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Study on viscosity mechanism of caffeine crystallization solutions Author links open overlay panel

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Particuology 2024年第8期91卷 341-350页

作者： Linjing Yu min su School of Chemical Engineering Hebei University of TechnologyTianjin300130China

The solid-liquid viscous system formed by high viscosity crystallization solution impacts the flow and separation ***,it is very important to study the viscosity mechanism to improve viscosity and regulate crystallization,ensuring a seamless production ***,the viscosity of crystallization solution was taken as the measurement parameter of caffeine as a model *** investigated the viscosity mechanism of caffeine crystallization solutions by combining experiment and *** results indicated that the weak interactions between caffeine and water result in increased viscosity of the caffeine crystallization ***,caffeine crystals possess elongated needle-like shapes,featuring a substantial specific surface ***,there isπ-πstacking occurring between the(200)crystal face and(110)crystal face,effectively fostering coalescence of the crystals towards the radial side of the crystal along its elongated axis,resulting in a more viscous crystallization *** results contribute to comprehending the viscosity mechanism of crystal systems and provide theoretical foundation to enhance engineering efficiency in crystallization.

关键词： Crystallization Caffeine Viscosity mechanism Molecular simulation

DDI-Transform:A neural network for predicting drug-drug interaction events

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Quantitative Biology 2024年第2期12卷 155-163页

作者： Jiaming su ying Qian School of Computer Science and Technology Shanghai Frontiers Science Center of Molecule Intelligent SynthesesEast China Normal UniversityShanghaiChina

Drug-drug interaction(DDI)event prediction is a challenging problem,and accurate prediction of DDI events is critical to patient health and new drug ***,many machine learning-based techniques have been proposed for predicting DDI ***,most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature *** address these limitations,we propose a DDI-Transform neural network framework for DDI event *** DDI-Transform,we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information.A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning,thus adaptively selecting the effective feature information for *** results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art *** on different scale datasets confirm the robustness of the method.

关键词： adaptive learning graph convolutional networks interaction prediction meta-path

Small-sized Ni-Co/Mo_(2)C/Co_(6)Mo_(6)C_(2)@C for efficient alkaline and acidic hydrogen evolution reaction by an anchoring calcination strategy

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Frontiers of Chemical Science and Engineering 2024年第5期18卷 95-103页

作者： Jianxia Gu ying Zhu Haiyan Zheng Chunyi sun Zhongmin su Department of Chemistry Xinzhou Normal UniversityXinzhou 034000China School of Chemistry Northeast Normal UniversityChangchun 130024China

A novel,cheap and highly efficient Ni-Co/Mo_(2)C/Co_(6)Mo_(6)C_(2)@C nanocomposite has been successfully constructed through simple one-step carbonization method in a nitrogen *** in the precursor can effectively anchor molybdenum-based Keggin-type polyoxometallate and NiCo-layered double hydroxide through electrostatic and coordination interactions,which avoids the aggregation of catalyst particles during the pyrolysis *** optimization,the obtained Ni-Co/Mo_(2)C/Co_(6)Mo_(6)C_(2)@C possesses small size(3-8 nm),large specific surface area and hierarchical pore *** importantly,Ni-Co/Mo_(2)C/Co_(6)Mo_(6)C_(2)@C presents remarkable hydrogen evolution reaction activity with low overpotentials in 0.5 mol·L^(-1)H_(2)SO_(4)(102.3 mV)and 1 mol·L^(-1)KOH(95 mV)to afford the current density of 10 mA·cm^(-2),as well as small Tafel slopes of 82.49 and 99.92 mV·dec^(-1),***,this catalyst also shows outstanding stability for 12 h without a significant change in current *** excellent catalytic performance of Ni-Co/Mo_(2)C/Co_(6)Mo_(6)C_(2)@C can put down to the synergistic effect between multiple components and the small size of the *** work provides unique insights into the preparation of efficient transition metal-based catalysts for HER.

关键词： polyoxometallates layered double hydroxide transition metal-based electrocatalysts hydrogen evolution reaction

Synergistic precipitant powered self-assembly of papain for cross-linked enzyme crystals preparation

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Particuology 2024年第2期85卷 102-112页

作者： Qi Hao Pan Guo Honghai Wang min su Chunli Li Yuqi Hu Weiyi su School of Chemical Engineering and Technology Hebei University of TechnologyTianjin300130China National-Local Joint Engineering Laboratory for Energy Conservation of Chemical Process Integration and Resources Utilization Tianjin300130China

In this study,we prepared cross-linked enzyme crystals(CLECs)of papain to further broaden the application of the enzyme with high activity in extreme ***,papain crystals were successfully obtained based on the micro-batch,batch,and expanded batch crystallization ***,ammonium sulfate and polyethylene glycol 6000(PEG6000)were synergistically used as the precipitants,while L-cysteine was applied to enhance the activity of ***,the interaction between L-cysteine and papain was modeled by molecular docking *** was found that L-cysteine could form a hydrogen bond with aspartic acid residue(Asp)at site 158,and the electrostatic attraction with lysine residue(Lys)at site 156 was also quite *** the enzyme crystals were cross-linked by glutaraldehyde at optimized *** papain CLECs were identified by various methods,and it was found that the thermal stability and enzymatic activity both increased compared to the raw *** importantly,it could be applied at more rigorous conditions,for example,pH of 4.

关键词： Papain Cross-linked enzyme crystals L-cysteine Molecular docking Crystallization

Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

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Forest Ecosystems 2024年第1期11卷 87-99页

作者： Yuwen Peng Huiyi su min sun mingshi Li Co-Innovation Center for sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037China College of Forestry Nanjing Forestry UniversityNanjing 210037China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from ***,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite *** study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern ***,the forest fire danger index(FFDI)was improved by supplementing slope and aspect *** compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to *** this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger *** results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,*** validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this stu

关键词： Forest fire risk reconstruction MFFDI Time series models LSTM ARIMA Prophet Anusplin

Strain Gradient Finite Element Formulation of Flexoelectricity in Ferroelectric Material Based on Phase-Field Method

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Acta Mechanica Solida Sinica 2024年第4期37卷 570-579页

作者： Shuai Wang Hengchang su min Yi Li-Hua Shao National Key Laboratory of Strength and Structural Integrity Institute of Solid MechanicsSchool of Aeronautic Science and EngineeringBeihang UniversityBeijing100191People’s Republic of China State Key Laboratory of Mechanics and Control for Aerospace Structures and Institute for Frontier Science and Key Laboratory for Intelligent Nano Materials and Devices of ministry of Education and College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016People’s Republic of China

Flexoelectricity is a two-way coupling effect between the strain gradient and electric field that exists in all dielectrics,regardless of point group ***,the high-order derivatives of displacements involved in the strain gradient pose challenges in solving electromechanical coupling problems incorporating the flexoelectric *** this study,we formulate a phase-field model for ferroelectric materials considering the flexoelectric effect.A four-node quadrilateral element with 20 degrees of freedom is constructed without introducing high-order shape *** microstructure evolution of domains is described by an independent order parameter,namely the spontaneous polarization governed by the time-dependent Ginzburg–Landau *** model is developed based on a thermodynamic framework,in which a set of microforces is introduced to construct the constitutive relation and evolution *** the flexoelectric part of electric enthalpy,the strain gradient is determined by interpolating the mechanical strain at the node via the values of Gaussian integration points in the isoparametric *** model is shown to be capable of reproducing the classic analytical solution of dielectric materials incorporating the flexoelectric *** model is verified by duplicating some typical phenomena in flexoelectricity in cylindrical tubes and truncated pyramids.A comparison is made between the polarization distribution in dielectrics and *** model can reproduce the solution to the boundary value problem of the cylindrical flexoelectric tube,and demonstrate domain twisting at domain walls in ferroelectrics considering the flexoelectric effect.

关键词： Flexoelectricity Phase-field method Ferroelectrics Domain configuration

Optimization on crystallization process of the magnetic core lysozyme

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Particuology 2024年第4期87卷 106-113页

作者： Lixia Hou Chunxiao Qu min su Zhihan Liang Qi Hao School of Chemical Engineering Hebei University of TechnologyTianjin300130China

The continuous growth of the biomedical market highlights the significance of improvement in the ef-ficiency of batch production for protein drug ***,we investigated the effect of polymer-modified Fe_(3)O_(4) nanoparticles on lysozyme(Lys)crystallization at different conditions,using function-alized Fe_(3)O_(4) magnetic nanoparticles(Polymer C@Fe_(3)O_(4))as nucleation *** study revealed that the surface charge density of Polymer C@Fe_(3)O_(4) was highest at a modification mass ratio of Fe_(3)O_(4) to Polymer C is 8∶*** ratio facilitates the augmentation of electrostatic interaction and the promotion of *** adding 15%of the PolymerC@Fe_(3)O_(4) prepared from Fe_(3)O_(4) with an average particle size of 150 nm,the magnetic core lysozyme(M-Lys)crystals with an average particle size of 11.08μm,narrow size distribution,and regular morphology were obtained in a yield of 82.42%within 60 min.

关键词： Protein Crystallization Magnetic particle Lysozyme

surfactant-assisted crystallization of lysozyme and its mechanism

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Particuology 2024年第7期90卷 199-208页

作者： Yujie Song Rongfei Ren min su Zhihan Liang Qi Hao School of Chemical Engineering Hebei University of TechnologyTianjin 300130China

surfactants were used to assist in the crystallization of *** types of surfactants with different structures(sodium dodecyl sulfate,cetyltrimethylammonium bromide,or betaine hydrochloride[BH])were *** study indicated that three surfactants were all capable of shortening the crystallization induction time of lysozyme,with BH inducing the shortest crystallization induction time(t=1 min)and the largest crystal size(37μm).The molecular interactions among each of the three surfactants aforementioned and lysozyme were also investigated by *** was found that hydrogen bonding dominantly acted between the carboxyl structure of the surfactant BH and the lysozyme,which produced the most favorable conformation for the self-assembly of lysozyme(promote lysozyme orderly aggregation and shorten the crystallization induction time).On this basis,the mechanism of surfactant-promoted lysozyme crystallization was proposed.

关键词： Protein Crystallization surfactant Lysozyme Molecular interaction Mechanism

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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Computers, Materials & Continua 2024年第3期78卷 3529-3549页

作者： ying su Morgan C.Wang Shuai Liu Department of Statistics and Data Science University of Central FloridaOrlandoFL32816-2370USA School of Mathematics and Statistics Beijing Technology and Business UniversityBeijing100048China

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual *** manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series *** complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple *** surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series ***,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series ***,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time *** is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing *** multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long ***,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural *** AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

关键词： Automated machine learning autoregressive integrated moving average neural networks time series analysis