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Staphylococcus aureus (S. aureus) infection is a primary cause of otitis media (OM), the most common disease for which children are prescribed antibiotics. However, the abuse of antibiotics has led to a global increase in antimicrobial resistance (AMR). Nanozymes, as promising alternatives to traditional antibiotics, are being extensively utilized to combat AMR. Here, we synthesize a series of single-atom nanozymes (metal-C3N4 SANzymes) by loading four metals (Ag, Fe, Cu, Ru) with antibacterial properties onto a crystalline g-C3N4. These metal-C3N4 display a rob-like morphology and well-dispersed metal atoms. Among them, Ru-C3N4 demonstrates the optimal peroxidase-like activity (285.3 U mg–1), comparable to that of horseradish peroxidase (267.7 U mg–1). In vitro antibacterial assays reveal that Ru-C3N4 significantly inhibits S. aureus growth compared with other metal-C3N4 even at a low concentration (0.06 mg mL–1). Notably, Ru-C3N4 acts as a narrow-spectrum nanoantibiotic with relative specificity against Gram-positive bacteria. Biofilms formed by S. aureus are easily degraded by Ru-C3N4 due to its high peroxidase-like activity. In vivo, Ru-C3N4 effectively eliminates S. aureus and relieves ear inflammation in OM mouse models. However, untreated OM mice eventually develop hearing impairment. Due to its low metal load, Ru-C3N4 does not exhibit significant toxicity to blood, liver, or kidney. In conclusion, this study presents a novel SANzyme-based antibiotic that can effectively eliminate S. aureus and treat S. aureus-induced OM.

Abstract A thin dielectric layer is an important constituent element in 2D materials-based electronics and photonics. Current methods of using hexagonal boron nitride (hBN) and direct deposition of dielectric layer induce either high leakage current or unintentional doping and defect. Here we report a technique for damaging free integration of dielectric layer to form high-quality van der Waals (vdW) heterostructure. The dielectric layer is grown by atomic layer deposition (ALD) on 2D materials and then deterministically transferred on the target 2D material. The much weaker binding energy between the ALD dielectric and the 2D materials enables the growth and exfoliation of the atomically thin dielectrics, which is confirmed by the X-ray photoelectron spectroscopy analyses and the density function theory calculations. The effectiveness of the technology is proven by the Raman and photoluminescence measurement on WS2 monolayer protected by the dielectric film through harsh plasma treatment. Furthermore, a 2D materials-based MOSFET is constructed as a demonstration of the viability of the technology for electronic device applications. The method produces flat surfaces and clean interfaces and would greatly benefit electronic and photonic applications as encapsulation or high-κ gate dielectric.

In ex vivo heart perfusion (EVHP), the control of aortic pressure (AoP) is critical for maintaining the heart’s physiologic aerobic metabolism. However, the complexity of and variability in cardiac parameters present a challenge in achieving the rapid and accurate regulation of AoP. In this paper, we propose a method of AoP control based on deep reinforcement learning for EVHP in Langendorff mode, which can adapt to the variations in cardiac parameters. Firstly, a mathematical model is developed by coupling the coronary artery and the pulsatile blood pump models. Subsequently, an aortic pressure control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. This method enables the regulation of the blood pump and the realization of closed-loop control. The control performance of the proposed DDPG method, the traditional proportional–integral–derivative (PID) method, and the fuzzy PID method are compared by simulating single and mixed changes in mean aortic pressure target values and coronary resistance. The proposed method exhibits superior performance compared to the PID and fuzzy PID methods under mixed factors, with 68.6% and 66.4% lower settling times and 70.3% and 54.1% lower overshoot values, respectively. This study demonstrates that the proposed DDPG-based method can respond more rapidly and accurately to different cardiac conditions than the conventional PID controllers.

Public open spaces make crucial contributions to the livability of communities and promote physical and mental health. Soundscapes play an important role in the overall physical comfort of public open spaces. However, owing to insufficient studies of high-density situations, soundscapes are ignored in public open spaces in high-density residential areas. This paper presents a case study of a soundscape in the overseas Chinese town (OCT) of Shenzhen, China. Through in situ observation, four distinct soundscapes were easily identified by performing soundscape conceptualization according to the ISO. In terms of the four identified soundscape areas, subjective evaluations of acoustic comfort and annoyance and their influencing factors were thoroughly explored. The results reveal that the natural soundscape had the best evaluations, whereas the artificial one did not have the worst result. It is interesting to note that acoustic factors do not always significantly influence a soundscape’s evaluation. A non-acoustic factor such as the spatial function may play a role as it is related to the context of an individual perceiving an acoustic environment. This study provides first-hand empirical evidence for understanding soundscapes and the influencing factors present in high-density residential public open spaces. The results provide useful knowledge for enhancing soundscape quality in such spaces.

In recent years, decellularized extracellular matrices (dECM) derived from organs have attracted much attention from researchers due to their excellent biocompatibility, capacity to promote cell proliferation and migration, as well as pro-vascularization. However, their inferior mechanical properties, slow cross-linking, weak strengths, and poor supporting properties remain their inevitable challenges. In our study, we fabricated a novel dECM hydrogel with better crosslinking strength and speed, stronger support properties, and better mechanical properties. The hydrogel, which we named gelatin-based dECM powder hydrogel (gelatin-dECM hydrogel), was prepared by mixing dECM microparticles in gelatin solution and adding certain amount of 1-Ethyl-3-(3-dimethyl-aminopropyl-1-carbodiimide) (EDC) and N-hydroxysuccinimide (NHS). We evaluated the utility of this hydrogel by assessing the degradation rate, mechanical properties, and biocompatibility. The results showed that the gelatin-dECM hydrogel has high mechanical properties and biocompatibility and also has the ability to promote cell proliferation and migration. After injection of this hydrogel around the surgical sites of urethras in rabbits, the incorporation of dECM powder was demonstrated to promote angiogenesis as well as scarless repair by histological sections after surgery. The application of this novel hydrogel provides a new perspective for the treatment of post-traumatic urethral stricture.

Agarwood is a precious aromatic plant which has good pharmacological effects such as antidepressant and sedation. It also has good ornamental and collection value. However, due to it is long and complex production process, the output of agarwood essential oils (AEOs) is scarce, so the price is expensive, the quality is uneven, and the adulteration events is endless. From the commercial and pharmaceutical point of view, the authenticity and quality of the commercial products labeled as AEOs is very important. This paper tested the applicability of Raman spectroscopy combined with chemometrics in classification and authenticity identification of AEOs. In this study, Raman spectroscopy and principal component analysis (PCA) combined with partial least square discriminant analysis (PLS-DA) were used to comprehensively evaluate AEOs from different geographical origins and/or extracted by different methods which showed different characteristic bands. The characteristic component of AEOs, chromone derivatives, and two commonly used adulterants were also detected. These characteristic bands provide spectrum information of AEO samples and reference materials, which can be used as Raman spectral markers for the qualitative identification of AEOs. This study can provide a novel, fast and convenient method for identification of AEOs.

Honeysuckle is a commonly used Chinese medicine for treating intestinal inflammation and other diseases. Compounds that are absorbed into the blood produce pharmacodynamic effects. However, it is still unclear which compounds in honeysuckle are absorbed into the blood. Thus, the purpose of this study was to investigate the composition and in vivo absorption of active components in honeysuckle in male Sprague-Dawley rats, and develop a controlled release hydrogel system. UPLC-LTQ-Orbitrap-MS was used to determine the active ingredients of honeysuckle in vitro and in vivo. A total of 80, out of which 42 components were found to be absorbed into the blood, which includes flavonoids, iridoids, organic acids and other compounds. FTIR analysis confirmed crosslinking between hydrogel content and drug loading, and TGA and DSC analysis indicated a high thermal stability. XRD analysis showed a decrease in crystallinity following crosslinking, and SEM revealed an irregular and hard surface. The maximum swelling and drug release were observed at pH 7.4 as compared to pH 1.2. The identified blood components can be used to determine Q-markers, while the prepared hydrogels can serve as an effective and promising carrier for the controlled release of honeysuckle extracts.

The quality of grains is influenced by storage pests, which are not only spoilers of stored grain, but also vectors of human and animal diseases. Chemical pesticides play an essential role in the cultivation and storage of cereals, however, due to the low degradability and residual toxicity of synthetic pesticides on the environment and non-target organisms, as well as the increasing resistance of target organisms to them, consideration should be given to the development of alternative pest control agents. Compounds isolated from natural sources have emerged as preferred targets for the development of novel insecticidal agents because of their eco-friendliness, safety, and effectiveness. In this review, we primarily focus on the natural product (NPs) control of storage pests. The effective monomer components of NPs and their anti-insect mechanisms were discussed, and natural sources of inspiration and models for insect repellents are described. This review aimed to provide guidelines for the exploitation and utilization of green and efficient natural insecticides.

Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model’s ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks.

Light-driven electrophoretic micromotors have gained significant attention recently for applications in drug delivery, targeted therapy, biosensing, and environmental remediation. Micromotors that possess good biocompatibility and the ability to adapt to complex external environments are particularly attractive. In this study, we have fabricated visible light-driven micromotors that could swim in an environment with relatively high salinity. To achieve this, we first tuned the energy bandgap of rutile TiO2 that was hydrothermally synthesized, enabling it to generate photogenerated electron-hole pairs under visible light rather than solely under UV. Next, platinum nanoparticles and polyaniline were decorated onto the surface of TiO2 microspheres to facilitate the micromotors swimming in ion-rich environments. Our micromotors exhibited electrophoretic swimming in NaCl solutions with concentrations as high as 0.1 M, achieving a velocity of 0.47 μm/s without the need for additional chemical fuels. The micromotors’ propulsion was generated solely by splitting water under visible light illumination, therefore offering several advantages over traditional micromotors, such as biocompatibility and the ability to operate in environments with high ionic strength. These results demonstrated high biocompatibility of photophoretic micromotors and high potential for practical applications in various fields.

CNC machine tools are a measure of national manufacturing and comprehensive national strength and how to reduce CNC machine errors in modern industry has received much attention from all walks of life. The thermal deformation of the feed axis of CNC machine tools is affected by the machine’s structure, installation, material properties, motion position, working conditions, and environmental temperature. Therefore, adopting a single thermal error model to the compensation implementation needs is difficult under changing motion parameters. In this paper, VMC655H is used as the research object and an embedded system is used as the control core. The model classification method is based on the approximate matching of the motion parameters of the thermal error model. Dynamic loading technology is used to fit multiple thermal error models as a technical means to reduce the impact of the machine feed axis’s thermal deformation on the positioning accuracy during the manufacturing process. The thermal error classification model for machine tool feed axes with variable motion parameters in an embedded environment can classify, identify, and load compensation device models within a limited range of motion parameters. This strategy can determine the current operating environment and compensate for the poor adaptability of a single thermal error model. The study of the actual operation effect of the compensation device under this strategy shows that the compensation strategy proposed in this paper can compensate for the thermal deformation of the ball screw feed axis under variable motion, and the X-axis positioning accuracy of its feed system can be improved by 53.11%. This study provides a new idea for the compensation method for thermogenic errors in machine tools.

Cannabidiol (CBD) is one specific kind of the cannabinoid in Cannabis sativa L with a wide range of pharmacological activities. However, the poor water solubility and specificity of CBD limits its application in pharmaceutical field. For solving these problems, in this work, we successfully prepared a targeted carrier by grafting biotin (BIO) onto ethylenediamine-β-Cyclodextrin (EN-CD) in a single step to generate a functionalized supramolecule, named BIO-CD. Subsequently, an amantadine-conjugated cannabinoids (AD-CBD) was prepared and self-assembled with the BIO-CD. A series of methods were used to characterize the inclusion behavior and physicochemical properties of AD-CBD and BIO-CD. The results showed that AD-CBD entered the cavity of BIO-CD and formed a 1:1 host-guest inclusion complex. MTT assay and confocal laser scanning microscopy (CLSM) revealed that the targeting effect and anticancer activity of AD-CBD/BIO-CD inclusion complex against three human cancer cell lines were higher than BIO-CD, AD-CBD and free CBD. Moreover, the inclusion complex could release drugs under weakly acidic conditions. These results demonstrated that AD-CBD/BIO-CD inclusion complex possess excellent targeted and anticancer activity, which is hopeful to be applied in clinic as a new therapeutic approach.

Multilevel resistive switching in memristive devices is vital for applications in non-volatile memory and neuromorphic computing. In this study, we report on the multilevel resistive switching characteristics in SnSe/SrTiO3(STO) heterojunction-based memory devices with silver (Ag) and copper (Cu) top electrodes. The SnSe/STO-based memory devices present bipolar resistive switching (RS) with two orders of magnitude on/off ratio, which is reliable and stable. Moreover, multilevel state switching is achieved in the devices by sweeping voltage with current compliance to SET the device from high resistance state (HRS) to low resistance state (LRS) and RESET from LRS to HRS by voltage pulses without compliance current. With Ag and Cu top electrodes, respectively, eight and six levels of resistance switching were demonstrated in the SnSe/SrTiO3 heterostructures with a Pt bottom electrode. These results suggest that a SnSe/STO heterojunction-based memristor is promising for applications in neuromorphic computing as a synaptic device.

The strong spin filtering effect can be produced by C-Ni atomic orbital hybridization in lattice-matched graphene/Ni (111) heterostructures, which provides an ideal platform to improve the tunnel magnetoresistance (TMR) of magnetic tunnel junctions (MTJs). However, large-area, high-quality graphene/ferromagnetic epitaxial interfaces are mainly limited by the single-crystal size of the Ni (111) substrate and well-oriented graphene domains. In this work, based on the preparation of a 2-inch single-crystal Ni (111) film on an Al2O3 (0001) wafer, we successfully achieve the production of a full-coverage, high-quality graphene monolayer on a Ni (111) substrate with an atomically sharp interface via ambient pressure chemical vapor deposition (APCVD). The high crystallinity and strong coupling of the well-oriented epitaxial graphene/Ni (111) interface are systematically investigated and carefully demonstrated. Through the analysis of the growth model, it is shown that the oriented growth induced by the Ni (111) crystal, the optimized graphene nucleation and the subsurface carbon density jointly contribute to the resulting high-quality graphene/Ni (111) heterostructure. Our work provides a convenient approach for the controllable fabrication of a large-area homogeneous graphene/ferromagnetic interface, which would benefit interface engineering of graphene-based MTJs and future chip-level 2D spintronic applications.

Conventional electricity generation methods are under the major revolution, and microgrids established on renewable energy sources are playing a vital role in this power generation transformation. This study proposes a hybrid AC/DC microgrid with a barrier function-based adaptive sliding mode controller, in which 8 kW wind energy system and 4.5 kW photovoltaic energy system perform as the hybrid RESs, and 33 Ah of battery works as the energy storage system. Barrier function-based adaptive sliding mode controller ensures the convergence of the system’s output variable independent of the knowledge of the upper bound of the disturbances. Firstly, global mathematical modeling of the suggested system is ensured. Then, the control laws are defined, providing the DC bus voltage regulation during islanding mode and AC/DC link bus voltage regulation during the grid-connected mode. The proposed barrier function-based adaptive sliding mode controller technique is analyzed through 20 s simulations on MATLAB/Simulink, which validates the controller’s robustness and effectiveness. Furthermore, a comparison of the proposed controller is made with the proportional integral derivative controller, Lyapunov controller, and sliding mode controller. In the end, hardware-in-loop tests are performed using C2000 Delfino MCU F28379D LaunchPad, showing the proposed structure’s real-time performance.

Metal nanostructured materials, with many excellent and unique physical and mechanical properties compared to macroscopic bulk materials, have been widely used in the fields of electronics, bioimaging, sensing, photonics, biomimetic biology, information, and energy storage. It is worthy of noting that most of these applications require the use of nanostructured metals with specific controlled properties, which are significantly dependent on a series of physical parameters of its characteristic size, geometry, composition, and structure. Therefore, research on low-cost preparation of metal nanostructures and controlling of their characteristic sizes and geometric shapes are the keys to their development in different application fields. The preparation methods, physical and chemical properties, and application progress of metallic nanostructures are reviewed, and the methods for characterizing metal nanostructures are summarized. Finally, the future development of metallic nanostructure materials is explored.

An effective approach composed of solution treatment, multipass cold rolling and aging was developed to improve the strength and ductility of novel Al-Cu-Mn alloys. This approach increased the yield strength by 214 MPa over that of the conventional peak-aged samples while maintaining a good elongation of 8.7%. The microstructure evolution was examined by confocal laser scanning microscopy (CLSM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). During postaging, deformed structures underwent a considerable decrease in dislocation density and typical dislocation network structures were formed. At the same time, highly dispersed nanoprecipitates and extensive ultrafine grains and nanograins were generated. These nanoprecipitations enabled effective dislocation pinning and accumulation during tension deformation. Therefore, composite nanostructures containing ultrafine grains, nanograins, dislocation network structures and nanoprecipitates were responsible for the simultaneous increases in strength and ductility. This paper provides a new understanding of designing composite nanostructure materials for achieving high strength and good ductility that is expected to be used for other age-hardenable alloys and steels.

Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as a non-convex regularization to approximate the low-rank component. Compared to various convex regularization, this new configuration not only can better capture the tensor rank but also provides a simplified approach. An optimization process is conducted via tensor singular decomposition and an efficient augmented Lagrange multiplier algorithm is established. Extensive experimental results demonstrate that our new approach outperforms current state-of-the-art algorithms in terms of accuracy and efficiency.

Over the past several years, coherent interface strengthening was proposed and has since drawn much attention. Unfortunately, many fabrication techniques are restricted to very small size. Recently, a brand new method of in-situ nanoparticle strengthening was systematically investigated, which was proved to be an efficacious way to optimize microstructure and improve mechanical property by utilizing uniformly dispersed nanoparticles. In this review, we summarized recent related advances in investigated steels and Cu alloys, including details of preparation technique and characterization of in-situ nanoparticles. In-situ nanoparticles formed in the melt possess a coherent/semi-coherent relationship with the matrix, which has a similar effect of coherent interface strengthening. In this case, bulk metallic structural materials with dispersed nanoparticles in the matrix can be fabricated through conventional casting process. The effects of in-situ nanoparticles on grain refinement, inhibiting segregation, optimizing inclusions and strengthening are also deeply discussed, which is beneficial for obtaining comprehensive mechanical response. Consequently, it is expected that in-situ nanoparticle strengthening method will become a potential future direction in industrial mass production.

In order to study the influence of freeze-thaw cycles on the multi-axial strength of AC (Asphalt Concrete)-13 and SMA (Stone Mastic Asphalt)-13 asphalt mixtures which are widely used in China, triaxial tests were carried out in the laboratory. Two nonlinear failure criterions under three-dimensional stress states in octahedral space were established. A linear model for engineering design and its simplified testing method were then presented. The three-dimensional failure criteria of asphalt mixtures after 0, 1, 3, 5, 10, 15, 20 freeze-thaw cycles were also proposed. The results indicated that the multi-axial strength decayed significantly after 20 freeze-thaw cycles. It is noteworthy that the strength degrades rapidly during the first 5 freeze-thaw cycles. Compared with AC-13 asphalt mixture, the SMA-13 asphalt mixture exhibits better performance on the resistance to freeze-thaw damage, and it is recommended as the upper surface layer material of pavement structure.

Conventional activated sludge treatment process has been widely employed to deal with a variety of municipal and industrial waste water, but the production of sludge by this method is considerable. It is urgent to find an appropriate method which can solve this problem. Shear force produced by rotating disk system (a cell lysis system) was employed to break cell wall of sludge microorganism in order to release intracellular materials which can be reused as nutrient materials for metabolism of other sludge microorganisms in this study. Special feature on surfaces of disks had been proved to be a novel improvement which can improve disintegration effect apparently. Ultrasonic system can further promote minimization of excess sludge after treatment by rotating disk system in shorter time.

(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

To evaluate the application of ultraviolet spectroscopy for the rapid determination of aesculin, aesculetin and fraxetin in Cortex fraxini extract solutions, ultraviolet spectra of Cortex fraxini extract solutions from different batches were collected in the spectral range from 200 nm to 400 nm. The relationship between ultraviolet spectra and chemical parameters displayed some non-linear characteristics. Thus, K-OPLS was proposed to establish the calibration models for the determination of Cortex fraxini extract solutions between the reference data and ultraviolet spectra. The calibration results were achieved for the determination of Cortex fraxini extract solutions. The coefficients of determination in calibration (R2) for aesculin, aesculetin and fraxetin were 0.989, 0.957 and 0.939, while in prediction (R2) were 0.982, 0.979 and 0.962, respectively. And the root-mean-square error of prediction (RMSEP) for aesculin, aesculet and fraxetin were 11.99, 3.02 and 1.59 μg/mL. The results demonstrated that ultraviolet spectroscopy could be used for the rapid determination of these three components in Cortex fraxini extract solutions.