Search Results (762)

The Silurian marine shale in the Sichuan Basin is currently the main reservoir for shale gas reserves and production in China. This study investigates the reservoir evolution of the Silurian marine shale based on fractal dimension, quantifying the complexity and heterogeneity of the shale’s pore structure. Physical simulation experiments were conducted on field-collected shale samples, revealing the evolution of total organic carbon, mineral composition, porosity, and micro-fractures. The fractal dimension of shale pore was characterized using the Frenkel–Halsey–Hill and capillary bundle models. The relationships among shale components, porosity, and fractal dimensions were investigated through a correlation analysis and a principal component analysis. A comprehensive evolution model for porosity and micro-fractures was established. The evolution of mineral composition indicates a gradual increase in quartz content, accompanied by a decline in clay, feldspar, and carbonate minerals. The thermal evolution of organic matter is characterized by the formation of organic pores and shrinkage fractures on the surface of kerogen. Retained hydrocarbons undergo cracking in the late stages of thermal evolution, resulting in the formation of numerous nanometer-scale organic pores. The evolution of inorganic minerals is represented by compaction, dissolution, and the transformation of clay minerals. Throughout the simulation, porosity evolution exhibited distinct stages of rapid decline, notable increase, and relative stabilization. Both pore volume and specific surface area exhibit a trend of decreasing initially and then increasing during thermal evolution. However, pore volume slowly decreases after reaching its peak in the late overmature stage. Fractal dimensions derived from the Frenkel–Halsey–Hill model indicate that the surface roughness of pores (D1) in organic-rich shale is generally lower than the complexity of their internal structures (D2) across different maturity levels. Additionally, the average fractal dimension calculated based on the capillary bundle model is higher, suggesting that larger pores exhibit more complex structures. The correlation matrix indicates a co-evolution relationship between shale components and pore structure. Principal component analysis results show a close relationship between the porosity of inorganic pores, microfractures, and fractal dimension D2. The porosity of organic pores, the pore volume and specific surface area of the main pore size are closely related to fractal dimension D1. D1 serves as an indicator of pore development extent and characterizes the changes in components that are “consumed” or “generated” during the evolution process. Based on mineral composition, fractal dimensions, and pore structure evolution, a comprehensive model describing the evolution of pores and fractal dimensions in organic-rich shale was established. Full article

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. Full article

The development of deep coalbed methane (buried depth > 2000 m) in the Yan’an block of Ordos Basin is limited by low permeability, the pore structure of the coal reservoir, and the gas–water occurrence relationship. It is urgent to clarify the key control mechanism of pore structure on gas migration. In this study, based on high-pressure mercury intrusion (pore size > 50 nm), low-temperature N₂/CO₂ adsorption (0.38–50 nm), low-field nuclear magnetic resonance technology, fractal theory and Pearson correlation coefficient analysis, quantitative characterization of multi-scale pore–fluid system was carried out. The results show that the multi-scale pore network in the study area jointly regulates the occurrence and migration process of deep coalbed methane in Yan’an through the ternary hierarchical gas control mechanism of ‘micropore adsorption dominant, mesopore diffusion connection and macroporous seepage bottleneck’. The fractal dimensions of micropores and seepage are between 2.17–2.29 and 2.46–2.58, respectively. The shape of micropores is relatively regular, the complexity of micropore structure is low, and the confined space is mainly slit-like or ink bottle-like. The pore-throat network structure is relatively homogeneous, the difference in pore throat size is reduced, and the seepage pore shape is simple. The bimodal structure of low-field nuclear magnetic resonance shows that the bound fluid is related to the development of micropores, and the fluid mobility mainly depends on the seepage pores. Pearson’s correlation coefficient showed that the specific surface area of micropores was strongly positively correlated with methane adsorption capacity, and the nanoscale pore-size dominated gas occurrence through van der Waals force physical adsorption. The specific surface area of mesopores is significantly positively correlated with the tortuosity. The roughness and branch structure of the inner surface of the channel lead to the extension of the migration path and the inhibition of methane diffusion efficiency. Seepage porosity is linearly correlated with gas permeability, and the scale of connected seepage pores dominates the seepage capacity of reservoirs. This study reveals the pore structure and ternary grading synergistic gas control mechanism of deep coal reservoirs in the Yan’an Block, which provides a theoretical basis for the development of deep coalbed methane. Full article

To address the poor thermal shock resistance and high brittleness of traditional ceramic tools, a novel Si₃N₄/Sc₂W₃O₁₂ (SNS) composite ceramic material was developed via in situ synthesis using WO₃ and Sc₂O₃ as precursors and consolidated by spark plasma sintering. Sc₂W₃O₁₂ with negative thermal expansion was introduced to compensate for matrix shrinkage and modulate interfacial stress. The effects of varying Sc₂W₃O₁₂ content on thermal expansion, residual stress, microstructure, and mechanical properties were systematically investigated. Among the compositions, SNS3 (12 wt.% Sc₂W₃O₁₂) exhibited the best overall performance: relative density of 98.8 ± 0.2%, flexural strength of 712.4 ± 30 MPa, fracture toughness of 7.5 ± 0.3 MPa·m^1/2, Vickers hardness of 16.3 ± 0.3 GPa, and an average thermal expansion coefficient of 2.81 × 10⁻⁶·K⁻¹. The formation of a spherical chain-like Sc-W-O phase at the grain boundaries created a “hard core–soft shell” interface that enhanced crack resistance and stress buffering. Cutting tests showed that the SNS3 tool reduced workpiece surface roughness by 32.91% and achieved a cutting distance of 9500 m. These results validate the potential of this novel multiphase ceramic system as a promising candidate for high-performance and thermally stable ceramic cutting tools. Full article

A salient feature of modern material surfaces used in cutting-edge technologies is their structural and spatial complexity, which endows them with novel properties and multifunctionality. The quantitative characterization of material complexity is a challenge that must be addressed to optimize their production and performance. While numerous metrics exist to quantify the complexity of spatial structures in various scientific domains, methods specifically tailored for characterizing the spatial complexity of material surface morphologies at the micro- and nanoscale are relatively scarce. In this paper, we utilize the concept of multiscale entropy to quantify the complexity of surface morphologies of rough surfaces across different scales and investigate the effects of amplitude fluctuations (i.e., surface height distribution) in both stepwise and smooth self-affine rough surfaces. The crucial role of the binning scheme in regulating amplitude effects on entropy and complexity measurements is highlighted and explained. Furthermore, by selecting an appropriate binning strategy, we analyze the impact of 2D imaging on the complexity of a rough surface and demonstrate that imaging can artificially introduce peaks in the relationship between complexity and surface amplitude. The results demonstrate that entropy-based spatial complexity effectively captures the scale-dependent heterogeneity of stepwise rough surfaces, providing valuable insights into their structural properties. Full article

Ceramic oxide coatings were fabricated on 7075 aluminum alloy via micro-arc oxidation (MAO) in a silicate-phosphate electrolyte under voltages of 250 V, 300 V, and 350 V for 600 s. The effect of the applied voltage on the surface morphology, microstructure, phase composition, microhardness, roughness, coating thickness, and corrosion resistance was systematically studied. The coating obtained at 300 V demonstrated a dense structure with relatively low surface roughness (2.3 μm) and a thickness of approximately 70 μm. This sample also exhibited the most balanced performance, combining relatively high microhardness (~422 HV) and the lowest corrosion current density (6.1 × 10⁻⁷ A/cm²) in a 3.5 wt.% NaCl solution. X-ray diffraction patterns revealed the presence of both γ- and α-Al₂O₃ phases in all coated samples, with a relative increase in α-phase intensity observed at an intermediate voltage. The results demonstrate that the applied voltage plays a critical role in determining the coating structure and performance, offering insights into the surface treatment of high-strength aluminum alloys for engineering applications. Full article

This paper presents the design and validation of a novel specialized fixture device for machining inclined planes with barrel cutters on 3-axis CNC machine tools. Barrel milling, also known as Parabolic Performance Cutting (PPC), is extensively used on 5-axis machines to enhance the efficiency of machining complex surfaces. While significant research has focused on optimizing barrel milling for aspects such as surface roughness and cutting forces, implementing this technique on 3-axis machines poses a challenge due to limitations in tool orientation. To overcome this, an innovative adaptable device was designed, enabling precise workpiece orientation relative to the barrel cutter. To overcome this limitation, an adaptable device was designed that enables precise workpiece orientation relative to the barrel cutter. The device utilizes interchangeable locating elements for different cutter programming angles (such as 18°, 20°, and 42.5°), thereby ensuring correct workpiece positioning. Rigid workpiece clamping is provided by the device’s mechanism to maintain precise workpiece positioning during machining, and probing surfaces are integrated into the device to facilitate the definition of the coordinate system necessary for CNC machine programming. Device control was performed using a Hexagon RA-7312 3D measuring arm. Inspection results indicated minimal dimensional deviations (e.g., surface flatness between 0.002 mm and 0.012 mm) and high angular accuracy (e.g., angular non-closure of 0.006°). The designed device enables the effective and precise use of barrel cutters on 3-axis CNC machines, offering a previously unavailable practical and economical solution for cutting tool tests and cutting regime studies. Full article

Objectives: This study aimed to evaluate the effects of different titanium (Ti) anodized surfaces on soft tissue healing around dental implant abutments. Methods: Discs of machined (MC), pink anodized (PA) and yellow anodized (YA) surfaces were morphologically characterized and evaluated in vitro. Cell adhesion and collagen synthesis by human gingival fibroblasts (hGFs) were assessed to evaluate the regenerative potential of the surfaces under study. Their inflammatory potential was evaluated in THP-1 cell cultures by measuring cytokine secretion, and their proteomic adsorption patterns were characterized using nano-liquid chromatography mass spectrometry (nLC-MS/MS). Statistical significance was considered at 5%. In relation to proteomics, statistical differences were evaluated using the Student t-test with the Perseus application. Results: The anodization process resulted in a reduction in the surface roughness parameter (Ra) relative to the machined titanium (p < 0.05). No differences in hGF adhesion were found between the surfaces after one day. PA induced increased hGF collagen synthesis after 7 days (p < 0.05). The secretion of TNF-α was lower for anodized surfaces than for MC, and its concentration was lower for PA than for YA (p < 0.05). In turn, TGF-β was higher for PA and YA versus MC after one and three days of culture. A total of 176 distinct proteins were identified and 26 showed differences in adhesion between the anodized surfaces and MC. These differential proteins were related to coagulation, lipid metabolism, transport activity, plasminogen activation and a reduction in the immune response. Conclusions: Anodized Ti surfaces showed promising anti-inflammatory and regenerative potential for use in dental implant abutments. Anodization reduced surface roughness, increased collagen synthesis and lowered TNF-α secretion while increasing TGF-β levels compared to machined surfaces. Identified proteins related to coagulation and lipid metabolism supported these findings. Clinical relevance: Anodized surfaces could offer improved short-term peri-implant soft tissue healing over machined surfaces. The analysis of abutment surface, instead of implant surface, is a new approach that can provide valuable information. Full article

The miniaturization of heat exchangers requires advanced manufacturing methods, as conventional techniques such as milling or casting are insufficient for producing complex microscale geometries. This study investigates the feasibility of using selective laser melting (SLM) with 316L stainless steel powder to fabricate compact heat exchangers with minichannels. The exchanger was designed using Autodesk Inventor 2023.3 software and produced under optimized process parameters. Measurements using a hydrostatic balance demonstrated that the applied process parameters resulted in a relative material density of 99.5%. The average microhardness in the core region of the SLM-fabricated samples was 255 HV, and the chemical composition of the final material differed only slightly from that of the feedstock material (stainless steel powder). Dimensional accuracy, surface quality, and internal structure integrity were assessed using computed tomography, optical microscopy, and contact profilometry. The fabricated component demonstrated high geometric fidelity and channel permeability, with local surface deformations associated with the absence of support structures. The average surface roughness (Ra) of the minichannels was 11.11 ± 1.63 µm. The results confirm that SLM technology enables the production of functionally viable heat exchangers with complex geometries. However, limitations remain regarding dimensional accuracy, powder removal, and surface roughness. These findings highlight the potential of metal additive manufacturing for heat transfer applications while emphasizing the need for further research on performance testing under real operating conditions, especially involving two-phase flow. Full article

The recirculating aquaculture system (RAS) has been rapidly adopted worldwide in recent years due to its high productivity, good stability, and good environmental controllability (and therefore friendliness to environment and ecology). Nevertheless, the effluent from seawater RAS contains a high level of ammonia nitrogen which is toxic to fish, so it is necessary to overcome the salinity conditions to achieve rapid and efficient nitrification for recycling. The moving bed biofilm reactor (MBBR) has been widely applied often by using PE fillers for efficient wastewater treatment. However, the start-up of MBBR in seawater environments has remained a challenge due to salinity stress and harsh inoculation conditions. This study investigated a new PE-filler surface modification method towards the enhanced start-up of mariculture MBBR by combining liquid-phase oxidation and maifanstone powder. The aim was to obtain a higher porous surface and roughness and a strong adsorption and alkalinity adjustment for the MBBR PE filler. The hydrophilic properties, surface morphology, and chemical structure of a raw polyethylene filler (an unmodified PE filler), liquid-phase oxidation modified filler (LO-PE), and liquid-phase oxidation combined with a coating of a maifanstone-powder-surface-modified filler (LO-SCPE) were first investigated and compared. The results showed that the contact angle was reduced to 45.5° after the optimal liquid-phase oxidation modification for LO-PE, 49.8% lower than that before modification, while SEM showed increased roughness and surface area by modification. Moreover, EDS presented the relative content of carbon (22.75%) and oxygen (42.36%) on the LO-SCPE surface with an O/C ratio of 186.10%, which is 177.7% higher than that of the unmodified filler. The start-up experiment on MBBRs treating simulated marine RAS wastewater (HRT = 24 h) showed that the start-up period was shortened by 10 days for LO-SCPE compared to the PE reactor, with better ammonia nitrogen removal observed for LO-SCPE (95.8%) than the PE reactor (91.7%). Meanwhile, the bacterial community composition showed that the LO-SCPE reactor had a more diverse and abundant AOB and NOB. The Nitrospira has a more significant impact on nitrification because it would directly oxidize NH₄⁺-N to NO₃⁻-N (comammox pathway) as mediated by AOB and NOB. Further, the LO-SCPE reactor showed a higher NH₄⁺-N removal rate (>99%), less NO₂⁻-N accumulation, and a shorter adaption period than the PE reactor. Eventually, the NH₄⁺-N concentrations of the three reactors (R1, R2, and R3) reached <0.1 mg/L within 3 days, and their NH₄⁺-N removal efficiencies achieved 99.53%, 99.61%, and 99.69%, respectively, under ammonia shock load. Hence, the LO-SCPE media have a higher marine wastewater treatment efficiency. Full article

This study investigates the potential of Selective Laser Melting (SLM) to tailor the surface characteristics of Ti6Al4V directly during fabrication, eliminating the need for post-processing treatments potentially for dental implants. By adjusting the Volumetric Energy Density (VED) through controlled variations in the laser scanning speed, we achieved customized surface textures at both the micro- and nanoscale levels. SLM samples fabricated at moderate VED levels (50–100 W·mm³/s) exhibited optimized dual-scale surface roughness—a macro-roughness of up to 25.5–27.6 µm and micro-roughness of as low as 58.8–64.2 nm—resulting in significantly enhanced hydrophilicity, with water contact angles (WCAs) decreasing to ~62°, compared to ~80° on a standard grade 5 machined Ti6Al4V plate. The XPS analysis revealed that the surface oxygen content remains relatively stable at low VED values, with no significant increase. The surface topography plays a significant role in influencing the WCA, particularly when the VED values are low (below 200 W·mm³/s) during SLM, indicating the dominant effect of surface morphology over chemistry in these conditions. Biological assays using osteoblast-like MG-63 cells demonstrated that these as-built SLM surfaces supported a 1.5-fold-higher proliferation and improved cytoskeletal organization relative to the control, confirming the enhanced early cellular responses. These results highlight the capability of SLM to engineer bioactive implant surfaces through process-controlled morphology and chemistry, presenting a promising strategy for the next generation of dental implants suitable for immediate placement and osseointegration. Full article

Magnesium alloy stents exhibit significant potential in the treatment of cardiovascular and cerebrovascular diseases due to their remarkable mechanical support and biodegradability. However, bare magnesium alloy stents often degrade too quickly and exhibit inadequate biocompatibility, which severely restricts their clinical applicability. Herein, a composite coating consisting of an MgF₂ conversion layer, a polydopamine (PDA) layer, fucoidan, and hyaluronic acid was prepared to enhance the corrosion resistance and biocompatibility of ZE21B alloy for a vascular stent application. The modified ZE21B alloy exhibited relatively high surface roughness, moderate wettability, and better corrosion resistance. Moreover, the modified ZE21B alloy with a low hemolysis rate and fibrinogen adsorption level confirmed improved hemocompatibility for medical requirements. Furthermore, the ZE21B alloy modified with fucoidan and hyaluronic acid enhanced the adhesion, proliferation, and NO release of endothelial cells (ECs). Simultaneously, it inhibits the adhesion and proliferation of smooth muscle cells (SMCs), promoting a competitive advantage for ECs over SMCs due to the synergistic effects of fucoidan and hyaluronic acid. The incorporation of fucoidan and hyaluronic acid markedly improved the corrosion resistance and biocompatibility of the ZE21B magnesium alloy. This development presents a straightforward and effective strategy for the advancement of biodegradable vascular stents. Full article

The increasing demand for precision-engineered machined components across diverse sectors highlights the importance of optimizing machining procedures. The improvement of milling strategies is significant in the production of flat surfaces and slots of different sizes. The choice between milling techniques can significantly impact the final product quality and production efficiency. This study provides a detailed examination of the relative effectiveness of plunge milling (axial feed) versus face milling (radial feed) techniques, concentrating on critical performance metrics such as cutting force and surface roughness. In our systematic approach, we varied key milling parameters (feed per tooth, depth of cut, and cutting speed). We conducted a series of experiments to quantify the resulting cutting forces and surface finish quality employed under different conditions. The analysis reveals notable performance differences between the two milling methods at various parameter settings. Through statistical and graphical analysis, we clarify the relationships between milling parameters and the resultant outputs, offering a deeper understanding of the factors influencing machining efficiency. The results reveal significant differences between plunge milling and face milling, with plunge milling exhibiting lower cutting forces, while face milling demonstrated superior surface quality. The insights granted from this research have implications for optimizing milling operations. Full article

Predicting real-time wear depth distribution and the coefficient of friction (COF) in tribological systems is challenging due to the dynamic and complex nature of surface interactions, particularly influenced by surface roughness. Traditional methods, relying on post-test measurements or oversimplified assumptions, fail to capture this dynamic behavior, limiting their utility for real-time monitoring. To address this, we developed a deep neural network (DNN) model by integrating experimental tribological testing and finite element method (FEM) simulations, using acoustic signals for non-invasive, real-time analysis. Experiments with brass pins (UNS C38500) of varying surface roughness (240, 800, and 1200 grit) sliding against a 304 stainless steel disc provided data to validate the FEM model and train the DNN. The DNN model predicted wear morphology with accuracy comparable to FEM simulations but at a lower computational cost, and the COF with relative errors below 10% compared to experimental measurements. This approach enables real-time monitoring of wear and friction, offering significant benefits for predictive maintenance and operational efficiency in industrial applications. Full article

A Digital twin (DT) enables the acquisition and subsequent analysis of large amounts of process data. Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. However, there is very little understanding of the relative merit of these methods. This paper proposes a DT framework in the context of predictive maintenance in smart manufacturing to compare the prediction efficacy of prevalent ML models. Data-driven models were developed using machine learning algorithms to predict surface roughness and power consumption during a CNC turning operation. Three process parameters, namely cutting velocity, feed rate, and depth of cut, and two dependent parameters, surface roughness and power consumption, were selected for model development. Seven ML algorithms were tested for each response parameter: Linear Regression, XGB Regressor, Random Forest Regressor, Average Ensemble, AdaBoost Regressor, SVR, and MLP. The results of the comparative analysis of the ML algorithms showed that the Random Forest Regressor is the best prediction model for surface roughness, with the highest R² (94.2% ± 2.4%), lowest MAE (0.011 ± 0.002), lowest MAPE (15.6% ± 4.0%), and lowest RMSE (0.017 ± 0.003), while the XGB Regressor demonstrated the best performance for power consumption prediction, with the highest R² (98.9% ± 0.5%), lowest MAE (22.513 ± 4.424), lowest MAPE (3.0% ± 0.5%), and lowest RMSE (42.650 ± 8.933). The best-performing machine learning algorithm was subsequently utilized in the data-driven models, helping to achieve an improved surface finish. This enables predictive maintenance, reducing energy usage for more sustainable production. Full article