The Mass-Balancing between Positive and Negative Electrodes for Optimizing Energy Density of Supercapacitors.

Supercapacitors (SCs) are some of the most promising energy storage devices, but their low energy density is one main weakness. Over the decades, superior electrode materials and suitable electrolytes have been widely developed to enhance the energy storage ability of SCs. Particularly, constructing asymmetric supercapacitors (ASCs) can extend their electrochemical stable voltage windows (ESVWs) and thus achieve high energy density. However, only full utilization of the electrochemical stable potential windows (ESPWs) of both positive and negative electrodes can endow the ASC devices with a maximum ESVW by using a suitable mass-ratio between two electrodes (the mass-balancing). Nevertheless, insufficient attention is directed to mass-balancing, and even numerous misunderstandings and misuses have appeared. Therefore, in this Perspective, we focus on the mass-balancing: summarize theoretic basis of the mass-balancing, derive relevant relation equations, analyze and discuss the change trends of the specific capacitance and energy density of ASCs with mass-ratios, and finally recommend some guidelines for the normative implementation of the mass-balancing. Especially, the issues related to pseudocapacitive materials, hybrid devices, and different open circuit potentials (OCPs) of the positive and negative electrodes in the mass-balancing are included and emphasized. These analyses and guidelines can be conducive to understanding and performing mass-balancing for developing high-performance SCs.

PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement.

PURPOSE: Pelvic X-ray (PXR) is widely utilized in clinical decision-making associated with the pelvis, the lower part of the trunk that supports and balances the trunk. In particular, PXR-based landmark detection facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXR has the advantages of low radiation and reduced cost compared to computed tomography (CT), it characterizes the 2D pelvis-tissue superposition of 3D structures, which may affect the accuracy of landmark detection in some cases. However, the superposition nature of PXR is implicitly handled by existing deep learning-based landmark detection methods, which mainly design the deep network structures for better detection performances. Explicit handling of the superposition nature of PXR is rarely done. METHODS: In this paper, we explicitly focus on the superposition of X-ray images. Specifically, we propose a pelvis extraction (PELE) module that consists of a decomposition network, a domain adaptation network, and an enhancement module, which utilizes 3D prior anatomical knowledge in CT to guide and well isolate the pelvis from PXR, thereby eliminating the influence of soft tissue for landmark detection. The extracted pelvis image, after enhancement, is then used for landmark detection. RESULTS: We conduct an extensive evaluation based on two public and one private dataset, totaling 850 PXRs. The experimental results show that the proposed PELE module significantly improves the accuracy of PXRs landmark detection and achieves state-of-the-art performances in several benchmark metrics. CONCLUSION: The design of PELE module can improve the accuracy of different pelvic landmark detection baselines, which we believe is obviously conducive to the positioning and inspection of clinical landmarks and critical structures, thus better serving downstream tasks. Our project has been open-sourced at https://github.com/ECNUACRush/PELEscores .

Zr-Based MOF-Stabilized CO2-Responsive Pickering Emulsions for Efficient Reduction of Nitroarenes.

A Pickering emulsion is a natural microreactor for interfacial catalysis in which an emulsifier is critical. Recently, a metal-organic framework (MOF) has attracted attention to emulsify water-organic mixtures for constructing a Pickering emulsion. However, a few stimuli-responsive Pickering emulsions based on MOFs have been reported, and the MOF emulsifiers cannot be regenerated at room temperature. Herein, the Zr-MOF with a rodlike morphology is synthesized using ionic liquid as a modulator and then modified with n-(trimethoxysilylpropyl)imidazole (C3im) to prepare a series of functionalized Zr-MOFs (MOF-C3im). It is found that MOF-C3im is an excellent emulsifier to construct stable and CO2-responsive Pickering emulsions even at low content (>0.20 wt %). Notably, the emulsification and demulsification of the emulsions can be easily and reversibly switched by bubbling of CO2 and N2 alternatively at room temperature because CO2 and imidazole molecules anchored on the Zr-MOF underwent a reversible acid-base reaction, resulting in an obvious change in the wettability of the emulsifier. As a proof of concept, the reduction reactions of nitrobenzene have been successfully carried out in these Pickering emulsions, demonstrating the efficient integration as a microreactor for chemical reaction, product separation, and emulsifier recycling under ambient conditions. This strategy provides an innovative option to develop stimulus-responsive Pickering emulsions for sustainable chemical processes.

Metal-Organic Framework Derived Bi-O-Sn/C Nanostructure: Tailoring the Adsorption Site of Dominant Intermediate for Highly Efficient CO2 Electroreduction to Formate.

Electrochemical CO2 reduction into high-value-added formic acid/formate is an attractive strategy to mitigate global warming and achieve energy sustainability. However, the adsorption energy of most catalysts for the key intermediate *OCHO is usually weak, and how to rationally optimize the adsorption of *OCHO is challenging. Here, an effective Bi-Sn bimetallic electrocatalyst (Bi1 -O-Sn1 @C) where a Bi-O-Sn bridge-type nanostructure is constructed with O as an electron bridge is reported. The electronic structure of Sn is precisely tuned by electron transfer from Bi to Sn through O bridge, resulting in the optimal adsorption energy of intermediate *OCHO on the surface of Sn and the enhanced activity for formate production. Thus, the Bi1 -O-Sn1 @C exhibits an excellent Faradaic efficiency (FE) of 97.7% at -1.1 V (vs RHE) for CO2 reduction to formate (HCOO- ) and a high current density of 310 mA cm-2 at -1.5 V, which is one of the best results catalyzed by Bi- and Sn-based catalysts reported previously. Impressively, the FE exceeds 93% at a wide potential range from -0.9 to -1.4 V. In-situ ATR-FTIR, in-situ Raman, and DFT calculations confirm the unique role of the bridge-type structure of Bi-O-Sn in highly efficient electrocatalytic reduction of CO2 into formate.

Pest recognition based on multi-image feature localization and adaptive filtering fusion.

Accurate recognition of pest categories is crucial for effective pest control. Due to issues such as the large variation in pest appearance, low data quality, and complex real-world environments, pest recognition poses challenges in practical applications. At present, many models have made great efforts on the real scene dataset IP102, but the highest recognition accuracy is only 75%. To improve pest recognition in practice, this paper proposes a multi-image fusion recognition method. Considering that farmers have easy access to data, the method performs fusion recognition on multiple images of the same pest instead of the conventional single image. Specifically, the method first uses convolutional neural network (CNN) to extract feature maps from these images. Then, an effective feature localization module (EFLM) captures the feature maps outputted by all blocks of the last convolutional stage of the CNN, marks the regions with large activation values as pest locations, and then integrates and crops them to obtain the localized features. Next, the adaptive filtering fusion module (AFFM) learns gate masks and selection masks for these features to eliminate interference from useless information, and uses the attention mechanism to select beneficial features for fusion. Finally, the classifier categorizes the fused features and the soft voting (SV) module integrates these results to obtain the final pest category. The principle of the model is activation value localization, feature filtering and fusion, and voting integration. The experimental results indicate that the proposed method can train high-performance feature extractors and classifiers, achieving recognition accuracy of 73.9%, 99.8%, and 99.7% on IP102, D0, and ETP, respectively, surpassing most single models. The results also show that thanks to the positive role of each module, the accuracy of multi-image fusion recognition reaches the state-of-the-art level of 96.1%, 100%, and 100% on IP102, D0, and ETP using 5, 2, and 2 images, respectively, which meets the requirements of practical applications. Additionally, we have developed a web application that applies our research findings in practice to assist farmers in reliable pest identification and drive the advancement of smart agriculture.

Liquid-Liquid Phase Separation of Aqueous Ionic Liquids in Covalent Organic Frameworks for Thermal Switchable Proton Conductivity.

Covalent organic frameworks (COFs) have regular channels that can accommodate guest molecules to provide highly conductive solid electrolytes. However, designing smart, conductive COFs remains a great challenge. Herein, we report the first example of PEG-functionalized ionic liquids (ILs) anchored on the COF walls by strong hydrogen bonding to fabricate thermally responsive COFs (ILm@COF). We found that similar to the traditional IL/water mixture, the ILs undergo lower critical solution temperature (LCST)-type phase behavior within COF nanopores under high moisture levels. However, the phase separation temperature of aqueous IL decreases in COF channels due to the strong interaction between the IL and COF. Thus, the proton conductivity of ILm@COF can be reversibly switched by phase miscibility and separation in COF nanopores, and there is no obvious decrease even after 20 switching cycles. Our work provides important clues for understanding liquid-liquid phase separation in a confined nanospace and opens a new pathway to switchable proton conductivity.

Early and long-term outcomes of young adult patients ≤30 years old with acute type A aortic dissection.

OBJECTIVES: The aim of this study was to investigate the early and long-term outcomes after total arch replacement (TAR) and frozen elephant trunk (FET) implantation in adult patients ≤30 years with acute type A aortic dissection (ATAAD). METHODS: All young adult patients (≤30 years) with ATAAD who underwent TAR and FET between 2009 and 2017 were enrolled. The end points were major organ morbidity and mortality, aortic-related events and reoperation. RESULTS: The mean age of all 83 patients was 25.9 (standard deviation, 3.3) years. The in-hospital mortality was 9.64% (8/83), and 9 (10.8%) patients required re-exploration for bleeding. The aortic-related events risk was 42.7% (32/75) and the aortic reoperation risk was 17.3% (13/75). Overall survival was 85.5% [95% confidence interval (CI), 75.9-91.5%] at 5 years and 75.9% (95% CI, 63.3-84.7%) at 10 years. The cumulative incidence of aortic-related events was 35% (95% CI, 24-47%) at 5 years and 58% (95% CI, 36-75%) at 10 years; the cumulative reoperation rate was 15% (95% CI, 7.9-24%) at 5 years and 17% (95% CI, 9.2-27%) at 10 years. Marfan syndrome significantly increased the aortic-related events (P = 0.036) and reoperation (P = 0.041) risks. CONCLUSIONS: Despite extensive repair in young ATAAD patients, the late aortic dilatation and reoperation risk remain high. The TAR and FET procedures achieved satisfactory early outcomes and reduced late aortic dilatation and reoperation in young patients compared with other records. Close follow-up and aggressive early reintervention are essential for patients with aortic-related risk factors early in life.

Spatial-Temporal Self-Attention Enhanced Graph Convolutional Networks for Fitness Yoga Action Recognition.

Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little expensive. To solve these problems, we propose spatial-temporal self-attention enhanced graph convolutional networks (STSAE-GCNs) that can analyze RGB yoga video data captured by cameras or smartphones. In the STSAE-GCN, we build a spatial-temporal self-attention module (STSAM), which can effectively enhance the spatial-temporal expression ability of the model and improve the performance of the proposed model. The STSAM has the characteristics of plug-and-play so that it can be applied in other skeleton-based action recognition methods and improve their performance. To prove the effectiveness of the proposed model in recognizing fitness yoga actions, we collected 960 fitness yoga action video clips in 10 action classes and built the dataset Yoga10. The recognition accuracy of the model on Yoga10 achieves 93.83%, outperforming the state-of-the-art methods, which proves that this model can better recognize fitness yoga actions and help students learn fitness yoga independently.

Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection.

Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detection of unseen classes by aligning object region proposals with semantic embeddings. However, these methods have several limitations, including poor region proposals for unseen classes, lack of consideration of semantic representations of unseen classes or their inter-class correlations, and domain bias towards seen classes, which can degrade overall performance. To address these issues, the Trans-ZSD framework is proposed, which is a transformer-based multi-scale contextual detection framework that explicitly exploits inter-class correlations between seen and unseen classes and optimizes feature distribution to learn discriminative features. Trans-ZSD is a single-stage approach that skips proposal generation and performs detection directly, allowing the encoding of long-term dependencies at multiple scales to learn contextual features while requiring fewer inductive biases. Trans-ZSD also introduces a foreground-background separation branch to alleviate the confusion of unseen classes and backgrounds, contrastive learning to learn inter-class uniqueness and reduce misclassification between similar classes, and explicit inter-class commonality learning to facilitate generalization between related classes. Trans-ZSD addresses the domain bias problem in end-to-end generalized zero-shot detection (GZSD) models by using balance loss to maximize response consistency between seen and unseen predictions, ensuring that the model does not bias towards seen classes. The Trans-ZSD framework is evaluated on the PASCAL VOC and MS COCO datasets, demonstrating significant improvements over existing ZSD models.

Ag Single Atoms Anchored on CeO2 with Interfacial Oxygen Vacancies for Efficient CO2 Electroreduction.

Ag single-atom catalysts (SACs) have great potential in selective electrocatalysis of the CO2 reduction reaction (CO2RR) to CO, while it is still a challenge to achieve high current density and high atom efficiency simultaneously. Here, we present a new and simple in situ adsorption-reduction method to prepare Ag SACs supported on CeO2 (Ag1/CeO2). It is found that Ag single atoms are anchored on CeO2 through strong metal-support interaction (SMSI), and each Ag atom is accompanied with three interfacial oxygen vacancies. This Ag1/CeO2 exhibits high performance in the electrocatalytic CO2RR with a high CO faradaic efficiency (FE) of >95% under a wide potential range. The turnover frequency (TOF) value can reach 50,310 h-1 at FECO = 99.5% in H-cells. Notably, Ag1/CeO2 achieves an industrial-grade current density of 403 mA cm-2 with a high FECO of 97.2% in flow cells. Experimental results combined with density functional theory calculation revealed that this superior performance was mainly ascribed to the existence of interfacial oxygen vacancies, which lead to the formation of Ag-O-Ce3+ atomic interfaces, and activates the Ce3+-O structures as the synergistic active center of Ag, thus promoting CO2 adsorption and activation and reducing the reaction potential barrier of *COOH-to-*CO.

Ingenious Artificial Leaf Based on Covalent Organic Framework Membranes for Boosting CO2 Photoreduction.

Covalent organic frameworks (COFs) hold the potential in converting CO2 with water into value-added fuels and O2 to save the deteriorating ecological environment. However, reaching high yield and selectivity is a grand challenge under metal-, photosensitizer-, or sacrificial reagent-free conditions. Here, inspired by microstructures of natural leaves, we designed triazine-based COF membranes with the integration of steady light-harvesting sites, efficient catalytic center, and fast charge/mass transfer configuration to fabricate a novel artificial leaf for the first time. Significantly, a record high CO yield of 1240 µmol g-1 in a 4 h reaction, approximately 100% selectivity, and a long lifespan (at least 16 cycles) were achieved under gas-solid conditions without using any metal, photosensitizer, or sacrificial reagent. Unlike the existing knowledge, the chemical structural unit of triazine-imide-triazine and the unique physical form of the COF membrane are predominant for such a remarkable photocatalysis. This work opens a new pathway to simulating photosynthesis in leaves and may motivate relevant research in the future.

Bodipy-Based Metal-Organic Frameworks Transformed in Solid States from 1D Chains to 2D Layer Structures as Efficient Visible Light Heterogeneous Photocatalysts for Forging C-B and C-C Bonds.

Boron dipyrromethene (also known as bodipy), as a class of versatile and robust fluorophores and a structural analogue of porphyrins, has received a great deal of interests in the field of light-harvesting and energy-transfer processes. However, the fabrication of bodipy monomers into metal-organic frameworks (MOFs) and the exploitation of their potential still lags behind the porphyrin MOFs. In this work, two bodipy-based MOFs, BMOF 1D with 1D chain structure and BMOF 2D with 2D layer structure, were assembled by using dicarboxyl-functionalized bodipy ligands. BMOF 1D can also be converted to BMOF 2D by inserting additional ligands into BMOF 1D to cross-link the adjacent chains into the rhombic grid layer. During this process, spontaneous exfoliation occurred simultaneously and resulted in the formation of several hundred nanometer thickness BMOF 2D (nBMOF 2D), which can be further exfoliated into one-layer MOF nanosheets (BMON 2D) by using the ultrasonic liquid exfoliation method in a high yield. Featuring the distinct bodipy scaffolds in the porous frameworks, both BMOF 2D and BMON 2D displayed high reactivity and recyclability in the photocatalytic inverse hydroboration and cross-dehydrogenative coupling reactions to afford α-amino organoborons and α-amino amides in moderate to high yields. This work not only highlights the cascade utilization of ligand installation and ultrasonic liquid exfoliation methods to provide the single-layer MOF sheets in high yields but also advances the bodipy-based MOFs as a new type of heterogeneous photocatalysts in the forging of C-B and C-C bonds driven by visible light.

Multi-weight susceptible-infected model for predicting COVID-19 in China.

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

Activation and Inhibition of Isomerization of a Cationic Azobenzene Surfactant in the Large Void Space of Polyglycerol Dendron Micelles.

Owing to the unique geometric structure of dendritic amphiphiles with voluminous dendrons, their micelles can harbor a large void space, which provides a new research focus and approach for micellar functionalization. In this work, we used the void space to construct a UV responsive micelle system of the mixed dendritic amphiphile (C12-(G3)2) and cationic azobenzene surfactant (C4AzoTAB). The synthesized C12-(G3)2 that possesses double third generation polyglycerol (PG) dendrons and a single alkyl chain is expected to highlight the large void space within the inside of the micelles. Thus, the aims of this work are to achieve the isomerization of C4AzoTAB in situ and to deeply understand the intermolecular interaction in the mixed micelles. The effect of the large void room with a wall decorated with the ether oxygen atoms on the isomerization of C4AzoTAB was studied by isomerization kinetics, conductivity measurements, isothermal titration calorimetry (ITC), and 1H NMR and 2D NOESY spectroscopies. The isomerization behavior of C4AzoTAB in C12-(G3)2 micelles was presented in terms of its kinetic constant, counterionic association, interaction enthalpy, and position and orientation of C4AzoTAB. The results of NMR and conductivity show that the quaternary ammonium group of C4AzoTAB situates on the surface of the mixed micelles with C12-(G3)2 both before and after UV-irradiation, while the position of azobenzene group in C12-(G3)2 micelles depends on its conformation. The C12-(G3)2 micelles can inhibit the UV response of the trans-isomer and activate the thermal relaxation of the cis-isomer, which has a potential application in the field of light-controlled smart nanocarriers.

Porous Covalent Organic Framework Based Hydrogen-Bond Nanotrap for the Precise Recognition and Separation of Gold.

Utilizing weak interactions to effectively recover and separate precious metals in solution is of great importance but the practice remains a challenge. Herein, we report a novel strategy to achieve precise recognition and separation of gold by regulating the hydrogen-bond (H-bond) nanotrap within the pore of covalent organic frameworks (COFs). It is found that both COF-HNU25 and COF-HNU26 can efficiently capture AuIII with fast kinetics, high selectivity, and uptake capacity. In particular, the COF-HNU25 with the high density of H-bond nanotraps exhibits an excellent gold uptake capacity of 1725 mg g-1 , which is significantly higher than that (219 mg g-1 ) of its isostructural COF (COF-42) without H-bond nanostrap in the pores. Importantly, the uptake capacity is strongly correlated to the number of H-bonds between phenolic OH in the COF and [AuCl4 ]- in water, and multiple H-bond interactions are the key driving force for the excellent gold recovery and reusability of the adsorbent.

Designing covalent organic frameworks with Co-O4 atomic sites for efficient CO2 photoreduction.

Cobalt coordinated covalent organic frameworks have attracted increasing interest in the field of CO2 photoreduction to CO, owing to their high electron affinity and predesigned structures. However, achieving high conversion efficiency is challenging since most Co related coordination environments facilitate fast recombination of photogenerated electron-hole pairs. Here, we design two kinds of Co-COF catalysts with oxygen coordinated Co atoms and find that after tuning of coordination environment, the reported Co framework catalyst with Co-O4 sites exhibits a high CO production rate of 18000 µmol g-1 h-1 with selectivity as high as 95.7% under visible light irradiation. From in/ex-situ spectral characterizations and theoretical calculations, it is revealed that the predesigned Co-O4 sites significantly facilitate the carrier migration in framework matrixes and inhibit the recombination of photogenerated electron-hole pairs in the photocatalytic process. This work opens a way for the design of high-performance catalysts for CO2 photoreduction.

Isomerization of DASA Molecules in the Nanopores of Metal-Organic Frameworks: What Determines Its Reversibility?

In recent years, light-responsive molecules have been incorporated in metal-organic frameworks (MOFs) to fabricate light-responsive intelligent devices, where reversible isomerization of the guest molecules in the nanopores is crucial. However, how to design a porous environment of MOFs to achieve a reversible isomerization remains unknown until now. In this work, donor-acceptor Stenhouse adducts (DASAs), a new kind of visible light responsive compound, were confined in the nanopores of different MOFs to study their isomerization upon visible-light irradiation/mild heating. We found that the polarity of the pore environment is the key to control the reversibility of isomerization of such guest molecules. Under the guidance of this principle, MIL-53(Al) was screened to investigate the proton conductivity and switching performance of the DASA-confined MOF. The proton conductance was up to 0.013 S cm-1 at 80 °C and 98 % RH, and at least 30 switching cycles were achieved thanks to the Grotthuss-type mechanism and the low polarity of MIL-53(Al) pore environment.

[EMmim][NTf2 ]-a Novel Ionic Liquid (IL) in Catalytic CO2 Capture and ILs' Applications.

Ionic liquids (ILs) have been used for carbon dioxide (CO2 ) capture, however, which have never been used as catalysts to accelerate CO2 capture. The record is broken by a uniquely designed IL, [EMmim][NTf2 ]. The IL can universally catalyze both CO2 sorption and desorption of all the chemisorption-based technologies. As demonstrated in monoethanolamine (MEA) based CO2 capture, even with the addition of only 2000 ppm IL catalyst, the rate of CO2 desorption-the key to reducing the overall CO2 capture energy consumption or breaking the bottleneck of the state-of-the-art technologies and Paris Agreement implementation-can be increased by 791% at 85 °C, which makes use of low-temperature waste heat and avoids secondary pollution during CO2 capture feasible. Furthermore, the catalytic CO2 capture mechanism is experimentally and theoretically revealed.

Chemical looping based ammonia production-A promising pathway for production of the noncarbon fuel.

Ammonia, primarily made with Haber-Bosch process developed in 1909 and winning two Nobel prizes, is a promising noncarbon fuel for preventing global warming of 1.5 °C above pre-industrial levels. However, the undesired characteristics of the process, including high carbon footprint, necessitate alternative ammonia synthesis methods, and among them is chemical looping ammonia production (CLAP) that uses nitrogen carrier materials and operates at atmospheric pressure with high product selectivity and energy efficiency. To date, neither a systematic review nor a perspective in nitrogen carriers and CLAP has been reported in the critical area. Thus, this work not only assesses the previous results of CLAP but also provides perspectives towards the future of CLAP. It classifies, characterizes, and holistically analyzes the fundamentally different CLAP pathways and discusses the ways of further improving the CLAP performance with the assistance of plasma technology and artificial intelligence (AI).

Correction: An azobenzene-modified redox-active ionic liquid electrolyte for supercapacitors.

Correction for 'An azobenzene-modified redox-active ionic liquid electrolyte for supercapacitors' by Yuhua Zhao et al., Chem. Commun., 2022, https://doi.org/10.1039/d2cc04081f.