Huzhou University

Background: More than 80% of sewage generated by human activities is discharged into rivers and oceans without any treatment, which results in environmental pollution and more than 50 diseases. 80% of diseases and 50% of child deaths worldwide are related to poor water quality. Methods: This paper selected 85 relevant papers finally based on the keywords of water pollution, water quality, health, cancer, and so on. Results: The impact of water pollution on human health is significant, although there may be regional, age, gender, and other differences in degree. The most common disease caused by water pollution is diarrhea, which is mainly transmitted by enteroviruses in the aquatic environment. Discussion: Governments should strengthen water intervention management and carry out intervention measures to improve water quality and reduce water pollution’s impact on human health.

Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.

Mixed convection is a mechanism of heat transport in a thermodynamic system in which the motion of fluid particles is produced by gravity as well as external forces like fans, pumps, or any other devices. Such type of heat transport has a fruitful application in daily life due to reliable maintenance. In this regard, numerous researchers and analyst have focused on the importance of mixed convective flow to explore its different aspects, and frequent research articles are published in this area. In this work, mixed convective entropy optimized nanomaterial magnetohydrodynamics (MHD) flow of Ree‐Eyring fluid is discussed between two rotating disks. The effects of porosity and velocity slip are considered. Both the disks are rotating with different angular frequency and stretching rates. Modeling is performed for the energy equation subject to heat generation/absorption, dissipation, radiative heat flux, and Joule heating. Four types of irreversibilities are discussed, and total entropy rate is calculated. The obtained results are compared with past studies and found good agreement with them. The physical curiosity like skin friction and Sherwood and Nusselt numbers are numerically calculated. Series solutions are computed via homotopy method. Our obtained outcomes show that the velocity and temperature fields show contrast behavior against larger magnetic parameter. It is also noticed that the entropy rate and Bejan number have opposite behaviors against higher values of Weissenberg number. The entropy rate increases for higher Weissenberg number while Bejan number decays.

The synergy between metal alloy nanoparticles (NPs) and single atoms (SAs) should maximize the catalytic activity. However, there are no relevant reports on photocatalytic CO2 reduction via utilizing the synergy between SAs and alloy NPs. Herein, we developed a facile photodeposition method to coload the Cu SAs and Au–Cu alloy NPs on TiO2 for the photocatalytic synthesis of solar fuels with CO2 and H2O. The optimized photocatalyst achieved record-high performance with formation rates of 3578.9 for CH4 and 369.8 μmol g–1 h–1 for C2H4, making it significantly more realistic to implement sunlight-driven synthesis of value-added solar fuels. The combined in situ FT-IR spectra and DFT calculations revealed the molecular mechanisms of photocatalytic CO2 reduction and C–C coupling to form C2H4. We proposed that the synergistic function of Cu SAs and Au–Cu alloy NPs could enhance the adsorption activation of CO2 and H2O and lower the overall activation energy barrier (including the rate-determining step) for the CH4 and C2H4 formation. These factors all enable highly efficient and stable production of solar fuels of CH4 and C2H4. The concept of synergistic SAs and metal alloys cocatalysts can be extended to other systems, thus contributing to the development of more effective cocatalysts.

Several mechanisms in industrial use have significant applications in thermal transportation. The inclusion of hybrid nanoparticles in different mixtures has been studied extensively by researchers due to their wide applications. This report discusses the flow of Powell–Eyring fluid mixed with hybrid nanoparticles over a melting parabolic stretched surface. Flow rheology expressions have been derived under boundary layer theory. Afterwards, similarity transformation has been applied to convert PDEs into associated ODEs. These transformed ODEs have been solved the using finite element procedure (FEP) in the symbolic computational package MAPLE 18.0. The applicability and effectiveness of FEM are presented by addressing grid independent analysis. The reliability of FEM is presented by computing the surface drag force and heat transportation coefficient. The used methodology is highly effective and it can be easily implemented in MAPLE 18.0 for other highly nonlinear problems. It is observed that the thermal profile varies directly with the magnetic parameter, and the opposite trend is recorded for the Prandtl number.

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.

Lithium metal anodes have attracted extensive attention owing to their high theoretical specific capacity. However, the notorious reactivity of lithium prevents their practical applications, as evidenced by the undesired lithium dendrite growth and unstable solid electrolyte interphase formation. Here, we develop a facile, cost-effective and one-step approach to create an artificial lithium metal/electrolyte interphase by treating the lithium anode with a tin-containing electrolyte. As a result, an artificial solid electrolyte interphase composed of lithium fluoride, tin, and the tin-lithium alloy is formed, which not only ensures fast lithium-ion diffusion and suppresses lithium dendrite growth but also brings a synergistic effect of storing lithium via a reversible tin-lithium alloy formation and enabling lithium plating underneath it. With such an artificial solid electrolyte interphase, lithium symmetrical cells show outstanding plating/stripping cycles, and the full cell exhibits remarkably better cycling stability and capacity retention as well as capacity utilization at high rates compared to bare lithium.

Small object detection remains an unsolved challenge because it is hard to extract the information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose an extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we introduce a cross resolution distillation mechanism to transfer the ability of perceiving details across the scales of the network, where a foreground-background-balanced loss function is designed to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100 K and small category of general object detection dataset MS COCO.

Abstract Polynary transition‐metal atom catalysts are promising to supersede platinum (Pt)‐based catalysts for oxygen reduction reaction (ORR). Regulating the local configuration of atomic catalysts is the key to catalyst performance enhancement. Different from the previously reported single‐atom or dual‐atom configurations, a new type of ternary‐atom catalyst, which consists of atomically dispersed, nitrogen‐coordinated Co–Co dimers, and Fe single sites (i.e., Co 2 –N 6 and Fe–N 4 structures) that are coanchored on highly graphitized carbon supports is developed. This unique atomic ORR catalyst outperforms the catalysts with only Co 2 –N 6 or Fe–N 4 sites in both alkaline and acid conditions. Density functional theory calculations clearly unravels the synergistic effect of the Co 2 –N 6 and Fe–N 4 sites, which can induce higher filling degree of Fe–d orbitals and favors the binding capability to *OH intermediates (the rate determining step). This ternary‐atom catalyst may be a promising alternative to Pt to drive the cathodic ORR in zinc–air batteries.

Abstract Pursuit of advanced batteries with high‐energy density is one of the eternal goals for electrochemists. Over the past decades, lithium–sulfur batteries (LSBs) have gained world‐wide popularity due to their high theoretical energy density and cost effectiveness. However, their road to the market is still full of thorns. Apart from the poor electronic conductivity of sulfur‐based cathodes, LSBs involve special multielectron reaction mechanisms associated with active soluble lithium polysulfides intermediates. Accordingly, the electrode design and fabrication protocols of LSBs are different from those of traditional lithium ion batteries. This review is aimed at discussing the electrode design/fabrication protocols of LSBs, especially the current problems on various sulfur‐based cathodes (such as S, Li 2 S, Li 2 S x catholyte, organopolysulfides) and corresponding solutions. Different fabrication methods of sulfur‐based cathodes are introduced and their corresponding bullet points to achieve high‐quality cathodes are highlighted. In addition, the challenges and solutions of sulfur‐based cathodes including active material content, mass loading, conductive agent/binder, compaction density, electrolyte/sulfur ratio, and current collector are summarized and rational strategies are refined to address these issues. Finally, the future prospects on sulfur‐based cathodes and LSBs are proposed.

In this article, we present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial–temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization and the backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude.

Abstract Single‐atom photocatalysis has been demonstrated as a novel strategy to promote heterogeneous reactions. There is a diversity of monoatomic metal species with specific functions; however, integrating representative merits into dual‐single‐atoms and regulating cooperative photocatalysis remain a pressing challenge. For dual‐single‐atom catalysts, enhanced photocatalytic activity would be realized through integrating bifunctional properties and tuning the synergistic effect. Herein, dual‐single‐atoms supported on conjugated porous carbon nitride polymer are developed for effective photocatalytic CO 2 reduction, featuring the function of cobalt (Co) and ruthenium (Ru). A series of in situ characterizations and theoretical calculations are conducted for quantitative analysis of structure–performance correlation. It is concluded that the active Co sites facilitate dynamic charge transfer, while the Ru sites promote selective CO 2 surface‐bound interaction during CO 2 photoreduction. The combination of atom‐specific traits and the synergy between Co and Ru lead to the high photocatalytic CO 2 conversion with corresponding apparent quantum efficiency (AQE) of 2.8% at 385 nm, along with a high turnover number (TON) of more than 200 without addition of any sacrificial agent. This work presents an example of identifying the roles of different single‐atom metals and regulating the synergy, where the two metals with unique properties collaborate to further boost the photocatalytic performance.

Abstract Work function strongly impacts the surficial charge distribution, especially for metal‐support electrocatalysts when a built‐in electric field (BEF) is constructed. Therefore, studying the correlation between work function and BEF is crucial for understanding the intrinsic reaction mechanism. Herein, we present a Pt@CoO x electrocatalyst with a large work function difference (ΔΦ) and strong BEF, which shows outstanding hydrogen evolution activity in a neutral medium with a 4.5‐fold mass activity higher than 20 % Pt/C. Both experimental and theoretical results confirm the interfacial charge redistribution induced by the strong BEF, thus subtly optimizing hydrogen and hydroxide adsorption energy. This work not only provides fresh insights into the neutral hydrogen evolution mechanism but also proposes new design principles toward efficient electrocatalysts for hydrogen production in a neutral medium.

We incorporate the color-screening effect due to light quark pair creation into the heavy quark-antiquark potential, and investigate the effects of screened potential on the spectrum of higher charmonium. We calculate the masses, electromagnetic decays, and E1 transitions of charmonium states in the screened potential model, and propose possible assignments for the newly discovered charmonium or charmoniumlike ``$X$, $Y$, $Z$'' states. We find the masses of higher charmonia with screened potential are considerably lower than those with unscreened potential. The ${\ensuremath{\chi}}_{c2}(2P)$ mass agrees well with that of the $Z(3930)$, and the mass of $\ensuremath{\psi}(4415)$ is compatible with $\ensuremath{\psi}(5S)$ rather than $\ensuremath{\psi}(4S)$. In particular, the discovered four $Y$ states in the initial state radiation process, i.e., $Y(4008)$, $Y(4260)$, $Y(4320/4360)$, $Y(4660)$ may be assigned as the $\ensuremath{\psi}(3S)$, $\ensuremath{\psi}(4S)$, $\ensuremath{\psi}(3D)$, $\ensuremath{\psi}(6S)$ states, respectively. The $X(3940)$ and $X(4160)$ found in the double charmonium production in ${e}^{+}{e}^{\ensuremath{-}}$ annihilation may be assigned as the ${\ensuremath{\eta}}_{c}(3S)$ and ${\ensuremath{\chi}}_{c0}(3P)$ states. Based on the calculated E1 transition widths for ${\ensuremath{\chi}}_{c1}(2P)\ensuremath{\rightarrow}\ensuremath{\gamma}J/\ensuremath{\psi}$ and ${\ensuremath{\chi}}_{c1}(2P)\ensuremath{\rightarrow}\ensuremath{\gamma}\ensuremath{\psi}(2S)$ and other results, we argue that the $X(3872)$ may be a ${\ensuremath{\chi}}_{c1}(2P)$ dominated charmonium state with some admixture of the ${D}^{0}{\overline{D}}^{*0}$ component. Possible problems encountered in these assignments and comparisons with other interpretations for these $X$, $Y$, $Z$ states are discussed in detail. We emphasize that more theoretical and experimental investigations are urgently needed to clarify these assignments and other interpretations.

Vanadium-based materials have been extensively studied as promising cathode materials for zinc-ion batteries because of their multiple valences and adjustable ion-diffusion channels. However, the sluggish kinetics of Zn-ion intercalation and less stable layered structure remain bottlenecks that limit their further development. The present work introduces potassium ions to partially substitute ammonium ions in ammonium vanadate, leading to a subtle shrinkage of lattice distance and the increased oxygen vacancies. The resulting potassium ammonium vanadate exhibits a high discharge capacity (464 mAh g–1 at 0.1 A g–1) and excellent cycling stability (90% retention over 3000 cycles at 5 A g–1). The excellent electrochemical properties and battery performances are attributed to the rich oxygen vacancies. The introduction of K+ to partially replace NH4+ appears to alleviate the irreversible deammoniation to prevent structural collapse during ion insertion/extraction. Density functional theory calculations show that potassium ammonium vanadate has a modulated electron structure and a better zinc-ion diffusion path with a lower migration barrier.

Electrocatalytic CO2 reduction (CO2RR), powered by renewable energy, has great potential in decreasing the concentration of CO2 in the atmosphere, as well as producing high value-added fuels or chemicals. The electrode and electrolyte together determine the catalytic performance of CO2RR. Despite the substantial progress has been made in the design and preparation of high-performance catalysts, the role of electrolyte at the electrode–electrolyte interface (EEI) which could largely affect the local catalytic environment has not been understood thoroughly. To maximize and balance the catalytic performance (i.e., activity, selectivity, and stability) of CO2RR from a standpoint of application, the fundamental understanding of interfacial electrolyte effects should be emphasized with equal importance to the intrinsic properties of the catalyst. In this Review, we will focus on the discussion of the role (effects) of electrolytes for CO2RR. We summarize the effects of electrolytes according to their compositions and local chemical environment, which include solvents, local pH, cations, anions, impurities, additives, and modifiers. In addition, in-depth investigations on the detection of intermediates during the catalytic reactions using in situ spectroscopy techniques are included. The mechanisms, current challenges, future developments, and perspectives are discussed.

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose SAM-Adapter, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. By integrating task-specific knowledge with general knowledge learnt by the large model, SAM-Adapter can significantly elevate the performance of SAM in challenging tasks as shown in extensive experiments. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection. Our code of adapting SAM in downstream applications have been released publicly at https://github.com/tianrun-chen/SAM-Adapter-PyTorch/ and has benefited many researchers. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more.

Abstract Halide perovskites have shown superior potentials in flexible photovoltaics due to their soft and high power‐to‐weight nature. However, interfacial residual stress and lattice mismatch due to the large deformation of flexible substrates have greatly limited the performance of flexible perovskite solar cells (F‐PSCs). Here, ammonium formate (HCOONH 4 ) is used as a pre‐buried additive in electron transport layer (ETL) to realize a bottom‐up infiltration process for an in situ, integral modification of ETL, perovskite layer, and their interface. The HCOONH 4 treatment leads to an enhanced electron extraction in ETL, relaxed residual strain and micro‐strain in perovskite film, along with reduced defect densities within these layers. As a result, a top power conversion efficiency of 22.37% and a certified 21.9% on F‐PSCs are achieved, representing the highest performance reported so far. This work links the critical connection between multilayer mechanics/defect profiles of ETL‐perovskite structure and device performance, thus providing meaningful scientific direction to further narrowing the efficiency gap between F‐PSCs and rigid‐substrate counterparts.

Low-dimensional metal-organic frameworks (LD MOFs) have attracted increasing attention in recent years, which successfully combine the unique properties of MOFs, e.g., large surface area, tailorable structure, and uniform cavity, with the distinctive physical and chemical properties of LD nanomaterials, e.g., high aspect ratio, abundant accessible active sites, and flexibility. Significant progress has been made in the morphological and structural regulation of LD MOFs in recent years. It is still of great significance to further explore the synthetic principles and dimensional-dependent properties of LD MOFs. In this review, recent progress in the synthesis of LD MOF-based materials and their applications are summarized, with an emphasis on the distinctive advantages of LD MOFs over their bulk counterparties. First, the unique physical and chemical properties of LD MOF-based materials are briefly introduced. Synthetic strategies of various LD MOFs, including 1D MOFs, 2D MOFs, and LD MOF-based composites, as well as their derivatives, are then summarized. Furthermore, the potential applications of LD MOF-based materials in catalysis, energy storage, gas adsorption and separation, and sensing are introduced. Finally, challenges and opportunities of this fascinating research field are proposed.

The present study examines the impact of unsteady viscous flow in a squeezing channel. Silver–gold hybrid nanofluid particles with different shapes are inserted in the base fluid engine oil. Flow and heat transfer mechanism is detected in the presence of magnetohydrodynamics between the two parallel infinite plates. The thermal conductivity models, that is, Yamada–Ota and Hamilton–Crosser models are used to investigate various shapes (Blade, platelet, cylinder, and brick) of hybrid nanoparticles. The model is made up of paired high nonlinear partial differential equations that are then transformed into ordinary differential equations which are coupled and strong nonlinear using the boundary layer approximation. The MATLAB solver bvp4c package is used to solve the numerical solution of this coupled system. The influence of different parameters on the physical quantities is addressed via graphs. A comparison with already reported results is given in order to confirm the current findings. The analysis shows that surprisingly the Yamada–Ota model of the Hybrid nanofluid gains high temperature and velocity profile than the Hamilton–Crosser model of the hybrid nanofluid. Also, both the models show increasing trends toward increasing the volume fraction rate of silver‐gold hybrid nanoparticles. It is also inferred that the hybrid‐nanoparticles performance is far better than the common nanofluids.