Engineering PapersSearch

DOE OSTI · 3012789

Self-oscillating synchronematic colloids

Abstract

Self-oscillators that sustain periodic dynamics under constant input are ubiquitous in natural and engineered systems, where their interactions enable spatiotemporal coordination among many individual units. New forms of organization can emerge when these self-oscillating units are free to move and rotate, coupling their spatial arrangement and alignment with their oscillation frequencies and phases. Here, we report experiments and simulations on populations of Quincke colloids that behave as self-oscillating units with position, orientation, frequency, and phase. Depending on the initial distribution, these active oscillators spontaneously organize into distinct collective states characterized by temporal synchronization and directional alignment, which we term synchronematic order. In fluid-like clusters, this order is short-ranged and decays over a length scale set by the competition between hydrodynamic interactions and athermal noise. In crystalline clusters, these interactions drive flobal synchronization and circular alignment-synchronematic crystals-whose collective frequency increases with cluster size due to non-reciprocal interactions. Our results establish self-oscillating colloids as a model system for active oscillatory matter and reveal fundamental principles by which synchronization, alignment, and structure co-emerge, offering new pathways for designing adaptive, frequency-tunable materials.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Leyva, Sergi G. [Northwestern Univ., Evanston, IL (United States)] (ORCID:0000000191769285), Zhang, Zhengyan [Columbia Univ., New York, NY (United States)], Olvera de la Cruz, Monica [Northwestern Univ., Evanston, IL (United States)] (ORCID:0000000298023627), Bishop, Kyle J. M. [Columbia Univ., New York, NY (United States)] (ORCID:0000000274673668). 2026-01-23. Self-oscillating synchronematic colloids. https://doi.org/10.1038/s41467-026-68552-8

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Integration of the Biot–Gassmann Fluid Substitution Method and Machine Learning-Based Velocity–Stress Relationship for Estimating In Situ Stresses

Recent advancements have shown that in situ stresses can be reliably estimated through an integrated machine/deep learning (ML/DL)-based framework, which relies on models trained and validated using true triaxial ultrasonic velocity (TUV) experimental data that involve measurements of ultrasonic velocity in saturated rocks under varying stress configurations. However, when the goal is to interpret lower frequency measurements, it may be more appropriate to run experiments on dry rocks and then obtain Biot–Gassmann-derived equivalent saturated velocities (low-frequency approximation) and employ these quantities for training ML/DL models to predict in situ stress. Whether the dispersion effect of frequency on the velocity–stress relationship substantially impacts in situ stress prediction is an important and unresolved question. This work presents an enhancement of ML/DL-based workflow by training and implementing ML/DL models using equivalent saturated acoustic velocities (low-frequency) obtained by applying Biot–Gassmann fluid substitution on the ultrasonic velocities of dry cores. The models were trained on TUV data sets derived from three subsurface cores extracted from the geothermal well 16B(78)-32 at the Utah FORGE site. Each core was subjected to 75 unique stress configurations for velocity measurement in the dry state. The ML/DL trained on the TUV data set with equivalent saturated velocities demonstrated promising performance to predict in situ stress in subsurface geological rocks using velocity–stress relationships with R 2 of 0.86, 0.971, and 0.975 and root mean squared error (RMSE) of 2.59, 1.92, and 1.80 for validation/testing phases of vertical, minimum horizontal, and maximum horizontal stress models, respectively. Additionally, interpretation and explanation by Shapley additive explanations (SHAP) analysis further improved scientific validation and model reliability for estimating in situ stresses.

colloids

Pinned in Microgravity: How Colloidal Suspensions Inhibit Droplet Motion

Understanding the fundamental behavior of colloidal fluids in microgravity is critical for advancing material science, manufacturing, and biological processes. In this study we investigate the dynamics of a droplet containing colloidal suspension when ejected in short term microgravity. These conditions are generated using a 2.2 second drop tower. A custom droplet generator was designed to produce consistent size-controlled droplets containing passive colloidal particles. The particles are suspended in water and subsequently ejected out of a hydrophobic wedge under microgravity conditions, where the droplet ejection time can be predicted [Torres & Weislogel, 2021]. The ejection times will be compared with de-ionized water ejected from the same system to investigate the effects of change in density and surface tension caused by the colloids. Successful passive colloidal ejection will lay the foundation for active colloid research in the future.

colloids