MLgrating: a program for simulating multilayer gratings for tender X-ray applications.

Multilayer gratings are increasingly popular optical elements at X-ray beamlines, as they can provide much higher photon flux in the tender X-ray range compared with traditional single-layer coated gratings. While there are several proprietary software tools that provide the functionality to simulate the efficiencies of such gratings, until now the X-ray community has lacked an open-source alternative. Here MLgrating is presented, a program for simulating the efficiencies of both multilayer gratings and single-layer coated gratings for X-ray applications. MLgrating is benchmarked by comparing its output with that of other software tools and plans are discussed for how the program could be extended in the future.

Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs.

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a feasible solution is graph knowledge distillation (KD), which can learn high-performance student Multi-layer Perceptrons (MLPs) to replace GNNs by mimicking the superior output of teacher GNNs. However, state-of-the-art graph knowledge distillation methods are mainly based on distilling deep features from intermediate hidden layers, this leads to the significance of logit layer distillation being greatly overlooked. To provide a novel viewpoint for studying logits-based KD methods, we introduce the idea of decoupling into graph knowledge distillation. Specifically, we first reformulate the classical graph knowledge distillation loss into two parts, i.e., the target class graph distillation (TCGD) loss and the non-target class graph distillation (NCGD) loss. Next, we decouple the negative correlation between GNN's prediction confidence and NCGD loss, as well as eliminate the fixed weight between TCGD and NCGD. We named this logits-based method Decoupled Graph Knowledge Distillation (DGKD). It can flexibly adjust the weights of TCGD and NCGD for different data samples, thereby improving the prediction accuracy of the student MLP. Extensive experiments conducted on public benchmark datasets show the effectiveness of our method. Additionally, DGKD can be incorporated into any existing graph knowledge distillation framework as a plug-and-play loss function, further improving distillation performance. The code is available at https://github.com/xsk160/DGKD.

Vaccine Hesitancy in Taiwan: Temporal, Multilayer Network Study of Echo Chambers Shaped by Influential Users.

BACKGROUND: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization's (WHO) infodemiology research agenda, which aims to propose new methods for social listening. OBJECTIVE: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time. METHODS: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the "Gossiping" subforum were scraped from January 2021 to December 2022 using the keyword "vaccine." A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users-or key nodes-in the network, the activity of high-diversity and hardliner nodes was analyzed. RESULTS: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a "healthy community." Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting. CONCLUSIONS: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of "influence." To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda.

Species-specific effect of particle viscosity and particle-phase reactions on the formation of secondary organic aerosol.

Secondary organic aerosol (SOA) is a major component of atmospheric fine particulate matter. Both particle viscosity and particle-phase chemistry play a crucial role in the formation and evolution of SOA; however, our understanding on how these two factors together with gas-phase chemistry collectively determine the formation of SOA is still limited. Here we developed a kinetic aerosol multilayer model coupled with gas-phase and particle-phase chemistry to simulate SOA formation. We take the atmospherically important α-pinene + OH oxidation system as an example application of the model. The simulations show that although the particle viscosity has negligible to small influences on the total SOA mass concentration, it strongly changes the concentration and distribution of individual compounds within the particle. This complicated effect of particle viscosity on SOA formation is a combined result of inhibited condensation or evaporation of specific organics due to slowed particle-phase diffusion. Furthermore, the particle-phase reactions alter the volatility and abundance of specific compounds and exacerbate their non-uniform distribution in highly viscous particles. Our results highlight an important species-specific effect of particle viscosity and particle-phase chemistry on SOA formation and demonstrate the capability of our model for quantifying such complicated effects on SOA formation and evolution.

Flexural Strength of Different Yttrium Oxide Containing Monochrome and Multilayer Partially Stabilized Zirconia upon Various Sintering Rates.

Purpose: Sintering procedures influence the properties of zirconia. This study evaluated the effect of varying sintering rates on flexural strength (σ) of 3, 4, and 5 mol% yttria (Y) containing monochrome (Mo) and multilayer (Mu) zirconia. Materials and Methods: 270 specimens (width×length×thickness = 11.2×20×1.5 mm) were prepared from Mo and Mu of 3Y, 4Y, and 5Y zirconia and randomly sintered at regular (RS: 10 °C/min), fast (FS: 35 °C/min), and speed (SS: 70 °C/min) sintering (n = 15/group). Three-point bending test was determined for σ. ANOVA and Bonferroni test were analyzed for significant differences of σ (a=0.05). The microstructure and crystalline [monoclinic (m), tetragonal (t), and cubic (c)] phases were evaluated by SEM and XRD.Results: σ of zirconia was affected by zirconia type, shade pattern, and sintering rate. Significant higher σ for 3Y>4Y>5Y (p<0.05). Significant greater σ for Mu>Mo (p<0.05). Significant reduction of σ upon SS than RS and FS (p<0.05). However, no significant effect on σ upon varying sintering rates within each type of zirconia (p>0.05). SEM indicated greater grain size in 5Y than in 4Y and 3Y. XRD indicated higher t phase in 3Y, whereas higher c phase in 5Y. Mo indicated a higher c phase than Mu.Conclusions: σ was influenced by Y content (3Y>4Y>5Y), shade pattern (Mu>Mo), and sintering rate (RS@FS>SS). Nonetheless, no influence of varying sintering rates on σ for each type of zirconia, suggesting sintering zirconia with RS and FS for expecting higher σ and at SS for acceptable σ for chair-side cost-effective restoration.

XPS depth profiling of nano-layers by a novel trial-and-error evaluation procedure.

In spite of its superior chemical sensitivity, XPS depth profiling is rarely used because of the alteration introduced by the sputter removal process and the resulting inhomogeneous in-depth concentration distribution. Moreover, the application of XPS becomes increasingly challenging in the case of the analysis of thin layers, if the thickness is in the range of 2-3 inelastic mean free paths (IMFP) of the photoelectrons. In this paper we will show that even in these unfavorable cases the XPS depth profiling is applicable. Herein the XPS depth profiling of a model system tungsten-carbide-rich nano-layer of high hardness and corrosion resistance is presented. We will show that the problems arising because of the relatively high IMFP can be corrected by introducing a layer model for the calculation of the observed XPS intensities, while the alteration, e.g. ion mixing, compound formation and similar artefact, introduced by the sputter removal process can be handled by TRIDYN simulation. The method presented here overcomes the limitation of XPS depth profiling.

Surface Acoustic Wave Sensors for Wireless Temperature Measurements above 1200 Degree Celsius.

High-temperature wireless sensing is crucial for monitoring combustion chambers and turbine stators in aeroengines, where surface temperatures can reach up to 1200 °C. Surface Acoustic Wave (SAW) temperature sensors are an excellent choice for these measurements. However, at extreme temperatures, they face issues such as agglomeration and recrystallization of electrodes, leading to loss of conductivity and reduced quality factor, hindering effective wireless signal transmission. This study develops an LGS SAW sensor with a Pt-10%Rh/Zr/Pt-10%Rh/Zr/Pt-10%Rh/Zr multilayer composite electrode structure to address these challenges. We demonstrate that the sensor can achieve wireless temperature measurements from room temperature to 1200 °C with an accuracy of 1.59%. The composite electrodes excite a quasi-shear wave on the LGS substrate, maintaining a Q-factor of 3526 at room temperature, providing an initial assurance for the strength of the wireless interrogation echo signal. The sensor operates stably for 2.18 h at 1200 °C before adhesion loss between the composite electrode and the substrate causes a sudden increase in resonant frequency. This study highlights the durability of the proposed electrode materials and structure at extreme temperatures and suggests future research to improve adhesion and extend the sensor's lifespan, thereby enhancing the reliability and effectiveness of high-temperature wireless sensing in aerospace applications.

A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications.

This paper proposes a novel multi-band textile monopole antenna for patient tracking applications. The designed antenna has compact footprints (0.13λ02) and works in the narrow band-internet of things (NB-IoT) 1.8 GHz, radio frequency identification (RFID), and industrial, scientific, and medical (ISM) 2.45 GHz and 5.8 GHz bands. The impedance bandwidths and gain of the antenna at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 310 MHz, 960 MHz, and 1140 MHz; 3.7 dBi, 5.3 dBi, and 9.6 dBi, respectively. Also, the antenna's behavior is checked on different body parts of the human body in various bending scenarios. As per the evaluated link budget, the designed antenna can easily communicate up to 100 m of distance. The specific absorption rate values of the designed antenna are also within acceptable limits as per the (FCC/ICNIRP) standards at the reported frequency bands. Unlike traditional rigid antennas, the proposed textile antenna is non-intrusive, enhancing user safety and comfort. The denim material makes it comfortable for extended wear, reducing the risk of skin irritation. It can also withstand regular wear and tear, including stretching and bending. The presented denim-based antenna can be seamlessly integrated into clothing and accessories, making it less obtrusive and more aesthetically pleasing.

Study on the Tribological Properties of Multilayer Concentric Hexagonal Laser Texturing on Rubber Surfaces of Screw Pumps.

Given the friction and drag reduction effects observed in various biological hexagonal structures in nature, a new design was implemented on the rubber surface of the stator of a submersible screw pump. This design featured a multilayer concentric hexagonal groove structure. Furthermore, a composite multilayer hexagonal structure integrating grooves and pits was also developed and applied. This study investigated the influence of groove layer number, groove depth, pit depth, and multilayer hexagonal groove texture arrangement on the rubber surface flow characteristics. Additionally, the pressure field state, the degree of influence on the oil film-bearing capacity, and the biomimetic and hydrodynamic lubrication theories were tested using the finite element analysis method. Tribological experiments were conducted on nanosecond laser-processed rubber textures under simulated liquid lubrication conditions, reflecting actual shale oil well experiments. These experiments aimed to investigate the influence of multilayer hexagonal shape parameters on the tribological characteristics of the stator-rotor friction pair of a submersible screw pump. The results indicated that with a constant overall size, a multilayer hexagonal structure with ~0.1 mm groove depth enhanced the oil film-bearing capacity, providing significant friction and drag reduction. For composite textures, a deeper pit depth within the study area enhanced the oil film-bearing capacity. Furthermore, a gradient arrangement of groove textures featuring wider outer grooves and shallower depth exhibited superior performance in terms of bearing capacity.

Confinement Effect in Multilayer Films Made from Semicrystalline and Bio-Based Polyamide and Polylactic Acid.

Bio-based multilayer films were prepared by using the innovative nanolayer coextrusion process to produce films with a number of alternating layers varying from 3 to 2049. For the first time, a semicrystalline polymer was confined by another semicrystalline polymer by nanolayering in order to develop high barrier polyamide (PA11)/polylactic acid (PLA) films without compromising thermal stability and mechanical behavior. This process allows the preparation of nanostratified films with thin layers (down to nanometric thicknesses) in which a confinement effect can be induced. The stratified structure has been investigated, and the layer thicknesses have been measured. Barrier properties were successfully correlated to the microstructure, as well as the thermal behavior, and mechanical properties. The layer continuity was fully achieved for most of the films, but some layer breakups have been observed on the film with the thinnest PLA layer (2049-layers film). Coextruding PLA with PA11 has induced an increase in PLA crystallinity (from 4 to 16%) along with an increase in thermal stability of the multilayer films without impacting PA11 properties. Gas barrier properties were driven by the PLA confined layers due to the microstructural rearrangement by increasing crystallinity, whereas water barrier properties were governed by the PA11 confining layers due to its lower water affinity. As a consequence, a decrease of water permeability (up to 11 times less permeable for the 6M film) but an increase of gas barrier properties (barrier improvement factor (BIF) of 66% for the 0M film for N2 and BIF of 36% for the 6M film for CO2 for instance) were evidenced as the layer number was increased. This study paves the way for the development of ecofriendly materials with outstanding barrier performances and highlights the importance of nonmiscible polymers adhesion at melt state and additives presence.

A Powerful Strategy for Carbon Reduction: Recyclable Mono-Material Polyethylene Functional Film.

Given the abundant plastics produced globally, and the negative environmental impacts of disposable plastic products throughout their life cycle, there has been significant attention drawn by the general public and governments worldwide. Mono-material multilayer packaging is a potent strategy to address the challenge of carbon emissions as it offers specific functionalities (such as strength and barrier properties) through its layers and facilitates recycling. In this study, a five-layer co-extruded polyethylene composite film LLDPE/mPE/PVA/mPE/LLDPE was taken as a model to investigate its mechanical properties and barrier properties after four recycling cycles. The result revealed that the longitudinal tensile strength and transvers tensile were, respectively, dropped from 29.66 MPa and 24.9 MPa to 21.972 MPa and 19.222 MPa after the recycling; it is shown that the film still has good mechanical properties after the recycling cycle. However, a noticeable decline in the barrier properties was observed after the second recycling. In contrast to traditional plastics, a mono-material film with a 10 wt.% circulating mass could reduce CO2 emissions by 3692.25 kg for every 1.0 ton of plastic products after four recycling cycles.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach.

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array.

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy.

To improve prediction performance and reduce artifacts in Raman spectra, we developed an eXtreme Gradient Boosting (XGBoost) preprocessing method to preprocess the Raman spectra of glucose, glycerol and ethanol mixtures. To ensure the robustness and reliability of the XGBoost preprocessing method, an X-LR model (which combined XGBoost preprocessing and a linear regression (LR) model) and a X-MLP model (which combined XGBoost preprocessing and a multilayer perceptron (MLP) model) were developed. These two models were used to quantitatively analyze the concentrations of glucose, glycerol and ethanol in the Raman spectra of mixed solutions. The proportion map of hyperparameters was firstly used to narrow down the search range of hyperparameters in the X-LR and the X-MLP models. Then the correlation coefficients (R2), root mean square of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used to evaluate the models' performance. Experimental results indicated that the XGBoost preprocessing method achieved higher accuracy and generalization capability, with better performance than those of other preprocessing methods for both LR and MLP models.

Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP).

Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.

Predicting Promoters in Multiple Prokaryotes with Prompt.

Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.

Coupling tensile test with LC-OCT and ultrasound imaging: investigation of the skin sublayers mechanical behaviour.

The skin is an envelope that covers the entire body. Nowadays, understanding and studying the mechanical, biological and sensory properties of the skin is essential, especially in dermatology and cosmetology. The in-depth study of the skin's mechanical behaviour is a highly intriguing challenge, enabling the differentiation of the behaviour of each layer. An extension device was developed to perform relaxation and extension tests to characterize the skin. The device has also been coupled with imaging tools (LC-OCT and ultrasound), allowing us to observe layer-by-layer deformations during the tests. Relaxation tests revealed significant skin anisotropy, as well as an influence of age and gender on skin viscoelastic parameters calculated from relaxation curves and a skin viscoelastic model. These tests also unveiled their ability to distinguish certain characteristic pathologies that alter the mechanical properties of the skin, such as scleroderma or heliodermatitis. Furthermore, the optical-mechanical coupling and deformation calculation through image analysis demonstrated that the skin layers exhibit distinct mechanical behaviours owing to their different structures. Finally, Poisson's ratio of the skin was obtained by calculating the deformation in two directions for each layer.

Unveiling the Role of Hydrophobicity on Multilayer Carbon Nanosheets Enriched in sp2-Carbon for Toluene Adsorption under Humid Conditions.

Carbon materials are regarded as a promising adsorbent for the adsorption of volatile organic compounds (VOCs). However, their adsorption behaviors are usually compromised at ambient conditions, attributed to the competitive VOCs adsorption with water vapor. In this study, we demonstrated that the selectivity for toluene than water of carbon can be effectively enhanced by introducing more sp2-carbon with two-dimensional nanosheets stacked. The multilayer carbon nanosheets enriched with sp2-carbon (CNS-MCA) exhibit a 151° H2O-contact angle, indicating hydrophobicity. Dynamic adsorption behaviors revealed that CNS-MCA retain 71% of their toluene adsorption capacity (91 mg/g) even at 60% relative humidity. Density functional theory (DFT) calculations, static adsorption studies, in situ Raman spectroscopy, and time-resolved in situ nuclear magnetic resonance (NMR) spectroscopy collectively indicate that toluene exhibits enhanced adsorption and selectivity due to π-π* interactions between its aromatic rings and the sp2-carbon. Conversely, water adsorption is attenuated, attributed to the reduced availability of surface-exposed hydrogen bonds associated with sp2-carbon and the inherent hydrophobic nature of multilayer graphene. This study extends a novel solution for the enhancement of VOCs adsorption under humid conditions.

Fracture resistance, marginal and internal adaptation of innovative 3D-printed graded structure crown using a 3D jet printing technology.

PURPOSE: This in vitro study aimed to create a graded structured dental crown using 3D printing technology and investigate the fracture resistance and the adaptation of this new design. MATERIALS AND METHODS: A dental crown with a uniform thickness of 1.5 mm was designed, and the exported stereolithography file (STL) was used to manufacture 30 crowns in three groups (n = 10), solid (SC), bilayer (BL), and multilayer (ML) crowns using  3D jet printing technology. Marginal and internal gaps were measured using the silicone replica technique. Crowns were then luted to a resin die using a temporary luting agent and the fracture resistance was measured using a universal testing machine. One-way ANOVA and Tukey post hoc tests were used to compare the fracture resistance and the adaptation of crowns at a significance level of 0.05. RESULTS: Mean marginal and internal gap of the ML group were 80 and 82 mm, respectively; which were significantly (p < 0.05) smaller than BL (203 and 183 mm) and SC (318 and 221 mm) groups. The SC group showed the highest mean load at fracture (2330 N) which was significantly (p < 0.05) higher than the BL (1716 N) and ML (1516 N) groups. CONCLUSION: 3D jet printing technology provides an opportunity to manufacture crowns in a graded structure with various mechanical properties. This study provided an example of graded structured crowns and presented their fracture resistance. SC group had the highest fracture resistance; however, ML had the best marginal and internal adaptation.

Development of an advanced in-line multilayer deposition system at Diamond Light Source.

A state-of-the-art multilayer deposition system with a 4200 mm-long linear substrate translator housed within an ultra-high vacuum chamber has been developed. This instrument is engineered to produce single and multilayer coatings, accommodating mirrors up to 2000 mm in length through the utilization of eight rectangular cathodes. To ensure the quality and reliability of the coatings, the system incorporates various diagnostic tools for in situ thickness uniformity and stress measurement. Furthermore, the system features an annealing process capable of heating up to 700°C within the load-lock chamber. The entire operation, including pump down, deposition and venting processes, is automated through user-friendly software. In addition, all essential log data, power of sputtering source, working pressure and motion positions are automatically stored for comprehensive data analysis. Preliminary commissioning results demonstrate excellent lateral film thickness uniformity, achieving 0.26% along the translation direction over 1500 mm in dynamic mode. The multilayer deposition system is poised for use in fabricating periodic, lateral-graded and depth-graded multilayers, specifically catering to the beamlines for diverse scientific applications at Diamond Light Source.