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Alloying high‐cost Pt with transition metals has been considered as an effective route to synthesize the electrocatalysts with low Pt loading and excellent activity towards oxygen reduction reaction (ORR) under acid solution. The galvanic replacement method, as featured with efficiency and simplicity, is widely reported to produce Pt‐based bimetallic alloys and thereby declare the significance of reductive transition metal precursor on the enhancement of ORR performance. Herein, mix‐phased Cu−Cu2O precursor was applied to prepare carbon black supported highly dispersed PtCu alloy nanoparticles (PtCu/C). The proper Cu−Cu2O ratios can exactly facilitate the generation of small sized PtCu alloy nanoparticles with regulated bimetallic content. Meanwhile, the Cu2O phase is revealed to benefit the electron transfer from Pt to Cu and thus improve the intrinsic activity of Pt active sites. And the metallic Cu can favor the promotion of electrochemical active surface area. Consequently, the as‐prepared PtCu/C behaves impressive ORR activity with half‐wave potential of 0.88 V (vs. RHE) and mass activity of 0.49 A cm−2 mgPt−1 at 0.8 V, which is 9.8 times of commercial Pt/C catalysts. Our work will offer helpful advices for the development and regulation of novel Pt‐based alloy materials towards diverse electrocatalysis. [ABSTRACT FROM AUTHOR]

Background: Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose–volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient‐specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly. Purpose: To develop a DVH‐based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH‐based PSQA. Methods: A DL model with a three‐dimensional squeeze‐and‐excitation residual blocks incorporated into a modified U‐net was developed to predict the measured PSQA DVHs of 208 head‐and‐neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training. Results: The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for Dmean of PTV6900 (p = 0.001), D50 of PTV6000 (p = 0.014), D2 of PTV5400 (p = 0.009), D50 of left parotid (p = 0.015), and Dmax of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases. Conclusions: The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH‐based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery. [ABSTRACT FROM AUTHOR]

Transition metal (e.g. Ni) ions dissolved from layered‐structured Ni‐rich cathodes can migrate to the anode side and accelerate the failure of lithium‐ion batteries. The investigations of the impact and distribution of Ni species on the solid electrolyte interphase (SEI) on the anode are crucial to understand the failure mechanism. Herein, we used time‐of‐flight secondary ion mass spectroscopy (TOF‐SIMS) coupled with multivariate curve resolution (MCR) analysis to intuitively characterize the distribution of Ni species in the SEI. We find that the SEI on the graphite electrode using an EC‐based electrolyte exhibits a multi‐stratum structure. During accelerated aging of the LiNi0.88Co0.08Mn0.04O2/graphite full cell, the dissolution of Ni aggravates significantly upon cycling. A strong correlation between the dissolved‐Ni and organic species in the SEI on graphite is illustrated. The ion‐exchange reaction between Ni2+ and Li+ ions in the SEI is demonstrated to be the main reason for the increase of SEI resistivity. [ABSTRACT FROM AUTHOR]

The purchase channels and certificate and ticket system of cosmetic enterprises were studied through the investigation of cosmetic enterprises in Hebei province of China. The results show that manufacturers (including registrants and filing applicants) account for 41.3% of the purchase channels, agents account for 56.41%, wholesale markets account for 8.6%, e-commerce platforms account for 5.4%, franchise brand headquarters account for 24.17%, and others account for 3.2%; the results also show some problems, such as incomplete or expired qualifications of business enterprises, hidden dangers in inspection on labels and poor implementation of purchase inspection system. The results show that it is necessary to improve the relevant laws and regulations for cosmetics market, strengthen the publicity of policies and regulations, set a reasonable transition period for regulations and policies, strengthen the professional training of law enforcement personnel and strengthen special inspections in supervision of cosmetic enterprises. [ABSTRACT FROM AUTHOR]