Search Results (718)

Three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrates have demonstrated remarkable abilities of ultrasensitive and reproducible molecular detection. The combination of both electromagnetic and chemical enhancement processes, light trapping, and multiple scattering effects of 3D structures are what enhance their performance. The principles of underlying enhancements are summarized systematically, and the main types of 3D substrates—vertically aligned nanowires, dendritic and fractal nanostructures, porous frameworks and aerogels, core–shell and hollow nanospheres, and hierarchical hybrid structures—are categorized in this review. Advances in fabrication techniques, such as template-assisted growth, electrochemical and galvanic deposition, dealloying and freeze-drying, self-assembly, and hybrid integration, are critically evaluated in terms of structural tunability and scalability. Novel developments in the field of biosensing are also highlighted, including non-enzymatic glucose sensing, tumor biomarker sensing, and drug delivery. The remaining limitations, such as low reproducibility, mechanical stability, and substrate standardization, are also noted, and future directions, such as stimuli-responsive designs, multifunctional hybrid platforms, and data-driven optimization strategies of SERS technologies, are also included. Full article

Three-dimensional (3D) printing offers new opportunities in surgical planning and medical education, yet high costs and technological complexity often limit its widespread use, especially in low-resource settings. This study presents a personalized, cost-effective, and anatomically accurate liver model designed using open-source tools and affordable 3D-printing techniques. Segmentation of hepatic CT images was performed in 3D Slicer using a region-growing method, and the resulting models were optimized and exported as STL files. The external mold was printed with Fused Deposition Modeling (FDM) using PLA+, while internal structures such as vessels and tumors were fabricated via Liquid Crystal Display (LCD) printing using PLA Pro resin. The final assembly was cast in food-grade gelatin to mimic liver tissue texture. The complete model was produced for under USD 50, with an average total production time of under 128 h. An exploratory pedagogical evaluation with five medical trainees yielded high Likert scores for anatomical understanding (4.6), spatial awareness (4.4), planning confidence (4.2), and realism (4.4). This model demonstrated utility in preoperative discussions and training simulations. The proposed workflow enables the fabrication of low-cost, realistic hepatic phantoms suitable for education and surgical rehearsal, promoting the integration of 3D printing into everyday clinical practice. Full article

With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

Hydrogen energy is a pivotal carrier for achieving carbon neutrality, requiring green and efficient production via water electrolysis. However, the anodic oxygen evolution reaction (OER) involves a sluggish four-electron transfer process, resulting in high overpotentials, while the prohibitive cost and complex preparation of precious metal catalysts impede large-scale commercialization. In this study, we develop a FeCo-based bimetallic deep eutectic solvent (FeCo-DES) as a multifunctional reaction medium for engineering a three-dimensional (3D) coral-like FeOOH-Co₂(OH)₃Cl/NF composite via a mild one-step impregnation approach (70 °C, ambient pressure). The FeCo-DES simultaneously serves as the solvent, metal source, and redox agent, driving the controlled in situ assembly of FeOOH-Co₂(OH)₃Cl hybrids on Ni(OH)₂/NiOOH-coated nickel foam (NF). This hierarchical architecture induces synergistic enhancement through geometric structural effects combined with multi-component electronic interactions. Consequently, the FeOOH-Co₂(OH)₃Cl/NF catalyst achieves a remarkably low overpotential of 197 mV at 100 mA cm⁻² and a Tafel slope of 65.9 mV dec⁻¹, along with 98% current retention over 24 h chronopotentiometry. This study pioneers a DES-mediated strategy for designing robust composite catalysts, establishing a scalable blueprint for high-performance and low-cost OER systems. Full article

To address the issues of bulky structure and complex transmission systems in current transplanters, a compact, electric-driven automatic transplanter was designed. Using pepper plug seedlings as the test subject, this study investigated plug tray dimensions and planting patterns. According to the design requirement that the width of the single-row transplanter must be less than 62.5 cm, a three-dimensional transplanter model was constructed. The transplanter comprises a coaxially installed dual-layer seedling conveying device and a sector-expanding automatic seedling picking and depositing device. The structural dimensions, drive configurations, and driving forces of the transplanter were also determined. Finally, the circuit and pneumatic system were designed, and the transplanter was assembled. Both bench and field tests were conducted to select the optimal working parameters. The test results demonstrated that the seedling picking and depositing mechanism met the required operational efficiency. In static seedling picking and depositing tests, at three transplanting speeds of 120 plants/min, 160 plants/min, and 200 plants/min, the success rates of seedling picking and depositing were 100%, 100%, and 97.5%, respectively. In the field test, at three transplanting speeds of 80 plants/min, 100 plants/min, and 120 plants/min, the transplanting success rates were 94.17%, 90.83%, and 88.33%, respectively. These results illustrate that the compact, electric-driven seedling conveying and picking and depositing devices meet the operational demands of automatic transplanting, providing a reference for the miniaturization and electrification of transplanters. Full article

Against the backdrop of global energy transition and strict environmental regulations, supercritical carbon dioxide (scCO₂) fracturing and oil displacement technologies have emerged as pivotal green approaches in shale gas exploitation, offering the dual advantages of zero water consumption and carbon sequestration. However, the inherent low viscosity of scCO₂ severely restricts its sand-carrying capacity, fracture propagation efficiency, and oil recovery rate, necessitating the urgent development of high-performance thickeners. The current research on scCO₂ thickeners faces a critical trade-off: traditional fluorinated polymers exhibit excellent philicity CO₂, but suffer from high costs and environmental hazards, while non-fluorinated systems often struggle to balance solubility and thickening performance. The development of new thickeners primarily involves two directions. On one hand, efforts focus on modifying non-fluorinated polymers, driven by environmental protection needs—traditional fluorinated thickeners may cause environmental pollution, and improving non-fluorinated polymers can maintain good thickening performance while reducing environmental impacts. On the other hand, there is a commitment to developing non-noble metal-catalyzed siloxane modification and synthesis processes, aiming to enhance the technical and economic feasibility of scCO₂ thickeners. Compared with noble metal catalysts like platinum, non-noble metal catalysts can reduce production costs, making the synthesis process more economically viable for large-scale industrial applications. These studies are crucial for promoting the practical application of scCO₂ technology in unconventional oil and gas development, including improving fracturing efficiency and oil displacement efficiency, and providing new technical support for the sustainable development of the energy industry. This study innovatively designed an amphiphilic modified amino silicone oil polymer (MA-co-MPEGA-AS) by combining maleic anhydride (MA), methoxy polyethylene glycol acrylate (MPEGA), and amino silicone oil (AS) through a molecular bridge strategy. The synthesis process involved three key steps: radical polymerization of MA and MPEGA, amidation with AS, and in situ network formation. Fourier transform infrared spectroscopy (FT-IR) confirmed the successful introduction of ether-based CO₂-philic groups. Rheological tests conducted under scCO₂ conditions demonstrated a 114-fold increase in viscosity for MA-co-MPEGA-AS. Mechanistic studies revealed that the ether oxygen atoms (Lewis base) in MPEGA formed dipole–quadrupole interactions with CO₂ (Lewis acid), enhancing solubility by 47%. Simultaneously, the self-assembly of siloxane chains into a three-dimensional network suppressed interlayer sliding in scCO₂ and maintained over 90% viscosity retention at 80 °C. This fluorine-free design eliminates the need for platinum-based catalysts and reduces production costs compared to fluorinated polymers. The hierarchical interactions (coordination bonds and hydrogen bonds) within the system provide a novel synthetic paradigm for scCO₂ thickeners. This research lays the foundation for green CO₂-based energy extraction technologies. Full article

The design of custom abutments (CA) can affect the mechanical reliability of implant-supported restorations. The purpose of the study was to evaluate the influence of design parameters on the fatigue limit of CA and to compare optimized custom designs with the reference abutment (RA). A morse-tapered dental implant, an anatomical abutment, and a connector screw were digitalized using microcomputed tomography. A cone beam computed tomography scan was obtained from one of the authors to virtually place the implant-abutment assembly in the upper central incisor. Ten design parameters were selected according to the structural geometry of the RA and the implant planning. A reverse-engineered RA model was created in SOLIDWORKS and was modified considering a Taguchi orthogonal array to generate 36 CAs with ±20% dimensional variations. Finite element analysis was conducted in ABAQUS, and fatigue limits were estimated using Fe-safe. ANOVA (α = 0.1) identified the most influential parameters. Von Mises stress values ranged from 229 MPa to 302 MPa, and 94.4% of the CAs had a higher fatigue limit than the RA. Three parameters significantly affected the fatigue performance of the implant system. The design process of custom abutments includes critical design parameters that can be optimized for longer lifetimes of implant-abutment restorations. Full article

Zirconium-89 (⁸⁹Zr) is a widely used radionuclide in immune-PET imaging due to its physical decay characteristics. Despite its importance, the production of ⁸⁹Zr radiopharmaceuticals remains largely manual, with limited cost-effective automation solutions available. To address this, we developed an automated system for the agile and reliable production of radiopharmaceuticals. The system performs transmutations, dissolution, and separation for a range of radioisotopes. Steps in the production of ⁸⁹Zr-oxalate are used as an exemplar to illustrate its use. Three-dimensional (3D) printing was exploited to design and manufacture a target holder able to include solid targets, in this case an ⁸⁹Y foil. Spot welding was used to attach ⁸⁹Y to a refractory tantalum (Ta) substrate. A commercially available CPU chiller was repurposed to efficiently cool the metal target. Furthermore, a commercial resin (ZR Resin) and compact peristaltic pumps were employed in a compact (10 × 10 × 10 cm³) chemical separation unit that operates automatically via computer-controlled software. Additionally, a standalone 3D-printed unit was designed with three automated functionalities: photolabelling, vortex mixing, and controlled heating. All components of the assembly, except for the target holder, are housed inside a commercially available hot cell, ensuring safe and efficient operation in a controlled environment. This paper details the design, construction, and modelling of the entire assembly, emphasising its innovative integration and operational efficiency for widespread radiopharmaceutical automation. Full article

Due to the need for reproducible, scalable, and environmentally friendly nanomaterial synthesis methods, an increasing amount of scientific interest revolves around microfluidic technologies. In this context, the present paper proposes a new three-dimensional (3D) spiral microfluidic platform designed and tested for the simultaneous synthesis and surface functionalization of magnetite (Fe₃O₄) nanoparticles with salicylic acid (SA). The microreactor was fabricated from overlaid polymethylmethacrylate (PMMA) sheets and assembled into a compact, reusable chip architecture, allowing continuous reagent mixing and enhanced hydrodynamic control. The performed physicochemical analyses confirmed that on-chip synthesized Fe₃O₄@SA NPs exhibit crystallinity, a uniform spherical morphology, a narrow size distribution, excellent colloidal stability, and successful surface functionalization. In vitro cytotoxicity assays using MRC-5 lung fibroblasts and HaCaT keratinocytes revealed a concentration-dependent response, identifying a safe dose range below 610 µg/mL. The integrated design, efficient synthesis, and favorable biocompatibility profile position this 3D microfluidic platform as a promising tool for scalable nanomaterial production in biomedical and environmental applications. Full article

The assembly optimization design of satellite components is a crucial element in the overall design of satellites. In this paper, a novel three-dimensional assembly optimization design problem (3D-AODP) for multi-module micro–nano satellite components is proposed according to the engineering requirements, aiming at optimizing the satellite mass characteristics, and taking into account constraints such as space interference, space occupation and special location. Multi-module micro–nano satellites are a new type of satellite configuration based on the assembly of multiple U-shaped cube units. The 3D-AODP of its components is a challenging two-layer composite optimization task involving discrete variable optimization of component allocation and continuous variable optimization of component layout, which interact with each other. To solve the problem, a hybrid assembly optimization method based on tabu search (TS) and multi-objective differential evolutionary (MODE) algorithms is proposed, in which the assignment problem of the components is converted into a domain search problem by the TS algorithm. The space interference constraints and space occupancy constraints of the components are considered, and an assignment scheme with the minimum mass difference is obtained. On this basis, a bi-objective differential evolutionary algorithm is used to develop the layout optimization problem for the components, which takes into account the spatial non-interference constraints and special location constraints of the components, and obtains the Pareto solution set of the assembly scheme under the optimal mass characteristics (moment of inertia and product of inertia). Finally, the feasibility and effectiveness of the proposed method is demonstrated by an engineering case. Full article

In advanced structural applications—aerospace and automotive—fiber-laminated composite (FRP) materials are increasingly used for their superior strength-to-weight ratios, making the reliability of their mechanical joints a critical concern. Mechanically fastened joints play a major role in ensuring the structural stability of FRP Composite structures; however, accurately predicting their failure behavior remains a major challenge due to the anisotropic and heterogeneous nature of composite materials. This paper presents a progressive damage modeling approach to investigate the failure modes and joint strength of mechanically fastened carbon fiber-laminated (CFRP) composite joints. A 3D constitutive model based on continuum damage mechanics was developed and implemented within a three-dimensional finite element framework. The joint model comprises a composite plate, a steel plate, a steel washer, and steel bolts, capturing realistic assembly behavior. Both single- and double-lap joint configurations, featuring single and double bolts, were analyzed under tensile loading. The influence of clamping force on joint strength was also investigated. Model predictions were validated against existing experimental results, showing a good correlation. It was observed that double-lap joints exhibit nearly twice the strength of single-lap joints and can retain up to 85% of the strength of a plate with a hole. Furthermore, double-lap configurations support higher clamping forces, enhancing frictional resistance at the interface and load transfer efficiency. However, the clamping force must be optimized, as excessive values can induce premature damage in the composite before external loading. The stiffness of double-bolt double-lap (3DD) joints was found to be approximately three times that of single-bolt single-lap (3DS) joints, primarily due to reduced rotational flexibility. These findings provide useful insights into the design and optimization of composite bolted joints under tensile loading. Full article

Hydrogels are ECM-mimicking three-dimensional (3D) networks that are widely used in biomedical applications; however, conventional natural and synthetic polymer-based hydrogels present limitations such as poor mechanical strength, limited bioactivity, and low reproducibility. Self-assembling peptides (SAPs) offer a promising alternative, as they can form micro- and nanostructured hydrogels through non-covalent interactions and allow precise control over their biofunctionality, mechanical properties, and responsiveness to biological cues. Through rational sequence design, SAPs can be engineered to exhibit tunable mechanical properties, controlled degradation rates, and multifunctionality, and can dynamically regulate assembly and degradation in response to specific stimuli such as pH, ionic strength, enzymatic cleavage, or temperature. Furthermore, SAPs have been successfully incorporated into conventional hydrogels to enhance cell adhesion, promote matrix remodeling, and provide a more physiologically relevant microenvironment. In this review, we summarize recent advances in SAP-based hydrogels, particularly focusing on their novel biofunctional properties such as anti-inflammatory, antimicrobial, and anticancer activities, as well as bioimaging capabilities, and discuss the mechanisms by which SAP hydrogels function in biological systems. Full article

This paper presents the first demonstration and comparison of two identical oscillator circuits employing piezoelectric zinc oxide (ZnO) microelectromechanical systems (MEMS) resonators, implemented on conventional printed-circuit-board (PCB) and three-dimensional (3D)-printed acrylonitrile butadiene styrene (ABS) substrates. Both oscillators operate simultaneously at dual frequencies (260 MHz and 437 MHz) without the need for additional circuitry. The MEMS resonators, fabricated on silicon-on-insulator (SOI) wafers, exhibit high-quality factors (Q), ensuring superior phase noise performance. Experimental results indicate that the oscillator packaged using 3D-printed chip-carrier assembly achieves a 2–3 dB improvement in phase noise compared to the PCB-based oscillator, attributed to the ABS substrate’s lower dielectric loss and reduced parasitic effects at radio frequency (RF). Specifically, phase noise values between −84 and −77 dBc/Hz at 1 kHz offset and a noise floor of −163 dBc/Hz at far-from-carrier offset were achieved. Additionally, the 3D-printed ABS-based oscillator delivers notably higher output power (4.575 dBm at 260 MHz and 0.147 dBm at 437 MHz). To facilitate modular characterization, advanced packaging techniques leveraging precise 3D-printed encapsulation with sub-100 μm lateral interconnects were employed. These ensured robust packaging integrity without compromising oscillator performance. Furthermore, a comparison between two transistor technologies—a silicon germanium (SiGe) heterojunction bipolar transistor (HBT) and an enhancement-mode pseudomorphic high-electron-mobility transistor (E-pHEMT)—demonstrated that SiGe HBT transistors provide superior phase noise characteristics at close-to-carrier offset frequencies, with a significant 11 dB improvement observed at 1 kHz offset. These results highlight the promising potential of 3D-printed chip-carrier packaging techniques in high-performance MEMS oscillator applications. Full article

Malicious software presents significant challenges in cybersecurity, leveraging rapidly evolving technologies to bypass traditional defense mechanisms. This research introduces a novel image-based malware classification framework that uses hybrid-model Convolutional Neural Networks to process RGB images generated from assembly code. We present MalevisAsm, an enriched dataset that merges MaleVis malware samples with benign files, and propose a hybrid deep learning model that combines EfficientNetB0 and DenseNet121 for robust feature extraction. The approach transforms Portable Executable files into assembly code, maps opcode transitions into three-channel images, and uses a fine-tuned CNN to classify malware families. Additionally, we implemented Uniform Manifold Approximation and Projection a contemporary nonlinear dimensionality reduction technique, to enhance the identification of previously unseen malware samples via binary classification. Our experiments achieve a top-tier accuracy of 98.45%, surpassing existing benchmarks on the MaleVis dataset. This research contributes to the field by integrating static binary analysis with advanced computer vision techniques, offering a scalable and effective solution for malware detection. Full article