This journal article is previously published as: Liu Huan. (2021). Ancient Chinese Eight Diagrams and Application on Chemistry Reaction Rate. Journal of Environment and Health Science (ISSN 2314-1628), 2021(02)., which is converted into Journal of Quantum Physics and Materials Chemistry (ISSN2958-4027) . Both Journals belong to the same publisher, Liu Huan. The previous journal article is closed to the public, but the previous reference is still valid.
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Cited as: DOI: 10.58473/JQPMC0010
ORCID: __https://orcid.org/0000-0003-4881-8509__
Article 10. Ancient Chinese Eight Diagrams and Application on Chemistry Reaction Rate/八卦与矩阵运算及其在化学反应速率中的应用研究 Author: Liu Huan (1983- ), Master of Science (First Class Honours), The University of Auckland.
Ancient Chinese Eight Diagrams is created by the first King in Chinese race by literature, Fuxi. This is a tool to deduce and analyze the inter-relationships across materials or affairs consisting of five elements (Metal, Wood, Water, Fire and Soil) at both temporal and spacial scales, with the key philosophy of Yin and Yang (translation as negative and positive poles respectively). As discussed in this journal, the basic attribute of materials contains magnetism, so the first one presenting this philosophy is just Fuxi in literature. The method of deducing and analyzing the inter-relationships across materials or affairs by Ancient Chinese Eight Diagrams is the embryo of matrix and linear algebra. However, for the matrix designed in this article, 0 represents negative pole and 1 represents positive pole like the matrix designed for genetic algorithm in this journal, and the philosophy of this originates from the Ancient Chinese Eight Diagrams. This binary algebra usually is applicable on the micro-scale substances. However, for the classification of bio-communities in a ecosystem, which possesses more advanced intelligence, more complex matrix is required. Unlike Taoists who utilize this tool to predict people’s fate or fortune, this book aims to succeed the philosophy of Ancient Chinese Eight Diagrams and further develop this on bio-medicine engineering or chemical engineering. This article chooses Netlogo software (installed in the School of Environment, The University of Auckland) to build a 3D model for the estimation of chemistry reaction rate at both spacial and temporal scales.
Hypotheses: in the spacial grid unit designed in this 3D modeling, the molecule motion frequently and regularly passes through this spacial grid unit. If this molecule motion exactly passes through this spacial grid unit, the magnetism value in this point is defined as 1; if this molecule motion leaves this spacial grid unit, the magnetism value in this point is defined as 0. Then the matrix of this spacial grid unit is defined as:
Matrix A = [a1,a2,a3,...]
Matrix B = [b1,b2,b3,...]
In this matrix, a1 is the magnetism value at time T1; a2 is the magnetism value at time T2; a3 is the magnetism value at time T3..... ;And a1, a2, a3 ... are the value 0 or 1. Matrix B is the same definition.
Spatial and Temporal Scales: the size of spacial grid unit designed in this 3D modeling is determined by the Max molecular length, and the parameter of time interval between T1 and T2 is determined by the molecular motion speed which required experiment test for parameterization. The Max molecular length is not necessary to be the very exact one, because usually when two molecules approach each other (does not need to exactly collide each other) at specific intersection angel, chemistry reaction occurs. Hence the size of spacial grid unit can be easily estimated according to the published molecular length of relevant chemistry species.
Matrix A is the matrix representing molecule A; Matrix B is the matrix representing molecule B. Chemistry reaction occurs between molecule A and molecule B. Only once molecule A and molecule B collides at specific intersection angle (or angles) of spacial magnetism curves between two molecule A and B, chemistry reaction occurs. The other intersection angles between molecule A and B collision can not lead to chemistry reaction. Consequently, it is hypothesized that molecule motion shows equal or random chances of occurrence in each spacial grid unit along motion orbit, which can be derived by the molar volume or molar concentration of chemistry molecules. With the chemistry reaction process, the chances of occurrence in each spacial grid unit decreases with the reduction in molar concentrations of molecule A and B, which can be edited into Netlogo as sub-models. Then the occurrence of collision between molecule A and B in a spacial grid unit is calculated as:
Chemistry Reaction Rate = Pa × Matrix A × Pb × (Matrix B)^T = Pa × Pb × [a1*b1, a2*b2, a3*b3,...], (Matrix B)^T is the transpose of Matrix B
In this matrix, if a1*b1 = 0, there is no occurrence of collision between two molecule A and B in a spacial grid unit; if a1*b1 = 1, collision between two molecules occurs in a spacial grid unit...; However, only once molecule A and molecule B collides at specific intersection angle (or angles) of spacial magnetism curves between two molecule A and B, chemistry reaction occurs. Consequently, Pa and Pb is the proportion of the specific spherical surface area of active chemistry bonds in molecule A and molecule B respectively, to its whole spherical surface area of a molecule during the molecular revolution, depended on the molecular structure. This can be derived from the 3D graph design of chemistry molecule.
Then the spacial diffusion models, such us Gaussian diffusion model, can be easily incorporated into Netlogo as sub-models, which determines the initial concentrations of each chemistry species in spatial distribution. These spatial distribution models (such us Gaussian diffusion model) usually focus on the chemistry transport only and do not consider the chemistry reaction conversions between chemistry species, so my model should fill in the academic/technological blanks. This matrix reflects the inter-dependent effects between two symmetric time spaces along the fourth dimension axis [1]. Please note: the sequence of steps in building up this model is also significantly different from the models set up in our Environmental Modeling Lab, so coping and transferring this sequence of steps is not allowed, which should be original too. Once the modeling chemistry reaction rate is compared and contrasted with the experimental test rate of chemistry reaction, it is easily to deduce the molecule motion speed under experimental conditions by this 3D modeling as parameterization. Consequently, the spacial points of intersection among the motion orbits of different molecules can be deduced by test and 3D graph. Obviously, the orbits of molecule motion is sphere shape, forming electromagnetic waves diffused around this.
To more accurately simulate the chemistry reaction rate, the frequency of molecule spinning motion is introduced into this 3D modeling, so the equation can be improved into:
Chemistry Reaction Rate = Pa × Matrix A × Pb × (Matrix B)^T × Fa × Fb
Fa and Fb represents the frequency of molecule spinning motion in molecule A and B respectively, which can be estimated according to the electromagnetic wave detector. Electromagnetic wave detector measures the electromagnetic waves generated by molecule spinning motion. The theory of this estimation has been further discussed in this article [4]. This 3D modeling does not only provide the empirical methods to calculate the results of chemistry reaction like this numerical modeling [3], but also plays the role in theoretical model to help to better understand the mechanism of chemistry reactions: Firstly, increased thermal energy increases the frequency of molecule motion along orbit and alters the structure of spacial magnetism curves in a molecule, which triggers the threshold energy for chemistry reaction. The effects of particles’ thermal motion on the electron magnetic moment is discussed in this paper [2], which means that the spherical surface area of active chemistry bonds in this 3D modeling increases under external thermal effects, to reach the threshold thermal energy for chemistry reaction (such as ignition point); Secondly, the effects of catalyzer can be also modeled by increasing the spherical surface area of active chemistry bonds in molecule revolution motion when catalyzer agents are applied; Thirdly, once the motion rhythm of spacial magnetism curves in synthesized molecule leads to higher thermal energy than previous molecules, which can be modeled by higher frequency of molecule spinning motion in synthesized molecule, this is the exothermic reaction; once the motion rhythm of spacial magnetism curves in synthesized molecule leads to lower thermal energy than previous molecules, which can be modeled by lower frequency of molecule spinning motion in synthesized molecule, this is the endothermic chemistry reaction. The modeling of thermal effects measured by temperature, which is divided into thermal energy generated by molecule spinning motion (expressed as the function of the frequency of molecule spinning motion) and thermal energy produced by molecule motions across different spacial grids (expressed as the function of the motion speed across different spacial grids). The theory of this has been discussed in this article [5]. The parameters of these two parts can be measured by electromagnetic wave detector and experimental test rate of chemistry reaction as discussed above. Modeling of the formation of magnetic materials by binary arithmetic: the formation mechanism of magnetic materials is discussed in my previous article [2], which can be simulated by another 3D modeling. In a new model, each molecule is evenly divided into 8 spacial grids. If the electron clouds or orbits in a spacial grid is clockwise orientation, its value is set to be 0 as Yin pole; If the electron clouds or orbits in a spacial grid is anti-clockwise orientation, its value is set to be 1 as Yang pole; Modeling of symmetric magnetism: if there is collision between two molecules, and the adjacent spacial grids between these two molecules is symmetric magnetism, which means that one is clockwise orientation as 0 value and the other is anti-clockwise orientation as 1 value, it strengthens the formation of magnetic materials. If the adjacent spacial grids between these two molecules is asymmetric magnetism, which means that two spacial grids show the same orientation orbits, they generate repulsive force against each other; the motion orbit of the central points of a molecule is set as random probability in each spacial grid, driven by thermal energy effects. Obviously, if there is more symmetric spacial array as [0,1] value by binary arithmetic, this materials tends to be ferromagnetism; if there is more asymmetric spacial array, including [0,0] and [1,1] by binary arithmetic, this materials tends to be anti-ferromagnetism.The probability of both symmetric spacial array and asymmetric spacial array in each grid can be simulated and estimated by this theoretical 3D modeling.
In this paper, the Chinese Eight Diagrams matrix operation essentially reflects that the magnetic line in the fourth dimension coordinate between two symmetric three-dimensional spaces dominates the motion law of microscopic particles. Its theory has been fully discussed in previous papers [1][2]. In this paper, the 3D modeling sets the occurrence probability of the particle motion trajectory in each spatial grid cell as the random one, but the probability distribution of randomness is still subject to statistical laws (such as normal distribution rate), which is fundamentally different from the disorder of probability. Just because the distribution probability of microscopic particles on the spatial motion trajectory is subject to the statistical laws, it can be predicted and modeled by the simulation. If it is the disordered motion trajectory, it cannot be estimated by the modeling. Thus, it also reveals that the magnetic line between two symmetric three-dimensional spaces is an intelligent magnetic line dominated by biotic forces.
译文：本文八卦矩阵运算从本质上反映了第四维度坐标上两个对称三维物质空间之间的磁力线主导着微观粒子的运动规律。其理论在之前论文已经充分论述[1][2]。本文空间模拟把粒子运动轨迹在各个空间网格单元中的出现概率设置为随机型，但是随机性的概率分布仍然在统计学上服从统计规律（比如正态分布率），与概率分布的无序性有本质区别。正是因为微观粒子在空间运动轨迹上的分布概率服从统计学规律，才能被模拟预测出来。如果是无序性的运动轨迹，则不可被模拟预测。从而也说明，两个对称三维物质空间之间的磁力线是一种有生力量在主导的智慧型磁力线。
图1. 八卦阵阴、阳二阵的排列组合。 八卦阵的神奇算术： 以上两个图片分别为阴、阳二阵，阴阵为矩阵 A=[A1, A2, ....A8], 阳阵为矩阵 B=[B1, B2,...B8], 其中 A1 与 B1 为 0, 1 二位进制算法的随机型数字 (如图所示) 排列组合，并且分别为矩阵 A 和 B 的第一列。与A1和B1相似，矩阵A和B中其它列中的数值亦为0, 1 二位进制算法的随机型数字排列组合。阴阳二阵的相互作用关系表达为矩阵 A 乘以矩阵 B 的倒置。每隔一段时间 T，矩阵就像时钟一样旋转一格，旋转一圈是一共八格。如图例所示，阴阵顺时针方向旋转一格，矩阵A 则变化为[A8,A1,A2,...A7], 阴阳二阵的相互作用关系表达为变化后的矩阵A乘以矩阵B的倒置。之后再次旋 转一格，矩阵变化则以此类推。
神奇算术：每旋转一圈，A 和 B 中的 0, 1 二位进制算法的数字排列组合就会重新随机型选取一次，代表阴、阳二阵中的事物随着时间发生变动，但是最终的行列式运算结果总体上一定符合统计学规律，并非无序性。如果喜欢应用数学，可以通过模拟计算并且探讨。
八卦矩阵算术反映了宇宙阴、阳两极的相互作用关系，本文已经应用于模拟微观 粒子的运动轨迹，其中二进制算法用于表示第四维度坐标上的磁力线在空间单元网格中是否出现。这类二进制算法不仅可以普遍应用于电路设计、天体物理学、生态环境评价等自然科学领域，也可以应用于“是、否”“有、无”等逻辑关系的社会科学领域。 Please note: This is the revised materials in book “Proceedings for Degree of Postgraduate Diploma in Environmental Science (3rd Edition).” published in 2016. Firstly revised on 31/ 12/2020; Secondly revised on 20/ 11/2021; Thirdly revised on 21/ 11/2021; Fourthly revised on 30/01/2022; Fifthly revised on 17/02/2022. This journal article is previously published as: Liu Huan. (2021). Ancient Chinese Eight Diagrams and Application on Chemistry Reaction Rate. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(02)., which is converted into Journal of Quantum Physics and Materials Chemistry (ISSN2958-4027) . Both Journals belong to the same publisher, Liu Huan. The previous journal article is closed to the public, but the previous reference is still valid. Latest revised on 04/02/2023; 05/02/2023; 06/02/2023;08/02/2023a; 08/02/2023b; 08/02/2023c; 10/02/2023; 12/02/2023.
声明：本文仅仅描述模拟软件制作的方法和自然科学理论的创新，但是不公开发表3D模拟程序，因为本文中的计算机模拟程序还需要用于国家软件著作权登记（受《香港版权条例》保护）。
References: [ 1]. Liu Huan. (2021). The anti-matter of symmetric three-dimensional spaces along the fourth dimension axis. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(2). [2]. Liu Huan. (2022). Essay: Electromagnetics and Materials. Journal of Environment and Health Science (ISSN 2314- 1628), 2022(11). [3]. Liu Huan (2021). Modeling of annual net primary production of a forest in the Taramakau Valley, Westland, New Zealand. Journal of Environment and Health Science (ISSN 2314- 1628). 2021(2). [4]. Liu Huan (2021). The particle dualism of electromagnetic waves. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(2). [5]. Liu Huan. (2021). The Principal of Thermodynamics: The Inner Energy, Energy Loss and Materials Perishing. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(02). |