Organic–inorganic hybrid perovskite solar cells (PVSCs) have become the front‐running photovoltaic technology nowadays and are expected to profoundly impact society in the near future. However, their practical applications are currently hampered by the challenges of realizing high performance and long‐term stability simultaneously. Herein, the development of inverted PVSCs is reported based on low temperature solution‐processed CuCrO2 nanocrystals as a hole‐transporting layer (HTL), to replace the extensively studied NiOx counterpart due to its suitable electronic structure and charge carrier transporting properties. A ≈45 nm thick compact CuCrO2 layer is incorporated into an inverted planar configuration of indium tin oxides (ITO)/c‐CuCrO2/perovskite/[6,6]‐phenyl‐C61‐butyric acid methyl ester (PCBM)/bathocuproine (BCP)/Ag, to result in the high steady‐state power conversion efficiency of 19.0% versus 17.1% for the typical low temperature solution‐processed NiOx‐based devices. More importantly, the optimized CuCrO2‐based device exhibits a much enhanced photostability than the reference device due to the greater UV light‐harvesting of the CuCrO2 layer, which can efficiently prevent the perovskite film from intense UV light exposure to avoid associated degradation. The results demonstrate the promising potential of CuCrO2 nanocrystals as an efficient HTL for realizing high‐performance and photostable inverted PVSCs.

No abstract

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node’s essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.

No abstract

Figure 5. Super-elastic properties. a) Stretching diagrams of the super-elastic magnetic structure color hydrogel: i) unstretched; ii) two times stretched; iii) three times stretched; iv) 3.5 times stretched. b) The corresponding dynamic reflection peaks of flower-shaped hydrogel film. c) The relationship between the reflection peak shift of the edge and the stretching times. The bars are 2 cm in (a). (6 of 6)www.advancedsciencenews.com

Environment‐related degradation and lead leakage in perovskite solar cells have posed a big challenge for their commercialization. Here, design of superhydrophobic surfaces is demonstrated as an effective strategy toward these issues, in which thiol‐functionalized perfluoroalkyl molecules are employed to chemically modify the lead halide perovskite film and metal electrode via a vapor‐assisted self‐assembly process. Due to the van der Waals forces, the generation of self‐assembly monolayer prefers to pack in a dense way, resulting in the formation of a closest‐packed, crystalline‐like molecular array. This dense array is endowed with a low‐surface‐energy chemistry that can not only enhance the water and oxygen resistance of the completed device but also reduce the defect density on the perovskite surfaces. These merits eventually boost the efficiency of inverted perovskite solar cells up to 21.79% along with a substantially improved long‐term stability. More importantly, the thiol‐functionalized superhydrophobic array can immobilize most of the undercoordinated lead ions on the perovskite surfaces by metal‐thiol coordination effect, which results in suppressing the lead leakage from the water‐soluble lead halide perovskites. Therefore, an avenue is pointed out here to fabricate stable perovskite solar cells with reducing lead leakage, representing a substantial step toward practical applications.

No abstract

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs).Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.