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In this work, a miniaturized, automated, and cost-effective ELISA device is designed and implemented, without the utilization of conventional techniques such as pipetting or microfluidic valve technologies. The device has dimensions of 24 cm × 19 cm × 14 cm and weighs <3 kg. The total hardware cost of the device is estimated to be approximately $1200, which can be further reduced through optimization during scale-up production. Three-dimensional printed disposable parts, including the reagent reservoir disk and the microfluidic connector, have also been developed. IL-6 is used as a model system to demonstrate how the device provides an ELISA measurement. The cost per test is estimated to be <$10. The compactness, automated operation, along with the cost-effectiveness of this ELISA device, makes it suitable for point-of-care applications in resource-limited regions. 

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements—up to an 8128-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors. 

Accurately measuring the viscoelastic properties of biomaterials is critical for understanding their functions in biological systems and optimizing their development for specific applications. Conventional methods often require direct physical contact, which hinders longitudinal studies of sterile samples and impose strict requirements in sample preparation. Here, we introduce tensile acoustic rheometry (TAR), a technique for rapid, contactless characterization of soft viscoelastic biomaterials. TAR uses a dual-mode ultrasound approach to apply an upward force impulse, generating oscillatory tensile and compressive motion in a small, free-standing sample (~30 mm3) with its bottom immobilized on a pre-wetted flat surface by capillary stiction. High frequency ultrasound pulse echo detection is employed to track this motion via the movement of the top surface of the sample in real time. In this study, we developed a theoretical framework of the tensile-compression motion of the sample from which Young’s modulus and viscosity of the sample are determined based on the TAR measurements. TAR was validated across a variety of samples, including engineered hydrogels and commercially available natural food products. Results from TAR measurements aligned closely with theoretical predictions, reported values, and shear wave elastography measurements. These findings underscore the versatility and flexibility of TAR as a robust, versatile rheological method for biomaterial characterization with minimal sample preparation requirements. 

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot® (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of in vitro migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and “hands-free,” schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.