Hydrogen Bonding
Inside Anionic Polymeric Brush Layer:
Machine Learning-Driven Exploration of the Relative Roles of the Polymer
Steric Effect, Charging, and Type of Screening Counterions
posted on 2024-02-10, 14:31authored byArka Bera, Tanmay Sarkar Akash, Raashiq Ishraaq, Turash Haque Pial, Siddhartha Das
This paper employs a combination
of all-atom molecular
dynamics
(MD) simulations and unsupervised machine learning (ML) for studying
the water–water hydrogen bonds (HBs) inside the anionic poly
acrylic acid (PAA) brushes modeled using all-atom MD simulations.
PAA brush layer with different charge fraction (f), namely, f = 0, 0.25, and 1, is considered. Water–water
interactions, both inside and outside the brush layer, are represented
through distinct clusters of tupules of variables representing distances
associated with the interacting water molecules. While clusters representing
the HBs are present for water inside and outside the brushes, several
clusters representing the long-range water–water interactions
are missing for the water molecules inside the highly charged (f = 1) PAA brushes. More importantly, inside highly charged
brushes, the edge of the clusters representing the water–water
HBs is progressively shortened as compared to that in the bulk. Both
of these results stem from the presence of the PAA brushes imparting
the steric effect and the charge effect, or the effect associated
with enhanced interactions of water molecules with PE charges and
counterions, thereby disrupting the water connectivity. This water-charged-species
interaction also increases the water–water HB angle, i.e.,
makes the water–water HBs less stable inside the highly charged
PAA brush layer. The narrowing of the clusters representing the HBs
and the alteration of the angle characterizing the HBs confirm that
the conditions defining the water–water HBs change inside the
PAA brush layer as a function of the charges on the PAA brush layer.
Furthermore, we show that the use of the generic definition of HBs,
as compared to using our simulation-motivated modified definition
of water–water HBs, overpredicts the number of water–water
HBs inside the PAA brush layer. Finally, we employ this all-atom-MD-ML
framework to quantify the effect of other types of screening counterions
(Li+, Ca2+, and Y3+ ions) in determining
the water–water interactions and water–water HB properties
inside the PAA brush layer. The findings of the present study, confirming
the weakening of water–water HBs inside the PAA brush layer,
point to the possibility that the water molecules will be more available
for hydrating the brush layer and counterions, thereby leading to
a more pronounced wetting of the PAA brush layer.