Anisotropic Effects in Local Anodic Oxidation Nanolithography on Silicon Surfaces: Insights from ReaxFF Molecular Dynamics.

Fully understanding the anisotropic effect of silicon surface orientations in local anodic oxidation (LAO) nanolithography processes is critical to the precise control of oxide quality and rate. This study used ReaxFF MD simulations to reveal the surface anisotropic effects in the LAO through the analysis of adsorbed species, atomic charge, and oxide growth. Our results show that the LAO behaves differently on silicon (100), (110), and (111) surfaces. Specifically, the application of an electric field significantly increases the quantity of surface-adsorbed -OH2 while reducing -OH on the (111) surface, and results in a higher charge on a greater number of Si atoms on the (100) surface. Moreover, the quantity of surface-adsorbed -OH plays a pivotal role in influencing the oxidation rate, as it directly correlates with an increased formation rate of Si-O-Si bonds. During bias-induced oxidation, the (111) surface appears with a high initial oxidation rate among three surfaces, while the (110) surface underwent increased oxidation at higher electric field strengths. This conclusion is based on the analysis of the evolution of Si-O-Si bond number, surface elevation, and oxide thickness. Our findings align well with prior theoretical and experimental studies, providing deeper insights and clear guidance for the fabrication of high-performance nanoinsulator gates using LAO nanolithography.

Driving factors and emission reduction scenarios analysis of CO2 emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics.

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO2 emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO2 emissions in the GBA and surrounding cities. The results show that (a) CO2 emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO2 emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO2 emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO2 emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

Ultra-Precision Manufacturing Technology for Difficult-to-Machine Materials.

Ultra-precision manufacturing requires superior components with an impeccable surface finish and accuracy [...].

Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning.

The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. The design of experiment was carried out to determine the impact of various factors and interaction effects on filament width and height through variance analysis. Five machine learning models (support vector regression, backpropagation neural network, decision tree, random forest, and K-nearest neighbor) were built to predict the geometric dimension of filaments. The models exhibited good predictive performance. The coefficients of determination of the backpropagation neural network model for predicting line width and line height were 0.9025 and 0.9604, respectively. The effect of various process parameters on the geometric morphology based on the established prediction model was also studied. The order of influence on line width and height, ranked from highest to lowest, was as follows: nozzle diameter, printing speed, extrusion pressure, and nondimensional nozzle height. Different nondimensional nozzle height settings may cause the extruded material to be stretched or squeezed. The material being in a stretched state leads to a thin filament, and the regularity of processing parameters on the geometric size is not strong. Meanwhile, the nozzle diameter exhibits a significant impact on dimensions when the material is in a squeezing state. Thus, this study can be used to predict the size of printing filament structures, guide the selection of printing parameters, and determine the size of 3D printing layers.

Three-dimensional biofabrication of nanosecond laser micromachined nanofibre meshes for tissue engineered scaffolds.

There is a high demand for bespoke grafts to replace damaged or malformed bone and cartilage tissue. Three-dimensional (3D) printing offers a method of fabricating complex anatomical features of clinically relevant sizes. However, the construction of a scaffold to replicate the complex hierarchical structure of natural tissues remains challenging. This paper reports a novel biofabrication method that is capable of creating intricately designed structures of anatomically relevant dimensions. The beneficial properties of the electrospun fibre meshes can finally be realised in 3D rather than the current promising breakthroughs in two-dimensional (2D). The 3D model was created from commercially available computer-aided design software packages in order to slice the model down into many layers of slices, which were arrayed. These 2D slices with each layer of a defined pattern were laser cut, and then successfully assembled with varying thicknesses of 100 µm or 200 µm. It is demonstrated in this study that this new biofabrication technique can be used to reproduce very complex computer-aided design models into hierarchical constructs with micro and nano resolutions, where the clinically relevant sizes ranging from a simple cube of 20 mm dimension, to a more complex, 50 mm-tall human ears were created. In-vitro cell-contact studies were also carried out to investigate the biocompatibility of this hierarchal structure. The cell viability on a micromachined electrospun polylactic-co-glycolic acid fibre mesh slice, where a range of hole diameters from 200 µm to 500 µm were laser cut in an array where cell confluence values of at least 85% were found at three weeks. Cells were also seeded onto a simpler stacked construct, albeit made with micromachined poly fibre mesh, where cells can be found to migrate through the stack better with collagen as bioadhesives. This new method for biofabricating hierarchical constructs can be further developed for tissue repair applications such as maxillofacial bone injury or nose/ear cartilage replacement in the future.

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network.

Due to the marked increase in the prevalence of overweight and obesity worldwide and an environment leading to a series of chronic diseases, physical exercise is an important way to prevent chronic diseases. Additionally, a good exercise smart bracelet can bring convenience to physical exercise. Quick and accurate evaluation of smart sports bracelets has become a hot topic and draws attention from both academic researchers and public society. In the literature, the analytic hierarchy process (AHP) and entropy weight method (EWM) were used to obtain the weights from both subjective and objective perspectives, which were integrated by the comprehensive weighting method, and furthermore the performance of sports smart bracelet was evaluated through fuzzy comprehensive evaluation. Also, to avoid complex weight calculations caused by the comprehensive weighting method, machine learning methods are used to model the structure and contribute to the comprehensive evaluation process. However, few studies have investigated all previous elements in the comprehensive evaluation process. In this study, we consider all previous parts when evaluating smart sports bracelets. In particular, we use the sparrow search algorithm (SSA) to optimize the backpropagation (BP) neural network for constructing the comprehensive score prediction model of the sports smart bracelet. Results show that the sparrow search algorithm-optimized backpropagation (SSA-BP) neural network model has good predictive ability and can quickly obtain evaluation results on the premise of effectively ensuring the accuracy of the evaluation results.

Editorial for the Special Issue on Ultra Precision Technologies for Micromachining, Volume II.

With the increasing demand for ultra-high-precision products and micro-products in fields such as aerospace, national defense, military, transportation, and people's livelihoods, it has become an important development trend in the field of machining to realize ultra-high-precision machining and miniaturization with a higher level and higher quality [...].

Investigation of Material Removal Distributions and Surface Morphology Evolution in Non-Contact Ultrasonic Abrasive Machining (NUAM) of BK7 Optical Glasses.

A non-contact ultrasonic abrasive machining approach provides a potential solution to overcome the challenges of machining efficiency in the high-precision polishing of optical components. Accurately modeling the material removal distribution (removal function (RF)) and surface morphology is very important in establishing this new computer-controlled deterministic polishing technique. However, it is a challenging task due to the absence of an in-depth understanding of the evolution mechanism of the material removal distribution and the knowledge of the evolution law of the microscopic surface morphology under the complex action of ultrasonic polishing while submerged in liquid. In this study, the formation of the RF and the surface morphology were modeled by investigating the cavitation density distribution and conducting experiments. The research results showed that the material removal caused by cavitation bubble explosions was uniformly distributed across the entire working surface and had a 0.25 mm edge influence range. The flow scour removal was mainly concentrated in the high-velocity flow zone around the machining area. The roughness of the machined surface increased linearly with an increase in the amplitude and gap. Increasing the particle concentration significantly improved the material removal rate, and the generated surface exhibited better removal uniformity and lower surface roughness.

Scanning Probe Lithography: State-of-the-Art and Future Perspectives.

High-throughput and high-accuracy nanofabrication methods are required for the ever-increasing demand for nanoelectronics, high-density data storage devices, nanophotonics, quantum computing, molecular circuitry, and scaffolds in bioengineering used for cell proliferation applications. The scanning probe lithography (SPL) nanofabrication technique is a critical nanofabrication method with great potential to evolve into a disruptive atomic-scale fabrication technology to meet these demands. Through this timely review, we aspire to provide an overview of the SPL fabrication mechanism and the state-the-art research in this area, and detail the applications and characteristics of this technique, including the effects of thermal aspects and chemical aspects, and the influence of electric and magnetic fields in governing the mechanics of the functionalized tip interacting with the substrate during SPL. Alongside this, the review also sheds light on comparing various fabrication capabilities, throughput, and attainable resolution. Finally, the paper alludes to the fact that a majority of the reported literature suggests that SPL has yet to achieve its full commercial potential and is currently largely a laboratory-based nanofabrication technique used for prototyping of nanostructures and nanodevices.

Challenges and issues in continuum modelling of tribology, wear, cutting and other processes involving high-strain rate plastic deformation of metals.

Contribution of finite element method (FEM) as a modelling and simulation technique to represent complex tribological processes has improved our understanding about various biomaterials. This paper presents a review of the advances in the domain of finite element (FE) modelling for simulating tribology, wear, cutting and other processes involving high-strain rate plastic deformation of metals used in bio tribology and machining. Although the study is largely focused on material removal cases in metals, the modelling strategies can be applied to a wide range of other materials. This study discusses the development of friction models, meshing and remeshing strategies, and constitutive material models. The mesh-based and meshless formulations employed for bio tribological simulations with their advantages and limitations are also discussed. The output solution variables including scratch forces, local temperature, residual stresses are analyzed as a function of input variables.

A cost-effective serpentine micromixer utilizing ellipse curve.

Due to high mixing performance and simple geometry structure, serpentine micromixer is one typical passive micromixer that has been widely investigated. Traditional zigzag and square-wave serpentine micromixers can achieve sufficient mixing, but tend to induce significant pressure drop. The excessive pressure drop means more energy consumption, which leads to low cost-performance of mixing. To mitigate excessive pressure drop, a novel serpentine micromixer utilizing ellipse curve is proposed. While fluids flowing through ellipse curve microchannels, the flow directions keep continuous changing. Therefore, the Dean vortices are induced throughout the whole flow path. Numerical simulation and visualization experiments are conducted at Reynolds number (Re) ranging from 0.1 to 100. Dean vortices varies with the changing curvature in different ellipse curves, and local Dean numbers are calculated for quantitative evaluation. The results suggest that the ellipse with a larger eccentricity induces stronger Dean vortices, thus better mixing performance can be obtained. A parameter, named mixing performance cost (Mec), is proposed to evaluate the cost-performance of micromixers. Compared with the zigzag, square-wave and other improved serpentine micromixers, the ellipse curve micromixer produces lower pressure drop while have the capability to maintain excellent mixing performance. The ellipse curve micromixer is proved to be more cost-effective for rapid mixing in complex microfluidic systems.

Optimal Controller Design for Ultra-Precision Fast-Actuation Cutting Systems.

Fast-actuation cutting systems are in high demand for machining of freeform optical parts. Design of such motion systems requires good balance between structural hardware and controller design. However, the controller tuning process is mostly based on human experience, and it is not feasible to predict positioning performance during the design stage. In this paper, a deterministic controller design approach is adopted to preclude the uncertainty associated with controller tuning, which results in a control law minimizing positioning errors based on plant and disturbance models. Then, the influences of mechanical parameters such as mass, damping, and stiffness are revealed within the closed-loop framework. The positioning error was reduced from 1.19 nm RMS to 0.68 nm RMS with the new controller. Under the measured disturbance conditions, the optimal bearing stiffness and damping coefficient are 1.1×105 N/m and 237.7 N/(m·s-1), respectively. We also found that greater moving inertia helps to reduce all disturbances at high frequencies, in agreement with the positioning experiments. A quantitative understanding of how plant structural parameters affect positioning stability is thus shown in this paper. This is helpful for the understanding of how to reduce error sources from the design point of view.

Rolling Nanoelectrode Lithography.

Non-uniformity and low throughput issues severely limit the application of nanoelectrode lithography for large area nanopatterning. This paper proposes, for the first time, a new rolling nanoelectrode lithography approach to overcome these challenges. A test-bed was developed to realize uniform pressure distribution over the whole contact area between the roller and the silicon specimen, so that the local oxidation process occurred uniformly over a large area of the specimen. In this work, a brass roller wrapped with a fabricated polycarbonate strip was used as a stamp to generate nanopatterns on a silicon surface. The experimental results show that a uniform pattern transfer for a large area can be achieved with this new rolling nanoelectrode lithography approach. The rolling speed and the applied bias voltage were identified as the primary control parameters for oxide growth. Furthermore, the pattern direction showed no significant influence on the oxide process. We therefore demonstrated that nanoelectrode lithography can be scaled up for large-area nanofabrication by incorporating a roller stamp.

Product and Process Fingerprint for Nanosecond Pulsed Laser Ablated Superhydrophobic Surface.

Superhydrophobic surfaces have attracted extensive attention over the last few decades. It is mainly due to their capabilities of providing several interesting functions, such as self-cleaning, corrosion resistance, anti-icing and drag reduction. Nanosecond pulsed laser ablation is considered as a promising technique to fabricate superhydrophobic structures. Many pieces of research have proved that machined surface morphology has a significant effect on the hydrophobicity of a specimen. However, few quantitative investigations were conducted to identify effective process parameters and surface characterization parameters for laser-ablated microstructures which are sensitive to the hydrophobicity of the microstructured surface. This paper proposed and reveals for the first time, the concepts of process and product fingerprints for laser ablated superhydrophobic surface through experimental investigation and statistical analysis. The results of correlation analysis showed that a newly proposed dimensionless functional parameter in this paper, Rhy, i.e., the average ratio of Rz to Rsm is the most sensitive surface characterization parameter to the water contact angle of the specimen, which can be regarded as the product fingerprint. It also proposes another new process parameter, average laser pulse energy per unit area of the specimen (Is), as the best process fingerprint which can be used to control the product fingerprint Rhy. The threshold value of Rhy and Is are 0.41 and 536 J/mm² respectively, which help to ensure the superhydrophobicity (contact angle larger than 150°) of the specimen in the laser ablation process. Therefore, the process and product fingerprints overcome the research challenge of the so-called inverse problem in manufacturing as they can be used to determine the required process parameters and surface topography according to the specification of superhydrophobicity.

A Generic Control Architecture for Hybrid Micro-Machines.

Hybrid micro-machining, which integrates several micro-manufacturing processes on one platform, has emerged as a solution to utilize the so-called "1 + 1 = 3" effect to tackle the manufacturing challenges for high value-added 3D micro-products. Hybrid micro-machines tend to integrate multiple functional modules from different vendors for the best value and performance. However, the lack of plug-and-play solutions leads to tremendous difficulty in system integration. This paper proposes a novel three-layer control architecture for the first time for the system integration of hybrid micro-machines. The interaction of hardware is encapsulated into software components, while the data flow among different components is standardized. The proposed control architecture enhances the flexibility of the computer numerical control (CNC) system to accommodate a broad range of functional modules. The component design also improves the scalability and maintainability of the whole system. The effectiveness of the proposed control architecture has been successfully verified through the integration of a six-axis hybrid micro-machine. Thus, it provides invaluable guidelines for the development of next-generation CNC systems for hybrid micro-machines.

Editorial for the Special Issue on Micro-Machining: Challenges and Opportunities.

Micro-machining is an enabling technology for the manufacture of micro-products in which functional features, or at least one dimension, are in the order of µm. [...].

Topic Review: Application of Raman Spectroscopy Characterization in Micro/Nano-Machining.

The defects and subsurface damages induced by crystal growth and micro/nano-machining have a significant impact on the functional performance of machined products. Raman spectroscopy is an efficient, powerful, and non-destructive testing method to characterize these defects and subsurface damages. This paper aims to review the fundamentals and applications of Raman spectroscopy on the characterization of defects and subsurface damages in micro/nano-machining. Firstly, the principle and several critical parameters (such as penetration depth, laser spot size, and so on) involved in the Raman characterization are introduced. Then, the mechanism of Raman spectroscopy for detection of defects and subsurface damages is discussed. The Raman spectroscopy characterization of semiconductor materials' stacking faults, phase transformation, and residual stress in micro/nano-machining is discussed in detail. Identification and characterization of phase transformation and stacking faults for Si and SiC is feasible using the information of new Raman bands. Based on the Raman band position shift and Raman intensity ratio, Raman spectroscopy can be used to quantitatively calculate the residual stress and the thickness of the subsurface damage layer of semiconductor materials. The Tip-Enhanced Raman Spectroscopy (TERS) technique is helpful to dramatically enhance the Raman scattering signal at weak damages and it is considered as a promising research field.

A New Grinding Force Model for Micro Grinding RB-SiC Ceramic with Grinding Wheel Topography as an Input.

The ability to predict the grinding force for hard and brittle materials is important to optimize and control the grinding process. However, it is a difficult task to establish a comprehensive grinding force model that takes into account the brittle fracture, grinding conditions, and random distribution of the grinding wheel topography. Therefore, this study developed a new grinding force model for micro-grinding of reaction-bonded silicon carbide (RB-SiC) ceramics. First, the grinding force components and grinding trajectory were analysed based on the critical depth of rubbing, ploughing, and brittle fracture. Afterwards, the corresponding individual grain force were established and the total grinding force was derived through incorporating the single grain force with dynamic cutting grains. Finally, a series of calibration and validation experiments were conducted to obtain the empirical coefficient and verify the accuracy of the model. It was found that ploughing and fracture were the dominate removal modes, which illustrate that the force components decomposed are correct. Furthermore, the values predicted according to the proposed model are consistent with the experimental data, with the average deviation of 6.793% and 8.926% for the normal and tangential force, respectively. This suggests that the proposed model is acceptable and can be used to simulate the grinding force for RB-SiC ceramics in practice.

Fabrication of Nanoscale Pits with High Throughput on Polymer Thin Film Using AFM Tip-Based Dynamic Plowing Lithography.

We show that an atomic force microscope (AFM) tip-based dynamic plowing lithography (DPL) approach can be used to fabricate nanoscale pits with high throughput. The method relies on scratching with a relatively large speed over a sample surface in tapping mode, which is responsible for the separation distance of adjacent pits. Scratching tests are carried out on a poly(methyl methacrylate) (PMMA) thin film using a diamond-like carbon coating tip. Results show that 100 µm/s is the critical value of the scratching speed. When the scratching speed is greater than 100 µm/s, pit structures can be generated. In contrast, nanogrooves can be formed with speeds less than the critical value. Because of the difficulty of breaking the molecular chain of glass-state polymer with an applied high-frequency load and low-energy dissipation in one interaction of the tip and the sample, one pit requires 65-80 penetrations to be achieved. Subsequently, the forming process of the pit is analyzed in detail, including three phases: elastic deformation, plastic deformation, and climbing over the pile-up. In particular, 4800-5800 pits can be obtained in 1 s using this proposed method. Both experiments and theoretical analysis are presented that fully determine the potential of this proposed method to fabricate pits efficiently.

Incipient plasticity in 4H-SiC during quasistatic nanoindentation.

Silicon carbide (SiC) is an important orthopedic material due to its inert nature and superior mechanical and tribological properties. Some of the potential applications of silicon carbide include coating for stents to enhance hemocompatibility, coating for prosthetic-bearing surfaces and uncemented joint prosthetics. This study is the first to explore nanomechanical response of single crystal 4H-SiC through quasistatic nanoindentation. Displacement controlled quasistatic nanoindentation experiments were performed on a single crystal 4H-SiC specimen using a blunt Berkovich indenter (300nm tip radius) at extremely fine indentation depths of 5nm, 10nm, 12nm, 25nm, 30nm and 50nm. Load-displacement curve obtained from the indentation experiments showed yielding or incipient plasticity in 4H-SiC typically at a shear stress of about 21GPa (~an indentation depth of 33.8nm) through a pop-in event. An interesting observation was that the residual depth of indent showed three distinct patterns: (i) positive depth hysteresis above 33nm, (ii) no depth hysteresis at 12nm, and (iii) negative depth hysteresis below 12nm. This contrasting depth hysteresis phenomenon is hypothesized to originate due to the existence of compressive residual stresses (upto 143MPa) induced in the specimen by the polishing process prior to the nanoindentation.