Ben-Naim Arieh
Department of Physical Chemistry, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9190401, Israel.
Entropy (Basel). 2018 Jul 9;20(7):514. doi: 10.3390/e20070514.
It is well known that the statistical mechanical theory of liquids has been lagging far behind the theory of either gases or solids, See for examples: Ben-Naim (2006), Fisher (1964), Guggenheim (1952) Hansen and McDonald (1976), Hill (1956), Temperley, Rowlinson and Rushbrooke (1968), O'Connell (1971). Information theory was recently used to derive and interpret the entropy of an ideal gas of simple particles (i.e., non-interacting and structure-less particles). Starting with Shannon's measure of information (SMI), one can derive the function of an ideal gas, the same function as derived by Sackur (1911) and Tetrode (1912). The new deviation of the same entropy function, based on SMI, has several advantages, as listed in Ben-Naim (2008, 2017). Here we mention two: First, it provides a simple interpretation of the various terms in this entropy function. Second, and more important for our purpose, this derivation may be extended to any system of interacting particles including liquids and solutions. The main idea is that once one adds intermolecular interactions between the particles, one also adds correlations between the particles. These correlations may be cast in terms of mutual information (MI). Hence, we can start with the informational theoretical interpretation of the entropy of an ideal gas. Then, we add correction due to correlations in the form of MI between the locations of the particles. This process preserves the interpretation of the entropy of liquids and solutions in terms of a measure of information (or as an average uncertainty about the locations of the particles). It is well known that the entropy of liquids, any liquids for that matter, is lower than the entropy of a gas. Traditionally, this fact is interpreted in terms of order-disorder. The lower entropy of the liquid is interpreted in terms of higher degree of order compared with that of the gas. However, unlike the transition from a solid to either a liquid, or to a gaseous phase where the order-disorder interpretation works well, the same interpretation would not work for the liquid-gas transition. It is hard, if not impossible, to argue that the liquid phase is more "ordered" than the gaseous phase. In this article, we interpret the lower entropy of liquids in terms of SMI. One outstanding liquid known to be a structured liquid, is water, according to Ben-Naim (2009, 2011). In addition, heavy water, as well as aqueous solutions of simple solutes such as argon or methane, will be discussed in this article.
众所周知,液体的统计力学理论远远落后于气体或固体的理论。例如,可参见:本 - 奈姆(2006年)、费舍尔(1964年)、古根海姆(1952年)、汉森和麦克唐纳(1976年)、希尔(1956年)、坦珀利、罗林森和拉什布鲁克(1968年)、奥康奈尔(1971年)。信息论最近被用于推导和解释简单粒子(即无相互作用且无结构的粒子)理想气体的熵。从香农信息测度(SMI)出发,可以推导出理想气体的函数,该函数与萨克(1911年)和特罗德(1912年)推导的相同。基于SMI的同一熵函数的新推导有几个优点,如本 - 奈姆(2008年、2017年)所列举的。这里我们提及两点:第一,它为该熵函数中的各项提供了简单的解释。第二,对我们的目的而言更重要的是,这种推导可以扩展到任何相互作用粒子的系统,包括液体和溶液。主要思想是,一旦在粒子之间添加分子间相互作用,也会在粒子之间添加相关性。这些相关性可以用互信息(MI)来表示。因此,我们可以从理想气体熵的信息理论解释开始。然后,我们以粒子位置之间MI的形式添加由于相关性引起的修正。这个过程保留了根据信息测度(或作为关于粒子位置的平均不确定性)对液体和溶液熵的解释。众所周知,任何液体的熵都低于气体的熵。传统上,这个事实是根据有序 - 无序来解释的。液体较低的熵被解释为与气体相比具有更高程度的有序性。然而,与从固体到液体或气相的转变不同,在固体到液体或气相的转变中,有序 - 无序解释效果良好,但同样的解释不适用于液 - 气转变。很难(如果不是不可能的话)认为液相比气相更“有序”。在本文中,我们根据SMI来解释液体较低的熵。根据本 - 奈姆(2009年、2011年)的说法,一种著名的结构化液体是水。此外,本文还将讨论重水以及简单溶质(如氩或甲烷)的水溶液。