PANDA-D: Interface-free determination of two dynamic contact angles

summary

Video file (mp4)

The gist

The gist PANDA-D introduces an interface-free method to determine both dynamic contact angles of a driven liquid roll by jointly fitting molecular density and layerwise center-of-mass displacement

In short

PANDA-D is a new method to find both dynamic contact angles of a liquid roll without needing to manually identify an interface contour. It achieves this by jointly fitting molecular density profiles and layerwise center-of-mass displacement. This approach resolves ambiguity inherent in using only one profile, providing reproducible angle estimates with low error.

Key concepts

PANDA-D
An extension of the PANDA method designed to determine both advancing and receding contact angles of a driven liquid roll by jointly fitting density and displacement profiles. It avoids the need to extract an explicit interface contour.
Density Profile ($ ho_k$)
This profile measures the number density of molecules within specific layers ($k$) relative to the roll's center. It indicates how much space is occupied in different vertical positions, but it cannot tell which meniscus (advancing or receding) is moving.
Layerwise Center-of-Mass Displacement ($q_k$)
This profile tracks the displacement of molecules in each layer relative to the roll's center of mass. By combining this with density, PANDA-D can distinguish between the two menisci and determine their respective angles.
Circular-Meniscus Model
A geometric model that maps the measured density and displacement profiles onto physical parameters like receding and advancing angles. It uses a circular arc parameterization to predict the roll's width and relate it to the contact line behavior.

Terminology used across episodes

This episode discusses

The paper

PANDA-D: Interface-free determination of two dynamic contact angles · Read on arXiv

Georgii Konstantinovich Vandyshev, Grigorii Stanislavovich Gorkaev, Nikolay Dmitrievich Kondratyuk, Kirill Mironovich Gerke, Ilia Vladimirovich Kopanichuk

Moscow Institute of Physics and Technology (National Research University)

Contact angles are central descriptors of wetting and capillary behavior at solid--fluid interfaces. Dynamic contact angles in molecular simulations are commonly extracted from two-dimensional liquid--vapor contours and near-wall tangent fits. We introduce PANDA-D, an extension of Predicting Angle from Nanoscale Density Analysis (PANDA) that determines both contact angles of a driven liquid roll without extracting an interface. The density profile measures occupied layer width but does not identify which meniscus is advancing. The method resolves this ambiguity by jointly fitting density and the layerwise center-of-mass displacement relative to the roll. A circular-meniscus model maps these profiles to receding and advancing angles and an effective near-wall offset. On synthetic rolls with prescribed angles, the reconstruction error is 0.29 root-mean-square. We then analyze force-driven Lennard--Jones rolls between crystalline walls. Joint fits reproduce both measured profiles, whereas density-only fits can match density while assigning incorrect asymmetry. The resulting angles agree with independently determined circle-fit angles to 0.84 root-mean-square. Across four wall--fluid attractions, the receding angle decreases and the advancing angle increases with force, approximately linearly over the studied ranges. By eliminating contour thresholds and near-wall tangent extrapolation from the primary estimate, the two-profile approach enables reproducible, automated angle analysis across molecular trajectories.

Transcript

Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: Today's paper: "PANDA-D: Interface-free determination of two dynamic contact angles".

Mira: The gist PANDA-D introduces an interface-free method to determine both dynamic contact angles of a driven liquid roll by jointly fitting molecular density and layerwise center-of-mass displacement profiles,

Kai: First, who's behind it and why it matters.

Paper summary: Kai: So, moving on from how they do it, PANDA-D’s core thesis is that you can determine both dynamic contact angles without ever extracting an interface. It takes the ambiguity out of the measurement by jointly fitting two profiles instead of just one.

Mira: The paper argues that this approach is useful because hydrodynamic accounts don't always identify the same local angle, and a measured angle should really be reported with its observation scale and wall convention, which this method handles by measuring two angles consistently before asking which dynamical law describes them.

Lev: From a real hardware standpoint, that consistency is key when you're trying to map simulation results onto actual experimental observations where the conditions are never perfectly controlled.

Kai: It matters because it lets us measure the fluid and solid and their fluctuating interface simultaneously within molecular dynamics, which is a significant capability for researchers studying these systems.

Mira: They also mention that in a confined bridge, the two menisci can respond differently to forcing even if the liquid layers next to the walls don't look like a sharp continuum interface. That’s something this method is designed to handle.

Lev: It means we don't have to rely on approximations about what the interface *looks* like in a specific force regime; we get two angles directly from the underlying particle trajectories.

Conclusion: Kai: Thinking about the whole paper, PANDA-D is really about turning that asymmetric geometry of a moving liquid roll into a two-profile inverse problem. It yields both dynamic contact angles directly without needing to select a specific liquid-vapor contour for the main estimate.

Mira: The authors are looking at how we report these angles—how they should be reported with their observation scale and wall convention—and PANDA-D provides a way to measure those two angles consistently from particle trajectories before you even have to ask which dynamical law describes them.

Lev: For the future, this means that when we look at simulations, instead of having to manually pick a threshold or an extrapolation method for contact line analysis, this joint fitting approach becomes the primary tool for extracting those dynamic angles.

Kai: It’s about making the process of getting these angles from molecular data more reproducible by removing that dependency on interface detection methods.

Mira: That reproducibility is what makes it useful across different molecular trajectories and different wall-fluid attractions, as they show a consistent relationship between force and the two resulting angles.

Lev: So, for anyone working with these simulations, the implication is a more robust way to characterize the wetting behavior of these nanoscale systems using what’s already there in the data.

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