In the past two decades, single-walled carbon nanotubes (SWNTs) have been explored for electronic applications because of their high charge carrier mobility, low-temperature solution processability ...and mechanical flexibility. Semiconducting SWNTs (s-SWNTs) are also considered an alternative to traditional silicon-based semiconductors. However, large-scale, as-produced SWNTs have poor solubility, and they are mixtures of metallic SWNTs (m-SWNTs) and s-SWNTs, which limits their practical applications. Conjugated polymer wrapping is a promising method to disperse and separate s-SWNTs, due to its high selectivity, high separation yield and simplicity of operation. In this review, we summarize the recent progress of the conjugated polymer wrapping method, and discuss possible separation mechanisms for s-SWNTs. We also discuss various parameters that may affect the selectivity and sorting yield. Finally, some electronic applications of polymer-sorted s-SWNTs are introduced. The aim of this review is to provide polymer chemist a basic concept of polymer based SWNT separation, as well as some polymer design strategies, influential factors and potential applications.
Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom ...investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.
The effective treatment of Alzheimer's disease (AD) is hindered due to the hard blood–brain barrier (BBB) penetration and non‐selective distribution of drugs in the brain. Moreover, the complicated ...pathological mechanism of AD involves various pathway dysfunctions that limit the effectiveness of a single therapeutic drug. Herein, a dendrigraft poly‐l‐lysines (DGL)‐based siRNA and D peptide (Dp) loaded nanoparticle is designed that could target and penetrate through the BBB, enter the brain parenchyma, and further accumulate at the AD lesion. In this system, T7 peptide, which specifically targets transferrin receptors on the BBB, is linked to DGL via acid‐cleavable long polyethylene glycol (PEG) to achieve high internalization, quick escape from endo/lysosome, and effective transcytosis. Then, the Tet1, which specifically targets diseased neurons, is modified onto DGL by short PEG. After being exposed, Tet1 could drive the nanoparticles to the AD lesion and release the drugs. As a result, the production of β amyloid plaques (Aβ) is inhibited. Neurotoxicity induced by Aβ plaques and tau proten phosphorylation (p‐tau) tangle is also alleviated, and the cognition of AD mice is significantly improved. Overall, this system programmatically targets BBB and neurons, thus, significantly enhances the intracephalic drug accumulation and AD treatment efficacy.
A dendrigraft poly‐l‐lysines‐based BACE siRNA and D peptide loaded nanoparticle are designed, which can target and penetrate through the blood–brain barrier by a long chain acid‐cleavable PEG‐T7, and further accumulate in brain neurons by a short chain PEG‐Tet1. This nanoparticle can significantly enhance the intracephalic drug accumulation and Alzheimer's disease treatment efficacy.
•Isothermal compressive deformation behaviors of a nickel-based superalloy are studied.•Fraction of low angle grain boundaries decreases with the increase of temperature.•Fraction of low angle grain ...boundaries decreases with the decrease of strain rate.•Continuous and discontinuous dynamic recrystallizations take place in hot deformation.•Discontinuous dynamic recrystallization is the dominant nucleation mechanism.
Hot deformation behaviors of a typical nickel-based superalloy are investigated by isothermal compression tests under the deformation temperature range of 920–1040°C and strain rate range of 0.001–1s−1. Scanning electron microscopy (SEM), electron backscattered diffraction (EBSD) technique and transmission electron microscopy (TEM) are employed to study the evolution of hot deformed microstructures. It is found that the fraction of low angle grain boundaries decreases with the increase of deformation temperature or the decrease of strain rate. This is related to the decrease of dynamic recrystallization degree under the low deformation temperature or high strain rate. The fraction of low angle grain boundaries shows a rapid increase at the relatively small deformation degree, and then a significant decrease due to the progress of dynamic recrystallization (DRX). The microstructural changes indicate that both continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX) take place during hot deformation. However, the small fraction of low angle boundaries with 10–15° misorientation indicates that the CDRX plays a minor role on the nucleation of dynamic recrystallization. Discontinuous dynamic recrystallization (DDRX) characterized by grain boundary bulging is the dominant nucleation mechanism for the studied superalloy.
Magnesium alloys have superior mechanical property for industry applications as structural materials, but their poor corrosion resistance is still a bottleneck problem. Mg-Al-Zn alloys are one of the ...most extensively used Mg alloys. In order to study the effects of the second phases on corrosion property systematically, Mg-xAl-(15−x) Zn (x = 12.5, 5.6, 3.3, 1.0 wt%) two-phase alloys containing a certain amount of the different second phases, two binary compounds γ, MgZn, and two ternary second phases Φ, q, were prepared based on the Mg-Al-Zn phase diagram. On this basis, corrosion property of the alloys in the 3.5 wt% NaCl solution was studied by corrosion morphology observation and electrochemical tests. The role of the different second phases in the corrosion processes was investigated. It was revealed that the second phases precipitated both in the Mg matrix and along the grain boundaries and acted as micro-cathode, accelerating corrosion dissolution of the Mg substrate. The acceleration effect of the second phases is in the order of γ-Mg17Al12 > q- Mg44Zn41Al1 > Φ-Mg21(Zn,Al)17 > MgZn. The alloys were also investigated for their diversity in corrosion product characters. The corrosion mechanism was discussed terminally by equivalent circuit of the electrochemical impedance spectrum.
•Two ternary and two binary second phases were confirmed stable at 300 °C.•The roles of the second phases in corrosion processes were investigated.•Corrosion property effected by different second phases were compared.•Corrosion mechanism was studied by corrosion morphology and the equivalent circuits.
Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the ...feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.
Geospatial data conflation is the process of combining multiple datasets about a geographic phenomenon to produce a single, richer dataset. It has received increased research attention due to its ...many applications in map making, transportation, planning, and temporal geospatial analyses, among many others. One approach to conflation, attempted from the outset in the literature, is the use of optimization‐based conflation methods. Conflation is treated as a natural optimization problem of minimizing the total number of discrepancies while finding corresponding features from two datasets. Optimization‐based conflation has several advantages over traditional methods including conciseness, being able to find an optimal solution, and ease of implementation. However, current optimization‐based conflation methods are also limited. A main shortcoming with current optimized conflation models (and other traditional methods as well) is that they are often too weak and cannot utilize the spatial context in each dataset while matching corresponding features. In particular, current optimal conflation models match a feature to targets independently from other features and therefore treat each GIS dataset as a collection of unrelated elements, reminiscent of the spaghetti GIS data model. Important contextual information such as the connectivity between adjacent elements (such as roads) is neglected during the matching. Consequently, such models may produce topologically inconsistent results. In this article, we address this issue by introducing new optimization‐based conflation models with structural constraints to preserve the connectivity and contiguity relation among features. The model is implemented using integer linear programming and compared with traditional spaghetti‐style models on multiple test datasets. Experimental results show that the new element connectivity (ec‐bimatching) model reduces false matches and consistently outperforms traditional models.
Research has revealed that parental attachment may be a protective factor against problematic smartphone use. However, few studies have examined the underlying mechanisms that may mediate or moderate ...this association. To fill this gap, this study examined the mediating role of interpersonal adaptation and moderating role of self-control in the association between parental attachment and problematic smartphone use. A sample of 764 Chinese young adults completed measures of parental attachment, problematic smartphone use, interpersonal adaptation, and self-control. Results showed that interpersonal adaptation mediated the relationship between parental attachment and problematic smartphone use. Moreover, this mediating effect of interpersonal adaptation between parental attachment and problematic smartphone use was moderated by self-control, with the effect being stronger for individuals with lower self-control. To our knowledge, this was the first study examining how and when parental attachment leads to problematic smartphone use. Limitations and implications of this study are discussed.
A physical system produces output due to impulse, which corresponds to a convolution process. Convolution has a very wide tolerance, therefore deconvolution is widespread. When seismic waves ...propagate in the underground medium, the stable wavelet is affected by several factors: complex factors at source, propagation factors from the source to reflection interface, the reflection interface, propagation factors from the reflection interface to receiver, and complex factors at the receiver. The purpose of surface-consistent correction is to eliminate the influence of complex factors at source and receiver on residual statics, phase, and amplitude of wavelets from the same stable reflector, which is typical deconvolution. Surface-consistent deconvolution can be referred to as a Bayesian estimation problem. However, it requires a great deal of computation for seismic data, and the statistical method should be more efficient. Based on statistics and physical understanding, maximizing the common midpoint (CMP) stack has been proven to eliminate residual statics and phase changes; particle swarm optimization (PSO) algorithm is used to explore the nonconvex parameter space. Then, under the physical assumption that the energy of wavelets from the same reflection interface changes steadily, the prediction-energy-change equation is introduced; the spatial mutations of amplitudes are corrected by solving a nonlinear equation system. Numerical experiments show that the statistical way is effective.
In addition to being the core factor in thrombosis, thrombin is involved in various inflammatory disease responses, but few studies have examined whether and how it is involved in membrane‐related ...inflammation. In this study, the thrombin inhibitor dabigatran is used to modify a polyethersulfone dialysis membrane. The modified membrane shows good hydrophilic properties and dialysis performance. It reduces the thrombin level in a targeted manner, thereby significantly inhibiting coagulation factor activation (based on the prothrombin time, international normalized ratio, activated partial thromboplastin time and thrombin time) and reducing the fibrinogen level and platelet adhesion. On thromboelastography, it shows excellent dynamic antithrombotic capacity. The modified membrane inhibited membrane‐related inflammation by inhibiting the production of the inflammatory mediators C‐reactive protein (CRP), interleukin‐6 (IL‐6), and interleukin‐1β (IL‐1β) via the thrombin/complement C5a pathway. Moreover, it is found to be safe in an in vivo study. Thus, the dabigatran‐modified polyethersulfone membrane may reduce dialysis‐related complications through its dual antithrombotic and anti‐inflammatory effects.
In addition to participating in coagulation, thrombin activates complement C5a to stimulate inflammatory cells (neutrophils) to produce inflammatory mediators and participate in inflammatory responses. The DMPES membranes inhibit inflammation and thrombin.
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