Search

A SERS substrate with high sensitivity and reusability was proposed. The chip consists of multiple ZnO microcavities loaded with silver particles. Based on structural characteristics, this coupling between cavity modes and localized surface plasmon modes can highly localize the electric field, where experimental results revealed a detection limit of 10−11 M for R6G. In addition, during carrier control in semiconductors with localized electromagnetic fields, our substrate also exhibits high self-cleaning efficiency and in situ detection stability. Even in a dry environment, it exhibits excellent light-mediated cleaning ability across multiple reuse test cycles. The convenient, rinse-free substrate, with its cost-effective and sustainable features, shows great promise for the study on detection and degradation of active materials. [ABSTRACT FROM AUTHOR]

Rapid and sensitive detection of food-borne bacteria has remained challenging over the past few decades. We propose a surface-enhanced Raman scattering sensing strategy based on a novel bioinspired surface-enhanced Raman scattering substrate, which can directly detect dye molecular residues and food-borne pathogen microorganisms in the environment. The surface-enhanced Raman scattering platform consists of a natural diatomite microporous array decorated with a metal-phenolic network that enables the in situ reduction of gold nanoparticles. The as-prepared nanocomposites display excellent surface-enhanced Raman scattering activity with the lowest limit of detection and the maximum Raman enhancement factor of dye molecules up to 10−11 M and 1.18 × 107, respectively. For food-borne bacterial detection, a diatomite microporous array decorated with a metal polyphenol network and gold nanoparticle-based surface-enhanced Raman scattering analysis is capable of distinguishing the biochemical fingerprint information of Staphylococcus aureus and Escherichia coli, indicating the great potential for strain identification. [ABSTRACT FROM AUTHOR]

In the aqueous electrochemical reduction of CO2, the choice of electrolyte is responsible for the catalytic activity and selectivity, although there remains a need for more in-depth understanding of electrolyte effects and mechanisms. In this study, using both experimental and simulation approaches, we report how the buffer capacity of the electrolytes affects the kinetics and equilibrium of surface reactant species and the resulting reaction rate of CO2 with varying partial CO2 pressure. Electrolytes investigated include KCl (nonbuffered), KHCO3 (buffered by bicarbonate), and phosphate-buffered electrolytes. Assuming 100% methane production, the simulation successfully explains the experimental trends in maximum CO2 flux in KCl and KHCO3 and also highlights the difference between KHCO3 and phosphate in terms of pKa as well as the impact of the buffer capacity. To examine the electrolyte impact on selectivity, the model is run with a constant total current density. Using this model, several factors are elucidated, including the importance of local pH, which is not in acid/base equilibrium, the impact of buffer identity and kinetics, and the mass-transport boundary-layer thickness. The gained understanding can help to optimize CO2 reduction in aqueous environments.

An integrated cell for the solar-driven splitting of water consists of multiple functional components and couples various photoelectrochemical (PEC) processes at different length and time scales. The overall solar-to-hydrogen (STH) conversion efficiency of such a system depends on the performance and materials properties of the individual components as well as on the component integration, overall device architecture, and system operating conditions. This Review focuses on the modeling- and simulation-guided development and implementation of solar-driven water-splitting prototypes from a holistic viewpoint that explores the various interplays between the components. The underlying physics and interactions at the cell level is are reviewed and discussed, followed by an overview of the use of the cell model to provide target properties of materials and guide the design of a range of traditional and unique device architectures.

We present a scheme to retrieve the initial spectrum of probe molecules in the near field. This is achieved by replacing conventional glass substrates with a hybrid mode photonics chip to create rich and persistent mode distributions and by observing the far-field scattering pattern. The manner of coupling from near field to far field is confirmed by the calculated dependence of the coupled emission signal on the unique material properties. By using an optimization algorithm to retrieve the intensity ratio of near-field optical signals in each frequency band gradually without the need for complicated instruments and time-consuming acquisition processes, our method can achieve broadband non-distortion spectral analysis in an enhanced optical antenna by selecting substrate parameters.

Thin-walled metal plates and cabin structures are widely found in ships and cargos which are susceptible to attacks or accidental explosions. The present work focuses on the dynamic response mechanism of steel plates under unconfined and confined blast loads. In the experiment, digital image correlation (DIC) technique was applied to record and analyze the dynamic response process of a large-scale field blast test. The DIC measured curve and the numerically calculated curves agree well in both trends and peak values. Then, the dynamic response mechanisms of steel plates under an unconfined blast (UB) load and confined blast (CB) load were compared and discussed. The results show that the dynamic response of plates can be divided into three phases under both UB and CB loads, with different mechanisms. In phase I, plastic hinges start from the center and move to the boundary in the UB condition, while in the case of CB, plastic hinges occur close to the boundary and move in the opposite direction. In phase II, two plastic hinge lines propagate towards each other, a platform exists between the boundary, and the central area remains undeformed in the UB condition, while in the CB condition, larger deformation occurs in the peripheral region rather than the central area. [ABSTRACT FROM AUTHOR]

Using health indices (HIs) to characterize machine conditions is greatly helpful to prevent machine failures and their subsequent catastrophe. Fusion and interpretation of the main contributions of HIs to machine condition monitoring are still challenging. In this paper, an interpretable fusion methodology of HIs is proposed for machine condition monitoring. The proposed methodology begins with elements of statistical learning for classification, following by an essence of how HIs are fused with their associated linear weights to realize machine condition monitoring. One main contribution of this paper gives a theoretical justification for positive and negative weights of the proposed fusion methodology for understanding their importance for machine condition monitoring and making the proposed methodology physically interpretable. In order to be suitable for two practical situations, in which whether faulty data are available or not, two solutions including an offline solution with healthy and faulty datasets and an online solution with only available healthy datasets are suggested to estimate interpretable weights of the proposed methodology. Finally, industrial turbine cavitation status data collected from our group are used to verify the proposed methodology and show its superiority to two existing popular machine fault diagnosis methods. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'. [ABSTRACT FROM AUTHOR]

The discrete outcome model is an important method for analyzing the factors affecting crash outcomes. However, the lack of effective approaches for discretizing continuous variables and mining interaction terms are two important problems confronted by such models. To address the above issues, this paper proposes a hybrid approach combining machine learning and econometric modelling to investigate fatal crash types in Shenzhen, China. First, the fatal crash data were collected from 2014 to 2016 in Shenzhen. Second, the minimum description length principle (MDLP), an outstanding representative of supervised discretization algorithms, was used for the discretization of continuous variables in the data. This algorithm selects the proper cut-point through the minimization of the entropy for the given interval. Subsequently, the feature subset selection algorithm based on association rule mining (FEAST), which has advantages over other interaction-mining algorithms in terms of structure freedom and the global search capability, was employed to mine the interaction effects between variables. Finally, the discretized continuous variables and the interaction terms were incorporated into the random parameters logit (RPL) model. Results reveal that the goodness of fit of the MDLP-FEAST-RPL model proposed in this paper is significantly better than that of the equal width discretization (EWD)-RPL, MDLP-RPL, and EWD-FEAST-RPL models. In addition, a total of eleven factors and interaction terms are associated with urban fatal crash types. These findings will facilitate the development of cost-effective policies or countermeasures for targeted crash types in large cities of developing countries. [ABSTRACT FROM AUTHOR]