Lab on a Chip最新文献

Conventional in vitro physiological models, relying on animal studies and 2D/3D cell cultures, are fundamentally limited by interspecies biological discrepancies, ethical constraints, or inadequate replication of human physiology. Organ-on-a-chip (OoC) technology overcomes these challenges through emulating organ-specific microphysiological systems. The transformative power of this innovation lies in multi-channel microfluidic chips. These chips facilitate the formation of 3D cellular organizations and tissue interfaces via integrated porous membranes, micropillar arrays, or perfusable vascular microchannels, simultaneously allowing for precise and dynamic modulation of chemical, biological, and physical factors. Rapid technological evolution has yielded functional models of lung alveoli, the blood–brain barrier, cardiac tissues, etc., thereby advancing drug testing and disease modeling accuracy. This review systematically examines the development of OoC technology through the lens of multi-channel microfluidics by focusing on four pivotal domains: (1) the biomimetic design overview for OoCs, (2) fabrication methods including soft lithography and 3D printing, (3) applications in pathophysiological investigations, preclinical drug evaluation, and toxicological assessment, and (4) current challenges and perspectives in structural design, materials and fabrication, biological applications, and other development directions. This review is intended to provide a reference for the technological iteration and interdisciplinary application of multi-channel microfluidic chip systems.

Global challenges such as climate change, escalating energy demands, and health equity require new scientific innovations able to deliver timely solutions. Self-driving laboratories (SDLs) combine robotics and lab automation with artificial intelligence to efficiently explore complex experimental spaces, reduce human effort, and speed up discovery through intelligent experimentation. Central to this transformation is responsible research acceleration (RRA). This ensures that advances are reproducible, transparent, and resource-efficient, and lays the foundation for sustainable innovation. Microfluidics, with its precise control of heat and mass transfer rates, minimal reagent use, and seamless integration with real-time sensing and automation, represents an ideal platform to embody RRA principles within SDLs. This perspective explores the synergy between microfluidics and autonomous experimentation, highlights key challenges, and proposes strategies for fully autonomous microfluidic workflows. We argue that flow-based platforms are essential to expedite discovery and that stronger academia–industry collaboration is critical in shortening the path from scientific insight to real-world implementation and impact.

Since their first report in 2007, microfluidic paper-based analytical devices (μPADs) have continued to gain attention as promising tools for point-of-care diagnostics due to their low cost, portability, ease of operation, and design flexibility. This review summarizes and discusses recent advances in the field, mostly based on works published between 2017 and 2025, with a focus on progress and remaining challenges in bridging the gap between proof-of-concept demonstrations in academic laboratories and real-world implementation. Special emphasis is placed on devices validated with clinical samples and capable of true sample-in–answer-out operation. To comprehensively assess recent developments, nearly one hundred reported examples were analysed not only in terms of analytical figures of merit but also with respect to practical criteria such as real-sample testing, long-term storage stability, the need for off-device sample pretreatment, reagent handling complexity, time-control requirements, and the number of operation steps. In parallel, topics of ongoing academic interest are highlighted, including automated sequential reagent delivery, strategies for accelerating liquid flow, and robust signal readout methods going beyond purely qualitative approaches to enhance assay sensitivity, precision, rapidity, and instrument-free usability. Finally, the review introduces emerging analytical technologies newly integrated into μPAD platforms, such as surface-enhanced Raman scattering (SERS), bioluminescence, CRISPR-based assays, and machine learning-driven data interpretation, which further expand the analytical capabilities and scope of μPADs.

Automating the isolation of rare cells such as circulating tumour cells (CTCs) within crowded microfluidic environments remains a bottleneck in liquid biopsy workflows. Optical tweezers offer contact-free, selective manipulation but traditionally rely on expert operators. We present MaGIC-OT (machine-guided isolation of cells using optical tweezers), a platform that integrates classical path planning and deep reinforcement learning (DRL) to automate single-cell manipulation inside a microfluidic chip. We built a high-fidelity simulation to train and benchmark control policies and show that cooperative, human-in-the-loop training improves DRL performance. Trained agents outperform expert users in speed and isolation success in silico, and we demonstrate proof-of-concept isolation of a cancer cell from a spiked blood sample on-chip. MaGIC-OT provides a flexible framework for intelligent optical manipulation, aligning microfluidic device design with autonomous control strategies and offering a pathway toward high-purity, label-free single-cell workflows.