Number Of Layers Research Articles

Self-healing superhydrophobic coating with durability based on EP + PDMS/SiO2 double-layer structure design

Superhydrophobic coatings often face challenges related to poor friction and corrosion resistance. In this study, composite coatings were developed, featuring a top layer of SiO2 nanoparticles and a bottom layer of epoxy resin (EP) and polydimethylsiloxane (PDMS). As the number of SiO2 nanoparticles layers increased, surface roughness escalated, and the coating structure transitioned from SiO2 nanoparticles entirely encapsulated by EP and PDMS to a small fraction of SiO2 exposed on the coating surface. Leveraging the rough surface and low-free-energy properties endowed by Si-(CH3)2 and Si-O-Si hydrophobic groups, coatings comprising 3–5 layers SiO2 nanoparticles exhibited superhydrophobicity and self-cleaning capabilities. These coatings retained their superhydrophobic properties even after enduring heavy friction from sandpaper, and demonstrated robust corrosion resistance over extended periods. Increasing the number of SiO2 layers not only enhanced superhydrophobicity, corrosion and friction resistance, but also improved the coating-substrate adhesion. The designed coating structure exhibited superior corrosion resistance and coating-substrate adhesion compared to those of EP/PDMS + SiO2 double-layer and EP + PDMS + SiO2 mixed coatings. Moreover, the coatings displayed a self-healing ability, regaining superhydrophobicity following O2 or Ar etching, facilitated by the upward movement of hydrophobic groups/bonds and intensity recovery. Coatings subjected to Ar etching exhibited better self-healing properties due to higher Si-O-Si and lower C-C/C-H bond intensities. This research presents novel insights for the practical application of superhydrophobic coatings.

Prediction of instantaneous flow characteristics of hydrocyclone with long short-term memory network based on computational fluid dynamics data

Accelerating the prediction time of separation performance and flow field characteristics in industrial hydrocyclones holds paramount importance for real-time control. Machine learning methods exhibit significant advantages in this particular aspect. This study presents a network model that integrates a long short-term memory (LSTM) layer with a fully connected layer to accurately forecast the flow field and separation performance under various operational conditions, leveraging CFD data. The study evaluates the effect of factors such as the number of LSTM layers and neurons per layer on the model's performance, aiming to identify the optimal network structure. The results demonstrate that the predicted flow field and performance of the hydrocyclones using LSTM closely align with the outcomes from CFD simulations. The average absolute percentage error is constrained to within 6%, which significantly enhances the prediction efficiency. The findings contribute to a more efficient and automated separation process in industrial environments.

CFRP-steel composite beams with seawater sea sand concrete cores subjected to bending

A redesign was attempted for the concrete-filled double-skin tube subjected to bending, and by adjusting the core tube position and applying carbon fiber-reinforced polymer (CFRP) sheets or steel bars in the tensile region, it was hoped to delay the cracking of the concrete in the tensile region. Epoxy mortar was used to modify the interface. Seawater and sea sand has been successfully applied from reinforced concrete beams to concrete-filled steel tube beams, benefiting from the corrosion-resistant properties of CFRP and epoxy mortar. The four-point bending test parameters included the type of core tube, the number of CFRP layers, and the diameter of the steel bars. The beams with FRP sheets exhibited better flexural capacity and residual stiffness, while the brittle fracture of FRP led to major crack in concrete. The beams with steel bars exhibited better initial stiffness and the cracks in concrete were more uniform in width. When the layers of CFRP or the diameter of the steel bars was increased, the flexural capacity of the composite beams increased by 11.0%-13.6% and 7.0%-9.1%, respectively. The enhancements provided by the core tubes were small, and the design of the composite beams should be considered from various perspectives. The moment-curvature relationships, ultimate states, and flexural stiffness were defined by considering concrete cracking, FRP fracturing, and steel buckling. Several formulas for calculating flexural stiffness in the code were evaluated in combination with the test data.

Study on Axial Compression Performance of Corroded Reinforced Concrete Columns Strengthened by Concrete Canvas and Carbon Fiber Reinforced Plastic under Secondary Corrosion

To investigate the effects of concrete canvas (CC) and carbon fiber reinforced plastic (CFRP) reinforcement on the mechanical properties of corroded reinforced concrete columns (compressive strength, flexure strength, strength of extension, and so on), 42 columns in four groups were designed and axial compression experiments were carried out. For the corroded reinforced concrete columns reinforced with CC and CFRP, the effects of initial corrosion rate (5%, 10%), secondary corrosion time (15 d, 30 d), number of CC layers (0, 1), and number of CFRP layers (1, 2, 3) on the failure morphology, load carrying capacity, and ductility of concrete columns were analyzed. The test results show that the properties of the single layer CC confined specimens are improved to a certain extent, and the ductility properties are enhanced. The properties of the CC–CFRP composite constrained specimens are greatly improved, the plastic deformation ability is enhanced, and the typical ductile damage characteristics are shown. The corrosion inhibition of CC for specimens with a theoretical corrosion rate lower than 20% showed an increasing trend, and the corrosion inhibition rate ranged from 23.0% to 31.2%. CC and CFRP restrain the concrete jointly, hindering the expansion inside the concrete, and the peak strain of the joint restraint specimen itself changes greatly, while the overall peak strain of the corrosion specimen is very small under the action of the joint steel bar. Finally, according to the existing peak stress–strain model and the experimental data in this paper, a peak stress–strain model suitable for corroded reinforced concrete columns is established. The established calculation model has a high accuracy, which provides a certain theoretical basis for subsequent research.

Исследование изменения геометрических параметров образцов, наплавленных методом GMAW при воздействии на электрическую дугу продольного магнитного поля

Introduction. The paper presents the results of research of additive manufacturing process by electric arc with axial feeding of steel filler wire in protective gas environment (GMAW technology) with additional influence of external longitudinal magnetic field on electric arc. Purpose of work: an experimental study of the effect of a longitudinal magnetic field during additive manufacturing by an electric arc with axial feed of filler wire made of structural steels in a protective gas environment on the change in the geometrical characteristics of the layers being surfaced. Research Methods. The manufacturing of specimens was carried out on a 5-axis additive machine based on a CNC machine. Surfacing was carried out in the following modes: voltage 17.5 V; current 55–65 A; wire diameter 1.2 mm; wire material Sv-08G2S; wire feed rate 2,267 mm/min; approximate roll diameter 3.0 mm; roll length 50 mm; number of wires per one roll 312.5 mm; number of layers when surfacing the wall 5; magnet operation mode: alternating current with frequency 50 Hz, voltage 30 V; measured magnetic induction 5.7 mTl; initial height of the magnet above the substrate 10 mm; electrode stickout 10 mm; shielding gas: welding mixture CO2-Ar; gas pressure (flow rate) 0.15 MPa. Results and discussion. The conducted experimental study showed that the effect of longitudinal magnetic field had a statistically significant effect on the change in the dimensions of the singular, namely an increase in the width of the layers being surfaced by 34.1 %, with a calculated significance index close to zero, and a decrease in height by 20.2 %, with a calculated significance index equal to 2.7105. The effect of longitudinal magnetic field had a statistically significant effect on the change of the overall dimensions of the specimens consisting of five layers, namely, the width of the specimens increased by 11.2 % with a calculated significance index of 4.3103, and the height of the specimens decreased by 10.3 % with a calculated significance index of 6.3105. The effect of longitudinal magnetic field had no statistically significant effect on the change of the vertical deviation from straightness for the side walls of the specimens, with a calculated significance index of 0.3277, and had no statistically significant effect on the change of the error of the width of the walls of the specimens, with a significance index of 0.098.

The Impact of Fiber Density and Layering in NFRP on Confined Concrete Compressive Strength

Strengthening columns holds a crucial role in structural engineering and is frequently called for due to a range of factors, including heightened load requirements, structural degradation, design flaws, or the need for seismic retrofitting. Natural Fiber-Reinforced Polymers (NFRP) in concrete reinforcement has gained significant attention in recent years as a sustainable and eco-friendly alternative to strengthened reinforced concrete. NFRP jacketing presents an adaptable option, as it delivers an improved load-carrying capability as a confining effect. This paper explores the fundamental reasons behind the need for column strengthening and the advantages of employing NFRP jacketing as a preferred method. The study examined the influence of varying fiber densities and the number of fiber layers in NFRP on the mechanical properties of concrete, with a specific focus on its confined concrete compressive strength. The test specimen was a cylinder with a diameter of 150mm and a height of 300mm. NFRP, made from abaca fiber and resin, was attached around the specimen’s circumference to provide a confinement effect. Axial load was applied to the test specimen. The findings indicated that introducing abaca fiber as an NFRP material increased confined concrete compressive strength by up to 37% compared to unconfined concrete. Moreover, the application of three layers of NFRP fibers results in a 15% rise in confined concrete stress, especially when higher-density fiber types are utilized. Study findings suggest that natural fiber density and the number of layers play a role in enhancing concrete strength, however, their influence may not be significantly pronounced.

Asymmetric metamaterial sandwich structure with NIM characteristics for THz imaging application

This study presented a unique, miniaturised asymmetric interconnected vertical stripe (IVS) design for terahertz (THz) frequency applications. Therefore, this research aimed to achieve a frequency response of 0 to 10 THz using a 5 × 5 µm2 Silicon (Si) substrate material. Meanwhile, various parametric examinations were conducted to investigate variations in the performance. For example, the unit cell selection process was carefully examined by using various design structures and modifying the structure by adding split gaps and connecting bars between vertical stripes. Furthermore, the proposed sandwich structure design was used to compute the absorbance and reflectance properties. All the analytical examinations were executed utilising the Computer Simulation Technology (CST) 2019 software. The introduced IVS metamaterial exhibits negative index behaviour and has a single resonance frequency of 5.23 THz with an acceptable magnitude of − 24.38 dB. Additionally, the quadruple-layer IVS structure exhibits optimised transmission coefficient behaviour between 3 and 6 THz and 7 to 9 THz, respectively. However, the magnitude of the transmission coefficient increased with the number of material layers. Besides that, the absorbance study shows that using a quadruple-layer structure obtains unique and promising results. Overall, the proposed asymmetric IVS metamaterial design achieves the required performance by using a compact structure rather than extending the dimensions of the design.

Biogenic carbonates (queras) in loess-palaeosol sequences of the Ebro Basin and their potential use as a palaeoenvironmental proxy

Secondary carbonate accumulations are very common in arid and semi-arid soils but are often poorly described and classified. This is the case for queras that are observed in several loess-palaeosol sequences of the Ebro Basin. Micromorphologically, queras are complex pedogenic features basically consisting of infillings of calcified root cells in channels, surrounded by decarbonated hypocoatings. They apparently result from biologically mediated calcification/decalcification processes at a microscale. The aim of this study is to systematically characterize biocalcifications found in dated loess-palaeosol sequences, to determine the main factors of formation, and to discuss their possible use as a palaeoenvironmental proxy. Different queras show a similar number of biosparite layers (four to five) around a central channel, and a decarbonated hypocoating around the queras. This supports the hypothesis that queras originate from the calcification of root tips and are related to the acidification of surrounding soil for nutrient absorption. Under cathodoluminescence microscopy, biosparite has a different behaviour than non-biological calcite crystals: the latter appears reddish while biosparite remains black. The queras, with ages corresponding to Marine Isotope Stages 1–3, are younger than that of the loess where they are found in. The stable isotope values likely correspond to C3 plants, and the estimated temperatures of formation reflect a Mediterranean temperate climate. This suggests that the biocalcifications developed during short moist seasons amidst arid periods of the Quaternary.

Enhancing the Thermal Conductivity of CNT/AlN/Silicone Rubber Composites by Using CNTs Directly Grown on AlN to Achieve a Reduced Filler Filling Ratio.

Achieving the thermal conductivity required for efficient heat management in semiconductors and other devices requires the integration of thermally conductive ceramic fillers at concentrations of 60 vol% or higher. However, an increased filler content often negatively affects the mechanical properties of the composite matrix, limiting its practical applicability. To address this issue, in this paper, we present a new strategy to reduce the required ceramic filler content: the use of a thermally conductive ceramic composite filler with carbon nanotubes (CNTs) grown on aluminum nitride (AlN). We combined catalyst coating technology with vacuum filtration to ensure that the catalyst was uniformly applied to micrometer-sized AlN particles, followed by the efficient and uniform synthesis of CNTs using a water-assisted process in a vertical furnace. By carefully controlling the number of vacuum filtration cycles and the growth time of the CNTs, we achieved precise control over the number and length of the CNT layers, thereby adjusting the properties of the composite to the intended specifications. When AlN/CNT hybrid fillers are incorporated into silicone rubber, while maintaining the mechanical properties of rubber, the thermal diffusivity achieved at reduced filler levels exceeds that of composites using AlN-only or simultaneous AlN and CNTs formulations. This demonstrates the critical influence of CNTs on AlN surfaces. Our study represents a significant advancement in the design of thermally conductive materials, with potential implications for a wide range of applications.

Three dimensional interface normal prediction for Volume-of-Fluid method using artificial neural network

In the numerical simulations of multi-phase flow using Volume-of-Fluid (VOF) method, the calculation of the interface normal is a crucial point. In this paper, a machine learning method is used to develop an artificial neural network (ANN) model to make more accurate prediction of the local normal vector from neighboring volume fractions. Spherical surfaces with different radii are intersected with a structural background grid to generate 84328 groups of data: 3×3×3 neighboring volume fractions are used as input, and normal vector as output. Using 90% data as training dataset, the ANN model is well trained by optimizing the number of hidden layers and the number of neurons on each layer. Using the remaining 10% data, normal predictions are made using ANN-VOF and the most used YOUNG and HEIGHT-FUNCTION methods. The RMSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.022/0.045 respectively. In the reconstruction of a sinusoidal surface, the MSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.018/0.041. It is demonstrated that the ANN-VOF method has better performance for interface normal prediction. The proposed method has a simple computational logic and does not need to deal with complex geometric topology, which lays the foundation for application in other more complex grids.

Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

Effect of Layers Number on The Bending Properties of Chestnut Glulam Beams

In recent years, advances in adhesive and lamination technologies have offered significant opportunities in the production of high-quality and valuable products from low-quality and non-durable cheap wood raw materials. Lamination generally refers to a multilayer material production method. The main goal of this production process is to develop and improve many properties of the created composite product, such as durability and stability. Laminated timber, called glulam, is a layered composite material formed by preparing timber fibers parallel to each other and gluing them together with the help of glue. In this study, the bending properties of solid, 3-layer and 5-layer glulam beams produced from chestnut tree species were investigated experimentally and numerically. The modulus of elasticity (MOE) of 5-layer glulam beams is 13.39% higher than 3-layer beams and 48.31% higher than solid beams. The modulus of rupture (MOR) value of the 5-layer beam is 24.21% higher than the 3-layer beam and 65.28% higher than the solid beam. There is a maximum difference of 2% between the experimental and numerical analysis results. When the results are compared, it is seen that the results are close to each other.

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

Digital workflow for printability checking and prefabrication in robotic construction 3D printing based on Artificial Intelligence planning

This paper introduces an innovative digital workflow for printability checking and prefabrication in robotic construction 3D printing based on Artificial Intelligence (AI) planning techniques. The proposed method generates operational sequences for a robotic manipulator tasked with concrete 3D printing by utilizing Planning Domain Definition Language (PDDL) 2.1-based AI planners. The planned sequences consider diverse geometry specifications, aligning with printability checks and prefabrication requisites. The sequences can then be executed in a robotic simulation environment, allowing operators to assess the 3D printing process in every aspect before real-world deployment, and potentially avoiding costly mistakes and the waste of resources. Compared to existing methods, the use of AI planning makes our approach more modular and easy to adapt across a variety of robotic platforms, diverse designs, materials, and printing conditions; and it is released as open-source software to the benefit of the community. The proposed method was tested by processing objects with multiple geometries and a varying number of printing layers, and by employing two different PDDL planners. Results prove the viability of the approach but also hint at possible scalability limitations for complex geometries that involve a large number of edges to be printed.

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud

“Surface‐Filming Assembly” Strategy for Facilely Constructing and Adjusting Circularly Polarized Luminescence from Perovskite/Chiral Helical Polyacetylene Films

AbstractTunable circularly polarized luminescence (CPL) has attracted substantial research interest. Till date, the main methods for adjusting CPL is by changing the ratio of fluorescent components and/or regulating external stimuli. Although these adjustment methods have been demonstrated to be reliable, most of them show disadvantages including challenging molecular design, complicated and cumbersome material fabrication, and rigorous adjustment conditions. Herein, the “Surface‐Filming Assembly” (SFA) strategy for conveniently and efficiently modulating CPL by taking the perovskite/chiral helical polyacetylene system as a model is proposed and established. This strategy can facilely achieve multicolor CPL by simply changing the color (or number) of perovskite fluorescent layers and/or the attaching order of the perovskite layers in the assembled multilayer film. Compared with previously reported methods, the SFA strategy integrates the preparation process and adjustment in one single step, simplifying the preparation process (especially the preparation of perovskite with different ratios of halogen atoms) and making adjustment simple and efficient. The SFA strategy and different CPL generation mechanisms to achieve multicolor and white‐color emission with glum up to 10−2 are combined. Based on the CPL film and the solvent sensitivity of perovskite, a solvent‐responsive CPL‐emitting switch and further applied the CPL sample in multiple information displays and encryption are realized.

Depth-based vessel position fixing by means of a neural network

A depth-based vessel position fixing method on the basis of a neural network is proposed. The network takes as an input a sequence of depth values measured by an echo-sounder and predicts vessel latitude and longitude for the moment of the latest depth measurement. The neural network has a fully-connected feedforward architecture with several layers which satisfies conditions of the universal approximation in compliance with the Stone-Weierstrass theorem. The Adamax algorithm for the neural network training with controlling a maximum value of position error at each epoch is implemented. Modeling is conducted with the Python programming language and the Tensorflow library. The model surface of seabed is performed as a second-order polynomial. Training samples on the basis of virtual soundings at the coordinate net knots with the space resolution not worse than one cable are obtained. After samples obtaining the training of the neural network is conducted. A validation set is not used. Several neural networks are trained. They have different number of hidden layers and different number of neurons per each hidden layer. After training the test procedure is performed. Test samples are generated in the assumption that a vessel is moving along meridians which are not used at the stage of the preliminary soundings survey. The cases of mean and random test meridians are considered. The random meridians are obtained with a uniform random number generator. As the result, all the tested neural networks have shown approximately identical navigational accuracy which is close to the accuracy for the training set.

Morphology analysis and process optimization of μ-SLA 3D manufactured micro-nano conic structure

PurposeThis paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.Design/methodology/approachThe µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.FindingsAfter the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.Originality/valueThis study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

Thylakoid ultrastructural variations in chlorophyll-deficient wheat: aberrations or structural acclimation?

Main conclusionA structural re-modeling of the thylakoid system, including granum size and regularity, occurs in chlorophyll-deficient wheat mutants affected by photosynthetic membrane over-reduction.In the chloroplast of land plants, the thylakoid system is defined by appressed grana stacks and unstacked stroma lamellae. This study focuses on the variations of the grana organization occurring in outdoor-grown wheat mutants characterized by low chlorophyll content and a tendency for photosynthetic membrane over-reduction. Triticum aestivum ANK-32A and Triticum durum ANDW-7B were compared to their corresponding WT lines, NS67 and LD222, respectively. Electron micrographs of chloroplasts were used to calculate grana ultrastructural parameters. Photosynthetic parameters were obtained by modulated chlorophyll fluorescence and applying Light Curves (LC) and Rapid Light Curves (RLC) protocols. For each photosynthetic parameter, the difference Δ(RLC–LC) was calculated to evaluate the flexible response to light in the examined lines. In the mutants, fewer and smaller disks formed grana stacks characterized by a marked increase in lateral and cross-sectional irregularity, both negatively correlated with the number of layers per granum. A relationship was found between membrane over-reduction and granum structural irregularity. The possible acclimative significance of a greater proportion of stroma-exposed grana domains in relieving the excess electron pressure on PSI is discussed.

Experimental Study on the Mechanical Properties of Nylon Fabric-Reinforced Elastomeric Isolators (N-FREIs)

In recent decades, carbon fiber, glass fiber, polyester and Kevlar fiber have been utilized to replace the steel shims in conventional steel-reinforced elastomeric isolators (SREIs). This study chose nylon fabrics owing to their extreme low cost, low elastic modulus and good adhesion to rubber, and nine nylon fabric-reinforced elastomeric isolators (N-FREIs) were manufactured with different design parameters. Compression and compression shear tests were, respectively, conducted to investigate the mechanical properties together with their influential factors of the N-FREIs. Results show that the vertical load-carrying capacity of the isolator is high enough to sustain a compressive stress of 10[Formula: see text]MPa without visible damage. The compressive stiffness of N-FREI is much smaller than that of SREI, and the vertical damping ratio under cyclic compression reaches up to 7%. In the compression shear tests, the shear stiffness of the isolator first decreases and then increases as the shear strain increases within 300%, and the equivalent damping ratio varies between 9% and 14% for different sizes of the isolator. Additionally, due to the flexibility and extensibility of the low modulus nylon fabric, both vertical and horizontal stiffness decrease a bit with an increase in the number of fabric layers. Finally, a formula for calculating the horizontal stiffness of N-FREI is proposed, it provides a comprehensive mathematical model to predict the behavior of the N-FREI under horizontal shear conditions.