Advancements in MXene Composite Materials for Wearable Sensors: A Review.

In recent years, advancements in the Internet of Things (IoT), manufacturing processes, and material synthesis technologies have positioned flexible sensors as critical components in wearable devices. These developments are propelling wearable technologies based on flexible sensors towards higher intelligence, convenience, superior performance, and biocompatibility. Recently, two-dimensional nanomaterials known as MXenes have garnered extensive attention due to their excellent mechanical properties, outstanding electrical conductivity, large specific surface area, and abundant surface functional groups. These notable attributes confer significant potential on MXenes for applications in strain sensing, pressure measurement, gas detection, etc. Furthermore, polymer substrates such as polydimethylsiloxane (PDMS), polyurethane (PU), and thermoplastic polyurethane (TPU) are extensively utilized as support materials for MXene and its composites due to their light weight, flexibility, and ease of processing, thereby enhancing the overall performance and wearability of the sensors. This paper reviews the latest advancements in MXene and its composites within the domains of strain sensors, pressure sensors, and gas sensors. We present numerous recent case studies of MXene composite material-based wearable sensors and discuss the optimization of materials and structures for MXene composite material-based wearable sensors, offering strategies and methods to enhance the development of MXene composite material-based wearable sensors. Finally, we summarize the current progress of MXene wearable sensors and project future trends and analyses.

The magnitude and frequency of detected precipitation determine the accuracy performance of precipitation data sets in the high mountains of Asia.

The grid-based precipitation dataset is an important source for studying precipitation change in the high mountains of Asia due to where precipitation stations are sparse. It is essential to evaluate the accuracy of grid-based precipitation datasets in the high mountains of Asia before selecting an appropriate grid-based dataset. Therefore, this study comprehensively evaluated the precipitation errors of four commonly utilized precipitation datasets (multi-source weighted-ensemble precipitation (MSWEP), global precipitation climatology centre (GPCC), global precipitation measurement (GPM), and soil moisture to rain-advanced scatterometer (SM2RAIN-ASCAT)) in the high mountains of Asia from temporal and spatial perspectives. It then decomposed the precipitation errors to reveal their sources. The results showed that MSWEP, GPCC, GPM, and SM2RAIN-ASCAT overestimated precipitation amount and probability compared with station observations. Meanwhile, all precipitation data sets except MSWEP data underestimated precipitation in the dry season. In terms of the average values of the error metrics, GPCC performed the best. There was an evident annual periodicity in the error assessment metrics for the four precipitation data sets. Multiple linear regression analysis revealed that four precipitation-related factors (false alarm precipitation, missed amount of precipitation, precipitation detected presented, and precipitation detected event) explained the root mean square error values for four precipitation data sets, with precipitation detected presented having the largest weight. The root mean square error of each product exhibits periodic fluctuations with changes in precipitation quantity, attributed to the occurrences of precipitation detected presented and precipitation detected events. These findings provide useful reference information for correcting biases in precipitation data sets for high mountains of Asia.