The catalytic conversion of alcohols under mild conditions is a great challenge because it is constrained by low selectivity and low activity. Herein, we demonstrate a hollow nanotube Fe2O3/MoO3 ...heterojunction (FeMo‐2) for the photoelectrocatalytic conversion of small‐molecule alcohols. Experimental and theoretical analyses reveal that the optical carrier transfer rate is enhanced by constructing interfacial internal electric fields and Fe‐O‐Mo charge transfer channels. For the formox process, heterojunctions possess superior HCHO‐selective reaction paths and free energy transitions, optimizing the selectivity of HCHO and enhancing the reactivity. FeMo‐2 shows a greatly improved performance compared to single Fe2O3; the photocurrent density of FeMo‐2 reaches 0.66 mA cm−2, which is 3.88 times that of Fe2O3 (0.17 mA cm−2), and the Faraday efficiency of the CH3OH‐to‐HCHO conversion is 95.7 %. This work may deepen our understanding of interfacial charge separation and has potential for the production of HCHO and for conversion reactions of other small‐molecule alcohols at cryogenic temperatures.
A Z‐Scheme Fe2O3/MoO3 hollow nanotube with a CH3OH‐to‐HCHO selectivity of 95.7 % was developed. The thin‐walled hollow structure facilitates a fast transfer of photogenerated carriers and enhances light utilization. The Fe‐O‐Mo charge transfer channel and internal electric field in the Fe2O3/MoO3 interface improve the charge transfer efficiency. PEC experiments and calculations demonstrate that C−H bond breaking is the rate‐determining step.
Emerging evidence has shown that COVID-19 survivors could suffer from persistent symptoms. However, it remains unclear whether these symptoms persist over the longer term. This study aimed to ...systematically synthesise evidence on post-COVID symptoms persisting for at least 12 months. We searched PubMed and Embase for papers reporting at least one-year follow-up results of COVID-19 survivors published by 6 November 2021. Random-effects meta-analyses were conducted to estimate pooled prevalence of specific post-COVID symptoms. Eighteen papers that reported one-year follow-up data from 8591 COVID-19 survivors were included. Fatigue/weakness (28%, 95% CI: 18-39), dyspnoea (18%, 95% CI: 13-24), arthromyalgia (26%, 95% CI: 8-44), depression (23%, 95% CI: 12-34), anxiety (22%, 95% CI: 15-29), memory loss (19%, 95% CI: 7-31), concentration difficulties (18%, 95% CI: 2-35), and insomnia (12%, 95% CI: 7-17) were the most prevalent symptoms at one-year follow-up. Existing evidence suggested that female patients and those with more severe initial illness were more likely to suffer from the sequelae after one year. This study demonstrated that a sizeable proportion of COVID-19 survivors still experience residual symptoms involving various body systems one year later. There is an urgent need for elucidating the pathophysiologic mechanisms and developing and testing targeted interventions for long-COVID patients.
As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank ...matrix/tensor factorization (LRMF/LRTF), many single-factor-regularized methods add various regularizations on the spatial factor to characterize its spatial prior knowledge. However, these methods neglect the common characteristics among different bands and the spectral continuity of HSIs. To tackle this issue, this article establishes a bridge between the factor-based regularization and the HSI priors and proposes a double-factor-regularized LRTF model for HSI mixed noise removal. The proposed model employs LRTF to characterize the spectral global low rankness, introduces a weighted group sparsity constraint on the spatial difference images (SpatDIs) of the spatial factor to promote the group sparsity in the SpatDIs of HSIs, and suggests a continuity constraint on the spectral factor to promote the spectral continuity of HSIs. Moreover, we develop a proximal alternating minimization-based algorithm to solve the proposed model. Extensive experiments conducted on the simulated and real HSIs demonstrate that the proposed method has superior performance on mixed noise removal compared with the state-of-the-art methods based on subspace representation, noise modeling, and LRMF/LRTF.
Anaerobic ammonium oxidation coupled to iron(III) reduction, termed Feammox, is a newly discovered nitrogen cycling process. However, little is known about the roles of electron shuttles in the ...Feammox reactions. In this study, two forms of Fe(III) (oxyhydr)oxide ferrihydrite (ex situ ferrihydrite and in situ ferrihydrite) were used in dissimilatory Fe(III) reduction (DIR) enrichments from paddy soil. Evidence for Feammox in DIR enrichments was demonstrated using the 15N-isotope tracing technique. The extent and rate of both the 30N2–29N2 and Fe(II) formation were enhanced when amended with electron shuttles (either 9,10-anthraquinone-2,6-disulfonate (AQDS) or biochar) and further simulated when these two shuttling compounds were combined. Although the Feammox-associated Fe(III) reduction accounted for only a minor proportion of total Fe(II) formation compared to DIR, it was estimated that the potentially Feammox-mediated N loss (0.13–0.48 mg N L–1 day–1) was increased by 17–340% in the enrichments by the addition of electron shuttles. The addition of electron shuttles led to an increase in the abundance of unclassified Pelobacteraceae, Desulfovibrio, and denitrifiers but a decrease in Geobacter. Overall, we demonstrated a stimulatory effect of electron shuttles on Feammox that led to higher N loss, suggesting that electron shuttles might play a crucial role in Feammox-mediated N loss from soils.
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•The unique Z-scheme ZOF@CN NTs photoanode exhibits outstanding CH3OH oxidation activity.•ZOF@CN NTs with high selectivity provides a green and clean pathway for CH3OH conversion ...process.•The CH3O* via the C–H bond scission to form CH2O* is the rate-determining step for the reactions.
Fe2O3 as photoanode photoelectrocatalytic (PEC) CH3OH conversion is a promising approach for industrial preparation of high value-added HCHO. However, the conversion efficiency is limited by its poor charge separation and sluggish oxidation mechanism. This study reports a direct Z-scheme system consisting of 1D nanotubes oxygen-deficient a-Fe2O3 and g-C3N4 (ZOF@CN NTs) with great enhancement of PEC CH3OH oxidation to HCHO activity. The ZOF@CN NTs presents a CH3OH oxidation photocurrent density of 1.05 mA cm−2 at 0.6 V vs Ag/AgCl, 5.25 times than that of oxygen-deficient a-Fe2O3 alone (0.20 mA cm−2), and HCHO selectivity of 81.5%. Combining by UV–vis, UPS, PL spectroscopy, PEC performance and density functional theory calculations, the mechanism of CH3OH oxidation to HCHO on Z-scheme ZOF@CN NTs photoanode was found along following pathway: CH3OH*–CH3O*- CH2O* –CH2O (g), which is starting from the O–H bond scission and then followed by C−H scission. The CH3O* via the C–H bond scission to form CH2O* is the key step for the reaction, and N atoms from g-C3N4 with higher electronegativity compared with O atoms, which is useful for C–H bond scission. This work serves as a solid foundation for understanding Fe2O3-based Z-scheme system photoanodes for CH3OH oxidation to HCHO.
The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD), has obtained promising results in hyperspectral image (HSI) denoising. However, the framework of the t-SVD ...lacks flexibility for handling different correlations along different modes of HSIs, leading to suboptimal denoising performance. This article mainly makes three contributions. First, we introduce a new tensor rank named tensor fibered rank by generalizing the t-SVD to the mode-<inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula> t-SVD, to achieve a more flexible and accurate HSI characterization. Since directly minimizing the fibered rank is NP-hard, we suggest a three-directional tensor nuclear norm (3DTNN) and a three-directional log-based tensor nuclear norm (3DLogTNN) as its convex and nonconvex relaxation to provide an efficient numerical solution, respectively. Second, we propose a fibered rank minimization model for HSI mixed noise removal, in which the underlying HSI is modeled as a low-fibered-rank component. Third, we develop an efficient alternating direction method of multipliers (ADMMs)-based algorithm to solve the proposed model, especially, each subproblem within ADMM is proven to have a closed-form solution, although 3DLogTNN is nonconvex. Extensive experimental results demonstrate that the proposed method has superior denoising performance, as compared with the state-of-the-art competing methods on low-rank matrix/tensor approximation and noise modeling.
A central challenge for citizens is to understand how their political system works. The classic “Levels of Conceptualization” measure proposed in The American Voter provided an answer for White ...Americans in the 1950s, but has limited relevance today for citizens of non-European ancestry. Expanding on the work of Campbell et al., this paper develops a measure of Political Conceptualization that combines views about parties and candidates with views on personal identity and ethnic fairness. The measure is based on open-ended responses in a survey of Asian Americans and Latinos. Results show how, across these quite different domains of politics, citizens vary in their Political Conceptualizations from narrow and concrete to broad and abstract. Results highlight the challenge for political organizers in building coalitions among citizens who vary in their understanding of how politics works.
The existence of thick clouds covers the comprehensive Earth observation of optical remote sensing images (RSIs). Cloud removal is an effective and economical preprocessing step to improve the ...subsequent applications of RSIs. Deep learning (DL)-based methods have attracted much attention and achieved state-of-the-art results. However, most of these methods suffer from the following issues: 1) ignore the physical characteristics of RSIs; 2) require paired images with/without cloud or extra auxiliary images; and 3) demand the cloud mask. These issues might have limited the flexibility of existing networks. In this article, we propose a novel low-rank regularized self-supervised network (LRRSSN) that couples model-driven and data-driven methods to remove the thick cloud from multitemporal RSIs (MRSIs). First, motivated by the equal importance of image and cloud components as well as their intrinsic characteristics, we decompose the observed image into low-rank image and structural sparse cloud components. In this way, we obtain a model-driven thick cloud removal method where the spectral-temporal low-rank correlation of the image component and the spectral structural sparsity of the cloud component are effectively exploited. Second, to capture the complex nonlinear features of different scenarios, the data-driven self-supervised network that does not require external training datasets is designed to explore the deep prior of the image component. Third, the coupled model-driven and data-driven LRRSSN is optimized by an efficient half quadratic splitting (HQS) algorithm. Finally, without knowing the exact cloud mask, we estimate the cloud mask to preserve information in cloud-free areas as much as possible. Experiments conducted in synthetic and real-world scenarios demonstrate the effectiveness of the proposed approach.
Hyperspectral images (HSIs) are often contaminated by several types of noise, which significantly limits the accuracy of subsequent applications. Recently, low-rank modeling based on tensor singular ...value decomposition (T-SVD) has achieved great success in HSI restoration. Most of them use the convex and nonconvex surrogates of the tensor rank, which cannot well approximate the tensor singular values and obtain suboptimal restored results. We suggest a novel HSI restoration model by introducing a fibered rank constrained tensor restoration framework with an embedded plug-and-play (PnP)-based regularization (FRCTR-PnP). More precisely, instead of using the convex and nonconvex surrogates to approximate the fibered rank, the proposed model directly constrains the tensor fibered rank of the solution, leading to a better approximation to the original image. Since exploiting the low-fibered-rankness of HSI is mainly to capture the global structure, we further employ an implicit PnP-based regularization to preserve the image details. Particularly, the above two building blocks are complementary to each other, rather than isolated and uncorrelated. Based on the alternating direction multiplier method (ADMM), we propose an efficient algorithm to tackle the proposed model. For robustness, we develop a three-directional randomized T-SVD (3DRT-SVD), which preserves the intrinsic structure of the clean HSI and removes partial noise by projecting the HSI onto a low-dimensional essential subspace. Extensive experimental results including simulated and real data demonstrate that the proposed method achieves superior performance over compared methods in terms of quantitative evaluation and visual inspection.
Image denoising is often empowered by accurate prior information. In recent years, data-driven neural network priors have shown promising performance for RGB natural image denoising. Compared to ...classic handcrafted priors (e.g., sparsity and total variation), the "deep priors" are learned using a large number of training samples, which can accurately model the complex image generating process. However, data-driven priors are hard to acquire for hyperspectral images (HSIs) due to the lack of training data. A remedy is to use the so-called unsupervised deep image prior (DIP). Under the unsupervised DIP framework, it is hypothesized and empirically demonstrated that proper neural network structures are reasonable priors of certain types of images, and the network weights can be learned without training data. Nonetheless, the most effective unsupervised DIP structures were proposed for natural images instead of HSIs. The performance of unsupervised DIP-based HSI denoising is limited by a couple of serious challenges, namely network structure design and network complexity. This work puts forth an unsupervised DIP framework that is based on the classic spatiospectral decomposition of HSIs. Utilizing the so-called linear mixture model of HSIs, two types of unsupervised DIPs, that is, U-Net-like network and fully connected networks, are employed to model the abundance maps and endmembers contained in the HSIs, respectively. This way, empirically validated unsupervised DIP structures for natural images can be easily incorporated for HSI denoising. Besides, the decomposition also substantially reduces network complexity. An efficient alternating optimization algorithm is proposed to handle the formulated denoising problem. Simulated and real data experiments are employed to showcase the effectiveness of the proposed approach.
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