Lane nucleation in complex active flows

Bacik, Karol A.; Bacik, Bogdan S.; Rogers, Tim

doi:10.1126/science.add8091

Cited by 12 publications

(3 citation statements)

References 54 publications

(91 reference statements)

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“…[3] Therefore, wearable sweat sensing holds great promise as a focal point of scientific research and a novel approach to extracorporeal diagnostics. [4][5][6][7] However, the natural secretion of sweat is both unstable and minimal. Environmental conditions and skin impurities may introduce contamination and compromise accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Bioinspired Droplet Self‐Actuated Sweat Transport Platform for Continuous Wearable Biochemical Monitoring

Shen,

Pang,

Yuan

et al. 2024

Adv Materials Technologies

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Wearable sweat sensors exhibit significant promise in the realm of smart healthcare. Efficient and non‐contaminating continuous sweat collection, transport, and disposal are crucial for enhancing detection accuracy. In this study, a novel wearable sweat sensor is presented that integrates various biomimetic structures, including cactus‐inspired spine, shorebird‐inspired conical through‐holes, and frog‐foot‐inspired micro‐grooves. These structures facilitate sweat droplets to be automatically transported from the skin to the sensing chamber and discharged without external power. First, theoretical analysis, simulation, and experiments based on self‐driven droplet mechanisms to determine the optimal transport efficiency parameters for biomimetic structures are conducted. Next, a novel sweat transport module is fabricated through ultra‐precision 3D printing according to these optimal parameters. And this module has excellent sweat collection performance contrastively. Finally, the novel biomimetic sweat transport module is integrated with a high‐sensitivity glucose detection electrode, resulting in a wearable sweat glucose detection system. Human experiments monitoring glucose metabolism in sweat verify the correlation between glucose concentrations in blood and sweat, which proves the feasibility of reflecting changes in the body's circulatory system by monitoring changes in sweat composition.

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Section: Introductionmentioning

confidence: 99%

Multi‐Bioinspired Droplet Self‐Actuated Sweat Transport Platform for Continuous Wearable Biochemical Monitoring

Shen,

Pang,

Yuan

et al. 2024

Adv Materials Technologies

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“…Active matter systems are composed of interacting agents that consume energy from their environment. They exhibit a rich variety of collective phenomena including flocking [1], lane formation [2,3], clustering [4][5][6] and pattern formation [7]. Active matter systems can be found in many natural and synthetic contexts [1], ranging from biological systems such as bacteria [8,9], microtubules [10] and animal groups [11], to physical and chemical systems such as colloids [4] and self-propelled rods [7].…”

Section: Introductionmentioning

confidence: 99%

Exact hydrodynamics and onset of phase separation for an active exclusion process

Mason,

Erignoux,

Jack

et al. 2023

Proc. R. Soc. A.

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We consider a lattice model of active matter with exclusion and derive its hydrodynamic description exactly. The hydrodynamic limit leads to an integro-differential equation for the density of particles with a given orientation. Volume exclusion results in nonlinear mobility dependent on spatial density. Such models of active matter can support motility-induced phase separation, which occurs despite the absence of attractive interactions. We study the onset of phase separation with linear stability analysis and numerical simulations.

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“…Human behavior studies [4], [5] reveal that humans anticipate each other's collision avoidance behavior when navigating in crowds. Empirical results further indicate that navigation algorithms that predict human cooperation during planning could improve both safety and navigation efficiency [6].…”

Section: Introductionmentioning

confidence: 99%

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Sun¹,

Baldini²,

Trautman³

et al. 2021

Robotics: Science and Systems XVII

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Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents' trajectories with the planning of the robot's trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents' intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav 1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance.

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Lane nucleation in complex active flows

Cited by 12 publications

References 54 publications

Multi‐Bioinspired Droplet Self‐Actuated Sweat Transport Platform for Continuous Wearable Biochemical Monitoring

Multi‐Bioinspired Droplet Self‐Actuated Sweat Transport Platform for Continuous Wearable Biochemical Monitoring

Exact hydrodynamics and onset of phase separation for an active exclusion process

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

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