Engineering topics

Pial, Turash Haque

Publications and source records attributed to Pial, Turash Haque.

3 records

DOE OSTI2024

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

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, f=0.25, and f=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 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, overpredict 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 + , Ca 2+ , and Y 3+ ions) in determining the water-water interactions and water-water HB properties inside the PAA brush layer. Furthermore, the findings of the present study, confirming the weakening of water-water HBs inside the PAA brush layer, points 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.

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DOE OSTI2022

Specific Ion and Electric Field Controlled Diverse Ion Distribution and Electroosmotic Transport in a Polyelectrolyte Brush Grafted Nanochannel

Controlling ion distribution inside a charged nanochannel is central to using such channels in diverse applications. Here, we show the possibility of using a charged polyelectrolyte (PE) brush grafted nanochannel for triggering diverse nanoscopic ion distribution and nanofluidic electroosmotic transport by controlling the valence and size of the counterions (that screen the charges of the PE brushes) and the strength of an externally applied axial electric field. We atomistically simulate separate cases of fully charged Polyacrylic acid (PAA) brush functionalized nanochannels with Na + , Cs + , Ca 2+ , Ba 2+ , and Y 3+ counterions screening the PE charges. Four key findings emerge from our simulations. First, we find that the counterions with a greater valence and a smaller size prefer to remain localized inside the brush layer. Second, for the case where there is an added chloride salt with the same cation (as the screening counterions), there are more coions (Cl - ions) in the brush-free bulk than counterions (for counterions Na + , Ca 2+ , Ba 2+ , Y 3+ ): this is a manifestation of the overscreening (OS) of the PE brush layer. Contrastingly, the number of Cs + ions remain higher than the Cl - ions inside the brush-free bulk, ensuring that there is no OS effect for this case. Third, large applied electric field enables a few Na + , Cs + , and Ba 2+ counterions to leave the brush layer and to go to the bulk: this makes the OS of the PE brush layer disappear for the cases of PE brushes being screened by the Na+ and Ba 2+ ions. On the other hand, no such electric-field-mediated disappearance of OS is observed for the cases of Ca 2+ and Y 3+ screening counterions; we attribute this to the firm attachment of these counterions to the negatively charged monomers. Free energy associated to a counterion binding to a PE chain corroborates this diversity in the counterion-specific response to the applied electric field. Lastly, we demonstrate that such diverse ion distributions, along with specific electric-field-strength-dependent ion properties, lead to (1) EOS transport in nanochannels grafted with PAA brushes screened with Cs + ions to be always counterion dominated, (2) EOS transport in nanochannels grafted with PAA brushes screened with Ca 2+ and Y 3+ ions to be always coion dominated, and (3) EOS transport in nanochannels grafted with PAA brushes screened with Na + and Ba 2+ ions to be coion dominated for smaller electric fields and counterion dominated for larger electric fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Machine learning enabled quantification of the hydrogen bonds inside the polyelectrolyte brush layer probed using all-atom molecular dynamics simulations

The configuration of densely grafted charged polyelectrolyte (PE) brushes is strongly dictated by the properties and behavior of the counterions that screen the PE brush charges and the solvent molecules (typically water) that solvate the brush molecules and these screening counterions. Only recently, efforts have been made to study the PE brushes atomistically, thereby shedding light on the properties of brush-supported ions and water molecules. However, even for such efforts, there are limitations associated with using a generic definition to estimate certain properties of water and ions inside the brush layer. For example, water–water hydrogen bonds (HBs) will behave differently for locations outside and inside the brush layer, given the fact that the densely closely grafted PE brush molecules create a soft nanoconfinement where the water connectivity becomes highly disrupted: therefore, using the same definition to quantify the HBs inside and outside the brush layer will be unwise. In this paper, we address this limitation by employing an unsupervised machine learning (ML) approach to predict the water–water hydrogen bonding inside a cationic PE brush layer modeled using all-atom molecular dynamics (MD) simulations. Here, the ML method, which relies on a clustering approach and uses the equilibrium coordinates of the water molecules (obtained from the all-atom MD simulations) as the input, is capable of identifying the structural modification of water–water HBs (revealed through appropriate clustering of the data) inside the PE brush layer induced soft nanoconfinement. Such capabilities would not have been possible by using a generic definition of the HBs. Our calculations lead to four key findings: (1) the clusters formed inside and outside the brush layer are structurally similar; (2) the margin of the cluster is shorter inside the PE brush layer confirming the possible disruption of the HBs inside the PE brush layer; (3) the average “hydrogen–acceptor-oxygen–donor-oxygen” angle that defines the HB is reduced for the HBs formed inside the brush layer; (4) the use of the generic definition (definition usable for characterizing the HBs in brush-free bulk) leads to an overprediction of the number of HBs formed inside the PE brush layer.

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