Search Results (107)

Additive manufacturing (AM) has expanded the design space for actuator systems by enabling complex internal geometries, multimaterial architectures, functional gradients, embedded channels, and application-specific components that are difficult to realize using conventional fabrication alone. These capabilities are particularly relevant when actuator performance is governed by the coupling between material selection, printed architecture, stimulus response, and system-level integration. This review examines additively manufactured actuators and actuator components using a material-architecture-function-integration framework. The actuator classes considered include soft pneumatic and fluidic actuators, electroactive and piezoelectric polymer actuators, shape-memory and 4D-printed actuators, magnetic and magnetoactive actuators, and printed pneumatic, hydraulic, mechanical, and aerospace-grade actuator components. Representative applications are discussed across biomedical and rehabilitation systems, aerospace and deployable mechanisms, soft robotics, and industrial automation. Beyond summarizing printed actuator demonstrations, the review analyzes the integration barriers that determine whether AM actuators can transition from laboratory prototypes to functional systems. These barriers include material durability, leakage, fatigue, dielectric breakdown, filler dispersion, interfacial failure, dimensional variability, environmental sensitivity, auxiliary hardware requirements, sensing, control, and benchmarking. By organizing recent developments across actuator classes, application domains, and integration strategies, this review clarifies where AM provides a functional advantage over conventional fabrication and where further validation is required for reliable deployment. Full article

The low signal-to-noise ratio (SNR) of existing magnetic sensors limits the detection of magnetic nanoparticles (MNPs) in microfluidic biosensing. We present a novel microfluidic coil-based impedance detection system for quantifying magnetic particles, including Fe filings and citrate-coated Fe₃O₄ MNPs, with potential applications in magnetically guided biosensing. Unlike conventional approaches that directly measure the magnetic properties of dispersed particles, our method employs an external collector magnet to concentrate particles within a copper coil detector. The accumulated particles alter the coil’s electromagnetic response through changes in the sample’s dielectric properties, producing an amplified impedance signal proportional to sample volume. We evaluated detection performance for 1–10 mg of ferromagnetic Fe filings and citrate-coated Fe₃O₄ MNPs across a broad frequency range. Results show a strong linear correlation between particle mass and impedance change, with SNR values from 25 dB to over 45 dB, demonstrating high sensitivity and precision. Coil sensitivity was further optimized by varying the number of turns (5, 10, and 15), enabling frequency-specific customization. This approach provides a scalable, low-cost platform adaptable to polymer-coated MNPs targeting biological analytes. Full article

This article presents an intelligent differential capacitive bioelectronic sensing system that provides an experimental foundation for future AI-assisted reliable microfluidic reagent delivery in automated pathology. The proposed platform integrates a slot-type microfluidic chamber, a differential slot-line capacitive sensor, embedded readout and signal-conditioning electronics, and a supervisory state assessment concept within a unified architecture. Its purpose is to support stable microliter-scale reagent exchange together with non-invasive process observability in automated staining workflows. The experimental study included flow calibration, analysis of feed direction and chamber tilt angle, preliminary vibration-assisted bubble mobilization, and evaluation of the sensing subsystem. The results showed that reliable operation is achieved only within a practically admissible regime in which fluidic stability and sensing informativeness overlap. In the investigated setup, upper-feed delivery and low chamber tilt angles provided the most favorable filling conditions, while the differential capacitive subsystem enabled stable detection of liquid-state changes in narrow microtubes. The reported results establish a foundation for future AI-assisted transport-state recognition and adaptive monitoring in automated pathology platforms. Full article

Automated multiplex immunohistochemistry (IHC) and in situ hybridization (ISH) require staining platforms that combine stable reagent exchange, low-volume operation, process observability, and protocol flexibility. Existing autostainers are often rigid and costly, whereas microfluidic and sensing solutions remain largely component-specific rather than system-oriented. This study proposes and partially validates a layered cyber-physical architecture for multiplex histological staining. The architecture integrates five functional layers—biochemical workflow, fluidic processing, capacitive sensing, protocol-driven control, and software-based process representation—within a unified formal framework and is supported at the subsystem level by experimental characterization of its fluidic and sensing layers. Fluidic experiments on a slot-type microfluidic chamber identified a practical operating window in which upper-feed operation, moderate calibrated flow conditions, and low chamber angles between 10° and 40° provide stable filling and acceptable drainage. The differential slot-line capacitive sensing subsystem detected liquid volumes as low as 0.5 µL, with stable threshold-based interpretation at a practical detection threshold of approximately 5 fF after digital filtering. The control and software layers are specified at the architectural and formal model level; their hardware implementation and closed-loop validation remain subjects of future work. Together, the reported results demonstrate that controlled reagent transport and sensing-based process observability are jointly feasible within the proposed modular framework, establishing a conceptual and experimental foundation for scalable, flexible, and resource-efficient multiplex IHC/ISH systems. Full article

This work numerically reveals a novel strategy to modulate two ionic conductance state in a nanochannel via pressure-dependent fluidic motion inside the channel. Steady and transient simulations based on Poisson–Nernst–Planck–Stokes equations demonstrate that the two states with distinct ionic conductance and ion selectivity can be reversibly switched by external pressure, with a characteristic time of ~100 μs. Furthermore, the two conductance states are found to depend on the transversal electric field, which gives rise to two distinct intrachannel fluidic flow patterns, namely laminar flow and vortex flow, respectively. This finding suggests the potential of pressure-controlled ionic conductance switching for applications in nanofluidic ionic circuits, flow-regulated sensing, and integrated micro/nanoscale devices. It also provides insights into nonlinear ionic current–voltage behaviors. Full article

This review deals with the development and progress of micro-electromechanical systems (MEMS) actuators, which are needed in microfluidic applications, such as lab-on-a-chip and diagnostics. In the last 10 years, there have been tremendous advances in materials, microfabrication and computational modeling that have increased the functionality and scope of MEMS-based microfluidic actuation. This study classifies MEMS actuators on the basis of the physical method of actuation, including electrostatic, piezoelectric, and pneumatic actuation designs, in comparison with their application in pumping, valving, and droplet control. It examines the suitability of emerging structural and functional materials, such as piezoelectric thin-films and electroactive polymers, paying special attention to their reliability and biocompatibility. It also highlights the progress in multiphysics modeling that incorporates electrical, thermal, mechanical, and fluidic models, which facilitates the efficient design and performance optimization procedures. Other trends are multifunctional actuators with built-in sensing capability and the use of artificial intelligence (AI)-assisted design in production. With these developments, however, there exist issues of power efficiency, thermal control, fabrication uniformity and operational durability, and also the absence of standardized benchmarking. Finally, future research directions are outlined, including hybrid MEMS actuation, intelligent microfluidic operations, to improve the performance of the system and enable the transfer of the lab demonstrations to the large scale application of the system. Full article

As a class of self-organized soft matter systems merging fluidic mobility with long-range molecular order, cholesteric liquid crystals (CLCs) possess immense potential for the development of high-sensitivity, visually tractable flexible sensors. Leveraging their unique helical superstructures and stimuli-responsive photonic bandgaps, CLCs can transduce subtle physical or chemical perturbations into discernible optical signatures, such as Bragg reflection shifts or mesomorphic textural transitions. Nonetheless, the intrinsic fluidity of CLCs often compromises their structural integrity, while conventional one-dimensional (1D) or two-dimensional (2D) confinement geometries exhibit pronounced angular dependence, significantly constraining their detection precision in complex environments. Recently, microfluidic technology has emerged as a pivotal paradigm for achieving sophisticated three-dimensional (3D) spatial confinement of CLCs through the precise manipulation of microscale fluid volumes. This review systematically delineates recent advancements in microfluidics-enabled CLC sensors. Initially, the fundamental self-assembly principles and optical properties of CLCs are introduced, emphasizing the unique advantages of 3D spherical confinement in mitigating angular sensitivity and intensifying interfacial interactions. Subsequently, the primary sensing mechanisms are bifurcated into bulk-driven sensing via pitch modulation and interface-driven sensing via topological configuration transitions. We then detail the microfluidic-based fabrication strategies and engineering protocols for diverse 3D architectures, including monodisperse/multiphase droplets, microcapsules, shells, and Janus structures. Building upon these structural frameworks, current sensing applications in physical (temperature, strain/stress), chemical (volatile organic compounds, ions, pH), and biological (biomarkers, pathogens) detection are evaluated. Lastly, in light of persistent challenges, such as intricate signal interpretation and limited robustness in complex matrices, we propose future research trajectories, encompassing the co-optimization of geometric parameters (size and curvature), artificial intelligence-enhanced automated diagnostics, and multi-field-coupled intelligent integration. This work seeks to provide a comprehensive roadmap for the design of next-generation, high-performance, and portable liquid-state photonic sensing platforms. Full article

Current liquid-phase resonant biosensors, such as Quartz Crystal Microbalance, Surface Acoustic Wave, or Surface Plasmon Resonance, typically rely on specialized piezoelectric substrates or complex optical setups. These requirements often necessitate cleanroom fabrication, thereby limiting cost-effective scalability. This study presents a high-integration sensing platform based on standard Printed Circuit Board (PCB) technology, incorporating an embedded inductor within a fluidic system for real-time monitoring. This design leverages industrial manufacturing standards to achieve a compact, low-cost, and scalable architecture. Detection is governed by shifts in the resonance frequency of an LC tank circuit; specifically, increases in bulk ionic strength induce a frequency decrease, whereas biomolecular adsorption at the sensor surface leads to a frequency increase. This phenomenon can be explained by the modulation of the inter-turn capacitance, which is modeled as a combination of capacitive elements accounting for contributions from the bulk electrolyte and the surface-bound dielectric layer. Such divergent responses provide an intrinsic self-discriminating capability, allowing for the analytical differentiation between surface interactions and bulk effects. To the best of our knowledge, this is the first demonstration of an inductor-based resonant sensor fully embedded in a PCB fluidic architecture for continuous liquid-phase analyte monitoring. Validated through a protein-antibody model (Bovine Serum Albumin-anti-Bovine Serum Albumin), the sensor demonstrated a limit of detection of 1.7 ppm (0.026 mM) and a linear dynamic range of 31–211 ppm (0.47–3.2 mM). These performance metrics, combined with a reproducibility of 4 ± 3%, indicate that the platform meets the requirements for robust analytical applications. Its inherent simplicity and potential for miniaturization position this technology as a viable candidate for point-of-care diagnostics in diverse environments. Full article

Fluidic thrust vectoring control (FTVC) enables highly agile flight without the mechanical complexity of traditional vectoring nozzles. However, a robust onboard identification of the jet deflection state remains challenging when only limited measurements are available. This study proposes a sparse reconstruction of the pressure field method for a wedge passive FTVC nozzle and validates the approach experimentally on a low-speed jet platform. By combining the proper orthogonal decomposition (POD) algorithm with an ${\mathcal{l}}_1$-regularized compressed sensing method, a full Coanda wall pressure distribution is reconstructed from the sparse measurements. A genetic algorithm is then employed to optimize the wall pressure tap locations, identifying an optimal layout. With only four pressure taps, the local pressure coefficient errors were maintained within $\left|\Delta Cp\right|$ < 0.02. In contrast, conventional Kriging interpolation requires increasing the sensor count to 13 to approach the reconstruction level of the proposed POD–compressed sensing method using 4 sensors, yet still exhibits a reduced fidelity in capturing key flow structure characteristics. Overall, the proposed approach provides an efficient and physically interpretable strategy for pressure field estimation, supporting lightweight, low-maintenance, and precise fluidic thrust vectoring control. Full article

Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO₂-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of $6\times {10}^{-6}$ W cm⁻¹ Hz^−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies. Full article

In this paper, we propose and experimentally demonstrate a dual-parameter fiber optic sensor, which combines a Fabry–Perot interferometer (FPI) and a Mach–Zehnder interferometer (MZI) for simultaneous pressure and temperature sensing. The Fabry–Perot (FP) cavity is formed by sandwiching a capillary fiber between a single-mode fiber and a dual-core fiber (DCF). A fluid channel is very close to the central core of the DCF. By precisely drilling micro-air chambers in the annular cladding of a capillary fiber (CF) using a femtosecond laser, external air pressure can directly affect the capillary fiber and induce changes in the refractive index of the air in the CF. The F-P cavity achieves a pressure sensitivity of 3.67 nm/MPa with a temperature cross-sensitivity of 2.82 pm/°C. The MZI is constructed using a dual-core fiber filled with silicone oil in the fluidic channel, which enhances temperature sensitivity through the thermo-optic effect. The MZI sensor exhibits a nonlinear temperature response with an average sensitivity of 103.43 pm/°C. The corresponding pressure cross-sensitivity is about –0.11 nm/MPa. Due to very low cross-sensitivity, simultaneous measurement of temperature and gas pressure is feasible. In addition, we implement a variant by replacing silicone oil with a UV-curable adhesive, which delivers a comparable FP-based pressure sensitivity of ~3.93 nm/MPa while yielding an MZI-based temperature sensitivity of 71.7 pm/°C and potentially improved long-term stability. Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article

Meissner’s corpuscles are essential mechanoreceptors that detect low-frequency vibrations. However, the internal fluid dynamic processes that convert directional mechanical stimuli into neural signals are not yet fully understood. This study aims to clarify the direction-specific sensing mechanism by analyzing internal fluid flow and shear stress distribution under different vibration modes. A biomimetic microfluidic platform was developed and coupled with a dynamic mesh computational fluid dynamics (CFD) model to simulate the response of the corpuscle to 20 Hz normal and tangential vibrations. The simulation results showed clear differences in fluid behavior. Normal vibration produced localized vortices and peak wall shear stress greater than 0.0054 Pa along the short axis. In contrast, tangential vibration generated stable laminar flow with a lower average shear stress of about 0.0012 Pa along the long axis. These results suggest that the internal structure of the Meissner corpuscle is important for converting mechanical inputs from different directions into specific fluid patterns. This study provides a physical foundation for understanding mechanotransduction and supports the design of biomimetic sensors with improved directional sensitivity for use in smart skin and soft robotic systems. Full article

The propagation of interface acoustic waves (IAWs) along rotated YX-LiNbO₃/SU-8/ZX-Si structures is theoretically investigated to identify the Y-rotation angles that support the efficient propagation of low-loss modes guided along the structure’s interface. A three-dimensional finite element analysis was performed to simulate IAW propagation in the layered structure and to optimize design parameters, specifically the thicknesses of the platinum (Pt) interdigital transducers (IDTs) and the SU-8 adhesive layer. The simulations revealed the existence of two types of IAWs travelling at different velocities under specific Y-rotated cuts of the LiNbO₃ half-space. These IAWs are faster than the surface acoustic wave (SAW) and slower than the leaky SAW (LSAW) propagating on the surface of the bare LiNbO₃ half-space. The mechanical displacement fields of both IAWs exhibit a rapid decay to zero within a few wavelengths from the LiNbO₃ surface. The piezoelectric coupling coefficients of the IAWs were found to be as high as approximately 7% and 31%, depending on the Y-rotation angle. The theoretical results were experimentally validated by measuring the velocities of the SAW and LSAW on a bare 90° YX-LiNbO₃ substrate, and the velocities of the IAWs in a 90° YX-LiNbO₃/SU-8/Si structure featuring 330 nm thick Pt IDTs, a 200 µm wavelength, and a 15 µm thick SU-8 layer. The experimental data showed good agreement with the theoretical predictions. These combined theoretical and experimental findings establish design principles for exciting two interface waves with elliptical and quasi-shear polarization, offering enhanced flexibility for fluidic manipulation and the integration of sensing functionalities. Full article

Gas bubbles are one of the main disturbances encountered when dispensing drugs of microliter volumes using portable miniaturized systems based on piezoelectric diaphragm micropumps. The presence of a gas bubble in the pump chamber leads to the inaccurate administration of the required dose due to its impact on the flowrate. This is particularly important for highly concentrated drugs such as insulin. Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. These solutions increase the complexity, size, and cost of the microdosing system. To address these problems, a radically new approach is taken by utilizing the sensing capability of the piezoelectric diaphragm during micropump actuation. This work demonstrates the workflow to build a self-sensing micropump based on artificial intelligence methods on an embedded system. This is completed by the implementation of an electronic circuit that amplifies and samples the loading current of the piezoelectric ceramic with a microcontroller STM32G491RE. Training datasets of 11 micropumps are generated at an automated testbench for gas bubble injections. The training and hyper-parameter optimization of artificial intelligence algorithms from the TensorFlow and scikit-learn libraries are conducted using a grid search approach. The classification accuracy is determined by a cross-training routine, and model deployment on STM32G491RE is conducted utilizing the STM32Cube.AI framework. The finally deployed model on the embedded system has a memory footprint of 15.23 kB, a runtime of 182 µs, and detects gas bubbles with an accuracy of 99.41%. Full article