二极管器件SPICE电路模型对锥形离子通道I-V特性曲线的模拟

离子整流性是纳米离子通道的一个重要特征,具有整流性的离子通道体系也被称为纳米流体二极管.本文比较了离子通道的泊松-能斯特-普朗克(PNP)方程组模型和固体半导体的扩散-漂移模型,提出可以使用二极管器件的仿真电路模拟器(SPICE)电路模型对离子通道体系的电流-电压(I-V)曲线进行模拟.以锥形离子通道的PNP数值模型的计算结果为基础,通过对这一体系进行讨论,给出一个锥形离子通道的SPICE电路模型,它可以较好地模拟I-V特性曲线.离子通道SPICE电路模型的建立可用于研究纳米流体二极管作为一个器件在电路中的应用.     

聚合物共混物脆韧转变性能研究：Ⅳ.橡胶粒子的分布对PVC／NBR共混物…

根据作者已建立的准网络形态模型和推导出的基体层厚度公式，从实验上研究了橡胶粒子的分布对聚氯乙烯／丁氰橡胶共混物脆韧转变的影响。结果表明，不仅无规形态ＰＶＣ／ＮＢＲ共混物存在脆韧转变主曲线，而且准网络形态ＰＶＣ／ＮＢＲ共混物也存在脆韧转变主曲线，但是条主曲线明显不重合，表明橡胶粒子分布对ＰＶＣ／ＮＢＲ共混物脆韧转变有显著影响，而且准网络形态ＰＶＣ／ＮＢＲ共混物的临界基体层厚度比无规形态ＰＶＣ／ＮＢＲ     

乙醇在环氧树脂／氨酯丙烯酸酯树脂互穿网络中的扩散研究 …

测定了乙醇在环氧树脂／氨酯丙烯酸酯树脂同步互穿网络内的吸收曲线，它们能被所建立的传递模型－络合模型很好地产联。实验结果表明，络合模型所给出的模拟曲线能普遍地关联实验测定的吸收曲线，络合因子ωτ的物理意义在于乙醇和ＳＩＮ中的亲醇基团发生氢键化。     

聚合物共混物脆韧转变性能研究 Ⅳ橡胶粒子的分布对PVC/NBR共混物脆韧转变的影响

根据作者已建立的准网络形态模型和推导出的基体层厚度计算公式，从实验上研究了橡胶粒子的分布对聚氯乙烯（ＰＶＣ）／丁氰橡胶（ＮＢＲ）共混物脆韧转变的影响．结果表明，不仅无规形态ＰＶＣ／ＮＢＲ共混物存在脆韧转变主曲线，而且准网络形态ＰＶＣ／ＮＢＲ共混物也存在脆韧转变主曲线．但是两条主曲线明显不重合，表明橡胶粒子的分布对ＰＶＣ／ＮＢＲ共混物脆韧转变有显著影响．而且准网络形态ＰＶＣ／ＮＢＲ共混物的临界基体层厚度比无规形态ＰＶＣ／ＮＢＲ共混物的临界基体层厚度大得多，表明准网络形态比无规形态明显有利于增韧．因此临界基体层厚度不仅是基体的特征参数，还是界面粘结和橡胶粒子分布的函数．     

乙醇在环氧树脂/氨酯丙烯酸酯树脂互穿网络中的扩散研究(Ⅰ)——吸收动力学曲线的测定及均相传递模型

测定了乙醇在环氧树脂(epoxy)/氨酯丙烯酸酯树脂(UAR)同步互穿网络(SIN)内的吸收曲线,它们能被所建立的传递模型--络合模型很好地关联.实验结果表明,络合模型所给出的模拟曲线能普遍地关联实验测定的吸收曲线,络合因子ω_r的物理意义在于乙醇和SlN中的亲醇基团发生氢键化.ω_r和实测的溶解度系数S、平均扩散系数D及渗透量P均与epoxy/UARSIN的组成有关,乙醇的传递主要在UAR相内进行.     

分布化能模型的理论及其在半焦气化和模拟蒸馏体系中的应用

通过对分布活化能模型所做的理论分析,给出了失重过程中活化能的解析表达式,阐明了失重实验中升温速率影响失重曲线的基本原理。按照本文给出的活化能求解方法,对大同煤半焦的空气气化动力学和煤焦油馏分的模拟蒸馏过程进行了分析,得到了半焦气化过程和模拟蒸馏过程的活化能变化曲线,与常规动力学分析结果的对比表明,新的DAEM方法能够很好地应用于简单反应体系。     

氙在活性炭纤维吸附床上穿透的动力学模型

研究了氙在活性炭纤维吸附床上的穿透特性,采用Boltzmann分布函数理论模拟了穿透曲线,根据Boltzmann函数曲线推导了穿透时间方程,给出了半穿透时间的理论表达式,较好地解释了实验数据。     

含时间变量的凝胶溶胶分配方程及其应用

在Ａａ－Ｂｂ型综聚反应的凝胶溶胶分配（ＧＳＤ）方程中引入相应的聚合反应动力学方程．模拟计算的溶胶分数（Ｓ）对反应程度（Ｐ_Ｂ，Ｐ_Ｂ＇）的关系曲线与实验结果基本吻合．利用含时间的ＧＳＤ方程，解决了非线型缩聚反应交联网络的模拟计算，给出了交联度随时间的变化曲线，也为凝胶点的计算提供了一种方法．     

氟化钙的分子动力学模拟——晶态、超离子态、熔融态和急冷非晶态

采用键序参数方法研究了Ca~(2+)亚晶格和F~-亚晶格,表明两者均可用键取向正态分布模型描述。超离子相Ca~(2+)亚晶格稳定地维持其原fcc结构,而F~-亚晶格表现为原sc结构的随机畸变。熔融相的模拟给出了实验上难以分离的三种径向分布函数。急冷非晶相的模拟表明体系是以Ca~(2+)离子为中心的等效球的无规密堆,由于Ca(2+)与其邻近的F~-离子没有形成某种确定的构形,体系不够稳定。     

柱阵列中高分子的爬行过程

利用Monte Carlo算法模拟了嵌入柱阵列中高分子的爬行过程.模拟结果显示窄间距的柱阵列可以有效地抑制嵌于其中的高分子链的长度涨落,并使得高分子链的弛豫时间符合爬行理论给出的标度律.通过对比爬行理论与模拟得到的链端关联函数可知,柱阵列中的爬行过程与高分子熔体中的爬行过程存在着微妙的差别.在柱阵列中高分子的链端无法完全消灭原有的管道,从而导致了链端关联函数被爬行理论低估.本文给出的二维自回避格点模型则可以很好地描述柱阵列中高分子的爬行过程.     

网络法数字模拟电活性物种吸附体系的循环伏安曲线

使用指数扩展的网络方法对电活性物质吸附与扩散共存的电化学体系进行了模拟,包括吸附为平衡和非平衡态的情形,所得结果跟文献报道一致并有所补充。该方法具有简便、直观、灵活性强等优点。     

指数扩展格网络方法：模拟球、柱和旋转圆盘电极体系电化学问题的有效方法

An exponentially expanded space grid technique has been employed in the network simulation of chronoamperometric and voltammetric problems in spherical, cylindrical and rotating-disk electrode systems, leading to an effective simulation strategy for electrochemical problems: exponentially expanded grid network approach (EEGNA). The success of this method is largely due to the improved ability in processing the boundary singularities existing for non-planar diffusions and the enhanced simulation speed and accuracy in contrast to the uniform or quasi-uniform grid network approach.     

Exponentially Expanded Grid Network Approach （EEGNA） ： An Efficient Way for the Simulation of Stiff Electrochemical Problems

The exponentially expanded space grid was incorporated into the network approach to overcome the problem of low simulation efficiency during the simulations of electrochemical problems with stiff kinetics or wide dlsperslon of diffusion coefficients, resulting in an effective electrochemical simulation method: exponentially expanded grid network approach (EEG-NA). The stability and accuracy of the EEGNA for the simulation of various electrode processes .coupled with different types of homogeneous reactions were investigated.     

Study of the permeability characteristics of porous media with methane hydrate by pore network model

The permeability in the methane hydrate reservoir is one of the key parameters in estimating the gas production performance and the flow behavior of gas and water during dissociation. In this paper, a three-dimensional cubic pore-network model based on invasion percolation is developed to study the effect of hydrate particle formation and growth habit on the permeability. The variation of permeability in porous media with different hydrate saturation is studied by solving the network problem. The simulation results are well consistent with the experimental data. The proposed model predicts that the permeability will reduce exponentially with the increase of hydrate saturation, which is crucial in developing a deeper understanding of the mechanism of hydrate formation and dissociation in porous media.     

Overcoming entropic barrier with coupled sampling at dual resolutions

An enhanced sampling method is proposed for ab initio protein folding simulations. The new method couples a high-resolution model for accuracy and a low-resolution model for efficiency. It aims to overcome the entropic barrier found in the exponentially large protein conformational space when a high-resolution model, such as an all-atom molecular mechanics force field, is used. The proposed method is designed to satisfy the detailed balance condition so that the Boltzmann distribution can be generated in all sampling trajectories in both high and low resolutions. The method was tested on model analytical energy functions and ab initio folding simulations of a beta-hairpin peptide. It was found to be more efficient than replica-exchange method that is used as its building block. Analysis with the analytical energy functions shows that the number of energy calculations required to find global minima and to converge mean potential energies is much fewer with the new method. Ergodic measure shows that the new method explores the conformational space more rapidly. We also studied imperfect low-resolution energy models and found that the introduction of errors in low-resolution models does decrease its sampling efficiency. However, a reasonable increase in efficiency is still observed when the global minima of the low-resolution models are in the vicinity of the global minimum basin of the high-resolution model. Finally, our ab initio folding simulation of the tested peptide shows that the new method is able to fold the peptide in a very short simulation time. The structural distribution generated by the new method at the equilibrium portion of the trajectory resembles that in the equilibrium simulation starting from the crystal structure.     

三元重叠色谱峰面积的定量方法

本从色谱峰的ＥＭＧ模型出发，通过对重叠色谱峰的模拟和回归分析，提出了一种三元重叠色谱峰的面积的定量方法，三元重叠色谱峰的每一个峰面积可以由峰面积比和总面积求得，此法所需的数据都由实验色谱图上测得，峰面积计算结果的相对误差小于±５％，适用于相对峰谷为５０％－９５％的三元重叠色谱峰面积的定量。     

Artificial neural network modification of simulation-based fitting: application to a protein-lipid system

Simulation-based fitting has been applied to data analysis and parameter determination of complex experimental systems in many areas of chemistry and biophysics. However, this method is limited because of the time costs of the calculations. In this paper it is proposed to approximate and substitute a simulation model by an artificial neural network during the fitting procedure. Such a substitution significantly speeds up the parameter determination. This approach is tested on a model of fluorescence resonance energy transfer (FRET) within a system of site-directed fluorescence labeled M13 major coat protein mutants incorporated into a lipid bilayer. It is demonstrated that in our case the application of a trained artificial neural network for the substitution of the simulation model results in a significant gain in computing time by a factor of 5 x 10(4). Moreover, an artificial neural network produces a smooth approximation of the noisy results of a stochastic simulation.     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

Simulation model for network formation in free-radical crosslinking copolymerization: Pregelation period

A new simulation model for network formation in free-radical copolymerization of vinyl and divinyl monomers is proposed. This model is based on the crosslinking density distribution of the primary polymer molecules that results from a kinetically controlled network formation. The crosslinking density distribution provides information on how each chain is connected to other chains and therefore, a detailed analysis of the kinetics of network formation becomes possible by application of Monte Carlo simulations. In this method, not only averages but also various distributions, such as molecular weight distribution and distribution of crosslinked units as well as of unreacted pendant double bonds among various polymer molecules, can be calculated. The present theory is a direct solution for the Bethe lattice formed under nonequilibrium conditions, and therefore, it can be used to examine the applicability of the earlier theories of network formation to kinetically controlled systems. The present method is quite general and can be applied to various complex reactions systems that involve crosslinking, branching, cryclization and degradation in a nonequilibrium system.     

Preprogramming Complex Hydrogel Responses using Enzymatic Reaction Networks

The creation of adaptive matter is heavily inspired by biological systems. However, it remains challenging to design complex material responses that are governed by reaction networks, which lie at the heart of cellular complexity. The main reason for this slow progress is the lack of a general strategy to integrate reaction networks with materials. Herein we use a systematic approach to preprogram the response of a hydrogel to a trigger, in this case the enzyme trypsin, which activates a reaction network embedded within the hydrogel. A full characterization of all the kinetic rate constants in the system enabled the construction of a computational model, which predicted different hydrogel responses depending on the input concentration of the trigger. The results of the simulation are in good agreement with experimental findings. Our methodology can be used to design new, adaptive materials of which the properties are governed by reaction networks of arbitrary complexity.