Mixing Entropy Threshold for Entropy-Tailored Materials

2021 ◽  
Author(s):  
Jie Wang ◽  
Yu Tang ◽  
Shun Li ◽  
Yicong Ye ◽  
Li’an Zhu ◽  
...  
Keyword(s):  
Mixing Entropy

scholarly journals Charge-Free Mixing Entropy Battery Enabled by Low-Cost Electrode Materials

ACS Omega ◽  
2019 ◽  
Vol 4 (7) ◽  
pp. 11785-11790 ◽  
Author(s):  
Meng Ye ◽  
Mauro Pasta ◽  
Xing Xie ◽  
Kristian L. Dubrawski ◽  
Jianqaio Xu ◽  
...  
Keyword(s):  
Electrode Materials ◽  
Low Cost ◽  
Mixing Entropy

scholarly journals Relations entre l’entropie physique, le codage de l’information et l’énergie de simulation

2007 ◽  
Vol 85 (12) ◽  
pp. 1381-1394 ◽  
Author(s):  
M Bigerelle ◽  
A Iost
Keyword(s):  
Free Energy ◽  
Computer Program ◽  
Turing Machine ◽  
Physical System ◽  
Ternary Systems ◽  
Thermodynamic System ◽  
Run Length ◽  
Mixing Entropy ◽  
Binary And Ternary Systems

It is shown that an isomorphism exists between the mixing entropy and the size of a computer program that simulates the physical system. This isomorphism must be constructed with respect to some theorems, and it is shown that the composition of two isomorphisms, one based on a run length encoding and another by encoding sequences in a dictionary allows us to quantify the entropy of binary and ternary systems at the equilibrium. Finally, it is shown that the energy consumed by a physical system encoded by this system and executed on a Turing machine is proportional to the free energy of the thermodynamic system.

Influence of mixing entropy on the nucleation ofCoSi2

2000 ◽  
Vol 62 (18) ◽  
pp. 12045-12051 ◽  
Author(s):  
C. Detavernier ◽  
R. L. Van Meirhaeghe ◽  
F. Cardon ◽  
K. Maex
Keyword(s):  
Mixing Entropy

Energy Harvesting by Nickel Prussian Blue Analogue Electrode in Neutralization and Mixing Entropy Batteries

Langmuir ◽  
2015 ◽  
Vol 31 (31) ◽  
pp. 8710-8717 ◽  
Author(s):  
Wellington J. A. S. Gomes ◽  
Cainã de Oliveira ◽  
Fritz Huguenin
Keyword(s):  
Energy Harvesting ◽  
Prussian Blue ◽  
Prussian Blue Analogue ◽  
Mixing Entropy

Static structure factor and chain dimensions in polymer blends with non-local mixing entropy

1998 ◽  
Vol 8 (1-2) ◽  
pp. 133-142 ◽  
Author(s):  
I.Ya. Erukhimovich ◽  
A.R. Khokhlov ◽  
T.A.V ilgis ◽  
A. Ramzi ◽  
F. Boué
Keyword(s):  
Polymer Blends ◽  
Structure Factor ◽  
Static Structure Factor ◽  
Static Structure ◽  
Mixing Entropy ◽  
Non Local

Statistic thermodynamic analysis of fructans – Part 3: Relationship between polymer structure and mixing entropy at equilibrium conditions

2017 ◽  
pp. 151-157
Author(s):  
Roland H.F. Beck
Keyword(s):  
Thermodynamic Analysis ◽  
Polymer Structure ◽  
Degree Of Polymerization ◽  
Average Degree ◽  
Equilibrium Conditions ◽  
Mixing Entropy ◽  
Branching Structures ◽  
Probable Distribution ◽  
Extremum Value ◽  
Most Probable Distribution

The reduced mixing entropy, which is a concentration and unimer independent equivalent to the polymer mixing entropy defined by Flory, for various probabilistic distributed polymer distributions is calculated. The unbranched most probable distribution proves to reach an extremum value at any given number average degree of polymerization, clearly differentiating it from both broader and narrower polymer distributions with branching structures. Entropy driven polymerization reactions thus inevitably produce unbranched polymer structures as discussed for the case of inulin biosynthesis.

Mixing entropy threshold for entropy-tailored materials

2022 ◽  
Vol 142 ◽  
pp. 107436
Author(s):  
Jie Wang ◽  
Yu Tang ◽  
Shun Li ◽  
Zhouran Zhang ◽  
Yicong Ye ◽  
...  
Keyword(s):  
Mixing Entropy

scholarly journals Mixing, entropy and competition

2012 ◽  
Vol 85 (6) ◽  
pp. 068201 ◽  
Author(s):  
A Y Klimenko
Keyword(s):  
Mixing Entropy

Some Aspects of Complexities for Quantum Processes

2009 ◽  
Vol 16 (02n03) ◽  
pp. 293-304 ◽  
Author(s):  
Noboru Watanabe
Keyword(s):  
Communication Systems ◽  
Von Neumann Entropy ◽  
Quantum Processes ◽  
Initial State ◽  
Dynamical Processes ◽  
Mixing Entropy ◽  
Quantum Dynamical ◽  
Dynamical Entropy ◽  
The Mean ◽  
Mutual Entropy

In quantum information theory, Emch, Conne, and Stormer were the first who studied the complexity of quantum dynamical processes. After that, Ohya introduced the [Formula: see text]-mixing entropy for general quantum systems and he defined the mean entropy and the mean mutual entropy for quantum dynamical systems based on the [Formula: see text]-mixing entropy. Conne, Narnhoffer and Thirring introduced the dynamical entropy (CNT entropy) and several researchers discussed this concept. Alicki and Fannes defined a different dynamical entropy — AF entropy. In 1995, Voiculescu proposed the dynamical approximation entropy. Accardi, Ohya and Watanabe defined yet another dynamical entropy (AOW entropy) through a quantum Markov process in 1997. In 1999, Kossakowski, Ohya and Watanabe introduced the dynamical entropy (KOW entropy) with respect to completely positive maps. In this paper, we discuss the complexity of quantum dynamical processes to calculate the dynamical entropy for noisy optical channels.In order to discuss the efficiency of information communication processes, a measure of complexity of initial state itself and a measure of transmitted complexity through communication channels are necessary. Quantum entropies were formulated on the basis of the quantum probability theory. In quantum communication systems, von Neumann entropy and Ohya mutual entropy relate to these measures of complexities, respectively. Recently, several mutual entropy type measures (Lindblad-Nielsen entropy and coherent entropy) were defined making use of entropy exchange with respect to a channel and initial state. In this paper, we show which of the measures is the most suitable one for discussing the efficiency of information transmission for quantum processes.

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