Lab on a Chip最新文献

Extracellular vesicles (EVs), especially the exosome-sized subset are increasingly exploited as minimally invasive cancer biomarkers. These small vesicles are abundant in biofluids and play crucial roles in intercellular communication and disease progression by transporting bioactive molecules. Exosomes offer distinct diagnostic and prognostic advantages over traditional cancer biomarkers, but purifying and extracting exosomes from blood remains challenging. There is a need to simply and cost-effectively isolate exosomes from milliliter quantities of whole blood for transcriptional and other omics-based research. Addressing this gap, we propose a microfluidic cartridge, the EV-Blade, for size and affinity-based purification of exosomes on a centrifugal microfluidic platform. We demonstrate a method to automate exosome purification from whole blood samples on a single microfluidic cartridge. The EV-Blade system combines blood centrifugation, plasma filtration for EV size selection and immunomagnetic capture using functionalized superparamagnetic nanoparticles targeting CD9, CD63, and CD81 exosomal surface proteins. We report on the device performance, purity of exosome recovery and the quality of RNA collected following on-chip EV lysis. We use this automated method to detect relevant long coding and non-coding RNA transcripts in circulating blood exosomes, showcasing the EV-Blade for use in cancer patient risk stratification. The system presented herein represents a significant advancement in exosome purification, offering a robust and automated platform for liquid biopsy-based cancer research and clinical applications. This innovation holds promise for cancer diagnosis, prognosis, and monitoring through non-invasive biomarkers.

Microfluidic probes (MFPs) are an emerging class of open microfluidic devices that use hydrodynamic flow confinement (HFC) to enable precise, contact-free delivery, and removal of fluids on biological surfaces. Unlike closed-channel microfluidics, MFPs operate in open environments, allowing localized chemical and biological interactions with high spatial and temporal resolution. Since their introduction in 2005, MFPs have advanced through major innovations, including multipolar flow designs, vertical configurations, 3D printing, and structural enhancements such as herringbone micromixers. This review presents a comprehensive overview of MFP technologies, covering core physical principles, flow dynamics, operating modes, and the influence of geometric and hydrodynamic design. We examine fabrication techniques such as photolithography, soft lithography, and 3D printing, highlighting their trade-offs in precision, scalability, and cost. We also explore biological applications of MFPs, including tissue assays, cellular manipulation, molecular patterning, and single-cell biopsy. Emerging integrations with heating, dielectrophoresis, and real-time feedback are expanding the utility of MFPs for adaptive high-throughput workflows. By tracing two decades of development, this review positions MFPs as transformative tools in open-space microfluidics and outlines opportunities for future progress.

In confined spaces where early fire detection and suppression are particularly challenging, failures in fire prevention and control can lead to severe personal injury and property loss. In such scenarios, the structured encapsulation and controlled release of fire-extinguishing agents are especially critical. However, systematic studies on the structural design of extinguishing agents and their fire-suppression mechanisms remain notably insufficient. Based on non-planar microfluidic technology, this study designed a novel PDMS microfluidic device for the highly efficient preparation of thermally responsive water-based fire-extinguishing microcapsules. Through rational design of the microcapsule structure and substrates, we achieved not only controllable adjustment of the agent dosage but also precise regulation over the release direction and coverage area of fine water mist. In accordance with the UL94 V-0 standard, the optimal microcapsule size was determined to be 550 μm in diameter with a shell thickness of 35 μm. Furthermore, integration of the microcapsules into a thermally responsive patch enabled effective flame suppression within 3 seconds. The water-based microcapsule system is environmentally benign, highly efficient, and cost-effective, offering a high-performance microencapsulated fire-extinguishing technology with directional release capability for early fire prevention and control in confined spaces, showing promising application potential.

Wearable biosensors leverage microfluidic technology for precise biofluid sampling and directional transport, and utilize electrical or optical sensing mechanisms for reliable detection of target physiological parameters. By synergizing microfluidics and sensing technologies, these devices provide innovative solutions for biomarker monitoring, demonstrating broad potential in health tracking and chronic disease management. With ongoing advances in smart materials, multiplex detection capabilities, and artificial intelligence-driven technologies, wearable biosensors are evolving into cornerstone tools for telemedicine and precision diagnostics. This work reviews recent progress in microfluidic-integrated wearable biosensors for disease diagnostics and health monitoring. We systematically examine sensing approaches for different analytes based on their biological characteristics, covering three key categories: (1) metabolite sensing, including microneedle-based detection, noninvasive optical/electrical methods, multimodal platforms, and closed-loop diabetes management systems; (2) protein sensing, encompassing both label-free and labeled electrical/optical techniques; and (3) nucleic acid sensing, which involves sampling protocols, amplification strategies, and label-free detection approaches. The review highlights the interaction between biomarker biological characteristics, sensing strategies, and microfluidic approaches in the development of wearable biosensing platforms, and is expected to guide the development of next-generation intelligent disease diagnostics and health monitoring devices.

Precise manipulation of small sample volumes through enrichment, metering, routing, and selective sorting defines the analytical performance of microfluidic systems. While passive approaches such as deterministic lateral displacement and inertial microfluidics offer robust geometry-encoded separations and field-based techniques like dielectrophoresis, magnetophoresis, and acoustofluidics provide dynamic control, they are limited by inability for tuning, susceptibility to sample media properties, and hardware complexity. Diaphragm-based actuation overcomes these constraints by introducing deformable membranes that dynamically reconfigure channel geometry to achieve sub-second fluidic control without direct exposure to external fields. This review consolidates diaphragm-actuated microfluidic strategies as a unified framework for active sample manipulation, spanning two key functions, enrichment (analyte/cell trapping, ion-transport focusing, and nanoconfinement) and activated sorting (label-based, label-free, and hybrid modalities). Diaphragm materials, geometries, and actuation schemes (pneumatic, piezoelectric, electrostatic, electromagnetic, thermo-pneumatic, and shape-memory) are benchmarked against quantitative performance metrics like pressure–deflection transfer, latency, enrichment efficiency, selectivity, and gating accuracy. Emerging directions include smart fatigue-resistant diaphragm materials, sensor-integrated feedback control, real-time programmable gating, scalable fabrication, and artificial intelligence (AI) to process multimodal data to trigger actuation. By bridging sample enrichment and activated sorting within a single mechanical paradigm, diaphragm-based actuation provides a versatile route towards autonomous, label-free, and high-content lab-on-a-chip systems for next-generation diagnostics, single-cell analytics, and biomanufacturing workflows.

Precise spatiotemporal manipulation of particles in complex microfluidic channel networks (MCNs) underlies numerous advanced applications, but remains constrained by the difficulty of rapidly translating prescribed trajectories into manufacturable device designs. In this work, we introduce a modular deep learning framework that overcomes these limitations by decomposing MCNs into standardized, reusable functional modules with well-characterized fluidic and structural properties. For each module, a dedicated neural network predicts the full spatiotemporal particle state—including position, velocity, and transit time—under diverse flow conditions. A multi-module reconfiguration algorithm (MMRA) assembles these local predictions into continuous, device-scale trajectories while rigorously preserving physical state continuity. This approach enables deterministic port routing and precise spatiotemporal scheduling on “DUT” and “grid” chips, with a mean absolute timing error below 0.031 s. Integrated into PathChip, our user-friendly end-to-end design platform, the proposed approach enables users to specify target particle behaviors and automatically generate optimized module sequences, geometries, and control parameters, producing fabrication-ready device blueprints. Using this reverse design workflow, the integration of 5000 modules can be completed in as little as 18 s. This work establishes a structurally scalable pathway toward programmable, device-level spatiotemporal particle manipulation in microfluidics, with broad implications for lab-on-a-chip automation, high-throughput screening, and adaptive microfluidic systems.

Traditional microfluidic chips for single-cell mechanical characterization face challenges such as cell aggregation and low throughput, limiting their clinical applicability. While fluid-driven methods such as constricted extrusion, pipette aspiration, and shear-induced or stretch-induced deformation have demonstrated laboratory success, they require improvements in accuracy and scalability. To overcome these limitations, integration of external physical fields, including acoustic, optical, electrical, and magnetic, enables non-contact, high-throughput cell operations and analysis. Acoustic waves and magnetic fields provide precise control over cell deformation, optical tweezers enable contact-free trapping, and electric fields facilitate dielectrophoretic manipulation. These techniques improve measurement sensitivity and throughput, making them more suitable for clinical applications, but also increase follow-up processing time. Artificial intelligence (AI) further enhances microfluidic automation across all these methodologies by enabling real-time image processing, parameter optimization, and data analysis to shorten processing time. This review particularly explores how AI is poised to solve fundamental, long-standing problems in cell mechanics that are intractable for conventional methods. Future microfluidic systems will integrate multiple physical fields controlled with AI, improving precision and scalability. The convergence of microfluidics, external fields, and AI is expected to revolutionize single-cell mechanobiology, advancing both fundamental research and clinical applications.