University of Shanghai for Science and Technology

This review summarizes recent research progress and perspectives on noble-metal-free bifunctional heterogeneous electrocatalysts towards hydrogen and oxygen evolution reactions in overall water splitting.

We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and [Formula: see text] credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5-[Formula: see text] requires at least three detectors of sensitivity within a factor of [Formula: see text] of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.

Finding pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be greatly enhanced by choosing suitable leaders. For instance in delicious.com, users subscribe to leaders' collection which lead to a deeper and wider reach not achievable with search engines. To consolidate such collective search, it is essential to utilize the leadership topology and identify influential users. Google's PageRank, as a successful search algorithm in the World Wide Web, turns out to be less effective in networks of people. We thus devise an adaptive and parameter-free algorithm, the LeaderRank, to quantify user influence. We show that LeaderRank outperforms PageRank in terms of ranking effectiveness, as well as robustness against manipulations and noisy data. These results suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.

In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed architecture, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.

Abstract Rechargeable aqueous zinc-ion batteries (RZIBs) provide a promising complementarity to the existing lithium-ion batteries due to their low cost, non-toxicity and intrinsic safety. However, Zn anodes suffer from zinc dendrite growth and electrolyte corrosion, resulting in poor reversibility. Here, we develop an ultrathin, fluorinated two-dimensional porous covalent organic framework (FCOF) film as a protective layer on the Zn surface. The strong interaction between fluorine (F) in FCOF and Zn reduces the surface energy of the Zn (002) crystal plane, enabling the preferred growth of (002) planes during the electrodeposition process. As a result, Zn deposits show horizontally arranged platelet morphology with (002) orientations preferred. Furthermore, F-containing nanochannels facilitate ion transport and prevent electrolyte penetration for improving corrosion resistance. The FCOF@Zn symmetric cells achieve stability for over 750 h at an ultrahigh current density of 40 mA cm −2 . The high-areal-capacity full cells demonstrate hundreds of cycles under high Zn utilization conditions.

Traditional photodynamic therapy (PDT) suffers from the critical issues of low tissue-penetrating depth of light and potential phototoxicity, which are expected to be solved by developing new dynamic therapy-based therapeutic modalities such as sonodynamic therapy (SDT). In this work, we report on the design/fabrication of a high-performance multifunctional nanoparticulate sonosensitizer for efficient in vivo magnetic resonance imaging (MRI)-guided SDT against cancer. The developed approach takes the structural and compositional features of mesoporous organosilica-based nanosystems for the fabrication of sonosensitizers with intriguing theranostic performance. The well-defined mesoporosity facilitates the high loading of organic sonosensitizers (protoporphyrin, PpIX) and further chelating of paramagnetic transitional metal Mn ions based on metalloporphyrin chemistry (MnPpIX). The mesoporous structure of large surface area also maximizes the accessibility of water molecules to the encapsulated paramagnetic Mn ions, endowing the composite sonosensitizers with markedly high MRI performance (r1 = 9.43 mM–1 s–2) for SDT guidance and monitoring. Importantly, the developed multifunctional sonosensitizers (HMONs-MnPpIX-PEG) with controllable biodegradation behavior and high biocompatibility show distinctively high SDT efficiency for inducing the cancer-cell death in vitro and suppressing the tumor growth in vivo. This report provides a paradigm that nanotechnology-enhanced SDT based on elaborately designed high-performance multifunctional sonosensitizers will pave a new way for efficient cancer treatment by fully taking the advantages (noninvasiveness, convenience, cost-effectiveness, etc.) of ultrasound therapy and quickly developing nanomedicine.

detection at room temperature.

This review summarizes recent progress in layered double hydroxide oxygen evolution reaction electrocatalysts including design strategies, key issues and future prospects.

Abstract Optical phase change materials (O-PCMs), a unique group of materials featuring exceptional optical property contrast upon a solid-state phase transition, have found widespread adoption in photonic applications such as switches, routers and reconfigurable meta-optics. Current O-PCMs, such as Ge–Sb–Te (GST), exhibit large contrast of both refractive index (Δ n ) and optical loss (Δ k ), simultaneously. The coupling of both optical properties fundamentally limits the performance of many applications. Here we introduce a new class of O-PCMs based on Ge–Sb–Se–Te (GSST) which breaks this traditional coupling. The optimized alloy, Ge 2 Sb 2 Se 4 Te 1 , combines broadband transparency (1–18.5 μm), large optical contrast (Δ n = 2.0), and significantly improved glass forming ability, enabling an entirely new range of infrared and thermal photonic devices. We further demonstrate nonvolatile integrated optical switches with record low loss and large contrast ratio and an electrically-addressed spatial light modulator pixel, thereby validating its promise as a material for scalable nonvolatile photonics.

Porphyrins and porphyrin derivatives have been widely explored for various applications owing to their excellent photophysical and electrochemical properties. However, inherent shortcomings, such as instability and self-quenching under physiological conditions, limit their biomedical applications. In recent years, metal-organic frameworks (MOFs) have received increasing attention. The construction of porphyrin-based MOFs by introducing porphyrin molecules into MOFs or using porphyrins as organic linkers to form MOFs can combine the unique features of porphyrins and MOFs as well as overcome the limitations of porphyrins. This Review summarizes important synthesis strategies for porphyrin-based MOFs including porphyrin@MOFs, porphyrinic MOFs, and composite porphyrinic MOFs, and highlights recent achievements and progress in the development of porphyrin-based MOFs for biomedical applications in tumor therapy and biosensing. Finally, the challenges and prospects presented by this class of emerging materials for biomedical applications are discussed.

Spectrometers with ever-smaller footprints are sought after for a wide range of applications in which minimized size and weight are paramount, including emerging in situ characterization techniques. We report on an ultracompact microspectrometer design based on a single compositionally engineered nanowire. This platform is independent of the complex optical components or cavities that tend to constrain further miniaturization of current systems. We show that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire. Our devices are capable of accurate, visible-range monochromatic and broadband light reconstruction, as well as spectral imaging from centimeter-scale focal planes down to lensless, single-cell-scale in situ mapping.

In the past 14 years, as a branch of high-entropy alloy (HEA) materials, HEA films and coatings have exhibited the attractive and unique properties, relative to the conventional film and coating materials. The recent research and development of HEA films and coatings are reviewed in this paper. At first, the basic concept of HEAs films and coatings are introduced. Then, their preparation technologies, microstructures and appealing properties are summarized. Moreover, the possible reasons and design criteria for achieving the excellent properties are discussed. Finally, the suggested future research work for the HEA films and coatings are outlined. Preparation technologies, microstructures, and properties of HEA films and coatings are reviewed in this paper. The design criteria and suggested research directions are further discussed and proposed.

Rechargeable sodium-ion batteries (SIBs) have been considered as promising energy storage devices owing to the similar "rocking chair" working mechanism as lithium-ion batteries and abundant and low-cost sodium resource. However, the large ionic radius of the Na-ion (1.07 Å) brings a key scientific challenge, restricting the development of electrode materials for SIBs, and the infeasibility of graphite and silicon in reversible Na-ion storage further promotes the investigation of advanced anode materials. Currently, the key issues facing anode materials include sluggish electrochemical kinetics and a large volume expansion. Despite these challenges, substantial conceptual and experimental progress has been made in the past. Herein, we present a brief review of the recent development of intercalation, conversion, alloying, conversion-alloying, and organic anode materials for SIBs. Starting from the historical research progress of anode electrodes, the detailed Na-ion storage mechanism is analyzed. Various optimization strategies to improve the electrochemical properties of anodes are summarized, including phase state adjustment, defect introduction, molecular engineering, nanostructure design, composite construction, heterostructure synthesis, and heteroatom doping. Furthermore, the associated merits and drawbacks of each class of material are outlined, and the challenges and possible future directions for high-performance anode materials are discussed.

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose an Efficient Attention Pyramid Transformer (EAPT). Specifically, we first propose the Deformable Attention, which learns an offset for each position in patches. Thus, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information among all patches to get more complete global attention information. Finally, we propose a position encoding specifically for vision transformers, which can be used for patches of any dimension and any length. Extensive experiments on the vision tasks of image classification, object detection, and semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.

This article proposes a memory-based eventtriggering H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach-Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

Abstract Aqueous Zn‐ion batteries (AZIBs) have attracted much attention due to their excellent safety, cost‐effectiveness, and eco‐friendliness thereby being considered as one of the most promising candidates for large‐scale energy storage. Zn metal anodes with a high gravimetric/volumetric capacity are indispensable for advanced AZIBs. However, pristine Zn metal anodes encounter severe challenges in achieving adequate cycling stability, including dendrite growth, hydrogen evolution reaction, self‐corrosion, and by‐product formation. Because all these reactions are closely related to the electrolyte/Zn interface, the subtle interface engineering is important. Many strategies targeted to the interface engineering have been developed. In this review, a timely update on these strategies and perspectives are summarized, especially focusing on the controllable synthesis of Zn, Zn surface engineering, electrolyte formulation, and separator design. Furthermore, the corresponding internal principles of these strategies are clarified, which is helpful to help seek for new strategies. Finally, the challenges and perspectives for the future development of practical AZIBs are discussed, including the conducting of in advanced in situ testing, unification of battery models, some boundary issues, etc. This review is expected to guide the future development and provi beacon light direction for aqueous zinc ion batteries.

During the past decades, with the increasing demands in lightweight structural materials, Mg alloys with low density and high performance have been extensively investigated and partly applied in some industries. Especially when rare earth (RE) elements are added as major alloying elements to Mg alloys, the alloy strength and creep resistance are greatly improved, which have promoted several series of Mg-RE alloys. This paper reviews the progress and developments of high-performance Mg-RE alloys in recent years with emphasis on cast alloys. The main contents include the alloy design, melt purification, grain refinement, castability, novel liquid casting and semisolid forming approaches, and the industrial applications or trials made of Mg-RE alloys. The review will provide insights for future developments of new alloys, techniques and applications of Mg alloys.

Abstract Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring, exhaled breath diagnosis, and food freshness analysis. Among various chemiresistive sensing materials, noble metal-decorated semiconducting metal oxides (SMOs) have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble metals. This review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures (e.g., nanoparticles, nanowires, nanorods, nanosheets, nanoflowers, and microspheres) for high-performance gas sensors with higher response, faster response/recovery speed, lower operating temperature, and ultra-low detection limits. The key topics include Pt, Pd, Au, other noble metals (e.g., Ag, Ru, and Rh . ), and bimetals-decorated SMOs containing ZnO, SnO 2 , WO 3 , other SMOs (e.g., In 2 O 3 , Fe 2 O 3 , and CuO), and heterostructured SMOs. In addition to conventional devices, the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also discussed. Moreover, the relevant mechanisms for the sensing performance improvement caused by noble metal decoration, including the electronic sensitization effect and the chemical sensitization effect, have also been summarized in detail. Finally, major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.