Back

Polyelectrolyte brush bilayers at thermal equilibrium: Density functional theory and molecular dynamic simulations

Edwards, M. J.

2022-02-27 biophysics
10.1101/2022.02.24.481861 bioRxiv
Show abstract

In this article, the equilibrium properties of Polyelectrolyte brush bilayers is studied. The density functional theory framework as well as the benchmark molecular dynamic simulations are employed to indicate the correct equation of state. Both techniques turn out that the equation of state neutral polymer brush bilayers scales with the wall distance as D-4. Additionally, it turns out that the electrostatic interactions does not change the universal power laws. The only difference is that the charged systems posses larger pressure due to stored electrostatic energy. SIGNIFICANCEStudying Polyelectrolyte brush bilayers is important because it exists widely in nature, biology and industry. In biology, they could be found in mammalian synivial joints as well as in the cellular structures.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Biophysical Journal
○ 631 papers in training set
Top 0.4%
15.2%
Similar papers in this journal
2
Soft Matter
● 60 papers in training set
Top 0.1%
9.0%
Similar papers in this journal
3
The Journal of Chemical Physics
● 56 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
4
PLOS ONE
● 5266 papers in training set
Top 23%
6.8%
Similar papers in this journal
5
Physical Review E
● 112 papers in training set
Top 0.3%
4.9%
Similar papers in this journal
6
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.1%
4.1%
7
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.3%
8
The Journal of Physical Chemistry B
● 167 papers in training set
Top 0.7%
3.2%
50% of probability mass above
9
The Journal of Physical Chemistry Letters
● 63 papers in training set
Top 0.3%
2.7%
10
Physical Chemistry Chemical Physics
● 36 papers in training set
Top 0.2%
2.4%
11
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 0.6%
2.4%
12
Physical Biology
○ 46 papers in training set
Top 0.2%
2.4%
13
Journal of Theoretical Biology
○ 162 papers in training set
Top 1%
2.1%
14
Scientific Reports
○ 3612 papers in training set
Top 49%
1.9%
15
Journal of Computational Chemistry
○ 13 papers in training set
Top 0.1%
1.7%
16
Biochimica et Biophysica Acta (BBA) - Biomembranes
○ 36 papers in training set
Top 0.2%
1.7%
17
The European Physical Journal Plus
○ 13 papers in training set
Top 0.2%
1.5%
18
Biosystems
○ 31 papers in training set
Top 0.4%
1.1%
19
European Biophysics Journal
○ 12 papers in training set
Top 0.1%
1.1%
20
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.7%
1.1%
21
Colloids and Surfaces B: Biointerfaces
○ 10 papers in training set
Top 0.2%
1.0%
22
Journal of Molecular Graphics and Modelling
○ 17 papers in training set
Top 0.3%
0.9%
23
Journal of Computational Neuroscience
○ 29 papers in training set
Top 0.4%
0.9%
24
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
25
Physical Review Research
● 49 papers in training set
Top 0.9%
0.6%
26
Journal of Colloid and Interface Science
○ 12 papers in training set
Top 0.3%
0.6%
27
RSC Advances
● 22 papers in training set
Top 1.0%
0.6%
28
Entropy
○ 21 papers in training set
Top 0.4%
0.6%
29
International Journal of Molecular Sciences
○ 494 papers in training set
Top 17%
0.6%
30
PeerJ
◐ 308 papers in training set
Top 12%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.