Multilayer Design Research Articles

Water vapor transport properties of bio-based multilayer materials determined by original and complementary methods

To enhance PLA gas barrier properties, multilayer designs with highly polar barrier layers, such as nanocelluloses, have shown promising results. However, the properties of these polar layers change with humidity. As a result, we investigated water transport phenomena in PLA films coated with nanometric layers of chitosan and nanocelluloses, utilizing a combination of techniques including dynamic vapor sorption (DVS) and long-term water vapor adsorption–diffusion experiments (back-face measurements) to understand the influence of each layer on the behavior of multilayer films. Surprisingly, nanometric coatings impacted PLA water vapor transport. Chitosan/nanocelluloses layers, representing less than 1 wt.% of the multilayer film, increased the water vapor uptake of the film by 14.6%. The nanometric chitosan coating appeared to have localized effects on PLA structure. Moreover, nanocelluloses coatings displayed varying impacts on sample properties depending on their interactions (hydrogen, ionic bonds) with chitosan. The negatively charged CNF TEMPO coating formed a dense network that demonstrated higher resistance to water sorption and diffusion compared to CNF and CNC coatings. This work also highlights the limitations of conventional water vapor permeability measurements, especially when dealing with materials containing ultrathin nanocelluloses layers. It shows the necessity of considering the synergistic effects between layers to accurately evaluate the transport properties.

Ion imprinted polymers integrated into a multi-functional microfluidic paper-based analytical device for trace cadmium detection in water.

A novel multi-functional microfluidic paper-based analytical device (μPAD) integrated with ion imprinted polymers (IIPs) was proposed for specific, portable and low-cost detection of cadmium (Cd(II)) in water. The IIP was grafted on paper and integrated into the μPAD for separation of Cd(II) through multi-layer design. The paper-based screen printed carbon electrode (pSPCE) modified with reduced graphene oxide was fabricated and combined with the μPAD for electrochemical sensing of the separated Cd(II). Reduced graphene oxide (rGO) was prepared via electroreduction on the working electrode surface of the pSPCE (rGO/pSPCE), which provided a sensitization effect with an improved signal for Cd(II) detection. The μPAD developed with the integrated IIP and combined with rGO/pSPCE is able to detect Cd(II) with a linear range from 1 ng ml-1 to 100 ng ml-1 and a detection limit of 0.05 ng ml-1. The accuracy of this μPAD was evaluated with spiked real water samples and compared with that of the inductively coupled plasma mass spectrometry (ICP-MS) method, from which the recovery values ranged from 96.5% to 114.2% with RSDs <10% between the two methods. This μPAD demonstrated its advantages of low-cost, portability, and suitability for highly sensitive detection of Cd(II), making it a valuable tool for on-site analysis.

A Neuro Phenotypic Evolution Algorithm for Recognizing Human Motion Type

Living in the modern world requires having a precise, intelligent system that may be suggested to do various activities. Due to the scalability of AI algorithms, we have proposed a phenotypic Evolutionary Algorithm (EA)-based system to assist the Artificial Neural Network algorithm (ANN) in the learning process. Combining the two strategies can result in a smart neuro-evolutionary model that is effective in accomplishing significant duties in various domains. The suggested multi-layer neural algorithm's design creates the conditions for learning via the EA’s processes of crossover, mutation, and selection. To aid in the selection and crossover processes, the learning process phase breaks up the original ANN into multiple ANNs according to the number of hidden layers. ANNs are ranked from worst to best in the selection phase based on the soring function that is applied to the fitness list. The fitness list retains each ANN’s accuracy even after breaking apart the original ANN. The crossover procedure is then applied between the two worst and best ANNs. Mutation provides a means of improvement for the less effective ANNs. Following completion of these processes, the ANN algorithms are combined to create a single ANN algorithm. The Vicon mobile robot (SCITOS G5) system’s multi-dimensional data, which extracted both aggressive and typical human movement, as well as Human Activity Recognition (HAR) datasets extracted by smartphones, both have been applied using the suggested method. The system achieved a high performance and efficiency rate on the intended recognition problem

Excellent mechanical and electromagnetic interference shielding properties of polylactic acid/polycaprolactone/multiwalled carbon nanotube composites enabled by a multilayer structure design.

In this work, a special multilayer structure consisting of polylactic acid (PLA) and a co-continuous PLA/polycaprolactone (PCL)/multiwalled carbon nanotube (MWCNT) (ALM) composite with a double-percolated conductive network was fabricated via layer-assembly coextrusion. It was revealed that PLA domains located at the layer interface could serve as rivets properly linking adjacent layers. Such a nacre-like structure with alternately stacked rigid PLA and flexible ALM increased the fracture strain to 354.4%, nearly quadruple that of the PLA/PCL/MWCNT conventional blending composite with the same composition, while maintaining an excellent strength above 46.0 MPa. In addition, the multilayer composites showed a special frequency-selective electromagnetic interference (EMI) shielding performance, with tunable shielding peak positions controlled by the layer number. Their maximum EMI shielding effectiveness almost contributed by absorption loss could reach 49.8 dB, which originated from two aspects: one was the high electrical conductivity offered by the double-percolated distribution of MWCNTs, and the other was the multiple wave attenuation effect that occurred at the interfaces between PLA and ALM layers and the blend interfaces in ALM layers. This effort paves a new way for developing composites with outstanding mechanical and EMI shielding properties that can be extended to other polymeric composite systems.

A microfluidic system for the cultivation of cyanobacteria with precise light intensity and CO2 control: enabling growth data acquisition at single-cell resolution.

Quantification of cell growth is central to any study of photoautotrophic microorganisms. However, cellular self-shading and limited CO2 control in conventional photobioreactors lead to heterogeneous conditions that obscure distinct correlations between the environment and cellular physiology. Here we present a microfluidic cultivation platform that enables precise analysis of cyanobacterial growth with spatio-temporal resolution. Since cyanobacteria are cultivated in monolayers, cellular self-shading does not occur, allowing homogeneous illumination and precise knowledge of the photon-flux density at single-cell resolution. A single chip contains multiple channels, each connected to several hundred growth chambers. In combination with an externally applied light gradient, this setup enables high-throughput multi-parameter analysis in short time. In addition, the multilayered microfluidic design allows continuous perfusion of defined gas mixtures. Transversal CO2 diffusion across the intermediate polydimethylsiloxane membrane results in homogeneous CO2 supply, with a unique exchange-surface to cultivation-volume ratio. Three cyanobacterial model strains were examined under various, static and dynamic environmental conditions. Phase-contrast and chlorophyll fluorescence images were recorded by automated time-lapse microscopy. Deep-learning trained cell segmentation was used to efficiently analyse large image stacks, thereby generating statistically reliable data. Cell division was highly synchronized, and growth was robust under continuous illumination but stopped rapidly upon initiating dark phases. CO2-Limitation, often a limiting factor in photobioreactors, was only observed when the device was operated under reduced CO2 between 50 and 0 ppm. Here we provide comprehensive and precise data on cyanobacterial growth at single-cell resolution, accessible for further growth studies and modeling.

A Wideband Circularly Polarized Filtering Antenna Based on Multi-Layer Structural Design

A Wideband Circularly Polarized Filtering Antenna Based on Multi-Layer Structural Design

DEEPSIDE A DEEP LEARNING FRAMEWORK FOR DRUG SIDE EFFECT PREDICTION

Drug failures brought on by unanticipated side effects during clinical trials put participants' health at risk and result in significant financial losses. Algorithms for predicting side effects may direct the development of new drugs. The LINCS L1000 dataset establishes a knowledge foundation for context-specific characteristics and offers a wealth of cell line gene expression data that has been altered by various medications. The state-of-the-art method, which seeks to use context-specific information, discards a significant number of the trials and only uses the high-quality experiments in LINCS L1000. Our aim in this research is to maximize the prediction performance by fully using this data. Five deep learning architectures are used in our experiments. We discover that when drug chemical structure (CS) and the whole collection of drug altered gene expression profiles (GEX) are employed as modalities, a multi-modal architecture yields the greatest prediction performance among multi-layer perceptron-based designs. We find that overall, the CS provides more information than the GEX. The best results are obtained by a convolutional neural network-based model that just employs the SMILES string representation of the medications; this model outperforms the state-of-the-art by 3:1% micro-AUC and 13:0% macro-AUC. Additionally, we demonstrate that the model can forecast drug-side effect couples that are absent from the ground truth side effect dataset but are described in the literature.

Efficient inverse design of optical multilayer nano-thin films using neural network principles: backpropagation and gradient descent.

Optical multilayer thin films have a wide range of applications due to their ability to manipulate transmissive or reflective wavelengths by adjusting the thickness of composed layers, enabling diverse uses. Although their light weight, flexible nature and ease of fabrication position them as promising components for future devices, determining their optimal layer thickness for the desired functionality demands extensive simulations, leading to inefficient utilization of computational resources and time. To overcome these challenges, inverse design methods, leveraging machine learning and deep learning, are being explored. However, these methods necessitate learning processes, despite the presence of well-established formulas that elucidate these phenomena. Furthermore, deriving accurate answers for conditions not included in the learning process proves to be challenging. This paper introduces an innovative inverse design approach that utilizes the backpropagation of a networked transfer matrix, effectively explaining the characteristics of optical multilayer thin films. By exploiting the chain rule of the network, this method calculates gradients to discern how each layer thickness influences the outcomes. Consequently, the optimal thickness is determined without the need for an additional learning process. Mathematical elucidation of the operational principle of this approach is precisely described. Optimization of computing resource utilization through network configuration reduces the calculation time compared to conventional methods. The efficacy of this method is demonstrated through its application in the inverse design of transmissive and reflective films, verifying its potential for enhancing efficiency and accuracy in optical multilayer thin-film design and manufacturing processes.

Technical and Economic Aspects of the Choice of Enclosing Wall Structures with a Similar Heat Flow for Low-rise Housing Construction

This article is devoted to the analysis of multilayer enclosing structures used for the construction of exterior walls of low-rise buildings based on a comparison of thermal and economic indicators. For the calculations in this article, the most relevant and popular multilayer wall designs are selected. The object of the study is the structural solutions of multilayer wall enclosing structures in low-rise housing construction, which are used in the city of Krasnoyarsk. The subject of the study is: the influence of the composition of multilayer wall enclosing structures with a similar heat flow on the estimated cost of their construction for the city of Krasnoyarsk and the estimated cost of 1 square meter of each wall structure is determined. At the moment, the construction of low-rise residential buildings is gaining popularity and is supported by state programs both at the federal level and at the levels of the subjects of the Russian Federation and municipalities. Low-rise housing construction is considered as one of the possibilities of solving the housing problem or improving housing conditions for the population. Multilayer enclosing structures with exterior facade decoration have been widely used in low-rise and multi-storey construction. As part of the study, we considered the most common types of multilayer structures and exterior wall facade finishing options, performed thermal engineering and cost estimates to determine the most profitable option for a multilayer wall structure in low-rise housing construction.

High-performance biosensors based on angular plasmonic of a multilayer design: new materials for enhancing sensitivity of one-dimensional designs.

In this study, a theoretical examination is conducted to investigate the biosensing capabilities of different surface plasmon resonance (SPR) based hybrid multilayer structures, which are composed of two-dimensional (2D) materials. The transfer matrix formulation is implemented to calibrate the results of this study. A He-Ne laser of wavelength = 632.8 nm is used to simulate the results. Many permutations and combinations of layers of silver (Ag), aluminum oxynitride (AlON), and 2D materials were utilized to obtain the optimized structure. Ten dielectrics and twelve 2D materials were tested for a highly sensitive multilayer hybrid sensing design, which is composed of the prism (Ohara S-FPL53)/Ag/AlON/WS2/AlON/sensing medium. The optimized biosensing design is capable of sensing and detecting analytes whose refractive variation is limited between 1.33 and 1.34. The maximum sensitivity, which is achieved by using the proposed design is 488.2° per RIU. Additionally, the quality factor, figure of merit, detection limit, and qualification limit values of the optimized design were also calculated to obtain a true picture of the sensing capabilities. The designing approach based on the multilayer hybrid SPR biosensors has the potential to develop various plasmonic biosensors that are related to food, chemical, and biomedical engineering fields.

Multi-Factor Approach on How to Enhance the System Stability of CO2 Electrochemical Conversion to C2+ Products from Hours to a Month

Renewable electricity-powered electrochemical conversion of CO2 into fuels and value-added chemicals has immense potential to defossilize related sectors and provide an alternative storage solution for intermittent renewable energy supply. Studies show that low-temperature CO2 electrochemical conversion can be economically feasible if operated efficiently, particularly when the final price of electrolysis products and market size are attractive [1-2]. Cu is the only catalyst material that can reduce CO2 electrochemically into C2+ products (ethylene, acetate, n-propanol, propionate, propionaldehyde, and allyl alcohol) in significant amounts. Higher current densities (>200 mA/cm2) for the electrochemical reduction reaction of CO2 (CO2RR) on Cu-based gas diffusion electrodes (GDE) are already achieved; however, researchers still report a wide range of challenges, poor reaction stability being arguably the most critical one. To realize its full potential for large-scale industrial implementation, research efforts in both industry and academia focus primarily on selective catalysts, novel electrode designs, and cell design improvements, leaving a research gap for a multi-aspect approach centering on long-term stability.In our work, based on our own research and state-of-the-art knowledge, we focused on three main factors which play significant roles in the stability of Cu-based CO2RR: Cu surface restructuring, GDE flooding and liquid product accumulation in the electrolyte. These factors were addressed by taking various countermeasures, which mainly included changes in the electrode layer design, material selection in the electrode and cell, and optimization of numerous operation conditions.First, the Cu surface restructuring factor was considered in the multi-layered GDE design. Carbon-containing layers were used on top of the catalyst layer to stabilize otherwise agglomerating Cu catalysts as the literature [3-4] hypothesized. Secondly, the GDE flooding factor was counteracted with multiple measures: 1) PTFE material was chosen as the gas diffusion layer due to its electrical insulator feature as well as its electrochemical inertness. 2) GDE layers were prepared with polymers binders consisting of hydrophobic backbones with hydrophilic functional groups, controlling the water uptake in the layers. 3) Coexistence of potassium, carbonate, and bicarbonate ions in the catalyst and gas diffusion layer was inhibited by the layer design in which the binders had specific permeance for cations or anions (i.e., ion exchange ionomers), undermining the potassium carbonate and potassium bicarbonate salt formation inside the catalyst and gas diffusion layers. 4) Temperature was optimized to prevent the salt formation in the gas chamber of the cell. Third, liquid product accumulation in the electrolyte was determined to be a destabilizing factor for the GDE and the membrane separator as a result of a prior experimental study [5]. Therefore, the liquid products of CO2RR were prevented from accumulating in the electrolyte by periodical electrolyte refurbishment.After implementing all the identified countermeasures in Siemens Energy test rigs, we conducted a series of experiments focusing only on the system's stability. Internally designed 10 cm2 flow-cells, which had comparable cell architecture to the cell upscaled to 5000 cm2 scale in previous work [6], were employed combined with Cu-based GDEs as cathode and Ir-based anodes in this study. We have achieved 720 hours (one month) of stable CO2RR at 100 mA/cm2 at around 20% faradaic efficiencies for the aimed product, C2H4. Faradaic efficiency for all C2+ products was up to 50%. Increasing current densities from 100 mA/cm2 to 300 mA/cm2 compromised the stability while enhancing the C2+ selectivity. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101006701.

Co‐Fired Multilayer Thermoelectric Generators Based on Textured Calcium Cobaltite

AbstractThermoelectric generators are very attractive devices for waste heat energy harvesting as they transform a temperature difference into electrical power. However, commercially available generators show poor power density and limited operation temperatures. Research focuses on high‐temperature materials and innovative generator designs. Finding the optimal design for a given material system is challenging. Here, a theoretical framework is provided that allows appropriate generator design selection based on the particular material properties. For high‐temperature thermoelectric oxides, it can be clearly deduced that unileg multilayer generators have the highest potential for effective energy harvesting. Based on these considerations, prototype unileg multilayer generators from the currently best thermoelectric oxide Ca3Co4O9 are manufactured for the first time by industrially established ceramic multilayer technology. These generators exhibit a power density of 2.2 mW cm−2 at a temperature difference of 260 K, matching simulated values and confirming the suitability of the technology. Further design improvements increase the power density by a factor of 22 to facilitate practicable power output at temperature differences as low as 7 K. This work demonstrates that reasonable energy harvesting at elevated temperatures is possible with oxide materials and appropriate multilayer design.

Multi-layered cladding based ultra-low loss, single mode antiresonant hollow core fibers

In reality, an efficient platform for high-power laser delivery is highly important, which can be justified by readily available fiber lasers, and hollow-core fiber can be the best platform for guiding high optical power over long distances. The constraints include designing new cladding geometry, which may lead to a higher laser induced damage threshold in the fiber’s structure, having low losses along with a single mode nature. This article reports a new antiresonant fiber that has a hollow core and a triple-layered cladding configuration with only circular tube elements. The effects of multiple layers corresponding to the number of tube rings in the cladding geometry are well explored, which leads to understanding the physical insight of inter-layers. In comparison to double-layered cladding elements fiber, the proposed fiber significantly reduces loss by an order of two and reveals a minimum leakage loss of 5.82 × 10−5 dB/km at the chosen wavelength of 1.06 µm through the proper arrangement of cladding elements. We achieved a maximal higher order mode extinction ratio of about 104 (indicates the excellent single mode properties) and comparatively a little bending-induced loss of 9.00 × 10−4 dB/km, when the radius of bending is 14 cm. As a result, our studies on new multilayer fiber designs are not only useful for delivering high laser power but also serve as guidelines for the experimental realization of future multilayered cladding fibers.

Enhancement of ultrasonic-assisted extraction and antioxidant potential of phenolic compounds from Moroccan Cannabis sativa: A green intelligent approach integrating optimal mixture process design and artificial neural networks

Environmentally-friendly extraction methods enables both efficient extraction of phenolic compounds from plant sources and safe use of the extracted substances. These characteristics render them as ideal choices for sustainable extraction processes. This study combines a green and sustainable extraction strategy with an intelligent statistical approach to optimize the recovery and the antioxidant activity of phytochemicals derived from Moroccan Cannabis sativa. The optimization of the ultrasound-assisted extraction is achieved using an optimal mixture-process design (OMPD) combined with artificial neural networks (ANNs). The OMPD consists of a mixture of water, glycerol, and ethanol, along with process parameters such as processing time, solvent-to-material (S/M) ratio, and temperature. The objective is to optimize the five responses extraction yield, total phenolic content (TPC), and antioxidant activities measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), total antioxidant capacity (TAC), and Ferric reducing antioxidant power (FRAP) assays. The results of the OMPD indicate that the recovery of phenolic compounds and antioxidant activity can be improved by using a binary mixture of water and ethanol, along with moderate processing time and temperature, and a high S/M ratio. The optimal responses were 26.06 %, 92.6 mg GAE/g, 0.1 mg/ml, 298 mg AAE/g, and 1.3 mg/ml for yield, TPC, TFC, DPPHIC50, TAC, and FRAPEC50, respectively. Subsequently, the OMPD matrix was used as input for the ANNs, employing a 6-6-5 multi-layer perceptron design. The findings of the ANNs closely matched those obtained from the OMPD models. However, the performance parameters of the ANNs models were slightly superior to those of the OMPD.

Fluorescence and Raman Micro-Spectroscopy of LiF Films Containing Radiation-Induced Defects for X-ray Detection

Lithium fluoride (LiF) film detectors for extreme ultraviolet radiation, soft and hard X-rays, based on the photoluminescence of radiation-induced electronic defects, have been proposed and are currently under further development and investigation. LiF film detectors are versatile and can be integrated in different experimental apparatus and imaging configurations. LiF can be grown in the form of polycrystalline thin films and it is compatible with several substrates. The radiation-induced color center (CCs) photoluminescence (PL) response can be enhanced through the appropriate choice of substrates and multilayer designs, and by tailoring the micro-structural properties of polycrystalline LiF films through the control of the growth conditions. In this work, we present the characterization, through fluorescence and Raman micro-spectroscopy, of LiF films, thermally evaporated on different substrates with thicknesses of up to 1 μm, irradiated with soft X-rays produced by a laser plasma source. The combination of these micro-spectroscopy techniques could represent an advanced method to investigate the role of the polycrystalline film structures in CC formation efficiency at the microscopic level, a fundamental aspect of the development of LiF film radiation-imaging detectors.

Uncertainties and inaccuracies of micro-perforated and micro-slit panel absorbers in multiple layers

Multilayer micro-perforated/slit panels (MPP/MSP) are used to achieve broadband absorption in Architectural Acoustics. Both micro-perforated and micro-slit panels can achieve a high absorption coefficient, but the frequency bandwidth is usually narrow for single-layer panels. For this reason, the multilayer design is proposed to achieve a broadband high absorption coefficient. This work applies Bayesian inference to the design of multilayered micro-perforated/-slit panels for a given design scheme. A higher level of inference, Bayesian model selection, estimates the parsimonious number of layers needed to achieve the design scheme. The lower level of inference estimates MPP/MSP parameters once the structure of the multilayer design is chosen. Once the exact parameters and the model are given, the theoretical absorption coefficient is compared with the measurement results. The causal inference is further applied to analyze the possible reasons for the deviations between the model and the experimental data. Applying the Bayesian framework and causal inference minimizes the uncertainties and inaccuracies during the manufacture and leads to a satisfactory design.

Multilayer designs comprising zirconium nitride and perovskites as a novel angular plasmonic biomedical sensor

Abstract In this paper, a comparison between different configurations of surface plasmon resonance (SPR) biosensors has been theoretically conducted to improve the performance of the designed biosensor. The proposed biosensor configurations contain zirconium nitride (ZrN) as an alternative plasmonic material, which comprises different perovskite materials (KNbO3, LiTaO3, LiNbO3, SrTiO3, and BaTiO3) in the visible region. Depending on the study calculations, the reflection spectra of the suggested designs were studied under the angular interrogation mode based on Fresnel coefficients for the transverse magnetic polarized light. The numerical findings demonstrated that the SPR biosensor, which has the configuration of [Prism/BaTiO3/ZrN/BaTiO3/Biosensing medium], represents the best biosensor due to its higher sensitivity and minimum reflectivity values. Meanwhile, sensitivity could receive 179.58 (deg/RIU). Therefore, it is believed that the proposed SPR biosensor designs could be promising through wide-ranging applications, specifically in biomedical, chemical, and environmental protection.

Preparation of microscale multi-layered viscoelastic polymers and analysis of their noise control effects on composite structures

Multi-layered design is an effective method to improve the damping performance of structures. The existing research focuses more on the multi-layered design of the structure from the macro-perspective, but lacks attention to the material itself from the micro-perspective. Thus, whether microscale multi-layered (MML) viscoelastic polymers have advantages in damping performance is not clear. Moreover, damping materials preparation and structural noise control is an interdisciplinary study. The noise and vibration control effect of MML viscoelastic polymers in actual composite structures needs to be further investigated. This paper conducts such a study on these issues. First, two different types of MML viscoelastic polymers, i.e., free damping (FD) and micro-constrained damping (MCD) composites, were prepared, and their material properties were characterized. Second, the frequency-dependent damping loss factors and elastic modulus of the different damping composites were identified. The influences of damping composite types and MML designs on material damping loss factor and elastic modulus were compared. Third, a prediction model of vibroacoustic behaviours of the honeycomb sandwich structure was established and validated to investigate the noise and vibration control effects of the MML damping composites on composite structures. The influences of the types, numbers of layers and application positions of the MML damping composites on the honeycomb sandwich structure were studied. The results indicated that MML design is helpful to improve the damping performance of viscoelastic polymers, but its influence on different damping composites is quite different. For noise control, MML-FD is more suitable for controlling airborne sound propagation, while MML-MCD is more suitable for controlling structural sound propagation.

Comparisons of student comprehension of 3D-printed, standard model, and extracted teeth in hands-on sessions.

Cavity preparation and direct and indirect pulp capping are difficult processes to learn in dentistry. Although plastic teeth are used in universities in Turkey, the standard model does not teach students how to distinguish between dental hard tissues from caries and how this relates to the pulp. The aim of this study was to investigate the differences in learning when a three-dimensional (3D)-printed tooth was employed in comparison with the standard model and extracted teeth. The differences are evaluated in the design, feasibility, and contribution of the 3D-printed dental tooth in pre-clinical education. The multi-layer 3D-printed tooth's authentic design and replication of the dental hard tissues and carious lesions are explored with 55 students for pre-clinical education, which includes caries excavation and direct and indirect pulp capping. The students completed questionnaires evaluating the 3D-printed teeth through comparison with the plastic and extracted teeth, rated with scores from 1 to 11 (1: poorest conformity; 11: excellent conformity). The questionnaire results indicated that students approved the printed tooth model for the practice of theoretical knowledge and the model received ratings between good and excellent. The results were statistically analysed using the Wilcoxon signed-rank test, and the printed teeth had the highest approval from the students (p < .001). The results of this study demonstrated that the use of the designed 3D-printed tooth is preferred by the students based on their perception of learning cavity preparation and pulp capping in a pre-clinical environment. Workflow and production were cost-effective with the use of 3D printing technology. The printed tooth allowed students to gain realistic experience before treating patients.

Improvement in surface performance of stainless steel by nitride and carbon-based coatings prepared via physical vapor deposition for marine application

To meet the escalating energy demands, marine resources have assumed a significant role in the field of renewable energy. High performance and extended lifespan of offshore equipment provide robust underpinnings for marine resource exploration. Stainless steel, as one of the most widely used marine materials, its friction and corrosion problems in extreme marine environments have become critical factors influencing the service life of marine equipment. Physical vapor deposition (PVD) technology is a compelling method for current and future applications due to the advantages of simple process, no pollution, few consumables, and uniform film formation. The preparation of suitable strengthening coatings by PVD provides a promising approach for enhancing the surface properties of stainless steel. Here, we review the current status of research on the preparation of wear resistance and anti-corrosion coatings on stainless steel surfaces by PVD in marine environments. First, an overview of PVD technology is given. Second, the influence of nitride and carbon-based coatings prepared by PVD technology on the tribological and corrosion properties of stainless steel in the marine environment is discussed, and the strengthening mechanisms of the two coatings are compared and analyzed from the aspects of elemental doping and multilayer structure design. Finally, the challenges and future directions in the development of nitride and carbon-based coatings for marine environments are prospected. The goal is to propose the design ideas of a new generation of anti-wear and corrosion-resistant coatings.