Femtosecond laser fabrication of 3D vertically aligned micro-pore network on thick-film Li_(4)Ti_(5)O_(12)electrode for high-performance lithium storage

The development of energy storage devices with high energy density relies heavily on thick film electrodes,but it is challenging due to the limited ion transport kinetics inherent in thick electrodes.Here,we report on the preparation of a directional vertical array of micro-porous transport networks on LTO electrodes using a femtosecond laser processing strategy,enabling directional ion rapid transport and achieving good electrochemical performance in thick film electrodes.Various three-dimensional(3D)vertically aligned micro-pore networks are innovatively designed,and the structure,kinetics characteristics,and electrochemical performance of the prepared ion transport channels are analyzed and discussed by multiple characterization and testing methods.Furthermore,the rational mechanisms of electrode performance improvement are studied experimentally and simulated from two aspects of structural mechanics and transmission kinetics.The ion diffusion coefficient,rate performance at 60 C,and electrode interface area of the laser-optimized 60-15%micro-porous transport network electrodes increase by 25.2 times,2.2 times,and 2.15 times,respectively than those of untreated electrodes.Therefore,the preparation of 3D micro-porous transport networks by femtosecond laser on ultra-thick electrodes is a feasible way to develop high-energy batteries.In addition,the unique micro-porous transport network structure can be widely extended to design and explore other high-performance energy materials.

Transfer film effects induced by 3D-printed polyether-ether-ketone with excellent tribological properties for joint prosthesis

Based on the building principle of additive manufacturing,printing orientation mainly determines the tribological properties of joint prostheses.In this study,we created a polyether-ether-ketone(PEEK)joint prosthesis using fused filament fabrication and investigated the effects of printing orientation on its tribological properties using a pin-on-plate tribometer in 25% newborn calf serum.An ultrahigh molecular weight polyethylene transfer film is formed on the surface of PEEK due to the mechanical capture of wear debris by the 3D-printed groove morphology,which is significantly impacted by the printing orientation of PEEK.When the printing orientation was parallel to the sliding direction of friction,the number and size of the transfer film increased due to higher steady stress.This transfer film protected the matrix and reduced the friction coefficient and wear rate of friction pairs by 39.13%and 74.33%,respectively.Furthermore,our findings provide a novel perspective regarding the role of printing orientation in designing knee prostheses,facilitating its practical applications.

Solar manipulations of perpendicular magnetic anisotropy for flexible spintronics

Flexible electronics/spintronics attracts researchers’attention for their application potential abroad in wearable devices,healthcare,and other areas.Those devices’performance(speed,energy consumption)is highly dependent on manipulating information bits(spin-orientation in flexible spintronics).In this work,we established an organic photovoltaic(OPV)/ZnO/Pt/Co/Pt heterostructure on flexible PET substrates with perpendicular magnetic anisotropy(PMA).Under sunlight illumination,the photoelectrons generated from the OPV layer transfer into the PMA heterostructure,then they reduce the PMA strength by enhancing the interfacial Rashba field accordingly.The coercive field(Hc)reduces from 800 Oe to 500 Oe at its maximum,and the magnetization can be switched up and down reversibly.The stability of sunlight control of magnetization reversal under various bending conditions is also tested for flexible spintronic applications.Lastly,the voltage output of sunlight-driven PMA is achieved in our prototype device,exhibiting an excellent angular dependence and opening a door towards solar-driven flexible spintronics with much lower energy consumption.

Robust Deadlock Avoidance Policy for Automated Manufacturing System With Multiple Unreliable Resources

This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To reach this goal, we propose a new modified Banker's algorithm(MBA) to ensure that all resources required by these jobs can be freed. Moreover,a Petri net based deadlock avoidance policy(DAP) is introduced to ensure that all jobs remaining in the system after executing the new MBA can complete their processing smoothly when their required unreliable resources are operational. The new MBA together with the DAP forms a new DAP that is robust to the failures of unreliable resources. Owing to the high permissiveness of the new MBA and the optimality of the DAP, it is tested to be more permissive than state-of-the-art control policies.

Optimal deadlock avoidance Petri net supervisors for automated manufacturing systems

Deadlock avoidance problems are investigated for automated manufacturing systems with flexible routings. Based on the Petri net models of the systems, this paper proposes, for the first time, the concept of perfect maximal resourcetransition circuits and their saturated states. The concept facilitates the development of system liveness characterization and deadlock avoidance Petri net supervisors. Deadlock is characterized as some perfect maximal resource-transition circuits reaching their saturated states. For a large class of manufacturing systems, which do not contain center resources, the optimal deadlock avoidance Petri net supervisors are presented. For a general manufacturing system, a method is proposed for reducing the system Petri net model so that the reduced model does not contain center resources and, hence, has optimal deadlock avoidance Petri net supervisor. The controlled reduced Petri net model can then be used as the liveness supervisor of the system.

Social Sensors (S^2ensors): A Kind of Hardware-Software-Integrated Mediators for Social Manufacturing Systems Under Mass Individualization

Currently, little work has been devoted to the mediators and tools for multi-role production interactions in the mass individualization environment. This paper proposes a kind of hardware-software-integrated mediators called social sensors （S2ensors） to facilitate the production interactions among customers, manufacturers, and other stakeholders in the social manufacturing systems （SMS）. The concept, classification, operational logics, and for- malization of S2ensors are clarified. S2ensors collect sub- jective data from physical sensors and objective data from sensory input in mobile Apps, merge them into meaningful information for decision-making, and finally feed the decisions back for reaction and execution. Then, an S2en- sors-Cloud platform is discussed to integrate different S2- ensors to work for SMSs in an autonomous way. A demonstrative case is studied by developing a prototype system and the results show that S2ensors and S2ensors- Cloud platform can assist multi-role stakeholders interact and collaborate for the production tasks. It reveals the mediator-enabled mechanisms and methods for production interactions among stakeholders in SMS.

Roll-to-roll imprint for high precision grating manufacturing

In this paper,a novel roll-to-roll imprint process is proposed to fabricate gratings in large area. The key challenges in roll-to-roll imprint process,such as the roller mold preparation,imprint system,misalignment of imprint roller and backup roller,and also the imprint pressure,are investigated. Various gratings with different periods and profiles are obtained by the roll-to-roll process. To calibrate the measuring ability of the self-made grating by imprint,a calibration system is built with a dual-frequency laser interferometer. By the calibration,the self-made gratings can achieve ±4μm accuracy in 45mm measuring distance.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.

Functionalized alginate-based bioinks for microscale electrohydrodynamic bioprinting of living tissue constructs with improved cellular spreading and alignment

Bioprinting has been widely investigated for tissue engineering and regenerative medicine applications.However,it is still difficult to reconstruct the complex native cell arrangement due to the limited printing resolution of conventional bioprinting techniques such as extrusion-and inkjet-based printing.Recently,an electrohydrodynamic(EHD)bioprinting strategy was reported for the precise deposition of well-organized cell-laden constructs with microscale filament size,whereas few studies have been devoted to developing bioinks that can be applied for EHD bioprinting and simultaneously support cell spreading.This study describes functionalized alginate-based bioinks for microscale EHD bioprinting using peptide grafting and fibrin incorporation,which leads to high cell viability(>90%)and cell spreading.The printed filaments can be further refined to as small as 30μm by incorporating polyoxyethylene and remained stable over one week when exposed to an aqueous environment.By utilizing the presented alginate-based bioinks,layer-specific cell alignment along the printing struts could be observed inside the EHD-printed microscale filaments,which allows fabricating living constructs with cell-scale filament resolution for guided cellular orientation.

Embedding aligned nanofibrous architectures within 3D-printed polycaprolactone scaffolds for directed cellular infiltration and tissue regeneration

Three-dimensional(3D) printing provides a promising way to fabricate biodegradable scaffolds with designer architectures for the regeneration of various tissues.However,the existing3D-printed scaffolds commonly suffer from weak cell-scaffold interactions and insufficient cell organizations due to the limited resolution of the 3D-printed features.Here,composite scaffolds with mechanically-robust frameworks and aligned nanofibrous architectures are presented and hybrid manufactured by combining techniques of 3D printing,electrospinning,and unidirectional freeze-casting.It was found that the composite scaffolds provided volume-stable environments and enabled directed cellular infiltration for tissue regeneration.In particular,the nanofibrous architectures with aligned micropores served as artificial extracellular matrix materials and improved the attachment,proliferation,and infiltration of cells.The proposed scaffolds can also support the adipogenic maturation of adipose-derived stem cells(ADSCs)in vitro.Moreover,the composite scaffolds were found to guide directed tissue infiltration and promote nearby neovascularization when implanted into a subcutaneous model of rats,and the addition of ADSCs further enhanced their adipogenic potential.The presented hybrid manufacturing strategy might provide a promising way to produce additional topological cues within 3D-printed scaffolds for better tissue regeneration.

3D printing of functional bioengineered constructs for neural regeneration: a review

Three-dimensional(3D)printing technology has opened a new paradigm to controllably and reproducibly fabricate bioengineered neural constructs for potential applications in repairing injured nervous tissues or producing in vitro nervous tissue models.However,the complexity of nervous tissues poses great challenges to 3D-printed bioengineered analogues,which should possess diverse architectural/chemical/electrical functionalities to resemble the native growth microenvironments for functional neural regeneration.In this work,we provide a state-of-the-art review of the latest development of 3D printing for bioengineered neural constructs.Various 3D printing techniques for neural tissue-engineered scaffolds or living cell-laden constructs are summarized and compared in terms of their unique advantages.We highlight the advanced strategies by integrating topographical,biochemical and electroactive cues inside 3D-printed neural constructs to replicate in vivo-like microenvironment for functional neural regeneration.The typical applications of 3D-printed bioengineered constructs for in vivo repair of injured nervous tissues,bio-electronics interfacing with native nervous system,neural-on-chips as well as brain-like tissue models are demonstrated.The challenges and future outlook associated with 3D printing for functional neural constructs in various categories are discussed.

Ubiquitous data computing and information using in a smart factory with wireless manufacturing

This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.

Soft Imprint Lithography for Pixel Array Manufacturing in OLED

Introduction During the last two decades,organic light-emitting diodes (OLED) have attracted considerable interest owing to their promising applications by replacing cathode ray tubes (CRTs) or liquid crystal displays (LCDs).

Novel deformable self-assembled magnetic anastomosis ring for endoscopic treatment of colonic stenosis via natural orifice

BACKGROUND Although endoscope-assisted magnetic compression anastomosis has already been reported for colonic anastomosis,there is no report on a single-approach operation using the natural orifice.AIM To design a deformable self-assembled magnetic anastomosis ring(DSAMAR)for colonic anastomosis for use in single-approach operation and evaluate its feasibility and safety through animal experiments.METHODS The animal model for colonic stenosis was prepared by partial colonic ligation in eight beagles.The magnetic compression anastomosis of their colonic stricture was performed by endoscopically assisted transanal implantation of the DSAMAR.The anastomotic specimen,obtained 2 wk after the operation,was observed by both the naked eye and a light microscope.RESULTS The DSAMAR was successfully inserted into the proximal end of colon stenosis through the anus.The DSAMAR of seven dogs was successfully transformed into rings,while that of the remaining dog was removed after the first deformation failed.The rings were successfully retransformed after optimization.All animals underwent colonic anastomosis using the DSAMAR.No device-related or procedure-related adverse events were observed.The colostomy specimens of the experimental dogs were obtained 2 wk after the operation.Both gross and histological observations showed good anastomotic healing.CONCLUSION The DSAMAR is a safe and feasible option for the treatment of colon stenosis.Its specific deformation and selfassembly capability maximize the applicability of the minimally invasive treatment.

Oxygen tension modulates cell function in an in vitro three-dimensional glioblastoma tumor model

Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor tissue has impeded the study of the effects of hypoxia on the progression and growth of tumor cells.This study reports a three-dimensional(3D)brain tumor model obtained by encapsulating U87MG(U87)cells in a hydrogel containing type I collagen.It also documents the effect of various oxygen concentrations(1%,7%,and 21%)in the culture environment on U87 cell morphology,proliferation,viability,cell cycle,apoptosis rate,and migration.Finally,it compares two-dimensional(2D)and 3D cultures.For comparison purposes,cells cultured in flat culture dishes were used as the control(2D model).Cells cultured in the 3D model proliferated more slowly but had a higher apoptosis rate and proportion of cells in the resting phase(G0 phase)/gap I phase(G1 phase)than those cultured in the 2D model.Besides,the two models yielded significantly different cell morphologies.Finally,hypoxia(e.g.,1%O2)affected cell morphology,slowed cell growth,reduced cell viability,and increased the apoptosis rate in the 3D model.These results indicate that the constructed 3D model is effective for investigating the effects of biological and chemical factors on cell morphology and function,and can be more representative of the tumor microenvironment than 2D culture systems.The developed 3D glioblastoma tumor model is equally applicable to other studies in pharmacology and pathology.

Carbon nanotube integrated circuit technology:purification,assembly and integration

As the manufacturing process of silicon-based integrated circuits(ICs)approaches its physical limit,the quantum effect of silicon-based field-effect transistors(FETs)has become increasingly evident.And the burgeoning carbon-based semiconductor technology has become one of the most disruptive technologies in the post-Moore era.As one-dimensional nanomaterials,carbon nanotubes(CNTs)are far superior to silicon at the same technology nodes of FETs because of their excellent electrical transport and scaling properties,rendering them the most competitive material in the next-generation ICs technology.However,certain challenges impede the industrialization of CNTs,particularly in terms of material preparation,which significantly hinders the development of CNT-based ICs.Focusing on CNT-based ICs technology,this review summarizes its main technical status,development trends,existing challenges,and future development directions.

Oxidation behavior of 4774DD1 Ni-based single-crystal superalloy at 980℃ in air

The oxidation behavior of a novel Ni-based single-crystal 4774DD1 superalloy for industrial gas turbine applications was investigated by the isothermal oxidation at 980℃ and discontinuous oxidation weight gain methods.The phase constitution and morphology of surface oxides and the characteristics of the crosssection oxide film were analyzed by XRD,SEM and EDS.Results show that the oxidation kinetics of the 4774DD1 superalloy follows the cubic law,indicating its weak oxidation resistance at this temperature.As the oxidation time increases,the composition of the oxide film evolves as following:One layer consisting of a bottom Al_(2)O_(3)sublayer and an upper(Al_(2)O_(3)+NiO)mixture sublayer after oxidized for 25 h.Then,two layers composed of an outermost small NiO discontinuous grain layer and an internal layer for 75 h.This internal layer is consisted of the bottom Al_(2)O_(3)sublayer,an intermediate narrow CrTaO_(4)sublayer,and an upper(Al_(2)O_(3)+NiO)mixture sublayer.Also two layers comprising an outermost relative continuous NiO layer with large grain size and an internal layer as the oxidation time increases to 125 h.This internal layer is composed of the upper(Al_(2)O_(3)+NiO)mixture sublayer,an intermediate continuous(CrTaO_(4)+NiWO_(4))mixture sublayer,and a bottom Al_(2)O_(3)sublayer.Finally,three layers consisting of an outermost(NiAl2O_(4)+NiCr2O_(4))mixture layer,an intermediate(CrTaO_(4)+NiWO_(4))mixture layer,and a bottom Al_(2)O_(3)layer for 200 h.

Twisted Integration of Complex Oxide Magnetoelectric Heterostructures via Water‑Etching and Transfer Process

Manipulating strain mode and degree that can be applied to epitaxial complex oxide thin films have been a cornerstone of strain engineering.In recent years,lift-off and transfer technology of the epitaxial oxide thin films have been developed that enabled the integration of heterostructures without the limitation of material types and crystal orientations.Moreover,twisted integration would provide a more interesting strategy in artificial magnetoelectric heterostructures.A specific twist angle between the ferroelectric and ferromagnetic oxide layers corresponds to the distinct strain regulation modes in the magnetoelectric coupling process,which could provide some insight in to the physical phenomena.In this work,the La_(0.67)Sr_(0.33)MnO_(3)(001)/0.7Pb(Mg_(1/3)Nb_(2/3))O_(3)-0.3PbTiO_(3)(011)(LSMO/PMN-PT)heterostructures with 45.and 0.twist angles were assembled via water-etching and transfer process.The transferred LSMO films exhibit a fourfold magnetic anisotropy with easy axis along LSMO<110>.A coexistence of uniaxial and fourfold magnetic anisotropy with LSMO[110]easy axis is observed for the 45°Sample by applying a 7.2 kV cm^(−1)electrical field,significantly different from a uniaxial anisotropy with LSMO[100]easy axis for the 0°Sample.The fitting of the ferromagnetic resonance field reveals that the strain coupling generated by the 45°twist angle causes different lattice distortion of LSMO,thereby enhancing both the fourfold and uniaxial anisotropy.This work confirms the twisting degrees of freedom for magnetoelectric coupling and opens opportunities for fabricating artificial magnetoelectric heterostructures.

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.