Efficient Manufacturing Research Articles

Virtual Development of a Single-Cylinder Hydrogen Opposed Piston Engine

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder’s geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction.

Process development and heat flow analysis for thin walls made of aluminum using extreme high-speed laser material deposition (EHLA3D)

Extreme high-speed laser material deposition (EHLA) is an adapted variant of directed energy deposition (DED-LB-P/M), also known as laser material deposition, and has been developed for the efficient manufacturing of thin layers with high deposition speeds. With precise control of the energy input into the powder gas jet and the substrate, EHLA allows deposition speeds of up to 200 m/min and weld beads as thin as 25 μm. Advantages include a smaller melt pool and a heat-affected zone, allowing the processing of difficult-to-weld material combinations. The development of EHLA for additive manufacturing (EHLA3D) aims to produce highly customized components with improved structural accuracy compared to standard LMD at increased build rates compared to laser powder bed fusion (PBF-LB/M). A promising application is complex lightweight structures for the aerospace industry. However, there is a lack of systematic investigation on lightweight materials processed with EHLA3D at feed rates >20 m/min. In this work, a specially designed tripod machine (maximum feed rate 200 m/min) was used to investigate the buildup of aluminum in process regimes at 30 m/min. After confirming the existing single-track parameters, the tracks were metallographically examined and checked for pores, cracks, and bonding defects. The process was applied to thin-wall geometries and line energies as well as return-times that were varied. To gain an understanding of process-induced heat development, the process was monitored using thermography. Since the process shows geometry-specific heat flow patterns, guidelines have been developed that enable the buildup using different process adaptions.

Efficient mass manufacturing of high-density, ultra-low-loss Si3N4 photonic integrated circuits

Silicon nitride (Si3N4) photonic integrated circuits (PICs) offer significant advantages over traditional silicon photonics, including low loss and superior power handling at optical communication wavelength bands. To facilitate high-density integration and effective nonlinearity, the use of thick, stoichiometric Si3N4 films is crucial. However, when using low-pressure chemical vapor deposition (LPCVD) to achieve high optical material transparency, Si3N4 films exhibit large tensile stress on the order of GPa, leading to wafer cracking that challenges mass production. Methods for crack prevention are therefore essential. The photonic Damascene process has addressed this issue, attaining record low-loss Si3N4 PICs, but it lacks control of the waveguide height, leading to large random variations of waveguide dispersion and unpredictable spectrum responses of critical functional devices such as optical couplers. Conversely, subtractive processes achieve better dimension control but rely on techniques unsuitable for large-scale production. To date, an outstanding challenge is to attain both lithographic precision and ultra-low loss in high-confinement Si3N4 PICs that are compatible with large-scale foundry manufacturing. Here, we present a single-step deposited, DUV-based subtractive method for producing wafer-scale ultra-low-loss Si3N4 PICs that harmonize these necessities. By employing deep etching of densely distributed, interconnected trenches into the substrate, we effectively mitigate the tensile stress in the Si3N4 layer, enabling direct deposition of thick films without cracking and substantially prolonged storage duration. A secondary ion mass spectrometry (SIMS) analysis reveals that these deep trenches simultaneously serve as gettering centers for metal impurities, in particular copper, thereby reducing the absorption loss in Si3N4 waveguides. Lastly, we identify ultraviolet (UV)-radiation-induced damage that can be remedied through a rapid thermal annealing. Collectively, we develop ultra-low-loss Si3N4 microresonators and 0.5-m-long spiral waveguides with losses down to 1.4 dB/m at 1550 nm with high production yield. This work addresses the long-standing challenges toward scalable and cost-effective production of tightly confined, low-loss Si3N4 PICs as used for quantum photonics, large-scale linear and nonlinear photonics, photonic computing, and narrow-linewidth lasers.

Towards atomic-scale smooth surface manufacturing of β-Ga2O3 via highly efficient atmospheric plasma etching

Abstract The highly efficient manufacturing of atomic-scale smooth β-Ga2O3 surface is fairly challenging because β-Ga2O3 is a typical difficult-to-machine material. In this study, a novel plasma dry etching method named plasma-based atom-selective etching (PASE) is proposed to achieve the highly efficient, atomic-scale, and damage-free polishing of β-Ga2O3. The plasma is excited through the inductive coupling principle and carbon tetrafluoride is utilized as the main reaction gas to etch β-Ga2O3. The core of PASE polishing of β-Ga2O3 is the remarkable lateral etching effect, which is ensured by both the intrinsic property of the surface and the extrinsic temperature condition. As revealed by density functional theory-based calculations, the intrinsic difference in the etching energy barrier of atoms at the step edge (2.36 eV) and in the terrace plane (4.37 eV) determines their difference in the etching rate, and their etching rate difference can be greatly enlarged by increasing the extrinsic temperature. The polishing of β-Ga2O3 based on the lateral etching effect is further verified in the etching experiments. The Sa roughness of β-Ga2O3 (001) substrate is decreased from 14.8 to 0.057 nm within 120 s, and the corresponding material removal rate reaches up to 20.96 μm/min. The polished β-Ga2O3 displays significantly improved crystalline quality and photoluminescence intensity, and the polishing effect of PASE is independent of the crystal faces of β-Ga2O3. In addition, the competition between chemical etching and physical reconstruction, which is determined by temperature and greatly affects the surface state of β-Ga2O3, is deeply studied for the first time. These findings not only demonstrate the high-efficiency and high-quality polishing of β-Ga2O3 via atmospheric plasma etching but also hold significant implications for guiding future plasma-based surface manufacturing of β-Ga2O3.

Study on hybrid 3D printing and milling process for customized polyether-ether-ketone components.

Polyether-ether-ketone (PEEK) has been widely applied in various fields due to its excellent mechanical properties and biocompatibility. The efficient and high-quality customized manufacturing of PEEK components are investigated in this study by the hybrid 3D printing and milling process. At first, the alternating hybrid process is selected and verified by comparing two typical hybrid process categories and conducting experiments, respectively. Second, a set of procedures are designed to automate the engineering application of the hybrid process trying to avoid the disadvantages of manual programing. Then, considering the tool length and possible interferences during the hybrid process, a model segmentation algorithm, namely, the exchange principle of avoiding interference (EPAI) is proposed. Based on the introduced EPAI and the programing language Python, the additive and subtractive hybrid manufacturing (ASHM) data processing procedure is proposed and realized by post-processing of the conventional 3D printing codes. Finally, the feasibility experiments have been conducted. The experimental results verify the hybrid manufacturing process in the fabrication of parts with complex internal features. The surface roughness Ra and dimensional error L of the parts have been reduced by 75.5% and 85.2%, respectively, while the shear strength τ has been increased by 14.1%. Compared with conventional milling process, the material consumption is reduced by 48.7%.

How environmentally friendly is the disposal of clear aligners? A gas chromatography-mass spectrometry study

Used clear aligner trays are often indiscriminately disposed of with general plastic waste and incinerated. This study aimed to analyze the smoke composition from incinerating 2 common aligner materials: glycol-modified polyethylene terephthalate (PET-G) and polyurethane. Each of the 2 materials in triplets was thermoformed. The thermoformed trays were shredded and subjected individually to open-fire combustion, ignited using a methane torch, in a specially designed combustion chamber. The resultant smoke was collected and analyzed using gas chromatography-mass spectrometry to study its in-depth composition. A total of 20 peaks, corresponding to 20 compounds, were identified from each of the 2 material samples. O-xylene (21.06%) showed the maximum concentration in the PET-G sample, whereas 1,4-dimethyl-1,3-cyclohexadiene in polyurethane (18.88%). The first peak in the PET-G sample corresponded to benzene with a relative concentration of 5.18%. Four compounds were common to both samples: 1,4-dimethyl-1,3-cyclohexadiene; 1,3-cyclohexadiene, 2,3-dimethyl-; 1-hydroxymethly-4-methylenecyclohexane; and cyclohexanemethanol, 4-methylene-. Benzene, a group 1 carcinogen, was identified in the PET-G smoke sample, whereas tetrahydrofuran, a suspected carcinogen, was found in the polyurethane sample. Some compounds were hazardous, whereas most were skin, eye, and respiratory irritants. Possible mitigation strategies include proper case selection, efficient manufacturing, direct 3-dimensional printing, and developing biodegradable materials. Clinicians can set up 'used aligner collection points' to ensure responsible disposal. Proper disposal guidelines and stringent regulations are the need of the hour.

Green Compression-Cast Concrete Material and Its Fiber-Reinforced Polymer (FRP)-Reinforced Concrete Structures

Concrete manufacturing is a significant contributor to carbon emission. This paper presents an extensive study on compression-casting, which is a newly developed and highly efficient concrete manufacturing technology. Compression-casting is a mechanical process that improves the properties of all types of concrete materials without using chemical additives or mineral admixtures. It is particularly suitable for producing high-quality concrete from secondary/substandard or waste/recycled aggregate materials, thereby promoting green, low-carbon, and sustainable construction. This paper introduces compression-casting and presents the experimental results of the material and relevant fiber-reinforced polymer (FRP)-reinforced structures. Various applications of the compression-casting technology on inferior concrete materials, including rubber, recycled aggregate, desert sand, and recycled powder concrete, have also been introduced. The behavior of FRP-reinforced structural members of compression-cast concrete (CCC) is evaluated and compared with that of normal concrete (NC). It is concluded that compared to NC, CCC has the following advantages: improved mechanical properties, microstructure, durability, cement saving, economy, and reduced carbon dioxide emissions. Finally, we provide recommendations for addressing CCC structure brittleness. As a result, this method can reshape the future of concrete manufacturing.

Improved wear and corrosion resistance of additively manufactured SS316L by laser remelting process

Directed energy deposition is an efficient and flexible additive manufacturing technique that has broad application prospects due to its ability to fabricate intricate structures with heterogeneous properties. In this study, the laser-based directed energy deposition (L-DED) technique is employed to deposit an austenitic stainless steel 316L powder at a significantly high deposition rate. In recent developments, laser remelting (LR) has been explored as a superior alternative for enhancing the surface integrity of the components produced by L-DED. This study endeavours to examine the impacts of LR, emphasizing the improvement of wear and corrosion resistance. The grain refinement is achieved through laser remelting, which eventually leads to a 34 % improvement in micro–hardness over the as-deposited sample; as a result of increased micro–hardness, wear resistance improved by 66 % when compared to the as-deposited specimen. Furthermore, the corrosion resistance performance of the laser-remelted samples is compared with as-deposited samples by electrochemical impedance spectroscopy (EIS) in a solution of 3.5 % NaCl solution. The remelted sample showed improved corrosion resistance, which is attributed to the grain refinement due to rapid solidification in LR. This study demonstrates that laser remelting can be an effective way to improve the wear and corrosion resistance of SS316L, which enlarges its application in harsh environments.

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Enhancing manufacturing efficiency and quality through automation and deep learning: addressing redundancy, defects, vibration analysis, and material strength optimization

This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.

Fabricating sandwich composite tubes using a new thermal expansion technique: Materials preparation and energy absorption characteristics

The extensive application of hollow composite structures in engineering increasingly demands innovations in highly efficient and economical manufacturing processes as well as materials to achieve lightweighting and enhance mechanical performance. To address the limitations of traditional molding processes in fabricating hollow composite structures with intricate geometries, this study proposes a novel thermal expansion molding technique. This method eliminates the need to remove the mandrel after molding, making it exceptionally suitable for the molding of foam sandwich composite structures. Leveraging this new technique, lightweight sandwich composite tubes filled with polymethacrylimide (PMI) foam and thermal expansion foam were successfully fabricated. Considering the importance of crashworthiness for automotive applications, detailed experimental studies were conducted to investigate the crushing performance of the sandwich composite tubes prepared using this technology. Experimental results reveal that the synergistic effect between the multi-component materials within the sandwich composite tubes confers surprising energy absorption characteristics and crushing stability to the structure. The sandwich composite tubes prepared by this innovative thermal expansion process exhibit promising prospects as crashworthy structures for automotive lightweighting applications, and this method provides critical inspiration for the fabrication of composite structures with complex shapes.

Three-dimensional interconnected nanofiber network derived from bacterial cellulose for strain sensing and energy harvesting

Flexible strain sensors have garnered widespread attention due to their potential applications, including human health monitoring, human-machine interfaces and intelligent robotics. However, the tradeoff between sensitivity and detection range needs to be considered. Herein, a dual-functional film based on bacterial cellulose is fabricated with a cost-effective freeze-drying and carbonization process, which is used for strain sensing and energy harvesting. The effect of the carbonization temperature on the structure is explored. Taking advantage of three-dimensional interconnected nanofiber of carbonized bacterial cellulose (CBC) network and the stretchability of the Ecoflex substrate, the optimized sensor demonstrates impressive strain sensing capabilities, featuring exceptional sensitivity (gauge factor = 100), large tolerable strain (100 %), swift response time (70 ms), and robust durability (3000 cycles). These features endow the CBC strain sensor with the capability to effectively discern and monitor multiple complex human activities, such as pulse rate and various human movements. Moreover, the CBC structure can serve as triboelectric nanogenerator (TENG) for energy harvesting. This work provides a green, efficient and cost-effective manufacturing method for the development of multifaceted wearable devices for human health monitoring and portable energy sources.

Capillary flow-MRI of micronized fat crystal dispersions: Effect of shear history on microstructure and flow

Micronized fat crystal (MFC) dispersions are a novel food ingredient enabling more efficient manufacturing of fat-based products as compared to established melt-cool processing. Yet, predicting rheological properties of MFC dispersions, remains a challenge. Here, we demonstrate how a capillary-flow Magnetic Resonance Imaging (MRI) platform provides quantitative measurements of local flow of MFC dispersions, inaccessible by global rheology. The measured 2D 1H MRI velocity maps unveiled a 5-fold velocity enhancement, and corresponding increase in wall slip, upon increasing the pre-shear time within 0–5 h, due to the 14 % increase in thickness of the crystalline nanoplatelets via recrystallization and aggregation of nanoplatelets. In the absence of pre-shearing, MFC dispersions exhibit an unsheared band at the walls of the capillary, likely arising from fouling and migration of thin nanoplatelets. In contrast to FCDs, flow cooperativity could not be observed for MFC dispersions due to the non-fractality of the fat crystal network. These results demonstrate that, by varying the pre-shear duration, the flow-microstructure properties of the MFC dispersions can be altered in a controlled manner. The approach presented here will allow for rapid assessment of shear-history dependence of flow properties of MFC dispersions under industrially relevant flow conditions.

Generative design and topology optimization research for single–layer aluminum alloy grid shell connections

The connection design of single–layer aluminum alloy grid shells traditionally relies on empirical strategies and is carefully tailored to specific construction requirements. However, confronting the intricacies inherent in the requirements of the contemporary era of intelligent construction poses rising challenges. This study aimed to enhance the connection design method for single–layer aluminum alloy grid shells on the basis of generative design (GD) and topology optimization (TO). First, mathematical models for GD and TO were outlined on the basis of the Audze–Eglais space–filling criterion and the solid isotropic material with penalization algorithm. Subsequently, GD and TO research were conducted on the single–layer aluminum alloy gird shell structure connection, with the glass skylight at Beijing Daxing International Airport used as a case study. Then, how various parameters, such as enhanced retention for different mass objectives, affect the diversity of outcomes in GD was explored. Ultimately, connections were created using 3D printing with polylactic acid, and the feasibility of using 3D printing technology to fabricate complex connections was assessed. Results showed that the GD surpassed TO in spatial exploration and model generation. The representative connection achieved through GD exhibited a 57 % decrease in mass and a 38 % reduction in maximum equivalent stress in comparison with the traditional plate–type connection. Additionally, 3D printing technology offered viable solutions for producing complex connections, boasting rapid and efficient manufacturing capabilities.

Ultrasonic Levitation as a Handling Tool for In-Space Manufacturing Processes

Abstract 3D printing is one of the key technologies in space exploration. The disparity in gravitational forces between Earth and space presents both challenges and opportunities with regard to material handling. This article examines the potential of employing ultrasonic levitation as a handling tool for substrate-free additive manufacturing processes in microgravity environments. Through preliminary experiments, we demonstrate the feasibility of manipulating polymer powders using acoustic fields while concurrently melting the levitated material. Subsequent experiments conducted in our drop tower facility confirm our ability to manipulate particles with acoustic traps under microgravity conditions. Building upon these findings, we outline plans to further advance our research using an expanded acoustic levitation system capable of three-dimensional object manipulation. Our objectives include moving and orienting large components beyond the wavelength limit in microgravity, manipulating granular raw material while melting it in proximity to the print part, and achieving a semi-continuous fusion of print material with the print part. Therefore, we present an intelligent control strategy based on the results of a digital twin simulation. Furthermore, we utilize a stereo camera combined with computer vision as feedback for the control system to ensure precise handling of the manipulated objects and particles. This study represents a significant advance toward the realization of efficient substrate-free additive manufacturing processes in microgravity environments, with potential applications for in-space manufacturing. Ultimately, this could result in long-term space missions becoming less reliant on supply deliveries, thus reducing cost and additionally enabling faster response to unforeseen issues.

Magnetic Hyperthermia in Glioblastoma Multiforme Treatment.

Glioblastoma multiforme (GBM) represents one of the most critical oncological diseases in neurological practice, being considered highly aggressive with a dismal prognosis. At a worldwide level, new therapeutic methods are continuously being researched. Magnetic hyperthermia (MHT) has been investigated for more than 30 years as a solution used as a single therapy or combined with others for glioma tumor assessment in preclinical and clinical studies. It is based on magnetic nanoparticles (MNPs) that are injected into the tumor, and, under the effect of an external alternating magnetic field, they produce heat with temperatures higher than 42 °C, which determines cancer cell death. It is well known that iron oxide nanoparticles have received FDA approval for anemia treatment and to be used as contrast substances in the medical imagining domain. Today, energetic, efficient MNPs are developed that are especially dedicated to MHT treatments. In this review, the subject's importance will be emphasized by specifying the number of patients with cancer worldwide, presenting the main features of GBM, and detailing the physical theory accompanying the MHT treatment. Then, synthesis routes for thermally efficient MNP manufacturing, strategies adopted in practice for increasing MHT heat performance, and significant in vitro and in vivo studies are presented. This review paper also includes combined cancer therapies, the main reasons for using these approaches with MHT, and important clinical studies on human subjects found in the literature. This review ends by describing the most critical challenges associated with MHT and future perspectives. It is concluded that MHT can be successfully and regularly applied as a treatment for GBM if specific improvements are made.

Improvement of High Temperature Wear Resistance of Laser-Cladding High-Entropy Alloy Coatings: A Review

As a novel type of metal material emerging in recent years, high-entropy alloy boasts properties such as a simplified microstructure, high strength, high hardness and wear resistance. High-entropy alloys can use laser cladding to produce coatings that exhibit excellent metallurgical bonding with the substrate, thereby significantly improvement of the wear resistance of the material surface. In this paper, the research progress on improving the high-temperature wear resistance of high entropy alloy coatings (LC-HEACs) was mainly analyzed based on the effect of some added alloying elements and the presence of hard ceramic phases. Building on this foundation, the study primarily examines the impact of adding elements such as aluminum, titanium, copper, silicon, and molybdenum, along with hard ceramic particles like TiC, WC, and NbC, on the phase structure of coatings, high-temperature mechanisms, and the synergistic interactions between these elements. Additionally, it explores the potential of promising lubricating particles and introduces an innovative, highly efficient additive manufacturing technology known as extreme high-speed laser metal deposition (EHLMD). Finally, this paper summarizes the main difficulties involved in increasing the high-temperature wear resistance of LC-HEACs and some problems worthy of attention in the future development.

Graph network-based human movement prediction for socially-aware robot navigation in shared workspaces

AbstractMethods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments.

In Situ Dewetting Assisted Plasma Etching of Large‐Scale Uniform Nanocones on Arbitrarily Structured Glass Elements

AbstractNanocones are ideal for wide‐angle antireflection of glass elements, yet their applications often encounter complex geometries and large sizes, bringing serious difficulty in developing a reliable and efficient manufacturing process. Here, an in situ dewetting assisted plasma etching (In situ DAPE) method is proposed to engrave nanocones on arbitrarily structured glass elements in one step. While exposed to high‐energy plasma, pre‐sputtered platinum (Pt) film undergoes dewetting, forming uniformly distributed nanoparticles that act as in situ sacrificial masks for plasma etching to generate nanocones. The nanocone size is decided by nanoparticle size, which can be modified through the Marangoni effect of the melted Pt film. Incorporating a passivation step can improve the aspect ratio of nanocones up to ≈8:1 due to the trench effect. Nanocones with diameters ranging from 62 to 136 nm and heights from 126 to 942 nm can be achieved. The nanocones help to achieve an average transmittance of 98% over the 0.3–2.5 µm spectrum and a 79.4% laser‐induced damage threshold of bare fused silica. The efficient fabrication of nanocones over multiple glass elements is demonstrated including a 2‐inch flat window, cylindrical microlens array, and diffractive grating, highlighting the efficacy of the In situ DAPE method for high‐performance optical elements.

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.