WO2022109935A1 - 基于从头算模型预测表面活性剂临界胶束浓度的方法 - Google Patents
基于从头算模型预测表面活性剂临界胶束浓度的方法 Download PDFInfo
- Publication number
- WO2022109935A1 WO2022109935A1 PCT/CN2020/131822 CN2020131822W WO2022109935A1 WO 2022109935 A1 WO2022109935 A1 WO 2022109935A1 CN 2020131822 W CN2020131822 W CN 2020131822W WO 2022109935 A1 WO2022109935 A1 WO 2022109935A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- concentration
- model
- surfactant
- calculated
- chemical potential
- Prior art date
Links
- 239000000693 micelle Substances 0.000 title claims abstract description 41
- 239000004094 surface-active agent Substances 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005263 ab initio calculation Methods 0.000 title abstract description 3
- 239000000126 substance Substances 0.000 claims abstract description 27
- 238000000329 molecular dynamics simulation Methods 0.000 claims abstract description 18
- 239000000178 monomer Substances 0.000 claims abstract description 18
- 239000002245 particle Substances 0.000 claims description 10
- 238000004088 simulation Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 4
- 238000005859 coupling reaction Methods 0.000 claims description 4
- 230000002776 aggregation Effects 0.000 claims description 3
- 238000004220 aggregation Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 239000012298 atmosphere Substances 0.000 claims description 2
- 238000012937 correction Methods 0.000 claims description 2
- 230000003993 interaction Effects 0.000 claims description 2
- 238000009826 distribution Methods 0.000 description 7
- 239000000539 dimer Substances 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 2
- 239000002563 ionic surfactant Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 229920002134 Carboxymethyl cellulose Polymers 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000005411 Van der Waals force Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000000149 argon plasma sintering Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000010948 carboxy methyl cellulose Nutrition 0.000 description 1
- 230000003197 catalytic effect Effects 0.000 description 1
- 229920006184 cellulose methylcellulose Polymers 0.000 description 1
- 239000011153 ceramic matrix composite Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012710 chemistry, manufacturing and control Methods 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000002209 hydrophobic effect Effects 0.000 description 1
- 230000009878 intermolecular interaction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 239000002736 nonionic surfactant Substances 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000013638 trimer Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
Definitions
- the invention belongs to the technical field of surfactants, and in particular relates to a method for predicting the critical micelle concentration of surfactants based on an ab initio calculation model.
- Surfactants are a class of functional molecules consisting of hydrophobic and hydrophilic groups that are widely used in consumer products, material synthesis, catalytic processes, and the chemical industry.
- the critical micelle concentration (CMC) is one of the most important properties of surfactants, which determines the concentration distribution of surfactants in solution and on the surface, which greatly affects the performance of surfactants. Predicting the critical micelle concentration of surfactant molecules by first-principles calculations allows for more cost-effective screening of molecules. In addition, computational simulations can elucidate microscopic details of polymerization-related structural and energy changes that are difficult to describe experimentally, and this information has high application value for designing new molecules or improving known surfactant molecules.
- Molecular dynamics (MD) simulations have been used for CMC prediction of some specific surfactant molecules. However, it is often difficult to predict directly for most surfactant molecules due to the limitations of the time and space scales of the simulated system.
- the CMC of the surfactant molecule is between 10 -3 and 10 -6 mol/L. Assuming that a surfactant solution with a concentration around CMC is to be simulated, in order to place 1000 surfactant molecules in the simulation box to form a sufficient number of aggregates, the number of water molecules required is about 5.5 x 10 8 , and the simulation The size of the system must be at least 255 ⁇ 255 ⁇ 255 nm 3 .
- SCMF single-chain mean field
- the purpose of the present invention is to provide a method for predicting the critical micelle concentration of surfactants based on an ab initio model, so as to solve the problems raised in the above background art.
- the present invention provides the following technical solutions:
- a method for predicting the critical micelle concentration of surfactants based on an ab initio model includes the following steps:
- k B is the Boltzmann constant
- ⁇ is the de Broglie wavelength of the particle
- V and N are the volume and number of particles of the system, respectively
- Z N+1 and Z N are (N+1) particles and N, respectively the configuration integral of a particle system
- V (n) The physical quantities calculated by molecular dynamics simulations are V (n) and the excess chemical potential in the exponential term, respectively; where V (n) , the occupied volume of n-mers, is calculated based on the assumption that the micelles are spherically distributed as a whole , and the radius of gyration of a single micelle obtained from the trajectory file of molecular dynamics simulation is obtained by simple geometric operations;
- the excess chemical potential on the exponential term is obtained by using the thermodynamic integration method by calculating the free energy change of eliminating one surfactant molecule in the monomolecular solution system and the monomicellar solution system respectively;
- the chemical potential of a single molecule of surfactant is calculated using a very dilute solution model (that is, a surfactant molecule or a single micelle is dissolved in a cubic box system filled with water molecules);
- thermodynamic integration method simulates different states of the system by modifying the Hamiltonian:
- H( ⁇ ) H 0 + ⁇ (H 1 ⁇ H 0 )
- H 0 and H 1 represent the Hamiltonian of the initial and final states, respectively, and ⁇ is the coupling constant between 0 and 1; the free energy change between the initial and final states is obtained by integrating the coupling constant owned:
- the present invention proposes a new step-by-step growth model based on the definition of statistical mechanics, dividing the chemical potential into ideal partial contribution and excess partial contribution.
- models 1 to 4 do not need to be calibrated by any empirical parameters. This means that all forecasts are based entirely on calculated data.
- the methods provided by the present invention can be applied to various types of surfactants.
- ionic surfactants or mixed surfactants how to accurately calculate the chemical potential of a single surfactant molecule is a relatively challenging problem.
- shape of the colloid may deviate more from spherical, which would introduce more error to some approximation assumptions in the overall prediction model.
- Fig. 1 is the variation relation of the excess chemical potential calculated in the embodiment with the micelle size
- Fig. 2a is the micelle size distribution in the C 12 EO 12 solution system under different surfactant concentrations in the embodiment
- Figure 2b shows the size distribution of micelles in the C 10 EO 6 solution system under different surfactant concentrations in the examples.
- the following takes the nonionic surfactant polyethoxy molecules C 12 EO 12 and C 10 EO 6 as examples to illustrate the CMC prediction process.
- Packmol software to build the corresponding single-molecule and single-micellar solution models for the target surfactant molecules.
- For each model we use the SDK coarse-grained force field to perform MD simulations respectively.
- the free energy change, ie, excess chemical potential, of eliminating a single surfactant molecule in each system is calculated by thermodynamic integration method.
- Figure 2 shows the calculated changes in the excess chemical potential of a single surfactant molecule in micelles of different sizes.
- the radius of gyration of micelles of different sizes can be calculated according to the trajectory file of MD simulation, and then the occupied volumes of micelles of different sizes can be estimated (see Table 1).
- the size data of micelles listed in the table can be estimated by interpolation. According to the calculated chemical potential changes and the volume occupied by the micelles, combined with the recursive equation derived earlier, given any monomer concentration, the concentration of micelles of different sizes in the solution can be calculated.
- trimer The concentration of trimer is:
- Table 1 The occupied volumes of micelles of different sizes estimated from the analysis of the simulated trajectories.
- Figures 2a and 2b show the variation of the size distribution of micelles in solution with the monomer concentration. According to the critical state assumption (the multimer concentration is greater than the monomer concentration is the critical point), the size of the CMC can be predicted.
- Table 2 shows the predicted CMC and maximum micelle size distributions and comparisons with experimental values and those predicted by other methods. It can be seen that the CMC value predicted by this method is in good agreement with the experimental value, and the deviation is much smaller than the error of the experiment itself. At the same time, the predicted maximum micelle size distribution is also more accurate than the prediction results of other theoretical methods. .
Landscapes
- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种基于从头算模型预测表面活性剂临界胶束浓度的方法，包括以下步骤：根据化学位的统计热力学得到一个递归模型（I）；通过分子动力学模拟计算得到占据体积以及不同尺寸胶束的化学位后，采用所述述递归模型将(n+1)聚体的浓度表达成了单体和n聚体浓度的解析函数。于是若以单体浓度ρ 1为起点，利用该递归方程可以依次计算出不同尺寸的n聚体的浓度，n＝2,3...，从而推导得出CMC大小。该方法可以应用于多种类型的表面活性剂。
Description
本发明属于表面活性剂技术领域，具体涉及一种基于从头算模型预测表面活性剂临界胶束浓度的方法。
表面活性剂是由疏水基团和亲水基团组成的一类广泛应用于消费品、材料合成、催化过程和化学工业的功能性分子。临界胶束浓度(CMC)是表面活性剂最重要的性质之一，它决定了表面活性剂在溶液和表面的浓度分配，从而在很大程度上影响表面活性剂的性能。通过第一性原理计算预测表面活性剂分子临界胶束浓度可以用更小的成本更高效的筛选分子。此外，计算模拟还可以阐明实验难以描述的与聚合相关的结构和能量变化的微观细节，这些信息对设计新分子或改进已知表面活性剂分子具有很高的应用价值。
实验上一般通过电导率、表面张力、光散射光谱等方法来检测表面活性剂溶液相关性质随表面活性剂分子浓度的变化情况。计算模拟方面同样可以通过分子动力学或者蒙特卡洛模拟表面活性剂溶液体系随溶液浓度的变化。通过在临界胶束浓度附近这些测试性质的突变情况可以确定临界胶束浓度的大小。另外从理论角度出发，也可以通过单链平均场(SCMF)法来预测CMC。
分子动力学(MD)模拟已用于某些特殊表面活性剂分子的CMC预测。但是，由于模拟体系时间和空间尺度的限制，对于大部分表面活性剂分子通常难以直接预测。一般表面活性剂分子的CMC在10
-3至10
-6mol/L之间。假如要模拟浓度处在CMC左右的表面活性剂溶液，为了在模拟盒中放置1000个表面活性剂分子，以便形成足够数量的聚合体，所需的水分子数量约为5.5×10
8，并且模拟体系的尺寸至少必须达到255×255×255nm
3。此外，胶束形成由缓慢的扩散和聚合动力学控制，至少需要毫秒级别的模拟才能得到足够的统计样本。然而在目前的计算机硬件水平下实现这一空间和时间尺度的分子动力学模拟的代价是相当昂贵的。为了解决模拟尺度的限制问题，不同的研究团队基于蒙特卡罗方法、粗粒化力场、耗散粒子动力学、隐式溶剂模型和增强采样法开发了多种不同技术策略。然而，这个问题并未得到完全解决，目前大多数预测工作仍仅局限于具有相对较高CMC的离子表面活性剂。
理论方法固然可以克服模拟的空间和时间尺度问题。譬如考虑了分子构象和分子间相互作用的单链平均场(SCMF)理论也被逐渐发展为预测CMC的一种方法，不过这些基础模型由于其模型本身的近似和简化，用于实际表面活性剂体系预测时容易带来较大的偏差。
发明内容
本发明的目的在于提供一种基于从头算模型预测表面活性剂临界胶束浓度的方法，以解决上述背景技术中提出的问题。
为实现上述目的，本发明提供如下技术方案：
基于从头算模型预测表面活性剂临界胶束浓度的方法，包括以下步骤：
首先将化学位的统计热力学表达式分解为理想和超额两部分贡献：
其中k
B是玻尔兹曼常数，Λ是粒子的德布罗意波长，V和N分别是体系的体积和粒子数，Z
N+1和Z
N分别是(N+1)个粒子和N个粒子体系的构型积分；
结合分步增长模型，即假设一个(n+1)聚体是由一个n聚体与一个单体相结合产生，在平衡状态下，有如下关系：
μ
n+1-μ
n-μ
1＝0
得到一个递归模型：
通过分子动力学模拟计算的物理量分别是V
(n)以及指数项中的超额化学势；其中V
(n)，即n聚体的占据体积的计算方法是根据胶束整体上呈球形分布的假设，结合从分子动力学模拟的轨迹文件中统计得到单个胶束的回旋半径尺寸通过简单的几何运算得出；
递归模型中，指数项上的超额化学势，采用热力学积分方法，通过计算在单分子溶液体系和单胶束溶液体系中分别消去一个表面活性剂分子的自由能变化得到；
表面活性剂单分子的化学位采用极稀溶液模型(即一个表面活性剂分子或者单个胶束溶解于一个充满水分子的立方盒子体系)计算得到；
单体在溶液中的化学位只需计算一次，而针对表面活性剂分子在不同尺寸胶束中的化学位，分别选择了聚集度n＝1,5,10,20,30,40,50,60,80,100的胶束作为模拟和计算对象，然后通过样条插值得到所有尺寸范围内的化学位大小。
进一步的，所述的热力学积分方法，通过修改哈密顿量模拟体系的不同状态：
H(λ)＝H
0+λ(H
1-H
0)
这里，H
0和H
1分别代表始态终态的哈密顿量，而λ是介于0到1之间的耦合常数；始态与终态之间的自由能变是通过对耦合常数的积分得到的：
由于在λ＝0时，被消分子与环境相互作用趋近于0，采用了软核模型(soft core)以减少采样误差；针对每个体系，选择19个λ点，即0,0.1,0.2,0.3,0.4,0.5,0.55,0.6,0.62,0.65,0.68,0.7,0.72,0.75,0.8,0.85,0.9,0.95,1.0；为了提高计算精度，在自由能变较大的区间，λ＝0.6-0.75，加入了更多的λ点；
针对每个窗口，使用巨正则系综(NPT)，在298K和一个大气压的条件下进行分子动力学模拟；温度和压强通过Nosé-Hoover方法来控制，其中时间常数分别使用100fs和1000fs；范德华力的截断距离为1.5nm，不包含长程校正；首先进行10ns的MD模拟使体系达到平衡，在随后的10ns内进行数据采集；在λ点的密集区域，数据采集时间加倍，以减少误差；每个λ点的标准误差σ
sim(λ)是通过块平均的方法计算得的，其中每个点使用四个数据块；所有的分子动力学模拟和热力学积分计算均使用开源软件LAMMPS完成。
通过分子动力学模拟计算得到占据体积以及不同尺寸胶束的化学位后，采用所述递归模型将(n+1)聚体的浓度表达成了单体和n聚体浓度的解析函数。于是若以单体浓度ρ
1为起点，利用该递归方程可以依次计算出不同尺寸的n聚体的浓度，n＝2,3...，从而推导得出CMC大小。
与现有技术相比，本发明的有益效果是：
为了建立一个从头算模型，从第一性原理出发预测CMC和尺寸分布，本发明提出了一种新的基于统计力学定义的分步增长模型，将化学势分为理想部分贡献和超额部分贡献。
对比以前基于热力学定义的模型，1～4的模型不需要通过任何经验参数来校准。这意味着所有预测都完全基于计算数据。
本发明提供的方法可以应用于多种类型的表面活性剂。针对离子表面活性剂或者混合表面活性剂，如何准确的计算单个表面活性剂分子的化学位是个相对有挑战性的问题。针对更大分子量的表面活性剂，胶体的形状可能偏离球型较多，这会对整个预测模型中的一些近似假设带来更多的误差。这些都是该方法目前可能存在的欠缺和将来可能进一步提高的方面。
图1为实施例中计算得到的超额化学位随胶束尺寸的变化关系；
图2a为实施例中不同表面活性剂浓度下C
12EO
12溶液体系中的胶束尺寸分布；
图2b为实施例中不同表面活性剂浓度下C
10EO
6溶液体系中的胶束尺寸分布。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
以下以非离子表面活性剂聚乙氧基分子C
12EO
12和C
10EO
6为例，阐述CMC预测流程。首先，我们针对目标表面活性剂分子采用Packmol软件搭建相应的单分子和单胶束溶液模型。针对每一个模型，我们采用SDK粗粒化力场分别进行MD模拟，模拟的细节条件参考上述发明内容里的具体说明。然后通过热力学积分方法计算各个体系中消去单个表面活性剂分子的自由能变化，即超额化学位。图2展示了计算得到的不同尺寸的胶束中单个表面活性剂分子超额化学位的变化情况。同时可以根据MD模拟的轨迹文件计算不同尺寸胶束的回旋半径，进而估算得到不同尺寸胶束的占据体积(见表1)，表中为列出的胶束尺寸数据可以根据插值估算得到。根据计算得到的化学位变化情况以及胶束占据体积，结合之前推导得出的递归方程，在给定任意单体浓度的前提下，就可以计算得到溶液中不同尺寸的胶束浓度。以C
10EO
6为例，假设单体浓度为10
-5mol/L，转化得到单体粒子数密度ρ
1约为6.02×10
21/m
3，单体和二聚体的占据体积V
(1)和V
(2)分别约为0.9nm
3，1.8nm
3，单体超额化学位差
约为0，二聚体胶束与单体超额化学位差
约为2.1kJ/mol，代入上述递归模型，可以得到，二聚体的浓度为：
三聚体的浓度为：
同理可以依次得到其他不同尺寸的胶束浓度。
表1分析模拟轨迹估算得到的不同尺寸的胶束占据体积.
图2a和图2b展示了溶液中胶束尺寸分布随单体浓度变化的情况，根据临界状态假设(多聚体浓度大于单体浓度即为临界点)，就可以预测得出CMC的大小。
表2预测得到的CMC值与实验以及其他理论方法预测的结果对比
表2展示了预测得到的CMC和最大胶束尺寸分布以及与实验值和其他方法预测得到的结果对比。可以看到，通过这个方法预测得到的CMC值与实验值的吻合程度相当高，偏差也远小于实验本身的误差，同时预测得到的最大胶束尺寸分布也比其他理论方法的预 测结果更为准确。
尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。
Claims (3)
- 基于从头算模型预测表面活性剂临界胶束浓度的方法，其特征在于，包括以下步骤：首先将化学位的统计热力学表达式分解为理想和超额两部分贡献：
其中k B是玻尔兹曼常数，Λ是粒子的德布罗意波长，V和N分别是体系的体积和粒子数，Z N+1和Z N分别是(N+1)个粒子和N个粒子体系的构型积分；结合分步增长模型，即假设一个(n+1)聚体是由一个n聚体与一个单体相结合产生，在平衡状态下，有如下关系：μ n+1-μ n-μ 1＝0得到一个递归模型：
通过分子动力学模拟计算的物理量分别是V (n)以及指数项中的超额化学势；其中V (n)，即n聚体的占据体积的计算方法是根据胶束整体上呈球形分布的假设，结合从分子动力学模拟的轨迹文件中统计得到单个胶束的回旋半径尺寸通过简单的几何运算得出；通过分子动力学模拟计算得到占据体积以及不同尺寸胶束的化学位后，采用所述述递归模型将(n+1)聚体的浓度表达成了单体和n聚体浓度的解析函数。于是若以单体浓度ρ 1为起点，利用该递归方程可以依次计算出不同尺寸的n聚体的浓度，n＝2,3...，从而推导得出CMC大小。 - 根据权利要求1所述的基于从头算模型预测表面活性剂临界胶束浓度的方法，其特征在于，所述的递归模型中，指数项中超额化学势，采用热力学积分方法，通过计算在单分子溶液体系和单胶束溶液体系中分别消去一个表面活性剂分子的自由能变化得到；表面活性剂单分子的化学位采用极稀溶液模型计算得到；单体在溶液中的化学位只需计算一次，而针对表面活性剂分子在不同尺寸胶束中的化学位，分别选择了聚集度n＝1,5,10,20,30,40,50,60,80,100的胶束作为模拟和计算对象，然后通过样条插值得到所有尺寸范围内的化学位大小。
- 根据权利要求2所述的基于从头算模型预测表面活性剂临界胶束浓度的方法，其特征在于，所述的热力学积分方法，通过修改哈密顿量模拟体系的不同状态：H(λ)＝H 0+λ(H 1-H 0)H 0和H 1分别代表始态终态的哈密顿量，而λ是介于0到1之间的耦合常数；始态与终态之间的自由能变是通过对耦合常数的积分得到的：
由于在λ＝0时，被消分子与环境相互作用趋近于0，采用了软核模型以减少采样误差；针对每个体系，选择19个λ点，即0,0.1,0.2,0.3,0.4,0.5,0.55,0.6,0.62,0.65,0.68,0.7,0.72,0.75,0.8,0.85,0.9,0.95,1.0；为了提高计算精度，在自由能变较大的区间，λ＝0.6-0.75，加入了更多的λ点；针对每个窗口，使用巨正则系综，在298K和一个大气压的条件下进行分子动力学模拟；温度和压强通过Nosé-Hoover方法来控制，其中时间常数分别使用100fs和1000fs；范德华力的截断距离为1.5nm，不包含长程校正；首先进行10ns的MD模拟使体系达到平衡，在随后的10ns内进行数据采集；在λ点的密集区域，数据采集时间加倍，以减少误差；每个λ点的标准误差σ sim(λ)是通过块平均的方法计算得的，其中每个点使用四个数据块。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2020/131822 WO2022109935A1 (zh) | 2020-11-26 | 2020-11-26 | 基于从头算模型预测表面活性剂临界胶束浓度的方法 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2020/131822 WO2022109935A1 (zh) | 2020-11-26 | 2020-11-26 | 基于从头算模型预测表面活性剂临界胶束浓度的方法 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2022109935A1 true WO2022109935A1 (zh) | 2022-06-02 |
Family
ID=81755100
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2020/131822 WO2022109935A1 (zh) | 2020-11-26 | 2020-11-26 | 基于从头算模型预测表面活性剂临界胶束浓度的方法 |
Country Status (1)
| Country | Link |
|---|---|
| WO (1) | WO2022109935A1 (zh) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024027410A1 (zh) * | 2022-08-03 | 2024-02-08 | 电子科技大学长三角研究院(湖州) | 一种模拟材料结构非晶化的新方法 |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050037509A1 (en) * | 2003-08-13 | 2005-02-17 | Richard Geisler | Method for determining surfactant concentration in aqueous solutions |
| CN203572738U (zh) * | 2013-11-26 | 2014-04-30 | 南京中医药大学 | 一种表面活性剂临界胶束浓度的检测系统 |
| CN105808933A (zh) * | 2016-03-03 | 2016-07-27 | 中国科学院微电子研究所 | 一种高分子表面活性剂的结构稳定性判断方法及系统 |
-
2020
- 2020-11-26 WO PCT/CN2020/131822 patent/WO2022109935A1/zh active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050037509A1 (en) * | 2003-08-13 | 2005-02-17 | Richard Geisler | Method for determining surfactant concentration in aqueous solutions |
| CN203572738U (zh) * | 2013-11-26 | 2014-04-30 | 南京中医药大学 | 一种表面活性剂临界胶束浓度的检测系统 |
| CN105808933A (zh) * | 2016-03-03 | 2016-07-27 | 中国科学院微电子研究所 | 一种高分子表面活性剂的结构稳定性判断方法及系统 |
Non-Patent Citations (1)
| Title |
|---|
| CHENG HAI －: "Quantitative Calculation Method of Surfactant Micelles Through the Molecular Structure Forecast", SHANDONG CHEMICAL INDUSTRY, 31 December 2011 (2011-12-31), pages 1 - 4, XP055932783, ISSN: 1008-021x, DOI: 10.19319/j.cnki.issn.1008-021x.2011.09.009 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024027410A1 (zh) * | 2022-08-03 | 2024-02-08 | 电子科技大学长三角研究院(湖州) | 一种模拟材料结构非晶化的新方法 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lyubartsev et al. | Osmotic and activity coefficients from effective potentials for hydrated ions | |
| Markland et al. | Nuclear quantum effects enter the mainstream | |
| Creasey et al. | Spreading out and staying sharp–creating diverse rotation curves via baryonic and self-interaction effects | |
| Krishnan et al. | Predicting the dissolution kinetics of silicate glasses using machine learning | |
| Hummer et al. | Free energy of ionic hydration | |
| CN112652364A (zh) | 基于从头算模型预测表面活性剂临界胶束浓度的方法 | |
| Donchev et al. | Quantum chemical benchmark databases of gold-standard dimer interaction energies | |
| Larriba et al. | Ion mobilities in diatomic gases: measurement versus prediction with non-specular scattering models | |
| Park et al. | A novel fixed-sectional model for the formation and growth of aerosol agglomerates | |
| Chen et al. | Petascale orbital-free density functional theory enabled by small-box algorithms | |
| Zucca et al. | Validation of bivariate DQMOM for nanoparticle processes simulation | |
| Xu et al. | Fast Monte Carlo simulation for particle coagulation in population balance | |
| Qiao et al. | Atomistic study of surface effects on polyelectrolyte adsorption: case study of a poly (styrenesulfonate) monolayer | |
| WO2022109935A1 (zh) | 基于从头算模型预测表面活性剂临界胶束浓度的方法 | |
| Waibl et al. | Conformational ensembles of antibodies determine their hydrophobicity | |
| Majumder et al. | Fast high-resolution method for solving multidimensional population balances in crystallization | |
| Flannigan et al. | Propagation of errors and quantitative quantum simulation with quantum advantage | |
| Steenbergen et al. | A two-dimensional liquid structure explains the elevated melting temperatures of gallium nanoclusters | |
| Yoon et al. | Representation of generally mixed multivariate aerosols by the quadrature method of moments: II. Aerosol dynamics | |
| Singh et al. | Effect of different discretizations on the numerical solution of 2D aggregation population balance equation | |
| Hertaeg et al. | Predicting coffee ring formation upon drying in droplets of particle suspensions | |
| Torres et al. | Using neural network force fields to ascertain the quality of ab initio simulations of liquid water | |
| Moura et al. | Parameter Estimation in Population Balance through Bayesian Technique Markov Chain Monte Carlo | |
| Kittelmann et al. | Robust high-throughput batch screening method in 384-well format with optical in-line resin quantification | |
| Peng et al. | OpenMSCG: A software tool for bottom-up coarse-graining |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20962826 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 20962826 Country of ref document: EP Kind code of ref document: A1 |