. The sound velocity formula can be developed into a Taylor polyno- mial formula of these two composite parameters. The change in the two composite parameters constitutes the sound velocity surface, which contains the complete information regarding ve- locity properties and sediment characteristics. The one-parameter velocity formula is a curve on the velocity surface. Each porosity-velocity empirical formula, which represents various sea locations and conditions, is transformed to a standard form. This result is the product of a reference velocity and a modulation function. Comparisons of the numerical calculation and measurements show that the derived modulation functions yield similar results. The difference between the velocity formula derived in this paper and the Wood velocity formula is due to the elastic modulus models.

Recent progress in COF-based electrode materials for rechargeable metal-ion batteries

Covalent organic frameworks(COFs)have emerged as promising electrode materials for rechargeable metal-ion batteries and have gained much attention in recent years due to their high specific surface area,inherent porosity,tunable molecular structure,robust framework,abundant active sites.Moreover,compared with inorganic materials and small organic molecules,COFs have the advantages of multi-electron transfer,short pathways,high cycling stability.Although great progress on COF-based electrodes has been made,the corresponding electrochemical performance is still far from satisfactory for practical applications.In this review,we first summarize the fundamental background of COFs,including the species of COFs(different active covalent bonds)and typical synthesis methods of COFs.Then,the key challenges and the latest research progress of COF-based cathodes and anodes for metal-ion batteries are reviewed,including Li-ion batteries,Na-ion batteries,K-ion batteries,Zn-ion batteries,et al.Moreover,the effective strategies to enhance electrochemical performance of COF-based electrodes are presented.Finally,this review also covers the typical superiorities of COFs used in energy devices,as well as providing some perspectives and outlooks in this field.We hope this review can provide fundamental guidance for the development of COFbased electrodes for metal-ion batteries in the further research.

化合物PbGa_(4)Se_(7)中[PbSe_(3)]和[GaSe_(4)]功能基元的强极化实现高的相位匹配二次谐波效应

二次谐波效应和相位匹配能力对于二阶非线性光学晶体的实际应用至关重要.在本工作中,我们通过将具有立体化学活性电子对的Pb^(2+)引入非相位匹配的Ga_(2)Se_(3),增强其各向异性和二阶超极化率.基于以上策略,我们制备了一种硒化物PbGa_(4)Se_(7),其晶体结构由类金刚石阴离子骨架和插入到间隙中的Pb^(2+)组成.在1910 nm的激光照射下,PbGa_(4)Se_(7)具有大的二次谐波系数(3.3×AgGaS_(2));同时,其具有低的热膨胀系数各向异性(0.5)和合适的光学带隙(2.1 eV),在2090 nm激光照射下,表现出高的激光诱导损伤阈值(7.0×AgGaS_(2)).PbGa_(4)Se_(7)的相位匹配能力和强二次谐波效应可归因于非线性光学功能基元(四面体GaSe_(4)和金字塔PbSe_(3)结构单元)的协同作用产生了额外的各向异性和极化率.

Design,synthesis and applications of functional zirconium-based metal-organic frameworks

Zirconium-based metal-organic frameworks(Zr-MOFs)have been explored for applications including but not limited to water adsorption,gas storage and separation,heterogeneous catalysis,and chemical sensing.Zr-MOFs serve as a major class of functional MOFs thanks to their high thermal,chemical and hydrolytic stability,large surface area,and tunable structures with the versatile connectivity.In this work,we highlight the design and synthesis of zirconium-based MOFs as well as their applications.Specifically,we demonstrate how reticular chemistry can direct the rational design and synthesis of functional ZrMOFs and describe their structure–property relationship.In addition,we feature synthetic strategies,including isoreticular expansion,linker functionalization,node functionalization,and defect engineering,as toolkits to construct tailored material for specific applications.