Structure-Property Relationships and Models of Controlled Drug Delivery of Biodegradable Poly (D, L-lactic acid) Microspheres

An oil-in-water (O/W) solvent evaporation method was used to prepare biodegradable microspheresbased on poly(D,L-lactic acid) (PLA). Nifedipine, a hydrophobic drug, was chosen as a model molecule in the studyof drug entrapment and release. Effect of preparation conditions on the size, morphology, drug loading, and releaseprofiles of micropheres was investigated. Based on in vitro release experimental findings, a diffusion/dissolutionmodel was presented for quantitative description of the resulting release behaviors and drug release kinetics fromPLA microspheres analyzed. The mathematical models were used to predict the effect of microstructure on theresulting drug release. It provided an approach to determine the suitable structure parameters for microspheres toachieve desired drug release behaviors.

Estimation of surface tension of organic compounds using quantitative structure-property relationship

A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out （LOO） cross-validated （CV） method. The correlation coefficient （R） and the leave-one-out cross-validation correlation coefficient （Rcv） of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.

Study of structure-property relationship of semiconductor nanomaterials by off-axis electron holography

As the scaling down of semiconductor devices, it would be necessary to discover the structure-property relationship of semiconductor nanomaterials at nanometer scale. In this review, the quantitative characterization technique off-axis electron holography is introduced in details, followed by its applications in various semiconductor nanomaterials including group IV, compound and two-dimensional semiconductor nanostructures in static states as well as under various stimuli. The advantages and disadvantages of off-axis electron holography in material analysis are discussed, the challenges facing in-situ electron holographic study of semiconductor devices at working conditions are presented, and all the possible influencing factors need to be considered to achieve the final goal of fulfilling quantitative characterization of the structure-property relationship of semiconductor devices at their working conditions.

Quantitative Structure-Property Relationship on Prediction of Surface Tension of Nonionic Surfactants

A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.

BiPh-m-BiDPO as a Hole-Blocking Layer for Organic Light-Emitting Diodes： Revealing Molecular Structure-Properties Relationship

We report a simple hole-blocking material （biphenyl-3,3＇-diyl）bis（diphenylphosphine oxide） （BiPh-m-BiDPO） based on our recent advance. The bis（phosphine oxide） compound shows HOMO/LUMO levels of ∽-6.71/- 2.51 eV. Its phosphorescent spectrum in a solid film features two major emission bands peaking at 2.69 and 2.4eV, corresponding to 0-0 and 01 vibronic transitions, respectively. The measurement of the electron-only devices reveals that BiPh-m-BiDPO possesses electron mobility of 2.28 × 10^-9-3.22× 10^-8cm2 V-1s-1 at E = 2- 5 × 10^5 V/cm. The characterization of the sky blue fluorescent and red phosphorescent pin organic light-emitting diodes （OLEDs） utilizing BiPh-m-BiDPO as the hole blocker shows that its shallow LUMO level as well as the low electron mobility affects significantly the power efficiency and hence operational stability, relative to the luminous efficiency, especially at high luminance. In combination with our recent results, the present study provides an indepth insight on the molecular structure-property correlation in the organic phosphinyl-containing hole-blocking materials.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Rational design of diamond through microstructure engineering:From synthesis to applications

Diamond possesses excellent thermal conductivity and tunable bandgap.Currently,the high-pressure,high-temperature,and chemical vapor deposition methods are the most promising strategies for the commercial-scale production of synthetic diamond.Although diamond has been extensively employed in jewelry and cutting/grinding tasks,the realization of its high-end applications through microstructure engineering has long been sought.Herein,we discuss the microstructures encountered in diamond and further concentrate on cutting-edge investigations utilizing electron microscopy techniques to illuminate the transition mechanism between graphite and diamond during the synthesis and device constructions.The impacts of distinct microstructures on the electrical applications of diamond,especially the photoelectrical,electrical,and thermal properties,are elaborated.The recently reported elastic and plastic deformations revealed through in situ microscopy techniques are also summarized.Finally,the limitations,perspectives,and corresponding solutions are proposed.

Structure-Property Relationship Analysis of D-Penicillamine-Derivedβ-Polythioesters with Varied Alkyl Side Groups

The ring-opening polymerization of heterocyclic monomers and the reversed ring-closing depolymerization of corresponding polymers with neutral thermodynamics are broadly explored to establish a circular economy of next-generation plastics.Polythioesters(PTEs),analogues of polyesters,are emerging materials for this purpose due to their high refractive index,high crystallinity,dynamic property and responsiveness.In this work,we synthesize and polymerize a series of D-penicillamine-derivedβ-thiolactones(NRPenTL)with varied side chain alkyl groups,and study the structure-property relationship of the resulting polymers.The obtained PTEs exhibit tunable glass transition temperature in a wide range of 130–50℃,and melting temperature of 90–105℃.In addition,copolymerizations of monomers with different side chains are effective in modulating material properties.The obtained homo and copolymers can be fully depolymerized to recycle monomers.This work provides a robust molecular platform and detailed structure-property relationship of PTEs with potential of achieving sustainable plastics.

Understanding Pseudocapacitance Mechanisms by Synchrotron X-ray Analytical Techniques

Pseudocapacitive materials that store charges via reversible surface or near-surface faradaic reactions are capable of overcoming the capacity limitations of electrical double-layer capacitors.Revealing the structure–activity relationship between the microstructural features of pseudocapacitive materials and their electrochemical performance on the atomic scale is the key to build high-performance capacitor-type devices containing ideal pseudocapacitance effect.Currently,the high brightness(flux),and spectral and coherent nature of synchrotron X-ray analytical techniques make it a powerful tool for probing the structure–property relationship of pseudocapacitive materials.Herein,we report a comprehensive and systematic review of four typical characterization techniques(synchrotron X-ray diffraction,pair distribution function[PDF]analysis,soft X-ray absorption spectroscopy,and hard X-ray absorption spectroscopy)for the study of pseudocapacitance mechanisms.In addition,we offered significant insights for understanding and identifying pseudocapacitance mechanisms(surface redox pseudocapacitance,intercalation pseudocapacitance,and the extrinsic pseudocapacitance phenomenon in battery materials)by combining in situ hard XAS and electrochemical analyses.Finally,a perspective for further depth of understanding into the pseudocapacitance mechanism using synchrotron X-ray analytical techniques is proposed.

Structure-property relationship on aggregation-induced emission properties of simple azine-based AIEgens and its application in metal ions detection

In recent twenty years,aggregation-induced emission(AIE),due to its excellent application prospect,has aroused widespread interests.The development of novel and easy to make AIE luminogens(AIEgens)is an attractive subject.For this purpose,it is very important to study the structure-property relationship of AIEgens.Because azine derivatives are easy to synthesis and some of them have nice AIE properties,herein,a series of azine derivatives(ADs)were employed as models to study the influence of different functional groups,electronic effects and structures on the AIE properties of azine derivatives.The AIE mechanism were studied by single crystal analysis,density functional theory(DFT)calculations and so on.The results indicated that the o-hydroxyl aryl substituted azine compounds could show good AIE properties.Meanwhile,the AIE properties of o-hydroxyl aryl substituted azine compounds were also influenced by the electronic effects of the aryl groups in the azine compounds.The o-hydroxyl groups could form intramolecular hydrogen bond with imine group,which play key role to restrict the intramolecular rotation of the aryl groups and act as base stone for the AIE process of this kind compounds.The HOMO-LUMO energy gaps of o-hydroxyl substituted azine are smaller than other homologous compounds,which is agree with the proposed AIE mechanism.Finally,thanks to the AIE properties,the o-hydroxy-substituted azines could be used as efficient Al^(3+)and Cu^(2+)fluorescent chemosensors in different conditions.In addition,test strips based on AD10 has been prepared,which can conveniently detect Cu^(2+)in industrial wastewater.This research supplied a way for the design of novel easy to make AIEgens through simple azine derivatives.

Low-viscosity oligoether esters(OEEs)as high-efficiency lubricating oils:Insight on their structure‒lubricity relationship

Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether esters(OEEs),as a low-viscosity ester base oil,have characteristics such as simple synthesis and excellent lubrication effect;however,the application of OEEs in tribology field has rarely been investigated.The objective of the present study is to investigate the effect of structure on the lubricating performance of OEEs and to develop a predictive model for OEEs based on quantitative structure‒property relationship(QSPR)through a combination of experiment and statistical modeling.Results showed that glycol chains contribute positively to lubrication with the ether functional groups increasing the sites of adsorption.Compared to branched-chain OEEs,straight-chain OEEs exhibited reduced wear,which was mainly due to the thicker adsorption film formed by the straight-chain structure.Furthermore,carbon films were detected on lightly worn surfaces,indicating that OEEs underwent oxidation during the friction process.Based on the results of principal component analysis(PCA)and partial least squares(PLS),it could be found that the predictive models of viscosity‒temperature performance,thermal stability performance,coefficient of friction(COF),and wear volume(WV)performed well and robustly.Among them,COF and WV can be best predicted with an R^(2) of about 0.90.

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants

In order to solve the problem of poor interpretability of support vector re- gression （SVR） applied in quantitative structure-property relationship （QSPR）, a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR.

A review of extractive distillation from an azeotropic phenomenon for dynamic control

Extractive distillation is an effective method for separating azeotropic or close boiling point mixtures by adding a third component.Various technologies for performing the extractive distillation process have been explored to protect the environment and save resources.This paper focuses on the improvement of these advanced technologies in recent years.Extractive distillation is retrieved and analyzed from the view of phase equilibrium,selection of solvent in extractive distillation,process design,energy conservation,and dynamic control.The quantitative structure–property relationship used in extractive distillation is discussed,and the future development of extractive distillation is proposed to determine how the solvent affects the relative volatility of the separated mixture.In the steady state design,the relationship between the curvature of the residue curve and parameters of the optimal steady state is also highlighted as another field worthy of further study to simplify the distillation process.

Quantitative Correlation of Chromatographic Retention and Acute Toxicity for Alkyl(1-phenylsulfonyl) Cycloalkane Carboxylates and Their Structural Parameters by DFT

Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.

Recent progress on discovery and properties prediction of energy materials:Simple machine learning meets complex quantum chemistry

In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.

In-silico Prediction of the Sweetness of Aspartame Analogues from QSPR Analysis

The extensive utilization of the low-energy dipeptide sweetener aspartame in foods leads to various studies on searching for new sweeteners in series. However, the real mechanistic cause of their sweetness power is still not completely known owing to their complex interactions with human sweet receptor, which may be different from that of other sweeteners to some extent. In this contribution, predictive quantitative structure-property relationship(QSPR) models have been developed for diverse aspartame analogues using Materials Studio 5.0 software. The optimal QSPR model(r2 = 0.913, r2 CV = 0.881 and r2 pred = 0.730) constructed by the genetic function approximation method has been validated by the tests of cross validation, randomization, external prediction and other statistical criteria, which shows that their sweetness power is mainly governed by their electrotopological-state indices(SssCH and SsNH), spatial descriptors(Shadow length: LX, ellipsoidal volume and Connolly surface occupied volume) and topological descriptors(Chi(3): cluster and Chi(0)(valence modified)), which partially supports both multipoint attachment theory proposed by Nofre and Tinti et al. and B-X theory proposed by Kier et al.. Present exploited results provide the key structural features for the sweetness power of aspartame analogues, supplement the mechanistic understanding of the sweet perception, and would be also helpful for the design of potent sweetener analogs prior to their synthesis.

Experimental and QSPR Studies on n-Octanol/water Partition Coefficient (lgK_(ow)) of Substituted Aniline

The n-octanol/water partition coefficients （lgKow） of 18 substituted anilines were determined at 25 ℃ by shake-flask method. The geometrical optimization of substituted anilines has been performed at B3LYP/6-311G^＊＊ level with Gaussian98 program, and the molecular surface areas of substituted anilines were calculated using ChemOffice 2004 program. The calculated structural parameters of substituted anilines were used as theoretical descriptors and the two-parameter （molecular surface area （MA） and the energy of the highest occupied molecular orbital （EaoMo）） quantitative structure-property relationship （QSPR） model of lgKow for substituted aniline with molecular structural parameters was developed by multi-linear regression method. The regression coefficient square （r^2） is 0.990 and the standard deviation SE 0.109. The model was validated by variance inflation factors （VIF） and t-test, and the results show that there exists small self-correlation between variables of the model with perfect stability. The model gives results in good qualitative agreement with experimental data. At last, the model was applied to predict lgKow values of five substituted anilines whose lgKow values have not been determined experimentally.

A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste disposals.During these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product properties.In this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so forth.The workflow of framework consists of three steps.In the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step.The whole framework has been carried out within a molecular property prediction toolbox.Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

Macroscopic and microscopic mechanical behaviors of climbing tendrils

Tendril-bearing climbing plants must recur to the tendril helices with chiral perversion or dual chirality for climbing and to obtain sun exposure. Despite researchers' prolonged fascination with climbing tendrils since Darwin's time and even earlier, why the soft and slender tendrils can bear heavy loads such as the self-weight of a plant or additional load caused by rain remains elusive. In this paper, we take towel gourd tendrils as an example and investigate the macroscopic and microscopic mechanical behaviors of tendrils through experiments and simulations. Our study indicates that the tendril filament exhibits rubber-like hyperelastic behaviors and can particularly endure large elongation, which is mainly attributed to the superelasticity of the cellulose fibril helix contained in the cell wall. Combination of the tendril helical structure with dual chirality or chiral perversion at a macroscale and a cellulose filament helix at a subcellular level creates superior elasticity for biological species relying on support and climbing. This study provides deep insight into the structure-property relationship of climbing tendrils, and the relationship is useful for the bioinspired design of composite systems with superior elasticity.