Why can a long-lived interface persist between fully miscible fluids?

We introduce the Co-Evolving Variational First Principle (CVFP), which explains the birth of long-lived interfaces in fully miscible fluids and quantitatively predicts their lifetime through the self-consistent co-evolution of the concentration field and free-energy landscape.

Published in Materials and Physics

Why can a long-lived interface persist between fully miscible fluids?

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

In our recent work, we introduce the Co-Evolving Variational First Principle (CVFP) to explain the emergence, persistence, and eventual decay of long-lived interfaces in fully miscible fluids.

The key idea is that the interfacial concentration field does not evolve on a fixed thermodynamic landscape. Instead, the Euler–Lagrange solution and the underlying free-energy landscape co-evolve self-consistently as a coupled pair. This hidden variational structure gives rise to optimal nonequilibrium pathways through a sequence of quasi-stationary states.

The framework reveals a dual structure between interfacial enthalpy and concentration-gradient energy, explains why long-lived miscible interfaces can form, and predicts their lifetime quantitatively across different fluid systems. It provides a unified description of the birth, persistence, broadening, and rupture of miscible interfaces within a first-principles variational framework.

The universal law behind long-lived interfaces in fully miscible fluids
Communications Physics 9, 300 (2026)
DOI: 10.1038/s42005-026-02869-0

Follow the Topic

Fluids
Physical Sciences > Physics and Astronomy > Condensed Matter Physics > Fluids
Soft Materials
Physical Sciences > Materials Science > Soft Materials
Thermodynamics
Physical Sciences > Physics and Astronomy > Classical and Continuum Physics > Thermodynamics
Statistical Physics
Physical Sciences > Physics and Astronomy > Theoretical, Mathematical and Computational Physics > Statistical Physics

Related Collections

With Collections, you can get published faster and increase your visibility.

Quantum machine learning: understanding capabilities, limitations, and perspectives for quantum advantage

This Collection highlights articles that investigate the capabilities and limitations of QML models, aiming at clarifying the prospects for quantum advantage in the near future.

Publishing Model: Open Access

Deadline: Dec 31, 2026

Advances in Quantum Networking: QKD and beyond

Communications Physics invites translational and pioneering work that describes the evolving capabilities in QKD and sets the stage for applications of quantum networking and distributed quantum technologies. Looking beyond the UNESCO International Year of Quantum Technologies, we hope to curate a cross-section of state-of-the-art research that showcases the rapid development of the quantum networking stack and its applications.

Publishing Model: Open Access

Deadline: Jan 31, 2027