In Vivo Photoacoustic Imaging of Livers Using Biodegradable Hyaluronic Acid‐Conjugated Silica Nanoparticles

The diagnosis of liver diseases is generally carried out via ultrasound imaging, computed tomography, and magnetic resonance imaging. The emerging photoacoustic imaging is an attractive alternative to diagnose even early stage of liver diseases providing high‐resolution anatomical and functional information in deep tissue noninvasively. However, the liver has insufficient photoacoustic contrast due to low optical absorbance in the near‐infrared windows. Here, a new hyaluronate–silica nanoparticle (HA–SiNP) conjugate for liver‐specific delivery and imaging for the diagnosis of liver diseases is developed. The HA–SiNP conjugates show high liver‐specific targeting efficiency, strong optical absorbance near‐infrared windows, excellent biocompatibility, and biodegradability. The liver‐specific targeting efficiency is verified by in vitro cellular uptake test, and in vivo and ex vivo photoacoustic imaging. In vivo photoacoustic imaging shows that photoacoustic amplitude in the liver injected with HA–SiNP conjugates is 4.4 times higher than that of the liver injected with SiNP. The biocompatibility and biodegradability of HA–SiNP conjugates are verified by cell viability test, optical spectrum analysis of urine, and inductively coupled plasma‐mass spectroscopy (ICP‐MS) analysis. Taken together, HA–SiNP conjugates may be developed as a promising liver targeted photoacoustic imaging contrast agent and liver‐targeted drug delivery agent.     

Influence of Bulky Organo‐Ammonium Halide Additive Choice on the Flexibility and Efficiency of Perovskite Light‐Emitting Devices

Perovskite light‐emitting diodes (LEDs) require small grain sizes to spatially confine charge carriers for efficient radiative recombination. As grain size decreases, passivation of surface defects becomes increasingly important. Additionally, polycrystalline perovskite films are highly brittle and mechanically fragile, limiting their practical applications in flexible electronics. In this work, the introduction of properly chosen bulky organo‐ammonium halide additives is shown to be able to improve both optoelectronic and mechanical properties of perovskites, yielding highly efficient, robust, and flexible perovskite LEDs with external quantum efficiency of up to 13% and no degradation after bending for 10 000 cycles at a radius of 2 mm. Furthermore, insight of the improvements regarding molecular structure, size, and polarity at the atomic level is obtained with first‐principles calculations, and design principles are provided to overcome trade‐offs between optoelectronic and mechanical properties, thus increasing the scope for future highly efficient, robust, and flexible perovskite electronic device development.     

Functionalization of Graphene Oxide Films with Au and MoOx Nanoparticles as Efficient p‐Contact Electrodes for Inverted Planar Perovskite Solar Cells

A graphene oxide (GO) film is functionalized with metal (Au) and metal‐oxide (MoO_x) nanoparticles (NPs) as a hole‐extraction layer for high‐performance inverted planar‐heterojunction perovskite solar cells (PSCs). These NPs can increase the work function of GO, which is confirmed with X‐ray photoelectron spectra, Kelvin probe force microscopy, and ultraviolet photoelectron spectra measurements. The down‐shifts of work functions lead to a decreased level of potential energy and hence increased V_oc of the PSC devices. Although the GO‐AuNP film shows rapid hole extraction and increased V_oc, a J_sc improvement is not observed because of localization of the extracted holes inside the AuNP that leads to rapid charge recombination, which is confirmed with transient photoelectric measurements. The power conversion efficiency (PCE) of the GO‐AuNP device attains 14.6%, which is comparable with that of the GO‐based device (14.4%). In contrast, the rapid hole extraction from perovskite to the GO‐MoO_x layer does not cause trapping of holes and delocalization of holes in the GO film accelerates rapid charge transfer to the indium tin oxide substrate; charge recombination in the perovskite/GO‐MoO_x interface is hence significantly retarded. The GO‐MoO_x device consequently shows significantly enhanced V_oc and J_sc, for which its device performance attains PCE of 16.7% with great reproducibility and enduring stability.     

Hierarchical NiMoS and NiFeS Nanosheets with Ultrahigh Energy Density for Flexible All Solid‐State Supercapacitors

Highly flexible supercapacitors (SCs) have great potential in modern electronics such as wearable and portable devices. However, ultralow specific capacity and low operating potential window limit their practical applications. Herein, a new strategy for the fabrication of free‐standing Ni?Mo?S and Ni?Fe?S nanosheets (NSs) for high‐performance flexible asymmetric SC (ASC) through hydrothermal and subsequent sulfurization technique is reported. The effect of Ni²⁺ is optimized to attain hierarchical Ni?Mo?S and Ni?Fe?S NS architectures with high electrical conductivity, large surface area, and exclusive porous networks. Electrochemical properties of Ni?Mo?S and Ni?Fe?S NS electrodes exhibit that both have ultrahigh specific capacities (≈312 and 246 mAh g^?1 at 1 mA cm^?2), exceptional rate capabilities (78.85% and 78.46% capacity retention even at 50 mA cm^?2, respectively), and superior cycling stabilities. Most importantly, a flexible Ni?Mo?S NS//Ni?Fe?S NS ASC delivers a high volumetric capacity of ≈1.9 mAh cm^?3, excellent energy density of ≈82.13 Wh kg^?1 at 0.561 kW kg^?1, exceptional power density (≈13.103 kW kg^?1 at 61.51 Wh kg^?1) and an outstanding cycling stability, retaining ≈95.86% of initial capacity after 10 000 cycles. This study emphasizes the potential importance of compositional tunability of the NS architecture as a novel strategy for enhancing the charge storage properties of active electrodes.     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.     

An efficient and secure 3‐factor user‐authentication protocol for multiserver environment

In the last decade, the number of web‐based applications is increasing rapidly, which leads to high demand for user authentication protocol for multiserver environment. Many user‐authentication protocols have been proposed for different applications. Unfortunately, most of them either have some security weaknesses or suffer from unsatisfactory performance. Recently, Ali and Pal proposed a three‐factor user‐authentication protocol for multiserver environment. They claimed that their protocol can provide mutual authentication and is secure against many kinds of attacks. However, we find that Ali and Pal's protocol cannot provide user anonymity and is vulnerable to 4 kinds of attacks. To enhance security, we propose a new user‐authentication protocol for multiserver environment. Then, we provide a formal security analysis and a security discussion, which indicate our protocol is provably secure and can withstand various attacks. Besides, we present a performance analysis to show that our protocol is efficient and practical for real industrial environment.     

Dirichlet Priors for MAP Inference of Protein Conformation Abundances from SAXS

Estimation of mixture coefficients of protein conformations in solution find applications in understanding protein behavior. We describe a method for maximum a posteriori (MAP) estimation of the mixture coefficients of ensemble of conformations in a protein mixture solution using measured small angle X-ray scattering (SAXS) intensities. The proposed method builds upon a model for the measurements of crystallographically determined conformations. Assuming that a priori information on the protein mixture is available, and that priori information follows a Dirichlet distribution, we develop a method to estimate the relative abundances with MAP estimator. The Dirichlet distribution depends on concentration parameters which may not be known in practice and thus need to be estimated. To estimate these unknown concentration parameters we developed an expectation-maximization (EM) method. Adenylate kinase (ADK) protein was selected as the test bed due to its known conformations Beckstein et al. (Journal of Molecular Biology, 394(1), 160 1). Known conformations are assumed to form the full vector bases that span the measurement space. In Monte Carlo simulations, mixture coefficient estimation performances of MAP and maximum likelihood (ML) (which assumes a uniform prior on the mixture coefficients) estimators are compared. MAP estimators using known and unknown concentration parameters are also compared in terms of estimation performances. The results show that prior knowledge improves estimation accuracy, but performance is sensitive to perturbations in the Dirichlet distribution’s concentration parameters. Moreover, the estimation method based on EM algorithm shows comparable results to approximately known prior parameters.