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Jinchao Chen

Publications and source records attributed to Jinchao Chen.

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Single Nanoparticle Dynamics in Opto-Thermal Tweezers: Resolving the Temporal Resolution of Depletion Force Trapping

Optothermal tweezers enable the manipulation of a wide range of nano-objects through optically induced depletion forces. Despite significant advances, the temporal dynamics of optothermal trapping remain elusive, as existing methodologies rely almost exclusively on time and ensemble averaging. Consequently, stable trapping cannot be distinguished from local transient accumulation, where the time-averaged concentration increases but particles exhibit rapid, dynamic motion in and out of the trap. Here we investigate optothermal trapping with single-nanoparticle-level analysis and sub-millisecond temporal resolution. Our data resolve the elusive dynamics of 40 nm polystyrene nanoparticles trapped within depletion force potentials in polyethylene glycol solutions, enabling to differentiate the conditions leading to extended trapping times from those leading to transient localization. Numerical simulations corroborate our experimental findings, elucidating how the interplay between thermophoresis and diffusiophoresis governs nanoparticle dynamics. These insights deepen our mechanistic understanding of optothermal trapping and unlock opportunities for single-molecule studies, nanoscale assembly, and targeted drug delivery.

physics.optics

CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.

cs.DC