各种生物体包括中药都有各自不同的元素分布,其氧化电势也各不相同,通常氧化电势越高,离子呈现的阳性越强;反之氧化电势越小,负值时离子的阴性越强。从大量中药元素的分析数据中发现,传统中药的阴阳性,与第Ⅳ长周期里的元素的氧化电势分布有密切关系。在阳性药中,氧化电势高的元素占优势,在阴性药中氧化电势低的元素占优势,可以用氧化电势来衡量中药阴阳性和药味。本文还考察了生命相关元素阳离子的亲电强度、氧化电势与中药三者之间关系,指出氧化电势(伏)和电荷强度(ξ)这两者间的线性关系;从氧化电势可以看出阳离子有阴阳性,从电荷强度可以看出不同阳离子对微环境负电荷中心有不同的亲和能力。本文定量地指出了中药性味(辛、甘、淡、苦、酸、涩、咸)的内在原因及其相应的中药有机成份的结构特征,进而提出通过中药生命动力元素的分布有可能预测中药的大体有机成分与药味。
All kinds of organisms have their own distribution law of life-related elements, including Chinese medicines, and their oxidation potential is different. Generally,when the oxidation potential is higher,the Yang(+) attribute of ions will be stronger; otherwise,the Yin(-)attribute is stronger. It is discovered in the data from the analysis of the elements of Chinese medicines that the so-called Yin and Yang in the traditional Chinese medicine have close relationship with the distribution of oxidation potential of the elements in the fourth period. In the medicines with Yang attribute, elements with high oxidation potential are superior and on the contrary in the medicines with Yin attribute, elements with low oxidation potential are advantageous. Therefore, oxidation potential can be used to distinguish between Yin and Yang and identify the flavor of Chinese medicines.This paper explores the relationship among the electrophilic intensity of the cations of life -related elements, oxidation potential and the organisms of Chinese medicines,pointing out that linear relationship exists between oxidation potential and charge strength of various cations and that the cation has its Yin and Yang attributes in the light of oxidation potential and different cations have their different offinities for negatively charged electrons in microcircumstance as far as charge strength is concernd. It also indicates the intrinsic reasons of the flavors (pungent,sweet,bitter,acerbity,salty and so on) of Chinese medicines and their corresponding structural characters of organic components and makes a further suggestion that the general organic components and flavors of Chinese medicines could be predicted through the distribution of the elements of biological power.