A multi-layer microfluidic chip integrates dynamic cell culture, processing, and in situ measurement of mechanical properties.

Microrobot swarms have powerful functions and reconfigurable collective behaviors, which enable them to be a possible way to completely solve the low execution efficiency and poor practicability of a single microrobot. However, some general deficiencies hinder the advancement of research in the application of microrobot swarms. For example, the limited number of individuals and the tiny spatial size make the current microrobot swarms too insignificant to meet practical needs. To release these restrictions, we designed a hybrid-style magnetic actuation control system that integrates the electromagnetic coils and permanent magnet. This hybrid-style actuation system can generate customized magnetic fields spatiotemporally, including powerful rotating and gradient magnetic fields, which enables the emergence of a large-scale magnetic microrobot swarm (LMMS) under the coupling effect of the magnetic field and gradient. Besides, the LMMS based on ferrofluid droplets is studied to verify the actuation system's feasibility, with a series of control strategies being proposed accordingly and the performance of the actuation system being discussed theoretically by mathematical models. Finally, physical or simulation experiments were carried out on these bases, demonstrating that the aforementioned method can realize the controllable emergence, dispersion, and locomotion of the LMMS with excellent manipulation ability and maneuverability.

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability.

Multi-fluid micromixing, which has rarely been explored, typically represents a highly sought-after technique in on-chip biochemical and biomedical assays. Herein, we propose a novel micromixing approach utilizing induced-charge electroosmosis (ICEO) to implement multicomplex mixing between parallel streams. The variations of ICEO microvortices above a sinusoidally shaped floating electrode (SSFE) are first investigated to better understand the microvortex development and the resultant mixing process within a confined channel. On this basis, a mathematical model of the vortex index is newly developed to predict the mixing degree along the microchannel. The negative exponential distribution obtained between the vortex index and mixing index demonstrates an efficient model to describe the mixing performance without solving the coupled diffusion and momentum equations. Specifically, sufficient mixing with a mixing index higher than 0.9 can be achieved when the vortex index exceeds 51, and the mixing efficiency reaches a plateau at an AC frequency close to 100 Hz. Further, a rectangle floating electrode (RFE) is deposited before SSFE to enhance the controlled sequence for three-fluid mixing. One side fluid can fully mix with the middle fluid with a mixing index of 0.623 above RFE in the first mixing stage and achieve entire-channel mixing with a mixing index of 0.983 above SSFE in the second mixing stage, thereby enabling on-demand sequential mixing. As a proof of concept, this work can provide a robust alternative technique for multi-objective issues and structural design related to mixers.

Concentration gradient generation plays a pivotal role in advancing applications across drug screening, chemical synthesis, and biomolecular studies, yet conventional methods remain constrained by labor-intensive workflows, limited throughput, and inflexible gradient control. This study presents a novel multilayer microfluidic chip leveraging shear flow-driven partitioning–recombination mechanisms to enable the flexible and high-throughput generation of concentration gradient droplets. The chip integrates interactive upper and lower polydimethylsiloxane (PDMS) layers, where sequential fluid distribution and recombination are achieved through circular and radial channels while shear forces from the oil phase induce droplet formation. Numerical simulations validated the dynamic pressure-driven concentration gradient formation, demonstrating linear gradient profiles across multiple outlets under varied flow conditions. The experimental results revealed that the shear flow mode significantly enhances mixing uniformity and droplet generation efficiency compared to continuous flow operations, attributed to intensified interfacial interactions within contraction–expansion serpentine channels. By modulating hydrodynamic parameters such as aqueous- and oil-phase flow rates, this system achieved tunable gradient slopes and droplet sizes, underscoring the intrinsic relationship between flow dynamics and gradient formation. The proposed device eliminates reliance on complex channel networks, offering a compact and scalable platform for parallelized gradient generation. This work provides a robust framework for optimizing microfluidic-based concentration gradient systems, with broad implications for high-throughput screening, combinatorial chemistry, and precision biomolecular assays.