Consecutive Layers Research Articles

Bonding Interaction Within Concentric Structural Layers in Gold Superatoms. The Concentric Bond.

Ligand-protected gold clusters display a rich structural diversity, featuring remarkable structures such as Au25(SR)18, Au55(PPh3)12Cl6, and CuAu144(SR)603+, involving a central core composed of consecutive layers. The respective Au@Au12, Au@Au12@Au42, and Cu@Au12@Au42@Au60cores with concentric structural layers enable a variable bonding/antibonding character between the electronic shells ascribed to each layer. Here, we rationalize the bonding within concentric structural layers in order to gain a further understanding of the related bonding patterns in such species. The proposed bonding concept differs from the classical situation in adjacent atoms, now being considered between concentric shells and, thus, coined as the concentric bond. From this approach, the bonding/antibonding character of each concentric shell is evaluated, and its contribution to the overall bonding is discussed. The concentric bond enables building a clear picture of the bonding acting in the overall cluster under the superatom concept. Such an approach expands the understanding of multi-layered cluster cores and is useful to further rationalize the bonding situation in metallic nanostructures.

Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring

Laser powder bed fusion of metals is increasingly used for fabricating complex parts requiring good mechanical properties. Simultaneously, researchers in the field are intensifying the efforts to reduce defects, such as internal porosities, which hinder a wider industrial adoption of this technology, urging process monitoring to a pivotal role in defect identification and mitigation. Therefore, understanding the correlation between in-process monitoring signals and post-process actual defects is fundamental to taking informed decisions and potential corrective actions during the process.This work focuses on developing models to predict spatter-related defects from specific process signatures detected through off-axis long-exposure imaging. Layer-wise images were properly aligned with corresponding cross-sections from tomographic reconstructions to investigate the relationship between spatter-related signatures and actual defects measured by X-ray computed tomography. This relationship was used as a knowledge basis to develop an analytical image-processing approach and a machine learning-based methodology, which were then compared in terms of their correlation performances. The advantages and limitations of both methods are discussed in the paper.Both approaches led to promising results in the prediction of lack-of-fusion defects caused by spatters, with the machine learning approach showing a prediction accuracy in the order of 90% for defects with equivalent diameter above 90µm, while the analytical model needed equivalent diameters larger than 130µm to reach a prediction accuracy in the order of 80%. Furthermore, the machine learning method led to strong results regarding early defect detection, with most of the investigated defects properly predicted by analysing two consecutive layers after the signature detection.

Chemically Reversible Translational Moiré Superlattice Formations in the Two-Dimensional Films of the Zinc Phthalate Complex.

The exciting electronic properties of two-dimensional layered materials introduced by twist angle and lattice mismatch between two consecutive layers are well-recognized. The major challenge lies in the control over the moiré periodicity. Herein, we report a chemically directed way to achieve the desired precision over moiré periodicity of the assembly of a complex of zinc ions and o-phthalic acid utilizing soft bonds. The reaction of o-phthalic acid with zinc ions in the aqueous medium at 80 °C for 1 h followed by preserving the reaction mixture at ambient temperature (35 °C) resulted in translational moiré superlattices with precise periods. Those lattices are further stacked angularly to give rotational super-moiré lattices. Further, the role of the π-zinc ion interaction in the origin of moiré fringes was examined with the reaction with 8-hydroxyquinoline-5-sulfonic acid (HQS) that also established the reversible nature of the superlattices. The present observations also suggest the formation of moiré superlattice through ion insertion in a chemical lattice.

Fractal Self-Similarity in Semantic Convergence: Gradient of Embedding Similarity across Transformer Layers

This paper presents a mathematical analysis of semantic convergence in transformer-based language models, drawing inspiration from the concept of fractal self-similarity. We introduce and prove a novel theorem characterizing the gradient of embedding similarity across layers. Specifically, we establish that there exists a monotonically increasing function that provides a lower bound on the rate at which the average cosine similarity between token embeddings at consecutive layers and the final layer increases. This establishes a fundamental property: semantic alignment of token representations consistently increases through the network, exhibiting a pattern of progressive refinement, analogous to fractal self-similarity. The key challenge addressed is the quantification and generalization of semantic convergence across diverse model architectures and input contexts. To validate our findings, we conduct experiments on BERT and DistilBERT models, analyzing embedding similarities for diverse input types. While our experiments are limited to these models, we empirically demonstrate consistent semantic convergence within these architectures. Quantitatively, we find that the average rates of semantic convergence are approximately 0.0826 for BERT and 0.1855 for DistilBERT. We observe that the rate of convergence varies based on token frequency and model depth, with rare words showing slightly higher similarities (differences of approximately 0.0167 for BERT and 0.0120 for DistilBERT). This work advances our understanding of transformer models’ internal mechanisms and provides a mathematical framework for comparing and optimizing model architectures.

DATA DRIVEN AND CELL SPECIFIC DETERMINATION OF NUCLEI-ASSOCIATED ACTIN STRUCTURE

In the specialized field of cellular mechanosignaling, the intricate relationship between the cytoskeleton and nucleus is crucial for determining the behavior and fate of mesenchymal stem cells (MSCs). Existing analytical methods for filamentous actin fibers (F-actin) suffer from issues of accuracy and reproducibility. To address this, our research introduces a significant advancement in image analysis by utilizing two deep-learning segmentation Convolutional Neural Networks (CNNs) based on U-Net architecture. This algorithm precisely quantifies F-actin and offers reproducible data from 3D confocal microscopy images. Our CNNs automatically extract the 3D shape of both the nucleus and individual actin fibers, thereby enabling the geometric reconstruction of these critical cellular components. We applied this methodology to MSCs exposed to low-intensity vibration, and hypothesized that mechanical stimuli will induce perinuclear actin remodeling. Confocal Z-stack images of MSCs were captured using a 40x oil lens and a Zeiss LSM 900 microscope. Nucleus and F-actin structures were stained with Hoechst 33342 and Phalloidin, respectively. Nucleus-based images were employed to mask and isolate actin fibers within the nuclear vicinity. A specialized deep-learning segmentation model was engineered within the PyTorch framework. For model training, a selective 2% of nuclei and F-actin images were annotated. Specifically, three layers from each of six representative nuclei, among approximately 500 cross-sectional layers, were meticulously selected; a similar approach was employed for actin. The model underwent 200 epochs of training. Post-training, the deep-learning model was employed to process all collected cross-sectional images of F-actin and nuclei. A subsequent analytical step involved the scrutiny of intersecting segmented fibers between consecutive layers for geometrically accurate actin fiber reconstruction. We analyzed 102 control and 110 vibration-treated mesenchymal stem cells (MSCs) by utilizing our deep-learning-based reconstruction algorithm on confocal Z-stack images. Our data revealed an 8% decrease in nuclear height from 5.73 µm to 5.28 µm (p < 0.001) upon application of low-intensity vibration. In contrast, the two groups observed no significant difference in nuclear volume. Accompanying these changes at the nuclear level, the following changes in the F-actin cytoskeleton were observed upon LIV application. The total number of actin filaments per cell increased from 117 to 150. The average volume of individual actin fibers rose from 0.40 µm³ to 0.43 µm³. Notably, the volume of fibers at the apical nuclear surface increased from 0.35 µm³ to 0.46 µm³. While the average length of actin fibers in the apical nuclear surface remained largely unchanged due to the addition of smaller fibers in LIV groups, the LIV group had a larger number of fibers that were larger than 15µm.

Fabrication of a 3D Printer Filamnet Extruder

3D printing, an additive manufacturing process, transforms digital designs into physical objects by layering material. This mechanization adds consecutive layers to build up the complete entity, resulting in a 3D object. It develops objects progressively to formulate the preferred look. Filaments are the main component in a die cast model (FDM) 3D printer, these are thermoplastic substances that are injected through a hot die to form the object. These filaments are available in different types such as PLA, ABS, PETG, HDPE each of which has specific characteristics that offer different applications and material options in 3D printing. This paper depicts about fabricating and developing a design of a 3D filament extruder that can produce 1.75 mm diameter filaments from recycled HDPE material. The extruder consists of a motor, a speed controller, a cylinder, a hopper, band heaters, a thermocouple, a temperature controller, a fan, a nozzle and a winder. This system works in the correct temperature range of 350-370 degrees Celsius for melting thermoplastic materials (recycled HDPE). Extruder function is to feed the material from the nozzle into the hot cylinder. A motor screw pushes the metal into the nozzle, where it becomes the filament. This filament is then injected onto a substrate for later use in 3D printing. The temperature is determined using thermocouples and a temperature controller, which ensures optimal extraction conditions. A fan is designed to quickly cool the removed filament. The objective of this research is to create an economical as well as efficient filament to produce high quality filaments. Extruder performance is evaluated based on filament diameter consistency, material penetration and energy efficiency.

Thin-Layer TiO2 Membrane Fabrication by Condensed Layer Deposition.

A novel approach to the fabrication of thin-film supported metal oxide membranes was investigated. Nanocoatings were obtained by the condensed layer deposition of TiO2 on tubular microporous supports, applying multiple consecutive layers of TiO2/polyaniline. The surface, cross-sectional structure, and morphology of the materials were investigated by electron microscopy. Their membrane-related properties were explored by permeability measurements, rejection, and fouling analysis, using polyethylene glycol (PEG) as test molecules. The SEM images showed that TiO2 was successfully deposited on the surface, creating a layer with partial coverage of the support after each layer was deposited; consequently, the permeability of the membranes decreased gradually. Overall, the results of the flux and permeability of the membranes confirmed the coating. The transmembrane pressure (TMP) increased with each coating layer, while the rejection of the membrane showed gradual improvement.

Molecular-Resolution Imaging of Ionic Liquid/Alkali Halide Interfaces with Varied Surface Charge Densities via Atomic Force Microscopy.

When in contact with charged solid surfaces, ionic liquids (ILs) are known to form solvation structures consisting of alternating cation and anion layers. This phenomenon is considered to originate from the adsorption layer of counterions overcompensating the surface charge, so-called overscreening. However, the response of these layers to surfaces with near-zero or extremely high surface charge density (σ) remains inadequately understood. Here, we probe the solvation structure of ILs on alkali halide surfaces with varied surface orientations: nearly zero-charged RbI(100) and highly charged RbI(111), by employing frequency modulation atomic force microscopy with atomic resolution. Two commonly used ILs are examined in this study: 1-methyl-1-propylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([C3mpyr][NTf2]) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([C2mim][NTf2]). On RbI(100) surfaces with near zero σ, we observe alternating cation and anion layers, diverging from the previously proposed monolayer model for IL/alkali halide(100) interfaces. These results support the argument that overscreening occurs under low σ, even approaching zero, and reconcile conflicting experimental conclusions about low σ systems. On RbI(111) surfaces with high σ, we identify solvation structures consisting of two consecutive counterion layers. This structure aligns with the theoretically predicted crowding; a phenomenon rarely observed in commonly used ILs due to typically unreachable σ in electrochemical IL/electrode systems. Our findings indicate that alkali halide(111) surfaces are potentially valuable for exploring the crowding phenomenon in ILs, addressing the current scarcity of experimental observations.

Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification.

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

Impact of carbon dots on the structural, morphological, magnetic, and electrochemical performance of Zn ferrite for energy storage applications

Magnetic core-shell Zn ferrite and carbon dots (C-dots) composite have been synthesized in this work using a simple coprecipitation and hydrothermal process, respectively. X-ray diffraction (XRD) patterns demonstrate an inverse spinel structure of Zn ferrites; the lattice parameter has decreased from 8.19 Å to 8.10 Å with C-dots. Scanning electron microscopy (SEM) images reveal rods-like structures for C-dots, and in the composite case, it is uniformly dispersed using low contrast consecutive C-dot layers to produce a core-shell structure. Zn ferrites are non-uniform in size, whereas C-dots and their composites are in uniform shape according to SEM images. The energy dispersive X-ray (EDX) study indicates that the substitution has been successful. For Zn ferrites, and composite, the Fourier transform infrared (FTIR) spectra shows that the absorption band of the tetrahedral site at 990 cm−1, and 870 cm−1, respectively. Different bonds in the infrared spectrum match specific function groups and bonding in various chemicals. The vibrating sample magnetometry (VSM) results indicates that the composite displays ferrimagnetic behavior. The specific capacity for composite has been found to be 2351 and 1790 F/g using cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) measurements, respectively. Higher electrochemical performance of the prepared composites may be helpful for energy storage systems.

An Optimal Radiation Shield Design of a Small Modular Nuclear Power Reactor by Genetic Algorithm

One of the critical concerns in developing small modular nuclear reactors (SMRs) is employing efficient radiation shielding consistent with the design and implementation requirements. Besides observing the radiation shielding requirements, the design process entails developing compact, lightweight shielding as well as ensuring the safety of the staff and radiation-sensitive equipment around the reactor under different operating conditions. Conventional methods of radiation shielding design are based on experiments conducted. Therefore, the resultant design involves trial and error, rarely achieving an optimal, effective, and efficient design. In this study, a multi-objective method based on the genetic algorithm coupled with the MNCP calculation code has been used to enhance the radiation shielding system of a SMR. Using this method, the thickness of different shielding layers has been optimized to minimize the total dose (neutrons and gamma) at the output, the weight, and the overall volume of the shielding. To assess and compare the method used to an implemented conventional method, a sample design of the MRX nuclear power reactor is considered. Using the optimization technique, the radiation shielding of the reactor has been redesigned and compared with the original design. This paper discusses the existing deficiencies in the initial design of the primary shield of the reactor as a design implemented based on conventional methods. Similar to the original design, the main materials considered for radiation shielding in consecutive layers were water and steel, and the final layer contained lead. The thickness of the different layers and their arrangements have been optimized to assess the upgraded method. The calculations indicate that the overall thickness of the proposed shielding is suggested as 93.8 cm compared to 140 cm to obtain the total dose of neutrons and gammas, which is suggested as less than 10 µSv/h. The results imply a 38.56% reduction in volume and a 17.24% reduction in weight of the radiation shielding compared to the reference reactor design.

The codriven assembly of molecular metalla-links (613, 623) and metalla-knots (41, 31) via coordination and noncovalent interactions

Although mechanically interlocked molecules (MIMs) display unique properties and functions associated with their intricate connectivity, limited assembly strategies are available for their synthesis. Herein, we presented a synergistic assembly strategy based on coordination and noncovalent interactions (π-π stacking and CH⋯π interactions) to selectively synthesize molecular closed three-link chains ([Formula: see text] links), highly entangled figure-eight knots ([Formula: see text] knots), trefoil knot ([Formula: see text] knot), and Borromean ring ([Formula: see text] link). [Formula: see text] links can be created by the strategic assembly of nonlinear multicurved ligands incorporating a furan or phenyl group with the long binuclear half-sandwich organometallic Cp*RhIII (Cp* = η5-pentamethylcyclopentadienyl) clip. However, utilizing much shorter binuclear Cp*RhIII units for union with the 2,6-naphthyl-containing ligand led to a [Formula: see text] knot because of the increased π-π stacking interactions between four consecutive stacked layers and CH⋯π interactions. Weakening such π-π stacking interactions resulted in a [Formula: see text] knot. The universality of this synergistic assembly strategy for building [Formula: see text] knots was verified by utilizing a 1,5-naphthyl-containing ligand. Quantitative conversion between the [Formula: see text] knot and the simple macrocycle species was accomplished by adjusting the concentrations monitored by NMR spectroscopy and electrospray ionization mass spectrometry (ESI-MS). Furthermore, increasing the stiff π-conjugated area of the binuclear unit afforded molecular Borromean ring, and this topology is a topological isomer of the [Formula: see text] link. These artificial metalla-links and metalla-knots were confirmed by single-crystal X-ray diffraction, NMR and ESI-MS. The results offer a potent strategy for building higher-order MIMs and emphasize the critical role that noncovalent interactions play in creating sophisticated topologies.

Design and fabrication of layered TiO2/rGO composited photoanode and its photovoltaic performance regulation for dye-sensitized solar cells

To improve the photovoltaic performance of the traditional TiO2 photoanode for dye-sensitized solar cells (DSSCs), a series of layered TiO2/rGO composited DSSCs photoanode was assembled by changing the morphologies of the TiO2 and introducing reduced graphene oxide (rGO) into different areas of the photoanode films. Firstly, TiO2 nanoflowers (TiO2-NFs), TiO2 nanorods (TiO2-NRs) were prepared employing the hydrothermal method and used as photoanode materials to explore the synergistic effect of TiO2 with different morphologies in layered photoanode. Then, TiO2-NFs/rGO composited photoanode (TNF/rGO) were fabricated to research the mechanism of rGO on improving electron transmission in the layered photoanode. The effect of adding rGO at different parts of the layered photoanode on the performance of DSSCs was also investigated. Attributing to the enhancement of light absorption and charge transfer, a champion power conversion efficiency (η) of 9.21 % (Jsc = 23.36 mA cm−2, Voc = 0.74 V, FF = 0.53) was achieved with the layered TiO2/rGO composited photoanode containing two consecutive layers of TiO2-NFs/rGO and P25/rGO, which exhibited an 82.01 % increase than traditional TiO2 (pure P25) photoanode (η = 5.06 %, Jsc = 12.09 mA cm−2, Voc = 0.73 V, FF = 0.58). It is an effective way to design and fabrication of layered TiO2/rGO composited photoanode to regulate the photovoltaic performance of traditional TiO2 photoanode for DSSCs based on the synergistic effect of its multi-layer structure, TiO2 morphologies and introduction of rGO.

Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory

<span lang="EN-US">The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.</span>

Dapagliflozin added to metformin reduces perirenal fat layer in type 2 diabetic patients with obesity

Sodium-glucose co-transporters type 2 inhibitors (SLGT2i) are highly effective in controlling type 2 diabetes, but reported beneficial cardiovascular effects suggest broader actions on insulin resistance. Weight loss may be initially explained by glycosuria-induced net caloric output and secondary volumetric reduction, but its maintenance could be due to loss of visceral fat mass. Structured ultrasound (US) imaging of abdominal adipose tissue (“eco-obesity”) is a recently described methodology used to measure 5 consecutive layers of abdominal fat, not assessable by DEXA or CT scan: superficial subcutaneous (SS), deep subcutaneous (DS), preperitoneal (PP), omental (Om) and right perirenal (RK). PP, Om and RK are predictors of metabolic syndrome (MS) with defined cut-off points. To assess the effect of SLGT2i on every fat depot we enrolled 29 patients with type 2 Diabetes (HbA1c 6.5–9%) and Obesity (IMC > 30 kg/m2) in an open-label, randomized, phase IV trial (EudraCT: 2019-000979-16): the Omendapa trial. Diabetes was diagnosed < 12 months before randomization and all patients were treatment naïve. 14 patients were treated with metformin alone (cohort A) and 15 were treated with metformin + dapaglifozin (cohort B). Anthropometric measures and laboratory tests for glucose, lipid profile, insulin, HOMA, leptin, ultrasensitive-CRP and microalbuminuria (MAL) were done at baseline, 3rd and 6th months. At 6th month, weight loss was −5.5 ± 5.2 kg (5.7% from initial weight) in cohort A and −8.4 ± 4.4 kg (8.6%) in cohort B. Abdominal circumference showed a −2.7 ± 3.1 cm and −5.4 ± 2.5 cm reduction, respectively (p = 0.011). Both Metformin alone (−19.4 ± 20.1 mm; −21.7%) or combined with Dapaglifozin (−20.5 ± 19.4 mm; −21.8%) induced significant Om fat reduction. 13.3% of cohort A patients and 21.4% of cohort’s B reached Om thickness below the cut-off for MS criteria. RK fat loss was significantly greater in cohort B group compared to cohort A, at both kidneys. Only in the Met + Dapa group, we observed correlations between Om fat with leptin/CRP/MAL and RK fat with HOMA-IR. US is a useful clinical tool to assess ectopic fat depots. Both Metformin and Dapaglifozin induce fat loss in layers involved with MS but combined treatment is particularly effective in perirenal fat layer reduction. Perirenal fat should be considered as a potential target for cardiovascular dapaglifozin beneficial effects.

Nature-Inspired Helicoidal Nanocellulose-Based Multi-Compartment Assemblies with Tunable Chiroptical Properties.

Cellulose-based nanocomposites are highly appealing for the development of next-generation sustainable functional materials. Although many advances have been made in this direction, the true potential of fibrillar nanocomposites has yet to be realized because available fabrication approaches are inadequate for achieving precise structural control at the sub-micrometer scale. Here a spray-assisted alignment methodology of cellulose nanofibrils is combined with the layer-by-layer assembly into an additive manufacturing process in which the alignment direction of each cellulose layer is rationally selected to achieve thin films with a helicoidal arrangement of the nanofibrils. The helicoidal structure of the films is verified by measuring the circular dichroism (CD) of the samples. The sign and position of the structural CD peak show that the handedness and the pitch of the chiral structures can be easily tuned by deliberately selecting simple parameters, such as the number of consecutive cellulose layers sprayed in the same direction, and the angle of rotation between successive stacks of layers. To the authors' knowledge, this approach is unique as it offers the possibility to prepare complex nanocomposite architectures with various nanoscale-controlled sub-structures from different anisometric objects, which is enabling novel designs of composite films with damage-resistant and/or optical filtering functionalities.

Fabrication and Characterization of Functionally Graded Nanocomposites: Impact of Graphene and Vanadium Carbide on Aluminum Matrix

Abstract Functional graded nanocomposites (FGNCs) based on Al are artificially tailored heterogeneous materials intended to serve the demand for diverse and contradicting properties used in various industrial applications. FGNCs and hybrid FGNCs (HFGNCs) based on Al reinforced with graphene and vanadium carbide (VC) were prepared using powder metallurgy techniques and investigated. Both samples were designed with a gradient composition, where the bottom layer consisted of 100% pure Al, followed by three consecutive layers containing progressively increasing amounts of reinforcement. The incorporation of graphene and VC into layer powders resulted in a decrease in both particle and crystal dimensions compared to pure Al. Adding graphene has a negative effect on bulk density samples, while VC has a positive effect. Reinforcing materials led to a decrease in thermal conductivity that reached 26.7 % for samples reinforced with VC reinforcement, except for FGNCs reinforced with graphene, which increased by ~3.3 compared to Al. The samples’ CTE and electrical conductivity values decreased, although adding graphene alone led to a slight decrease in electrical conductivity. A significant improvement in all mechanical properties was noted with additional. The HFGCNs reinforced with the largest amount of hybrid reinforcement recorded an improvement in CTE value, Young's modulus, and compressive strength by about 38.1%, 22.2%, and 20.5%, respectively, compared to Al

In-situ construction of functional carbon layer on the plasma electrolytic oxidation-pretreated Ti-6Al-4V alloy to enhance anti-static properties

Thermal control coatings prepared by plasma electrolytic oxidation (PEO) have shown broad application potential, especially in the aerospace field. However, the high insulation nature results in charge accumulation during the real application in spacecraft, aggravating the risk of electrostatic discharges and endangering equipment's safety. Herein, a functional anti-static thermal control composite coating on the surface of Ti-6Al-4V alloys was constructed by preparation route involving in-situ polymerization and carbonization of polydopamine (PDA) on the PEO layer. After the introduction of carbonized PDA, the composite coating retained the pristine porous structure of the PEO coating with improved crystallinity. Notably, the consecutive functional carbon layer ingrates the graphite nanocrystals and N-doped graphite to achieve high conductivity and then renders the composite coating with the lowest volume resistivity (6.04 × 104 Ω m) and surface resistance (9.0 × 107 Ω/□). Moreover, the two-step prepared coating showed high absorption (200–2500 nm, 0.929) and emission (2–16.6 μm, 0.921) along with excellent stability at high temperature (∼200 ℃) and low temperature (∼−200 ℃) even over 48 h. This contribution reveals a promising direction to widen the practical application of the PEO thermal control coating.

High-Performance Defect Detection Methods for Real-Time Monitoring of Ceramic Additive Manufacturing Process Based on Small-Scale Datasets

Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnormal points on training results and playing a crucial role in stabilizing the training process, which is suitable for training on small-scale datasets. Comparative experiments are implemented and the results show that using a Resnet50 backbone for feature extraction and a HED network for the differential Siamese network module yields the best detection performance, with an obtained F1 score of 0.89. Additionally, as a single-stage defect object detector, the model achieves a detection frame rate of 54.01 frames per second, which meets the real-time detection requirements. By monitoring the recoating process in real-time, the manufacturing fluency of industrial equipment can be effectively enhanced, contributing to the improvement of the yield of ceramic additive manufacturing products.

Evaluation of the influence of ultrasonic vibration on physical, tensile, and morphological properties of fused deposition modeled specimens

The use of fused deposition modeling (FDM) in printing polymers for various applications has been ever increasing. However, its utilization in printing polymers for high-strength and superior surface finish applications is still a challenge, primarily due to process intrinsic defects, i.e., voids between the layers and the rough exterior arising from unrestrained deposition of molten polymer. This research hypothesizes that application of ultrasonic vibration (USV) post-fabrication could minimize these shortcomings. For this investigation, ASTM D638 Type IV samples were FDM-printed using poly(lactic) acid (PLA). Through screening experiments, an optimized set of ultrasonic parameters was determined. Then, the effect of both-sided ultrasonic application was characterized. Subsequently, the impact of USV on the samples’ physical, tensile, and morphological properties was examined by varying the layer height, infill patterns, and % infill density. Up to 70% roughness reduction was observed as a result of post-FDM ultrasonic application. Additionally, the tensile strength of the samples increased by up to 15.31%. Moreover, for some lower % infill samples, post-ultrasonic tensile strengths were higher than 100% infill control samples. Analysis of scanning electron microscopy (SEM) and X-ray computed tomography (CT) imagery indicated enhanced layer consolidation and reduced void presence in samples treated with ultrasonic. The combination of ultrasonic-generated heat and downward pressure promoted a synergistic squeeze flow and intermolecular diffusion across consecutive layers of polymers. As a result, increased tensile strength and surface finish were achieved while dimensional change was marginal.