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Flexible and wearable physical sensors have gained significant interest owing to their potential in attachable devices, electronic skin, and multipurpose sensors. The physical stimuli of these sensors typically consist of vertically and horizontally applied pressures and strains, respectively. However, owing to their similar response characteristics, interference occurs between the two types of signals detected, complicating the distinction between pressure and strain stimuli, leading to inaccurate data interpretation and reduced sensor specificity. Therefore, we developed a dual-sensing-mode physical sensor with separate response mechanisms for the two types of physical stimuli based on a unique structural design that can independently induce changes in the piezocapacitance and piezoresistance for pressure and strain stimuli, respectively. The asterisk-shaped piezoresistive pathway (electrode), designed for multifunctionality, effectively detected the intensity and direction of tensile deformation, and an elastomeric sponge structure positioned between the two electrodes detected the pressure signals via changes in capacitance. This dual-sensing-mode sensor offers clearer signal differentiation and enhanced multifunctionality compared to those of traditional single-mode sensors. Additionally, extensive experimentation demonstrated that our sensor has a good sensitivity, high linearity, and stability in detecting signals, proving its applicability for sophisticated monitoring and control tasks that require the differential detection between pressure and deformation signals.

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A physical sensor with a sensing medium comprising multiparallel-connected (MPC) piezoresistive pathways in both the vertical and horizontal directions was developed to achieve improved sensing performance. The MPC sensing medium reduces the total resistance and offsets noise, offering enhanced signal stability and device reliability and providing a high-performance sensing platform. The signal change and gauge factor (GF) of the 3PW-5L strain sensor (comprising three lines and five layers of piezoresistive pathways horizontally and vertically, respectively) were, respectively, 5.9 and 4.7 times higher than those of the 1PW-1L sensor composed of a monosensing pathway; the hysteresis of the detected signal was also significantly reduced. The linearity of the detected signal increased from 0.912 for 1PW-1L to 0.995 for 3PW-5L, indicating a greater sensing reliability. The direction of the applied tensile strain was successfully detected using the MPC sensing medium with an orthogonal configuration. The MPC piezoresistive sensor composing vertically stacked piezoresistive pathways demonstrated excellent performance as a pressure sensor; the 3PW-5L pressure sensor afforded a GF of 0.121 ± 0.002 kPa-1 with a linearity of 0.998 under an applied pressure ≥16.4 kPa. The MPC piezoresistive physical sensor offers a superior sensing performance and should contribute to the future development of wearable sensors and electronic devices.

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Fiber-type electronics is a crucial field for realizing wearable electronic devices with a wide range of sensing applications. In this paper, we begin by discussing the fabrication of fibers from conjugated polymers. We then explore the utilization of these fibers in the development of field-effect and electrochemical transistors. Finally, we investigate the diverse applications of these fiber-type transistors, encompassing chemical and physical sensors. Our paper aims to offer a comprehensive understanding of the use of conjugated polymers in fiber-type transistor-based sensors.

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With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues in order to conform to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, and stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation, while traditional medical sensors cannot meet such requirements. In addition, to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. Based on the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

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This paper describes the use of an optical instrument, the Fabry-Perot interferometer, adapted to measure very low pressures. The interferometer consists of two high-reflectance flat mirrors placed one in front of another. In addition, a metallic chamber contains air or a gas. In one of the faces of the chamber, a flexible thin silicone membrane is attached and, over it, one of the mirrors is glued. The other mirror rests in a fixed mechanical mounting. Light crosses both mirrors and, when it leaves them, forms an interference pattern consisting of concentric circular fringes. When the pressure is increased/decreased within the chamber, a displacement of the fringes is observed due to the movement of the glued mirror. By measuring the fringe displacement and knowing the pressure, a calibration plot can be made. Minimum pressure measurements of about tens of Pascals were achieved.

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The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

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In recent years, the considerable importance of healthcare and the indispensable appeal of curative issues, particularly the diagnosis of diseases, have propelled the invention of sensing platforms. With the development of nanotechnology, the integration of nanomaterials in such platforms has been much focused on, boosting their functionality in many fields. In this direction, there has been rapid growth in the utilisation of nanocellulose in sensors with medical applications. Indeed, this natural nanomaterial benefits from striking features, such as biocompatibility, cytocompatibility and low toxicity, as well as unprecedented physical and chemical properties. In this review, different classifications of nanocellulose-based sensors (biosensors, chemical and physical sensors), alongside some subcategories manufactured for health monitoring, stand out. Moreover, the types of nanocellulose and their roles in such sensors are discussed.

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Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

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Organ-on-a-chip systems are miniaturized microfluidic 3D human tissue and organ models designed to recapitulate the important biological and physiological parameters of their in vivo counterparts. They have recently emerged as a viable platform for personalized medicine and drug screening. These in vitro models, featuring biomimetic compositions, architectures, and functions, are expected to replace the conventional planar, static cell cultures and bridge the gap between the currently used preclinical animal models and the human body. Multiple organoid models may be further connected together through the microfluidics in a similar manner in which they are arranged in vivo, providing the capability to analyze multiorgan interactions. Although a wide variety of human organ-on-a-chip models have been created, there are limited efforts on the integration of multisensor systems. However, in situ continual measuring is critical in precise assessment of the microenvironment parameters and the dynamic responses of the organs to pharmaceutical compounds over extended periods of time. In addition, automated and noninvasive capability is strongly desired for long-term monitoring. Here, we report a fully integrated modular physical, biochemical, and optical sensing platform through a fluidics-routing breadboard, which operates organ-on-a-chip units in a continual, dynamic, and automated manner. We believe that this platform technology has paved a potential avenue to promote the performance of current organ-on-a-chip models in drug screening by integrating a multitude of real-time sensors to achieve automated in situ monitoring of biophysical and biochemical parameters.

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