Multilayer Layer Research Articles

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

The resolution of flow fields represents a significant factor influencing the accuracy of turbulent flow analysis. Nevertheless, the acquisition of high-resolution turbulence data remains a challenge due to the limitations imposed by computing resources. The interpolation method, while capable of achieving high-resolution turbulence at low cost, faces challenges in capturing details of turbulent flows. In this study, a local and global feature fusion network (LGFN) is designed for the reconstruction of high-resolution turbulent flows with high quality. First, dual parallel branches made of dense blocks are introduced into the LGFN to extract local features of turbulent flows. Moreover, the features after the first dense block of each branch are summarized into the self-attention block to obtain global features. The extracted local and global features are aggregated through learnable weight parameters to achieve feature fusion. Finally, the fused turbulence features are scaled to the same dimensional size as the high-resolution turbulence through the implementation of multilayer pixel shuffle layers and convolution layers. The effectiveness of the proposed network was evaluated using datasets of forced isotropic turbulence and channel turbulence. The results demonstrate that the reconstructed velocity fields of the LGFN exhibit the highest degree of similarity to the direct numerical simulation results, in comparison with bicubic interpolation, static convolutional neural network, and super-resolution dense connection network results. In addition, compared to alternative methods, the proposed network effectively captures the characteristics of isotropic or anisotropic turbulence even at larger scales.

Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.

Brain tumors are widely recognized as the primary cause of cancer-related mortality globally, necessitating precise detection to enhance patient survival rates. The early identification of brain tumor is presented with significant challenges in the healthcare domain, necessitating the implementation of precise and efficient diagnostic methodologies. The manual identification and analysis of extensive MRI data are presented as a challenging and laborious task, compounded by the importance of early tumor detection in reducing mortality rates. Prompt initiation of treatment hinges upon identifying the specific tumor type in patients, emphasizing the urgency for a dependable deep learning methodology for precise diagnosis. In this research, a hybrid model is presented which integrates the strengths of both transfer learning and the transformer encoder mechanism. After the performance evaluation of the efficacy of six pre-existing deep learning model, both individually and in combination, it was determined that an ensemble of three pretrained models achieved the highest accuracy. This ensemble, comprising DenseNet201, GoogleNet (InceptionV3), and InceptionResNetV2, is selected as the feature extraction framework for the transformer encoder network. The transformer encoder module integrates a Shifted Window-based Self-Attention mechanism, sequential Self-Attention, with a multilayer perceptron layer (MLP). These experiments were conducted on three publicly available research datasets for evaluation purposes. The Cheng dataset, BT-large-2c, and BT-large-4c dataset, each designed for various classification tasks with differences in sample number, planes, and contrast. The model gives consistent results on all three datasets and reaches an accuracy of 99.34%, 99.16%, and 98.62%, respectively, which are improved compared to other techniques.

Inter-temporal dynamic joint learning model considering intra- and inter-day mutable correlations for blood glucose level prediction

Accurate and low clinical risk blood glucose level (BGL) prediction plays an important role in blood glucose management of type 1 diabetes (T1D). Due to the diurnal variation in physiological regulation and the influence of external life events, blood glucose patterns change over time and events within a day. The fact that humans have an inherent biological cycle of approximately one day and an individual has similar responses to the same life events leading to day-to-day similarities in blood glucose trajectories. However, the daily schedules are not entirely same under free-living conditions so that similar glycemic patterns may occur at different periods on each day. Thus, the mutable intra-day and inter-day temporal correlations in multi-day BGL data challenge the BGL prediction. To this end, we propose an inter-temporal dynamic joint learning method, named as iTDJL. Firstly, considering the variability of the association between days in the free-living, we design a discovery strategy based on discrete cross-correlation to dynamically select the highly correlated series slices from previous days. Then, based on the multi-head self-attention mechanism, the intra-day and inter-day attention layers are developed and connected in sequence to capture the correlation relationships between different time spans that varies with input. Finally, the future multi-step predictions are obtained through a multi-layer perception layer. We evaluate the iTDJL model on the real-world OhioT1DM dataset. Compared to other advanced methods, the iTDJL produces more reliable BGL predictions without requiring meal announcement and other lifestyle information from the patients under free-living conditions. Our proposed method may promote the T1D management, such as clinical decision support assisting insulin injection and abnormal glucose warning.

Continuous Graded Catalyst Layers for PEM Fuel Cells with Improved Humidity Range Tolerance

Grading electrodes is a promising approach to reduce ohmic and mass transport-related voltage losses in proton exchange membrane fuel cells. In graded electrodes, the ionomer and/or catalyst are spatially non-uniformly distributed to optimize performance over a wide operating range. In this study, through-plane ionomer gradients were fabricated with a simple wet-layer deposition technique that can readily be adapted in a roll-to-roll process. The presence of ionomer gradients was verified using scanning transmission electron microscopy and the profiles were found to be continuous despite using only two coating steps. Single cell tests revealed that the gradients outperform conventional electrodes and maintain the optimal performance for different relative humidities. These improvements were traced back to enhanced mass transport and protonic conduction properties identified by detailed electrochemical analysis. This manufacturing approach for electrodes offers an accessible toolbox to produce versatile and specialized multi-layered catalyst layers for different applications.

Improvement in the oxidative stability of microencapsulated linseed oil using carob protein hydrolysates and multilayer emulsions

The microencapsulation of linseed oil in multilayer emulsions stabilized by carob protein hydrolysates was evaluated in this study. Linseed oil was emulsified in both multilayer and single layer interfacial emulsions using either carob protein concentrate or carob protein hydrolysate. The protein hydrolysate was able to increase the encapsulation efficiency by up to 12 % compared to non-hydrolyzed concentrated protein. Larger particles containing the hydrolysates (mean diameter ∼3 µm) were observed; however, the size distribution and microstructure were similar for all samples, regardless of the use of protein concentrate or protein hydrolysate, in single or multilayer emulsion systems. Physical aspects of the particles, such as porosity and glass transition temperature (Tg), were also similar, showing low porosity (<7.5 %) and high Tg (>80 °C). The antioxidant capacity of the protein hydrolysates, combined with the protective effect provided by the multilayer systems, enhanced the oxidative stability of the microencapsulated oil during processing and storage. The use of both strategies seems to provide an improved alternative for the microencapsulation of linseed oil, resulting in particles with superior physicochemical and oxidative stability.

Cartilage Tissue Engineering in Multilayer Tissue Regeneration.

The functional and structural integrity of the tissue/organ can be compromised in multilayer reconstructive applications involving cartilage tissue. Therefore, multilayer structures are needed for cartilage applications. In this review, we have examined multilayer scaffolds for use in the treatment of damage to organs such as the trachea, joint, nose, and ear, including the multilayer cartilage structure, but we have generally seen that they have potential applications in trachea and joint regeneration. In conclusion, when the existing studies are examined, the results are promising for the trachea and joint connections, but are still limited for the nasal and ear. It may have promising implications in the future in terms of reducing the invasiveness of existing grafting techniques used in the reconstruction of tissues with multilayered layers.

Effectiveness of activated carbon from rubber seed shell as adsorbent of liquid waste of heavy metal

Iron or Fe is classified as a metal that has low toxicity, but if the concentration exceeds the quality standard, it can potentially cause pollution to the environment. This study aims to make activated carbon from rubber seed coat shells as an adsorbent for artificial Fe liquid waste using Fe2(SO4)3. 05 grams of activated carbon was activated using a ratio of 2:1, 3:1, 4:1 (NaOH: charcoal), and then an FTIR test was carried out to see the characteristics of the activated carbon. Measurement of Fe concentration using UV-VIS spectrophotometry, with various concentrations of 1, 1.5, 2, 2.5, 3, 4, 6, and 10 mg/L. After that, an analysis of the effect of contact time with the addition of activated carbon as an adsorbent for heavy metal Fe liquid waste was carried out, using variations of 05, 10, 15, 20, 30, 45, 90, and 120 minutes. An analysis of the effect of Fe concentration, with various concentrations of 10, 20, 30, 40, and 50 mg/L. Obtained from the linear equation of the calibration curve y = 0.0679x - 0.0013 with a correlation coefficient of 0.9996. The results of the third FTIR test showed that the ratio of NaOH and carbon activators did not change significantly. The functional groups that appeared were O-H, C=O carboxylate, C-C-C, aromatic C-H, aromatic C=C, C-O, and Si-O. Activated carbon 2:1, 3:1, and 4:1 have an absorption percentage of 86.72%, 89.45% and 96.37%. The greatest absorption efficiency is at a ratio of 4:1, optimum at 90 minutes. Sample 2:1 has an adsorption capacity of 0.163 mg/g, sample 3:1 of 0.259 mg/g, and 4:1 of 1.340 mg/g. The greatest adsorption capacity of activated carbon is at a ratio of 4:1, adsorption occurs physically (Freundlich) with a multilayer layer.

Enhanced Photocatalytic Properties and Photoinduced Crystallization of TiO2-Fe2O3 Inverse Opals Fabricated by Atomic Layer Deposition.

The use of solar energy for photocatalysis holds great potential for sustainable pollution reduction. Titanium dioxide (TiO2) is a benchmark material, effective under ultraviolet light but limited in visible light utilization, restricting its application in solar-driven photocatalysis. Previous studies have shown that semiconductor heterojunctions and nanostructuring can broaden the TiO2's photocatalytic spectral range. Semiconductor heterojunctions are interfaces formed between two different semiconductor materials that can be engineered. Especially, type II heterojunctions facilitate charge separation, and they can be obtained by combining TiO2 with, for example, iron(III) oxide (Fe2O3). Nanostructuring in the form of 3D inverse opals (IOs) demonstrated increased TiO2 light absorption efficiency of the material, by tailoring light-matter interactions through their photonic crystal structure and specifically their photonic stopband, which can give rise to a slow photon effect. Such effect is hypothesized to enhance the generation of free charges. This work focuses on the above-described effects simultaneously, through the synthesis of TiO2-Fe2O3 IOs via multilayer atomic layer deposition (ALD) and the characterization of their photocatalytic activities. Our results reveal that the complete functionalization of TiO2 IOs with Fe2O3 increases the photocatalytic activity through the slow photon effect and semiconductor heterojunction formation. We systematically explore the influence of Fe2O3 thickness on photocatalytic performance, and a maximum photocatalytic rate constant of 1.38 ± 0.09 h-1 is observed for a 252 nm template TiO2-Fe2O3 bilayer IO consisting of 16 nm TiO2 and 2 nm Fe2O3. Further tailoring the performance by overcoating with additional TiO2 layers enhances photoinduced crystallization and tunes photocatalytic properties. These findings highlight the potential of TiO2-Fe2O3 IOs for efficient water pollutant removal and the importance of precise nanostructuring and heterojunction engineering in advancing photocatalytic technologies.

StochCA: A novel approach for exploiting pretrained models with cross-attention

Utilizing large-scale pretrained models is a well-known strategy to enhance performance on various target tasks. It is typically achieved through fine-tuning pretrained models on target tasks. However, naï ve fine-tuning may not fully leverage knowledge embedded in pretrained models. In this study, we introduce a novel fine-tuning method, called stochastic cross-attention (StochCA), specific to Transformer architectures. This method modifies the Transformer’s self-attention mechanism to selectively utilize knowledge from pretrained models during fine-tuning. Specifically, in each block, instead of self-attention, cross-attention is performed stochastically according to the predefined probability, where keys and values are extracted from the corresponding block of a pretrained model. By doing so, queries and channel-mixing multi-layer perceptron layers of a target model are fine-tuned to target tasks to learn how to effectively exploit rich representations of pretrained models. To verify the effectiveness of StochCA, extensive experiments are conducted on benchmarks in the areas of transfer learning and domain generalization, where the exploitation of pretrained models is critical. Our experimental results show the superiority of StochCA over state-of-the-art approaches in both areas. Furthermore, we demonstrate that StochCA is complementary to existing approaches, i.e., it can be combined with them to further improve performance. We release the code at https://github.com/daintlab/stochastic_cross_attention.

Water stress corrosion at wafer bonding interface during bond strength evaluation

Wafer-to-wafer bonding is becoming a critical unit process step with the increase in the demand for 3D integration. The widely used bonding method is plasma activated bonding, where it is essential to ensure sufficient bond strength to withstand the stresses generated during subsequent processes and achieve multi-layer stacking or layer transfer. Although the double cantilever beam method is widely used to evaluate wafer bond strength, significant variation occurs due to water stress corrosion at the bonding interface. These factors have not been adequately addressed, and precise measurement conditions have yet to be established. In this study, a semi-auto tool was introduced to conduct measurements under an inert atmosphere, whereby the influence of water stress corrosion was eliminated. Furthermore, the study elucidated the effects of the crystal orientation and blade insertion conditions. The impacts of interfacial voids and the annealing conditions on bond strength were revealed. Observation of the progress of delamination during measurements under an inert atmosphere enabled the effective estimation of the moisture content that remained inside the wafers, which then contributed to the selection of appropriate annealing conditions.

Bending Dielectric Elastomer Actuators

Dielectric Elastomer Actuators (DEAs) are a type of electroactive polymers that transform electric energy into mechanical work and are capable of large strains and high energy densities. DEAs are often described as “artificial muscles” because they exhibit strains, energy density, and responsiveness similar to natural human muscles. Their applications as soft robotic grippers, artificial arms, underwater robots, and aerial robots have previously been demonstrated. Single-layer DEAs have a simple design, with a dielectric layer sandwiched between compliant electrodes. However, by alternating positive and negative electrodes, multilayer DEAs can exhibit larger forces and strains than single-layer DEAs. DEAs are actuated by applying voltage and forming an electric field, generating a Maxwell stress between the electrodes. The process of fabricating a multilayer DEA consists of spin coating and curing layers of elastomer, stamping carbon nanotubes (CNT), and making connections to the electrodes. By alternating positive and negative electrodes, multilayer DEAs are constructed, which can exhibit larger forces and strains than single-layer DEAs. When electrically activated, DEAs expand laterally; however, by fabricating a bimorph DEA actuator, we demonstrate bending DEAs. A bimorph DEA consists of a multilayer DEA layer attached to a thicker inactive elastomer layer. When the bimorph is actuated, only the DEA layer expands, causing the actuator to bend. We study the relationship between the bending and stiffness of the inactive layer. By changing the shape of the DEA to a triangle or wing-like shape, we also emulate the high-frequency flapping of birds. Work begun on characterizing the voltage-controlled deflection of bimorph DEAs and understanding the mechanics of the bending of a simple beam in terms of forces at Harvard University will be continued on the Navajo Reservation at Navajo Technical University.

Oxidation and wear behaviors of MoNbTaW high entropy alloy fabricated via laser energy deposition at elevated temperature

The microstructure, mechanical properties at room temperature, oxidation, and wear behaviors of the MoNbTaW refractory high-entropy alloy (RHEA) at elevated temperatures are investigated. The as-deposited MoNbTaW RHEA maintains a single BCC structure and a dendritic morphology. Furthermore, it demonstrates a high yield stress of 1478 MPa and an ultimate yield stress of 1787 MPa at room temperature. The oxidation tests reveal that the oxidation rate constant initially decreases from 0.83 mg2 cm−4 h−1 to 0.78 mg2 cm−4 h−1, then increases to 12.43 mg2 cm−4 h−1 as the temperature rises from 500 °C to 800 °C. Additionally, a linear oxidation kinetic is observed at 500 and 600 °C, while at 800 °C, the parabolic oxidation kinetics is observed. The transition is linked to the development of a multilayer oxide layer structure and the formation of a thicker oxide scale. The results of wear tests at high-temperature show that the wear depth at 600 °C is about 24.886 μm, which is 3.9 times as much as 500 °C (6.437 μm). This indicates that the wear resistance is reduced at 600 °C. In addition, with the increase in temperature, the wear mechanism changes from adhesive wear to abrasive wear.

Fault diagnosis of induction motor in the cooling water supply system using a multi-channel data fusion transformer with limited sample conditions*

Abstract Induction motors play a vital role in the cooling water supply system of hydropower facilities. However, it is not feasible to collect sufficient fault samples in a hydropower station. The scarcity of labeled samples poses a challenge in developing powerful diagnostic models with high classification accuracy. To address this challenge, this paper proposes a multi-channel data fusion strategy based on a transformer for feature enhancement. Initially, the original signals are transferred into non-overlapping single-channel data patches to preserve correlation features across different channels. Next, temporal and spatial attention modules are applied to process the data patches, which can learn and fuse temporal and spatial information, respectively. Subsequently, the data patches are embedded to retain position information and represent fault-related features through class embedding, which are further processed by a transformer encoder with self-attention mechanisms. Finally, the classification task is achieved by using a multilayer perceptron layer connected to the class embedding. While dealing with limited training samples, the proposed method can learn robust features that are beneficial to improve the fault diagnosis ability of induction motors. The comparison of the proposed method with three basic models and two improved methods demonstrates the superiority of the proposed method in accuracy and feature clustering performance under limited sample conditions. In addition, ablation experiments demonstrate the necessity of each module in the proposed method.

Microstructure and properties of Zircaloy-4 alloy under low-temperature diffusion bonding with a Ni-plated surface

Zircaloy-4, which is used as a structural material for nuclear cladding, requires precise bonding at low temperature in nuclear power applications. We achieved this using diffusion bonding with Ni plating on the surface of Zircaloy-4. We evaluated the performance of joints at temperatures between 650 and 780 °C. At 650 °C, the joint was successfully bonded. The joint exhibited a maximum shear strength of 207 MPa at 780 °C, which was twice as high as that produced by direct diffusion bonding at 700 °C under the same parameters. The interface was analyzed by using a combination of SEM and TEM to identify the presence of multilayered intermetallic compound layers in the joints. We investigated the mechanism of interface generation and determined the sequence of phase formation by Gibbs free energy calculations to be NiZr, Ni10Zr7, NiZr2, Ni7Zr2, and Ni3Zr. Through X-ray diffraction and cross-section elemental analysis of the fractures, we observed that, at lower temperatures, the joint fracture tended to occur in the Ni layer, whereas at higher temperatures, it occurred between Ni7Zr2 and Ni3Zr. The utilization of diffusion welding with Ni plating applied to the surface of Zircaloy-4 reduces the joining temperature and enhances the properties of the joint.

Interfacial reaction mechanism between Y-containing DD5 alloy and Al2O3 ceramic during directional solidification process

This study systematically investigated the influence of varying yttrium(Y) additions on the interfacial reaction mechanism between the DD5 superalloy melt and Al2O3-based ceramic molds during directional solidification. The analysis revealed the formation of a complex multilayer reaction layer at the alloy-ceramic interface. The interfacial products changed from CaO·6Al2O3 to a mixture of various yttrium oxides after Y was added. As the reaction time increased, the sequence of interfacial reaction products observed on the surface of Y-containing alloys progressed as follows: Y2O3, Y4Al2O9, YAlO3, and ultimately Y3Al5O12. Increasing the Y additions promoted the formation of a reaction layer with a higher atomic percentage of Y. The interfacial reaction process between the DD5 superalloy and Al2O3-based ceramic molds involved ceramic decomposition and diffusion, Y element diffusion and displacement reactions, Al2O3 dissolution and further diffusion of the Al element. This process resulted in the formation of different yttrium-aluminum oxides and related to the bidirectional erosion of the Y2O3. These interfacial reactions significantly contributed to the consumption of Y element within the alloy.

Microstructure and properties of multi-layer laser cladding Cu-10Pb-10Sn anti-friction coating

The preliminary experimental results show that the laser cladding technology can produce a well-formed single-layer lead bronze cladding layer with a thickness of 250 μm. The ideal process parameters of the single-layer laser cladding test were selected to study the multi-layer cladding, and the microstructure and properties of the multi-layer cladding were evaluated. The results show that, the multi-layer cladding is composed of α-Cu, Cu41Sn11, Cu5.6 Sn, Pb, and SnO2 phases. There are fine equiaxed crystals at the top of the cladding layer while columnar crystals growing perpendicular to the fusion line at the bottom. The maximum hardness of cladding layer is 248.2 HV near the bottom fusion line. The wear mechanism of multi-layer cladding layer combines abrasive wear and adhesive wear, with wear weight loss and average friction coefficient of 0.0025 g and 0.1161, respectively. The average shear strength between the substrate and the cladding layer is 129 MPa, the shear fracture mode is ductile fracture. Compared with single-layer cladding, the anti-friction performance of multi-layer cladding is significantly improved.

Ultrasmooth Ti/Al multilayer with a Ti seed layer for EUV applications

Al-base multilayers have attracted much interest in the extreme ultraviolet (EUV) optics field, but high roughness of this multilayer due to the Al film is still a big concern. Here, a strategy of the seed layer was proposed to reduce the surface roughness and intermixing layer thickness of the Al-base multilayer. Ti film is not only a seed layer, but also an absorption layer in this novel multilayer. An optimized Ti/Al multilayer film structure was designed to work at 21.1 nm, while investigating the use of Ti as a seed layer to reduce the roughness and enhance the peak reflectivity. The experimental results showed that the Ti seed layer effectively reduced the surface roughness and intermixing layer thickness and improved the reflectance. At 21.1 nm, the peak reflectance reached 39.6%, with a bandwidth of only 1.0 nm and an RMS roughness of 0.17 nm. Ti/Al multilayer also exhibits good stability. This multilayer has potential application in high-precision optics, such as corona detection, which requires extreme low light scattering of multilayer mirror.

Research on Improved Crystallization Properties and Underlying Mechanism of the Sb2Te3 Phase-Change Thin Film by Inserting Sn15Sb85 Layers.

As a non-volatile semiconductor memory technology, phase-change memory has a wide range of application prospects as a result of the excellent comprehensive performance. In this paper, multilayer thin films based on Sb2Te3 material were designed and prepared by inserting the Sn15Sb85 layer. The thermal and electrical properties of superlattice-like Sb2Te3/Sn15Sb85 phase-change films can be adjusted by controlling the thickness ratio of Sb2Te3/Sn15Sb85. In comparison to the monolayer Sb2Te3 film, the multilayer layer Sb2Te3/Sn15Sb85 materials have a higher crystallization temperature, larger crystallization activation energy, and longer data lifetime, indicating the great improvement of thermal stability. The changes in the phase structure and vibrational modes of multilayer Sb2Te3/Sn15Sb85 films were characterized by X-ray diffraction and Raman spectroscopy, respectively. The presence of Sn15Sb85 layers restrains grain growth and refines the grain size. The multilayer Sb2Te3/Sn15Sb85 films exhibit better surface flatness, smaller surface potential undulation, and lower thickness variations than single-layer Sb2Te3. Phase-change memory cells based on the [Sb2Te3 (1 nm)/Sn15Sb85 (9 nm)]5 thin film has a lower threshold voltage (1.9 V) and threshold current (3.1 μA) compared to the Ge2Sb2Te5 film. Meanwhile, the electrical heating model of a phase-change memory cell based on a [Sb2Te3 (1 nm)/Sn15Sb85 (9 nm)]5 multilayer structure was established by multiphysics coupling analysis, proving the great improvement in heat transfer performance and efficiency. The experimental and theoretical studies confirm that the insertion of the Sn15Sb85 layer can significantly improve the crystallization properties of Sb2Te3 films, paving the way for optimizing the phase-change materials by regulating the microstructural parameters.

Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies.

Carrageenan (CG) and ion exchange resins (IERs) are better metal chelators. Kappa (κ) CG and IERs were synthesized and subjected to copper ion (Cu2+) adsorption to obtain DMSCH/κ-Cu, DC20H/κ-Cu, and IRP69H/κ-Cu nanocomposites (NCs). The NCs were studied using statistical physics formalism (SPF) at 315-375 K and a multilayer perceptron with five input nodes. The percentage of Cu2+ uptake efficiency was used as an outcome variable. Via the grand canonical ensemble, SPF gives models for both monolayer and multilayer sorption layers. For in vitro anticoagulant activity (ACA), the activated partial thromboplastin time were calculated using 100 μL of rabbit plasma incubated at 37 °C. After 2 min, 100 L of 0.025 M CaCl2 was added, and the clotting time was recorded for each group (n = 6). The results demonstrated that the key covariables for the adsorption process were pH and concentration. The results of artificial neural network models were comparable with the experimental findings. The error rates varied between 4.3 and 1.0%. The prediction analysis results ranged from 43.6 to 89.2. The ΔG and ΔS values for IRP69H/κ-Cu obtained were -18.91 and -16.32 and 26.21 and 22.74 kJ/mol for the temperatures 315 and 345 K, respectively. Adsorbate species were perpendicular to the adsorbent surfaces, notwithstanding the apparent importance of macro- and micropore volumes. These adsorbents typically fluctuate with temperature changes and contain one or more layers of sorption. Negative and positive sorption energies correspond to endothermic and exothermic processes. The biosorption energy (E1 and E2) values in this experiment have a value of less than 23 kJ mol-1. Complex SPF models' energy distributions validate surface properties and interactions with adsorbates. At a concentration of 100 μg/mL, DC20H/κ-Cu2+ exhibited an ACA of only 8 s. These NCs demonstrated better greater ACA with the order DC20H/κ < DMSCH/κ < IRP69H/κ. More research is needed to rule out the chemical processes behind the ACA of CG/IER-Cu NCs.

Martensite transformation characteristics in the heat affected zone of laser cladding on rail

This paper studied the macroscopic morphology and properties of the heat affected zone (HAZ) in multi-layer multi-track cladding layers on rail. The results showed that in the two-layer laser cladding, there are martensitic structures and cracks in the HAZ. With the increase of the number of laser cladding layers, the tempering structure in the HAZ expands gradually from the bottom to the top, from the middle to the edge, and no cracks appear in the HAZ. At ten-layer laser cladding, the martensitic structure has been completely eliminated and transformed into tempered sorbite. Finally, the hardness test shows that the hardness of cladding layer, HAZ and substrate are basically the same, which meets the performance requirements of rail repair.