Chemical Design of Palladium‐Based Nanoarchitectures for Catalytic Applications

Palladium (Pd) plays an important role in numerous catalytic reactions, such as methanol and ethanol oxidation, oxygen reduction, hydrogenation, coupling reactions, and carbon monoxide oxidation. Creating Pd‐based nanoarchitectures with increased active surface sites, higher density of low‐coordinated atoms, and maximized surface coverage for the reactants is important. To address the limitations of pure Pd, various Pd‐based nanoarchitectures, including alloys, intermetallics, and supported Pd nanomaterials, have been fabricated by combining Pd with other elements with similar or higher catalytic activity for many catalytic reactions. Herein, recent advances in the preparation of Pd‐based nanoarchitectures through solution‐phase chemical reduction and electrochemical deposition methods are summarized. Finally, the trend and future outlook in the development of Pd nanocatalysts toward practical catalytic applications are discussed.     

An enhanced approach for the progressive mean control charts

To maintain and improve the quality of the processes, control charts play an important role for reduction of variation. To detect large shifts in the process parameters, Shewhart control charts are commonly applied but for small shifts, exponentially weighted moving averages (EWMA), cumulative sum (CUSUM), double exponentially weighted moving average (DEWMA), double CUSUM, moving average (MA), double moving average (DMA), and progressive mean (PM) control charts, are used. This study proposes double progressive mean (DPM) and optimal DPM control charts to enhance the performance of the PM chart. As the proposed DPM control charts use information sequentially, hence their performance is compared with natural competitors EWMA, CUSUM, DEWMA, double CUSUM, MA, DMA, and PM control charts. Run length and its different properties are evaluated to compare the performance of the proposed charts and counterparts. Results reveal that proposed optimal DPM outperforms the other charts. An example related to voltage on fixed capacitance level is also provided to illustrate the proposed charts.     

光触发增强葡萄糖氧化酶催化活性的等离子体囊泡用于程序化光热-饥饿疗法

在肿瘤的饥饿治疗及协同治疗中,基于葡萄糖氧化酶(GOx)的纳米诊疗剂展现出具大的应用前景.自组装等离子体金囊泡(GV),由于具有独特的光学性能、巨大空腔和强局域表面等离子体共振等特性,可作为协同治疗的多功能纳米载体.本文中,我们开发了一种装载GOx的GV(GV-GOx)用于光触发释放GOx,同时增强GOx的催化活性,从而实现程序化光热-饥饿治疗.在近红外激光照射下,由于GV具有等离子体耦合效应, GV-GOx可以产生很强的局部高热,引起封装的GOx释放,同时高热可提高GOx催化活性,从而增强肿瘤的饥饿效应.此外,高光热效应可促进细胞对GV-GOx的摄取,并可通过活体光声/光热双模态成像对协同治疗进行有效监测.令人印象深刻的是,协同光热/饥饿疗法能完全消融4T1荷瘤小鼠的肿瘤,抗肿瘤效果明显优于单一疗法,且没有明显的系统毒性.本工作展示了一种光触发的纳米平台,可用于癌症协同治疗.     

Conditional mean- and median-based cumulative sum control charts for Weibull data

This article deals with the monitoring of censored data using the cumulative sum (CUSUM) control charts for Weibull lifetimes under type-I censoring. To develop an efficient CUSUM structure for censored data, we use the conditional expected value (CEV) and conditional median (CM) approaches. In particular, we focus on the detection of shifts in the mean of Weibull lifetimes assuming censored data. In addition to fixed/known parameter values, the effect of estimation is assessed on the detection power of control charts. The performance of the proposed charts is evaluated by the average run length (ARL). Furthermore, the ARL performance of CUSUM charts is compared with CEV- and CM-based exponentially weighted moving average (EWMA) control charts. Besides an extensive simulation study, the significance of the current work is illustrated by a data set on the response time of a thermostat experiment.     

Two successive occasions resubmitted sampling scheme-based control chart

In this paper, a resubmitted sampling-based successive sampling over two successive occasions control chart is proposed to monitor the underlying characteristic of interest. Auxiliary information of the first occasion is utilized to monitor the relative change in the study variable over the second occasion successively despite high degree of correlation. The structural and operational design is presented along with the comparative performance evaluation. The average run length is used as a performance evaluation measure and proved the argument in favor of the presented concept in comparison with the other auxiliary data control charts. The implementation is explained through two real examples.     

Distribution-free composite Shewhart-GWMA Mann-Whitney charts for monitoring the process location

The Mann-Whitney (MW) statistic is one of the most recommended two-sample statistical tests when the assumption of normality fails to hold due to its robustness and fascinating properties especially when small sample sizes are involved. In order to improve the sensitivity of the generally weighted moving average (GWMA) monitoring scheme toward the detection of large shifts, in this paper, a new distribution-free phase II composite Shewhart-GWMA (CSG) scheme is proposed using the MW U statistic. The performance of the proposed scheme is investigated using the average run-length (ARL) and average extra quadratic loss (AEQL) values through extensive simulations. The performance of this newly proposed monitoring scheme is found to be superior when compared to numerous memory-type MW schemes (some existing and others introduced in this paper) in many situations. An illustrative example is given to demonstrate the implementation of the CSG MW U scheme using real-life data.     

Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme.