Combined effects of BET and PF on the mechanism and material removal in alkaline chemical mechanical polishing of InP

Geometrically induced coupling effects on material removal and damage mechanisms during dry hard turning of large-pitch gothic-arch ball screws

Novel full-frequency material removal models verified by polishing experiments using three kinds of polishing pads performed on a developed five-axis polisher

Mechanisms of anisotropic material removal and deformation in ultra-precision grinding of polycrystalline AlN ceramics

Hybrid additive-subtractive manufacturing of surface-modified CF/PEEK porous implants with high-low temperature assistance.

Multi-scale analysis and modeling of material removal process for ultrasonic vibration-assisted polishing

Erosion resistance of basalt fiber-aeolian sand concrete under wind-sand erosion: Experimental analysis and mechanisms

Navigating the unavoidable: Enhancing the EBSD indexability through adaptation to three hurdles in femtosecond laser ablation.

Ultrasonic vibration affects three-phase flow behavior under the hydrodynamic contact mode in sapphire ultrasonic vibration chemical mechanical polishing (UVCMP). Nevertheless, the effects of slurry dynamics on material removal remain unclear due to challenges in experimental characterization. In this paper, the influences of key factors on the material removal of three-phase flow were investigated via the dense discrete phase model. The simulation results reveal that concentration and particle size present insignificant impacts on the multiphysical fields. However, the shear stress and material removal rate (MRR) increase with the concentration. While shear stress increases by 6 orders of magnitude as abrasive diameter grows from 20 to 5000 nm, the MRR only triples. As the ultrasonic amplitude and frequency rise, the multiphysical fields and shear stress are enhanced. The numerical MRR increases by 59% when the amplitude rises from 1 to 3 μm. The ultrasonic frequency is found to markedly affect the mechanical removal capability of sapphire UVCMP. The gray relational analysis reveals that frequency has the most significant effects on the simulation MRR (χnm = 0.8608). The influences of various factors on simulation MRR are experimentally validated. This paper establishes a theoretical foundation and provides novel insights for the optimization of sapphire UVCMP.

The effect of polishing pads is generally incorporated as a coefficient in the conventional Preston equation and its modified forms during the past century, and macroscopic stress distribution and microscopic motion states are discussed separately. To solve this challenge, we propose a novel cross-scale model using a unified physical framework integrating the macroscopic and microscopic states. The proposed model decomposes the effect of polishing pads into stress transfer and abrasive constraint factors. It connects microstructure, stress transfer, abrasive constraint and material removal in sequence and establishes a relationship between the microstructure of pads and the evolution of a polished surface. Finite element simulations show that the maxima of von Mises stresses on fused silica are 0.171, 0.749 and 0.446 MPa for non-woven, polyurethane, and asphalt pads, respectively, corresponding to the support of discrete fibers, local stress concentration and continuous transfer of stress. Furthermore, single-abrasive scratching confirms that the maxima of equivalent stress exerted by the associated three pads are 2.059, 4.701 and 7.771 MPa, respectively, relevant to weak, unstable and strong constraints of abrasives. Polishing experiments were performed on fused silica with ceria slurry. They demonstrate that the peak-to-valley value obtained using an asphalt pad decreases from 385.976 to 115.237 nm and the attenuation of power spectral density is 88%. The surface roughness Sa achieved using a non-woven pad is reduced from 2.145 to 0.721 nm. The predictions of the proposed model are in good agreement with the simulation and experimental results. Our outcomes provide new insights into achieving error convergence of full bands on polished surfaces using different polishing pads.

Fracture-related infection (FRI) remains a serious complication in orthopedic trauma surgery, with microbiological confirmation based on positive cultures from deep tissue samples. However, the clinical interpretation of positive microbiological findings in asymptomatic patients remains unclear. This prospective study aimed to evaluate the incidence and characteristics of peri-implant soft tissue colonization in patients undergoing routine removal of osteosynthetic material without clinical or radiological signs of FRI. Indications for implant removal surgery included routine removal, prominence of osteosynthetic material, discomfort, removal prior to prosthetic joint implantation or patient request. It is a single-center prospective study. A total of 137 adult patients undergoing implant removal between 01/09/2023 and 30/11/2025 were included. For each patient, five deep tissue samples and the removed implant were collected for microbiological analysis, including sonication fluid culture. Histological evaluation was performed in a subset of cases. Peri-implant colonization was defined as the presence of identical pathogens in at least two tissue samples. All patients underwent routine postoperative clinical follow-up after implant removal surgery. Wound healing and local clinical status were assessed at the time of suture removal, typically 12-14 days postoperatively. In addition, all patients were instructed to return for further clinical evaluation in the event of any wound healing problems, local inflammatory signs or other symptoms involving the operated region. Peri-implant soft tissue colonization was detected in 32.8% of patients, while overall microbiological positivity (including sonication) reached 38.7%. Sonication and tissue cultures demonstrated comparable detection rates, with isolated positivity observed in 5.8% of cases. Histological positivity was rare (2.75%) and showed limited concordance with microbiological findings. Colonization rates varied significantly by anatomical location, with markedly higher prevalence in the shoulder region compared to other sites (70.0 vs. 20.7%, p < 0.001). In multivariable logistic regression analysis, shoulder region location was a strong independent predictor of culture positivity (OR 25.45, 95% CI 8.00-80.65), while male sex was also independently associated with positive cultures (adjusted OR 6.42, 95% Cl 2.11-19.51, p = 0.001). The microbiological spectrum was dominated by low virulence, predominantly polymicrobial flora, with Cutibacterium acnes as the most frequently identified organism. These findings demonstrate that peri-implant colonization is common in clinically asymptomatic patients, particularly in the shoulder region. Shoulder region localization and male sex were independently associated with microbiological positivity. Positive microbiological results should therefore be interpreted with caution and in the context of clinical, radiological and histopathological findings to avoid overdiagnosis of FRI and unnecessary treatment.

This study investigates the abrasive water jet machining (AWJM) performance of an Al6061-0.5 wt.% B[Formula: see text]C-1 wt.% ZrO[Formula: see text] hybrid composite fabricated using ultrasonic-assisted stir casting. A Taguchi L27 orthogonal array was adopted to systematically evaluate the effects of five machining parameters, namely abrasive flow rate (AFR), water jet pressure (WJP), abrasive jet cutting speed (AJCS), stand-off distance (SOD), and abrasive grit size (GS) each at three levels. Material removal rate (MRR), surface roughness (Ra), and kerf taper angle (KTA) were considered as the key performance responses. The experimental results revealed that MRR varied from 7.86 to 15.24 mm[Formula: see text]/min, Ra ranged between 3.220 and 3.980μm, and KTA varied from 0.142[Formula: see text] to 0.309[Formula: see text]. Analysis of variance (ANOVA) was performed to identify the statistically significant machining parameters influencing the responses, indicating that AFR was the dominant factor affecting MRR, whereas AJCS predominantly governed Ra and KTA. Furthermore, a hybrid Grey Relational Analysis-Analytic Hierarchy Process (GRA-AHP) multi-criteria optimization approach was employed to simultaneously maximize MRR and minimize Ra and KTA. The optimal machining condition was obtained at an AFR of 430 g/min, WJP of 280 MPa, AJCS of 80 mm/min, SOD of 1.5 mm, and GS of 120 mesh. The findings demonstrate the effectiveness of the proposed hybrid optimization framework in enhancing AWJM performance of advanced aluminium-based hybrid composites. Also, machine learning (ML) models, including Support Vector Regression (SVR), Random Forest (RF), and Multi-layer perceptron (MLP), were developed to predict machining output responses - MRR, Ra, and KTA based on AWJM process parameters. Among the developed models, the RF model demonstrated superior predictive capability for MRR and Ra with maximum [Formula: see text] values of 0.9882 and 0.9919, respectively, whereas the SVR model achieved the highest prediction accuracy for KTA with an [Formula: see text] value of 0.9955. The low RMSE and MAPE values further confirmed the robustness and reliability of the developed ML models for AWJM response prediction. The models exhibited high predictive accuracy with strong agreement between experimental and predicted results, even with a limited dataset. These results demonstrate the effectiveness of ML-based approaches as reliable tools for performance estimation and process optimization in AWJM of hybrid aluminium matrix composites.

The evaluation and control of energy efficiency in machine tools are crucial to achieving energy conservation and emission reduction in manufacturing. However, existing energy efficiency models primarily focus either on the energy efficiency of material removal or on the inherent energy efficiency of machine tools themselves, without considering the impact of speed loss caused by low-load operation on the overall energy efficiency of the machining process. To address this gap, this paper establishes a novel load-energy efficiency model for machining systems to specifically assess the impact of speed loss on machining energy efficiency. Furthermore, a statistical process control method for load-energy efficiency is proposed to monitor energy use. Finally, a case study of load-energy efficiency modelling and control for the CK6153i lathe is conducted, demonstrating the effectiveness of the proposed method through a reduction in energy loss of 417.54 kJ and an increase in load-energy efficiency by 4.59%. The proposed method reveals previously untapped opportunities for energy saving in machining processes. The study provides a more nuanced understanding of the relationship between speed loss and machining energy efficiency, thereby offering practical insights for machining energy optimisation.

Zinc selenide (ZnSe) convex aspherical optical components have been widely used because of their excellent optical properties. Single-point diamond turning (SPDT) is the mainstream method for machining of ZnSe aspherical optical surfaces. However, the high brittleness and extremely low fracture toughness of ZnSe make it prone to surface defects such as cracks and pits during the SPDT process, which seriously impair the quality of ultraprecision machined ZnSe surfaces. At present, remarkable achievements have been made in the research on inducing ductile-brittle transition (DBT) to improve the crystal surface quality via the precise control of cutting parameters during the ultraprecision SPDT of ZnSe crystals. Nevertheless, few studies have reported the influence of the ZnSe crystal orientation on the surface quality. The anisotropy of ZnSe crystal grains also exerts a significant impact on the machined surface quality. Therefore, it is crucial to explore the anisotropic cutting mechanism of ultraprecision turning for ZnSe crystals and improve the surface and subsurface quality of ZnSe. In this study, molecular dynamics simulations of different ZnSe crystal planes were combined with gradient ultraprecision turning experiments to systematically investigate the material removal mechanisms of ultraprecision turning for the ZnSe (100), (110), and (111) crystal planes and polycrystalline ZnSe, as well as the effect of cutting depth on the surface quality of different ZnSe crystal planes. The results show that the surface morphologies of different ZnSe crystal planes exhibit distinct differences at the same cutting depth and that the morphologies of different crystal planes also vary at an identical cutting depth. The machined surface quality of ZnSe crystals is significantly affected by the crystal orientation. Among the four crystal planes investigated in the experiments, the machined surface quality is ranked as (110) > (111) > polycrystalline ZnSe > (100).

Sternal osteomyelitis (OM), particularly in the context of late-presenting non-union following cardiothoracic surgery, remains a poorly understood and inconsistently managed condition. Delayed presentations involve established infection, sternal instability, and biofilm formation, requiring a multidisciplinary approach. BOAST guidelines for fracture-related infection (FRI) recommend a combined orthoplastic approach to manage these complex cases. This retrospective case series identified all patients referred to a specialist bone infection unit with delayed onset sternal OM from March 2015 to March 2025. All patients were managed through a multidisciplinary pathway involving cardiothoracic surgery, orthopaedics, plastic surgery, and microbiology. Each case involved planning through a bone infection MDT, radical debridement, multiple deep tissue sampling, removal of infected foreign material, skeletal stabilisation where indicated, definitive soft tissue reconstruction, and culture-directed antimicrobial therapy. Clinical outcomes, microbiological findings, and recurrence were assessed. Fourteen patients were included (mean age 63·7years), all with significant comorbidity burden. The interval from index cardiac surgery to definitive debridement ranged from 25 to 3,251days. Eleven patients underwent single-stage debridement with immediate reconstruction: three required staged procedures. The most commonly isolated organisms wereStaphylococcus epidermidis(7/14),Cutibacterium acnes(5/14), andStaphylococcus aureus(4/14); mixed infections were frequent. Mean length of stay following single-stage procedures was 10·5days. During a mean follow-up of 410days, one patient experienced recurrence requiring further surgery. This case series demonstrates that delayed sternal OM and non-union can be effectively managed through an orthoplastic approach aligned with FRI principles. Isolated soft tissue coverage, which has historically been the default management, fails to address the underlying pathology. Approaches focused solely on soft tissue reconstruction risk, persistent infection, and instability. This study supports the application of fracture-related infection principles to delayed sternal osteomyelitis, emphasising radical debridement, hardware removal, multidisciplinary decision-making, and definitive reconstruction as key components of successful treatment.

Electrical Discharge Machining (EDM) has emerged as an effective technique for machining difficult-to-cut superalloys such as Waspaloy, which are widely used in aerospace applications due to their superior mechanical strength and thermal stability but exhibit poor machinability under conventional processes. The present study focuses on enhancing EDM performance through the combined use of copper-graphite (Cu-Gr) composite electrodes and eco-friendly dielectric media, along with multi-objective optimization. Composite electrodes with graphite content 5 wt% were fabricated, and machining experiments were conducted under different process parameters, including discharge current, pulse-on time, pulse-off time, and inter-electrode gap. Machining performance was evaluated in terms of material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra), while surface integrity was analyzed using scanning electron microscopy (SEM). A TOPSIS-based multi-objective optimization approach was employed to determine the optimal machining conditions. The results indicated that discharge current and pulse-on time significantly influence MRR, with a maximum MRR of approximately 0.43mm3/min achieved at 32A and 30 µs. The minimum TWR of about 0.06mm3/min was also observed at 8A, indicating optimal discharge stability. Surface roughness was minimized to 1.8μm using the CuGr5 electrode, while eco-friendly sunflower oil dielectric produced superior surface integrity with Ra as low as 1.013μm and reduced recast layer thickness. Sensitivity analysis revealed non-linear parameter behavior with distinct optimal regions, and uncertainty analysis confirmed high experimental reliability with minimal variability. Multi-objective optimization identified the optimal condition with a performance index of 0.6213. The study demonstrates that the integration of composite electrode, bio-based dielectrics, and optimization techniques significantly enhances machining efficiency, surface quality, and sustainability in EDM of Waspaloy.

Low refractive index contrast in long-wavelength nitride laser diodes (LDs) limits optical confinement, motivating new architectural approaches. Here, we report the first electrically driven edge-emitting LDs featuring top and bottom air-claddings. To enable the top air cladding, the architecture employs a tunnel junction, which converts the current flow from holes to electrons. This allows for low series resistance lateral current flow and placement of the metal contact on the side of the LD mesa. The bottom air-cladding is realized postepitaxy through lateral electrochemical etching (ECE) of a highly doped InGaN:Ge sacrificial layer. Depending on the geometry of the openings for electrolyte access, wing-like and membrane LD devices are obtained. Very high backside smoothness of the membrane has been achieved thanks to an abrupt doping profile and excellent selectivity in material removal by ECE. Synchrotron-based scanning X-ray diffraction microscopy shows that laser membranes exhibit slight elastic relaxation, which results in bending of the LDs by a few nanometers over a distance of a dozen microns. LDs with dual air-claddings operated in pulse mode at a wavelength of λ = 456 nm with a slope efficiency of 0.4 W/A, similar to their reference counterparts without under-etching. This architecture is expected to provide greater benefits of refractive index engineering for longer wavelength LDs, where high refractive index contrast is more challenging. Moreover, the work highlights ECE as an extremely effective method for device liftoff, enabling GaN substrate reuse and facilitating transfer and integration of LDs into advanced photonic platforms, including therapeutic applications.

Fabricating microelectronic devices for two-dimensional (2D) materials research is essential but often limited by the high cost and need for specialized facilities. This study establishes a practical method for cleaning and reusing substrates with pre-patterned electrodes. The cleaning protocol involves the use of an ultrasonic bath in warm N-methyl-2-pyrrolidone (NMP), enabling the removal of 2D materials without damaging the electrodes. Electrical measurements, Raman analysis, and Kelvin probe force microscopy measurements collectively confirm the feasibility of repeatedly reusing the same pre-patterned chip, showing that the cleaned regions exhibit no detectable Raman signatures of the transferred 2D material, retain a largely homogeneous surface-potential distribution, and preserve comparable electrical performance after reuse. By extending the lifetime of pre-patterned chips, this approach can reduce substrate consumption and lower the cost of 2D device prototyping.

Intelligent manufacturing demands accurate prediction of machining quality characteristics with conflicting behaviour, which is challenging with limited experimental data. Taguchi L27 experimental data sets were collected with 6 influencing variables (workpiece material: PA66, PA66 + GF30, PA66 + MoS₂, tool approach angle, tool nose radius, cutting speed, feed rate and depth of cut) and 8 machining quality characteristics (surface roughness, cutting force, temperature, amplitude of vibration, tool wear rate, specific cutting energy, material removal rate, and sound pressure level). The total 27 experimental datasets were stratified by material into a 3-fold cross-validation protocol. The present work develops five base learners such as Gaussian Process Regression (GPR), Least Squares Boosting (LSB), Support Vector Regression (SVR), Random Forest (RF) and extreme gradient boosting (XGBoost) for predictions of machining quality characteristics. All three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and crayfish optimization algorithm (COA)) determine identical weights for three best predictive base learners (GPR, SVR, and LSB) for developing Ensemble model. The COA converge to a minimum composite cost with comparatively lesser computation time than GA and PSO. Therefore, CrayStack ensemble model is constructed with a hybrid combination of GPR, SVR, and LSB and COA methods. The COA efficiently optimizes the adaptive fusion weights by assigning a higher weight fraction to GPR and LSB for nonlinear models. CrayStack Ensemble predictions outperforms all individual learners (SVR, GPR, LSB, XGBoost, and RF) with material stratified three-fold cross validation across all eight outputs of training data. CrayStack Ensemble requires a total training cost of 16.13s (which includes base model training: 94.92% & 15.31s, COA weight optimization: 1.88% & 0.30s, and bootstrap confidence interval estimation: 3.20% & 0.52s) ensuring its practical usefulness. CrayStack Ensemble achieves near-instantaneous inference (0.003 ms/sample; 290,592 samples/s) with a speed of 58.33, 79.33, 4899.33, 5630.67, 5608.3 over GPR, SVR, LSB, RF and XGBoost ensuring practicality suitable for real-time monitoring systems. Wilcoxon single-rank test confirmed that improvements are statistically significant (with a preset confidence level, p < 0.05) for 7 of the 8 responses, validating the practical utility of the developed models. CrayStack Ensemble showed superior prediction performances against nine randomly generated test cases with a mean absolute percent error of 9.8%, followed by GPR, LSB, XGBoost, SVR, and RF of 12.69%, 13.81%, 15.39%, 21.52% and 42.73% considering all responses. The results demonstrated that the intelligent ensemble stack ensures robustness and higher prediction accuracy for limited experimental datasets offering a practical solution for industrial process optimization.

Alumina abrasives are well-known for their high material removal rates in chemical mechanical polishing (CMP), but poor suspension stability remains a key limitation to their performance. This study aims to identify effective dispersants to enhance the dispersion stability of alumina suspensions, which are crucial for CMP in semiconductor manufacturing. A systematic investigation is conducted to examine the effects of silica sol and three organic dispersants, namely, cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and polyvinylpyrrolidone (PVP), on the dispersion behavior of alumina suspensions. The results demonstrate that silica sol displays much better dispersion performance than the organic dispersants. The dispersion stability of alumina suspensions increases with SiO2 content in silica sol up to 10 wt %, where uniform particle distribution, a zeta potential of -39.1 mV, and optimal dispersion were achieved before declining at higher contents. Further analysis shows that silica sol stabilizes alumina particles through electrostatic and chemical interactions, forming a dense, stable adsorption layer on their surface. The resulting electrostatic repulsion effectively prevents agglomeration, thereby ensuring a stable alumina suspension. These results show that silica sol stabilizes alumina suspensions through chemical bonding and electrostatic repulsion, enabling uniform, long-term dispersion for high-performance CMP applications.