arXiv subjects

Shuqi Zhou

Publications and source records attributed to Shuqi Zhou.

4 recordsLinked to original sources

arXiv2026

Laser-Liquid Interaction in Laser-Induced Forward Transfer (LIFT) Printing: A Multiscale Perspective on Bubble Dynamics and Material Ejection

Laser-induced forward transfer (LIFT) is a nozzle-free laser-assisted printing method that provides an advanced manufacturing route for spatially selective deposition of functional inks, nanoparticle suspensions, polymers, hydrogels, biological materials, and other difficult-to-nozzle formulations. The apparent simplicity of LIFT, however, conceals a strongly coupled laser-liquid interaction. Laser energy is absorbed within a confined donor architecture, converted into thermal and plasma responses, and then transformed into bubble-mediated motion of the donor material. The cavitation bubble provides the transient mechanical bridge between optical energy deposition and the hydrodynamic ejection process. This chapter presents LIFT from a multiscale perspective centered on bubble dynamics and material ejection. It first reviews major LIFT donor architectures. Then, it examines how donor ribbon design, absorbing-layer properties, laser parameters, material rheology, control bubble inception/growth, jet formation, droplet breakup, and final deposition. Modeling approaches are discussed as tools for connecting experimental observations across time and length scales, ranging from reduced-order estimates to interface-resolving simulations and data-driven process maps. As one illustrative mechanistic example, thermal-only, plasma-mediated, and coupled plasma-thermal-thermoelastic frameworks for early-stage bubble inception are briefly compared to show how different inception assumptions can provide initial conditions for downstream bubble growth and jetting models. This chapter concludes by identifying opportunities for bubble-aware donor design, time-resolved diagnostics, benchmark datasets, and predictive LIFT process maps based on intermediate bubble and jet observables.

physics.flu-dyn↗

arXiv2026

From Optical Breakdown to Bubble Inception: A Coupled Plasma-Thermal Framework for Nanosecond Laser-Induced Cavitation in Water

Laser-induced cavitation under nanosecond optical breakdown is central to applications such as laser-induced forward transfer (LIFT), microsurgery, and microfluidic actuation, yet the physical origin of the earliest cavity and its connection to subsequent bubble growth remain unresolved. Existing models typically describe bubble formation either as a plasma-driven mechanical response or as a thermally driven nucleation process, without resolving how these mechanisms interact during inception. In this study, we developed a coupled plasma-thermal framework that unifies free-electron dynamics, plasma absorption, thermoelastic acoustic response, residual thermal energy retention, and post-inception bubble evolution within a single description. The model shows that bubble inception is governed primarily by plasma-induced thermoelastic acoustic relaxation, which generates transient tensile rarefaction pressures sufficient for cavitation on nanosecond timescales, while residual thermal energy sustains subsequent bubble growth. Because plasma-mediated energy deposition is spatially anisotropic under moving breakdown conditions, the initial cavity is predicted to form as an elongated, asymmetric bubble rather than as a spherical nucleus. This initial bubble shape reflects the spatial structure of the plasma absorption region and provides the starting geometry for subsequent bubble dynamics. Comparison with time-resolved experiments demonstrates that the coupled framework captures both early-time cavity formation and long-time bubble expansion more accurately than plasma-only or thermal-only models. These results show how breakdown-induced asymmetry controls the birth shape of the cavitation bubble and its early fluid-mechanical evolution, providing a physics-based basis for modeling laser-driven liquid motion and material transport.

physics.flu-dyn↗

arXiv2025

Automated Model-Free Sorting of Single-Molecule Fluorescence Events Using a Deep Learning Based Hidden-State Model

Single-molecule fluorescence assays enable high-resolution analysis of biomolecular dynamics, but traditional analysis pipelines are labor-intensive and rely on users' experience, limiting scalability and reproducibility. Recent deep learning models have automated aspects of data processing, yet many still require manual thresholds, complex architectures, or extensive labeled data. Therefore, we present DASH, a fully streamlined architecture for trace classification, state assignment, and automatic sorting that requires no user input. DASH demonstrates robust performance across users and experimental conditions both in equilibrium and non-equilibrium systems such as Cas12a-mediated DNA cleavage. This paper proposes a novel strategy for the automatic and detailed sorting of single-molecule fluorescence events. The dynamic cleavage process of Cas12a is used as an example to provide a comprehensive analysis. This approach is crucial for studying biokinetic structural changes at the single-molecule level.

q-bio.QM↗

arXiv2015

Shifted Hecke insertion and the K-theory of OG(n,2n+1)

Patrias and Pylyavskyy introduced shifted Hecke insertion as an application of their theory of dual filtered graphs. We use shifted Hecke insertion to construct symmetric function representatives for the K-theory of the orthogonal Grassmannian. These representatives are closely related to the shifted Grothendieck polynomials of Ikeda and Naruse. We then recover the K-theory structure coefficients of Clifford-Thomas-Yong/Buch-Samuel by introducing a shifted K-theoretic Poirier-Reutenauer algebra. Our proofs depend on the theory of shifted K-theoretic jeu de taquin and the weak K-Knuth relations.

math.CO↗