The booming market of portable and wearable electronics has aroused the requests for advanced flexible selfpowered energy systems featuring both excellent performance and high safety. Herein, we report a safe, flexible, self-powered wristband system by integrating high-performance zinc-ion batteries (ZIBs) with perovskite solar cells (PSCs). ZIBs were first fabricated on the basis of a defective MnO 2−x nanosheetgrown carbon cloth (MnO 2−x @CC), which was obtained via the simple lithium treatment of the MnO 2 nanosheets to slightly expand the interlayer spacing and generate rich oxygen vacancies. When used as a ZIB cathode, the MnO 2−x @CC with a ultrahigh mass loading (up to 25.5 mg cm −2 ) exhibits a much enhanced specific capacity (3.63 mAh cm −2 at current density of 3.93 mA cm −2 ), rate performance, and long cycle stability (no obvious degradation after 5000 cycles) than those of the MnO 2 @CC. Importantly, the MnO 2−x @CC-based quasi-solid-state ZIB not only achieves excellent flexibility and an ultrahigh energy density of 5.11 mWh cm −2 (59.42 mWh cm −3 ) but also presents a high safety under a wide temperature range and various severe conditions. More importantly, the flexible ZIBs can be integrated with flexible PSCs to construct a safe, selfpowered wristband, which is able to harvest light energy and power a commercial smart bracelet. This work sheds light on the development of high-performance ZIB cathodes and thus offers a good strategy to construct wearable self-powered energy systems for wearable electronics.
Bi and Sn co-doped perovskite BaFe0.8−XSn0.2BiXO3−δ materials have been designed and characterized as a series of new cathodes for proton-conducting solid oxide fuel cells (SOFCs), providing a new life for the traditional BaFeO3-based cathodes.
Background: Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don’t necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data. Objective: The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation. Materials and methods: A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score. Results: In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk. Conclusions: Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
The lack of stable p-type van der Waals (vdW) semiconductors with high hole mobility severely impedes the step of low-dimensional materials entering the industrial circle. Although p-type black phosphorus (bP) and tellurium (Te) have shown promising hole mobilities, the instability under ambient conditions of bP and relatively low hole mobility of Te remain as daunting issues. Here we report the growth of high-quality Te nanobelts on atomically flat hexagonal boron nitride (h-BN) for high-performance p-type field-effect transistors (FETs). Importantly, the Te-based FET exhibits an ultrahigh hole mobility up to 1370 cm2 V−1 s−1 at room temperature, that may lay the foundation for the future high-performance p-type 2D FET and metal–oxide–semiconductor (p-MOS) inverter. The vdW h-BN dielectric substrate not only provides an ultra-flat surface without dangling bonds for growth of high-quality Te nanobelts, but also reduces the scattering centers at the interface between the channel material and the dielectric layer, thus resulting in the ultrahigh hole mobility "Image missing".
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