One neutral tripodal semi-rigidity ligand tri(4-imidazolylphenyl)amine (TIPA) with excellent hole-transfer nature, was selected as a linker to construct MOFs. Two two-dimensional (2D) microporous ...metal–organic frameworks (MOFs) were synthesized solvothermally: Ni(TIPA)(COO−)2(H2O)·2(DMF)2(H2O) (1) and Cd(TIPA)2(ClO4−)2·(DMF)3(H2O) (2). Compound 1 incorporated carboxylic groups into the channel and exhibited the high capacity of light hydrocarbons as well as the remarkable selectivity of C2H2/CH4. The value is in excess of 100 at room temperature, which is the highest value reported to date. Compound 2, as a cationic framework with high water stability, was not only applied as a sensor, displaying the ultrahigh sensitivity against Cr2O72− with a detection limit as low as 8 ppb, but also possessed excellent Cr(vi) sorption with good repeatability in aqueous solution. This study provides an efficient strategy to design cationic MOFs for the selective separation of light hydrocarbons and the sensing and trapping of toxic chromate for the purification of water.
Collinear limit usually provides strong constraints for scattering amplitudes. At strong coupling, collinear limit of the amplitudes in
SYM is related to the large mass limit of the corresponding
Y
...system. In this paper, we consider a special case in which all mass parameters are taken to be large, which corresponds to a multi-double-collinear limit in which a
n
-side polygon becomes pentagons. This limit provides a useful constraint for the amplitudes, in particular can be used to fix the periods part for the case of 4
K
gluons, which is the last missing piece of full amplitudes.
With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate ...supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective.
Background
Obesity is highly prevalent in patients with hypertrophic cardiomyopathy (HCM) and believed to influence its phenotype.
Purpose
To explore the effects of obesity on left ventricular (LV) ...remodeling and long‐term clinical course in Chinese patients with HCM.
Study Type
Longitudinal.
Population
A total of 247 patients with HCM classified according to body mass index (BMI) (normal weight: BMI = 18.0–22.9 kg/m2 N = 90; overweight: BMI = 23.0–24.9 kg/m2 N = 58; and obese: BMI ≥ 25 kg/m2 N = 99).
Field Strength/Sequence
3.0 T/Balanced steady‐state free precession sequence and phase‐sensitive inversion recovery late gadolinium enhancement (LGE) sequence.
Assessment
LV function and geometry were measured. LV peak strain analysis was performed. The presence and percentage of LGE in the LV were recorded. The endpoints including heart failure, sudden cardiac death, and overall composite outcome were assessed during a median follow‐up of 4.1 years (interquartile range, 3.0–6.2 years).
Statistical Tests
One‐way analysis of variance, Kruskal–Wallis test, or chi‐square test; Pearson correlation coefficient (r); multivariable linear regression analysis; Kaplan–Meier survival analysis; and Cox proportional hazards model analysis were conducted. A two‐tailed P‐value < 0.05 was considered statistically significant.
Results
Obese patients exhibited a significant progressive increase in LV mass compared with normal‐weight patients. The magnitude of all LV strain indices gradually and significantly decreased as BMI increased, whereas LV ejection fraction was not significantly different among BMI groups (P = 0.364). Multivariable linear regression analysis showed that obesity had a significant association with impaired strain indices as well as with indexed LV mass. Multivariable Cox model analysis retained obesity as an independent marker for future endpoints, and conveyed a > 3‐fold increase in risk compared with patients with normal weight (hazard ratio, 3.04; 95% confidence interval, 1.07–6.57).
Data Conclusion
Obesity is an important environmental modifier that is associated with adverse LV remodeling and is independently associated with future clinical outcomes in Chinese patients with HCM.
Level of Evidence
3
Technical Efficacy
Stage 2
We present results of ab initio electronic structure and molecular dynamics simulations (AIMD), as well as a microkinetic model of CO oxidation catalyzed by TiO2 supported Au nanocatalysts. A ...coverage-dependent microkinetic analysis, based on energetics obtained with density functional methods, shows that the dominant kinetic pathway, activated oxygen species, and catalytic active sites are all strongly depended on both temperature and oxygen partial pressure. Under oxidizing conditions and T < 400 K, the prevalent pathway involves a dynamic single atom catalytic mechanism. This reaction is catalyzed by a transient AuCO species that migrates from the Au-cluster onto a surface oxygen adatom. It subsequently reacts with the TiO2 support via a Mars van Krevelen mechanism to form CO2 and finally the Au atom reintegrates back into the gold cluster to complete the catalytic cycle. At 300 ≤ T ≤ 600 K, oxygen-bound single OadAu+CO sites and the perimeter Au-sites of the nanoparticle work in tandem to optimally catalyze the reaction. Above 600 K, a variety of alternate pathways associated with both single-atom and the perimeter sites of the Au nanoparticle are found to be active. Under low oxygen pressures, OadAu+CO species can be a source of catalyst deactivation and the dominant pathway involves only Au-perimeter sites. A detailed comparison of the current model and the existing literature resolves many apparent inconsistencies in the mechanistic interpretations.
Effective integration of contextual information is crucial for salient object detection. To achieve this, most existing methods based on 'skip' architecture mainly focus on how to integrate ...hierarchical features of Convolutional Neural Networks (CNNs). They simply apply concatenation or element-wise operation to incorporate high-level semantic cues and low-level detailed information. However, this can degrade the quality of predictions because cluttered and noisy information can also be passed through. To address this problem, we proposes a global Recurrent Localization Network (RLN) which exploits contextual information by the weighted response map in order to localize salient objects more accurately. Particularly, a recurrent module is employed to progressively refine the inner structure of the CNN over multiple time steps. Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position. The learned propagation coefficients can be used to optimally capture relations between each pixel and its neighbors. Experiments on five challenging datasets show that our approach performs favorably against all existing methods in terms of the popular evaluation metrics.
Despite the prodigious potential of lithium-sulfur (Li-S) batteries as future rechargeable electrochemical systems, their commercial implementation is hindered by several vital issues, including the ...shuttle effect and sluggish migration of lithium-polysulfides leading to rapid capacity fading. Here, we systematically investigate the potential of first-row two-dimensional transition metal carbides (TMCs) as sulfur cathodes for Li-S batteries. The adsorption strength of lithium-polysulfides on TMCs is induced by the amount of charge transfer from the former to the latter and the proposed periodic relationship between sulfur in Li
2
S and 3d-transition metals. Our findings show that the VC nanosheet possesses immense anchoring potential and exhibits a comparatively low migration energy barrier for lithium-ion and Li
2
S molecules. Additionally, we report
ab initio
molecular dynamics simulations for lithiated polysulfide species anchored on a TMC-based model with a liquid-electrolyte medium. The microscopic reaction mechanism, revealed by the evolution of the reaction voltage during lithiation, demonstrates that the dissolution of high-order lithium-polysulfides in the electrolytes can be prevented due to their robust interaction with TMC-based cathode materials. These appealing features suggest that TMCs present colossal performance improvements for anchoring lithium-polysulfides, stimulating the active design of sulfur cathodes for practical Li-S batteries.
First-row two-dimensional transition metal carbides present colossal performance improvements for anchoring lithium-polysulfides, stimulating the active design of sulfur cathodes for practical Li-S batteries.
A sparse and low-rank near-isometric linear embedding (SLRNILE) method has been proposed to make dimensionality reduction and extract proper features for hyperspectral imagery (HSI) classification. ...The SLRNILE stands on the theory of the John-Lindenstrauss lemma, and tries to estimate a sparse and low-rank projection matrix that satisfies the restricted isometric property (RIP) condition on all secants of the HSI data. The RIP condition guarantees that the desired linear mapping near-isometrically preserves nearest neighbor points of all HSI pixels. Seeking the desired mapping is then modeled into minimizing a Lagrange multipliers formulation. The alternating direction method of multipliers framework is utilized to solve the above convex program, and column generation techniques are adopted to alleviate the computation memory burden during the optimization procedure. Five experiments on three widely used HSI data sets are designed to completely test the performance of SLRNILE, and experimental results are compared against those of six state-of-the-art feature extraction methods, including principal component analysis, Laplacian eigenmaps, locality preserving projections, neighborhood preserving embedding, sparse nonnegative matrix underapproximation, and random projections. The results show that SLRNILE performs best among all the seven methods, and its computational time is longest of all but still bearable for regular users. Therefore, the SLRNILE can be a good choice for feature extraction in HSI classification.
In the pursuit of renewable energy storage technologies, ion batteries are intended to play a decisive role in accomplishing the energy requirements of the modern world. Herein, we theoretically ...designed porous three-dimensional (3D) allotropes of silicene (termed 3D-ortho-silicene and 3D-monosilicene); these new 3D-silicenes exhibit stable structures, high porosities, conductive natures, and the high uptake of monovalent and multivalent ions. Owing to the unique 3D porous inner skeleton, small mass density, and inherent conductivity, both 3D-ortho-silicene and 3D-monosilicene materials indicate marvelous potential as anode materials. Importantly, 3D porous silicene structures exhibit high theoretical capacities in the range 718–1117 mAh g–1 with high average potentials and extremely low volume expansion during the charge and discharge processes for lithium and calcium ions. This contribution not only expands the family of 3D-silicene allotropes but also predicts their electrochemical performances for ion batteries.
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