Tunable Radiative Cooling by Mechanochromic Electrospun Micro-Nanofiber Matrix.

Radiative thermoregulation has been regarded as an energy-efficient method for thermal management. In this study, the development of a mechanoresponsive polydimethylsiloxane (PDMS) micro-nanofiber matrix capable of both sub-ambient radiative cooling and solar heating is presented, achieved through a core-shell electrospinning technique. The electrospun PDMS micro-nanofibers, with diameters comparable to the solar wavelengths, exhibit excellent solar reflectivity (≈93%) while preserving its pristine high infrared (IR) emissivity. As a result, the electrospun PDMS radiative cooler (EPRC) successfully demonstrated sub-ambient radiative cooling performance (≈3.8°C) during the daytime. Furthermore, the exceptional resilient property of PDMS facilitated the reversible alteration of the structural morphology created by the fiber-based matrix under mechanical force, resulting in the modulation of solar reflectivity (≈80%). The precise modulation of solar reflectivity enabled reversibly switchable multi-step radiative thermoregulation, offering enhanced flexibility in addressing varying thermal environments even in maintaining the desired temperature. The findings of this work offer a promising approach toward dynamic radiative thermoregulation, which holds significant potential for addressing global climate change concerns and energy shortage.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Pebax® 2533/Graphene Oxide Nanocomposite Membranes for Carbon Capture.

In this work, the behavior of new GO-based mixed matrix membranes was tested in view of their use as CO2-selective membrane in post combustion carbon capture applications. In particular, the new materials were obtained by mixing of Pebax® 2533 copolymer with different types of graphene oxide (GO). Pebax® 2533 has indeed lower selectivity, but higher permeability than Pebax® 1657, which is more commonly used for membranes, and it could therefore benefit from the addition of GO, which is endowed with very high selectivity of CO2 with respect to nitrogen. The mixed matrix membranes were obtained by adding different amounts of GO, from 0.02 to 1% by weight, to the commercial block copolymers. Porous graphene oxide (PGO) and GO functionalized with polyetheramine (PEAGO) were also considered in composites produced with similar procedure, with a loading of 0.02%wt. The obtained films were then characterized by using SEM, DSC, XPS analysis and permeability experiments. In particular, permeation tests with pure CO2 and N2 at 35°C and 1 bar of upstream pressure were conducted for the different materials to evaluate their separation performance. It has been discovered that adding these GO-based nanofillers to Pebax® 2533 matrix does not improve the ideal selectivity of the material, but it allows to increase CO2 permeability when a low filler content, not higher than 0.02 wt%, is considered. Among the different types of GO, then, porous GO seems the most promising as it shows CO2 permeability in the order of 400 barrer (with an increase of about 10% with respect to the unloaded block copolymer), obtained without reducing the CO2/N2 selectivity of the materials, which remained in the order of 25.