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Wearable pressure sensors have received widespread attention owing to their potential applications in areas such as medical diagnosis and human–computer interaction. However, current sensors cannot adapt to extreme environments (e.g., wet and underwater) or show moderate sensitivity. Herein, a highly sensitive and superhydrophobic fabric sensor is reported based on graphene/PDMS coating. This wearable sensor exhibits great superhydrophobicity (water contact angle of 153.9°) due to the hydrophobic alkyl long chains and rough structure introduced by the Ar plasma. Owing to the network structure created by the electric-induced alignment of graphene sheets, an enhanced sensitivity (ΔI/I0 of 55) and fast response time (~100 ms) are observed. Due to its superhydrophobicity and sensitivity, this wearable sensor demonstrates efficient and stable monitoring of various underwater activities, including pressure, blowing, and tapping. Our approach provides an alternative idea for highly sensitive wearable sensors while broadening the practical application scope. [ABSTRACT FROM AUTHOR]

The growing awareness about the global energy crisis and extreme weather from global warming drives the development of smart windows market. Compared to conventional electrochromic, photochromic, or thermochromic smart windows, mechanoresponsive smart windows present advantages of simple construction, low cost, and excellent stability. In this review, we summarize recent developments in mechanoresponsive smart windows with a focus on the structures and properties. We outline the categories and discuss the advantages and disadvantages. Especially, we also summarize six unconventional driving modes to generate mechanical strain, including pneumatic, optical, thermal, electric, magnetic, and humidity modes. Lastly, we provide practical recommendations in prospects for future development. This review aims to provide a useful reference for the design of novel mechanoresponsive smart windows and accelerate their practical applications. [ABSTRACT FROM AUTHOR]

In this work, the compatibility and long-term integrity of candidate structural materials, including the austenitic stainless steel 316NG, the Fe-Ni-based alloy 800H, and the Ni-based alloy 625, were tested in high-temperature and high-pressure SCO2. The exposure time was up to 3000 h. The results showed that the corrosion kinetics approximately followed a near-cubic law for 316NG and 800H. After 3000 h exposure, all oxide layers, mainly composed of Cr2O3, were continuous, compact, and protective, and their thicknesses were about 21~45 nm, 64~88 nm, and 34~43 nm, respectively. In the case of carburization, dark spots corresponding carbon deposition were observed on the surface and a little enriched in the underside of the oxide for 800H. Moreover, the enrichment of trace elements was found at the oxide/substrate interface through GDOES and TEM analyses, i.e., the enrichment of Mn and Si for 316NG, the enrichment of Mn, Si, Al, and Ti for 800H, and the enrichment of Ti and Al for alloy 625. [ABSTRACT FROM AUTHOR]

The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML algorithms based on the same dataset vary significantly. Therefore, selection of an efficient ML algorithm would significantly reduce the number and cost of the experiments. In this work, phase prediction of HEAs (PPH) is proposed by integrating criterion and machine learning recommendation method (MLRM). First, a meta-knowledge table based on characteristics of HEAs and performance of candidate algorithms is established, and meta-learning based on the meta-knowledge table is adopted to recommend an algorithm with desirable accuracy. Secondly, an MLRM based on improved meta-learning is engineered to recommend a more desirable algorithm for phase prediction. Finally, considering poor interpretability and generalization of single ML algorithms, a PPH combining the advantages of MLRM and criterion is proposed to improve the accuracy of phase prediction. The PPH is validated by 902 samples from 12 datasets, including 405 quinary HEAs, 359 senary HEAs, and 138 septenary HEAs. The experimental results shows that the PPH achieves performance than the traditional meta-learning method. The average prediction accuracy of PPH in all, quinary, senary, and septenary HEAs is 91.6%, 94.3%, 93.1%, and 95.8%, respectively. [ABSTRACT FROM AUTHOR]