Sensors

Organic Electrochemical Transistors (OECTs) have emerged as continuous monitoring sensors for in vitro models. Source Measure Units (SMUs) are typical instruments used to simultaneously control input and output signals in OECTs. Conventional SMUs are unsuitable for in vitro applications due to their limited channel count, large footprint and high cost. This work presents the design and development of a multichannel, portable and cost-effective SMU platform for the continuous and long-term characterization of OECT biosensors. The hardware is based on a breadboard interfacing via custom connections to a six-OECT setup integrated into a standard culture plate and operated within a cell incubator. System functionality is managed by an Arduino Nano microcontroller (Arduino, Monza, Italy) through custom-developed software, enabling automated characterization of the OECTs. Platform performance was evaluated by comparing OECT I–V characteristics and time responses against the commercial B2912A SMU (Keysight Technologies, Santa Rosa, CA, USA), using phosphate-buffered saline (PBS) to mimic biological samples. Long-term continuous operation was verified through a 5 day recording session with a 30 min sampling interval. Experimental results demonstrated consistent functionality with satisfactory accuracy for the targeted application. The proposed platform is a promising alternative to conventional SMUs for the continuous and long-term monitoring of OECT biosensors. Future work will focus on hardware optimization and full biological validation through OECT-based monitoring of in vitro models.

Differential interferometric synthetic aperture radar (D-InSAR) provides a potential approach for retrieving mountain snow depth at high spatial resolution using Sentinel-1 interferometric phase information. However, its performance in complex mountainous terrain remains insufficiently evaluated. This study assessed the applicability and uncertainty of Sentinel-1 D-InSAR for multi-temporal snow depth retrieval in the Maxian Mountains. Sentinel-1 interferometric pairs from 2020 to 2022 were used for snow depth retrieval, and UAV-LiDAR snow depth estimates served as independent reference data. The spatial distribution of interferometric coherence was examined, and coherence statistics were compared among elevation intervals and land-cover types. Snow depth was derived from interferometric phase using snow density and local incidence angle as model inputs, and sensitivity to both parameters was evaluated. The retrievals characterized the main spatial distribution of snow depth, but validation accuracy varied markedly among periods. Compared with November 2020, the root mean square error (RMSE) and mean absolute error (MAE) in April 2021 increased by 3.62 and 2.55 cm, respectively, indicating larger errors under spring snow conditions. Interferometric coherence was a key factor controlling retrieval reliability, and areas with low coherence were more prone to phase-unwrapping errors, missing retrievals, and local anomalies. Sensitivity analysis showed that snow density had a greater influence than local incidence angle.

The world has faced pandemics throughout its history. The most recent, COVID-19, took lives and disrupted healthcare and economies. COVID-19 vaccine programs were launched worldwide; however, the record time in which vaccines were developed raises questions about side effects and their severity. Also, as vaccines are delicate medical products, their cold supply chain must be continuously tracked and monitored to ensure their efficacy. Moreover, vaccine supply chain management is complex and requires trained staff to handle its different activities. As such, it is important to connect beneficiaries’ symptoms and registration systems with the vaccine supply chain to enable healthcare authorities to investigate whether observed side effects may be linked to cold chain handling conditions or production factors. Consequently, the need arises for a system that manages trained personnel certificate generation, which could also be used as a verification tool for employing the right experts for the right job. Therefore, our study developed a blockchain platform that integrates individuals’ registration and side effect data with vaccine cold supply chain information. The developed platform also includes a certification system for cold supply chain personnel training to support authenticated certificate generation and role assignment management. Blockchain was selected due to its features of security, decentralization, traceability, and trust. The key contributions of this work lie in providing a digital collaborative management and data tracing platform for vaccine side effects that are linked to beneficiaries’ registration and cold supply chain data to enable healthcare authorities to investigate whether any observed side effects may be linked to cold chain handling conditions or production factors while managing the issuance of certificates for trained cold supply chain personnel and controlling their employment in the correct chain(s). Our platform creates training certificate IDs without a central entity to regulate certificate generation and uses the created certificate information to control the employment of personnel with the appropriate training in the right chain to perform the right role. Our platform identified personnel employed in any chain where violations were recorded and side effects were subsequently reported, and presents their training and employment data, providing the interconnected data record necessary for accountability. The platform was tested successfully, producing the desired outcome of secure, traceable, and transparent vaccine data sharing, linking, and verification.