Surface Characteristics and Microbiological Analysis of a Vat-Photopolymerization Additive-Manufacturing Dental Resin

The wide application of additive manufacturing in dentistry implies the further investigation into oral micro-organism adhesion and biofilm formation on vat-photopolymerization (VP) dental resins. The surface characteristics and microbiological analysis of a VP dental resin, printed at resolutions of 50 μm (EG-50) and 100 μm (EG-100), were evaluated against an auto-polymerizing acrylic resin (CG). Samples were evaluated using a scanning electron microscope, a scanning white-light interferometer, and analyzed for Candida albicans (CA) and Streptococcus mutans (SM) biofilm, as well as antifungal and antimicrobial activity. EG-50 and EG-100 exhibited more irregular surfaces and statistically higher mean (Ra) and root-mean-square (rms) roughness (EG-50-Ra: 2.96 ± 0.32 µm; rms: 4.05 ± 0.43 µm / EG-100-Ra: 3.76 ± 0.58 µm; rms: 4.79 ± 0.74 µm) compared to the CG (Ra: 0.52 ± 0.36 µm; rms: 0.84 ± 0.54 µm) (p < 0.05). The biomass and extracellular matrix production by CA and SM and the metabolic activity of SM were significantly decreased in EG-50 and EG-100 compared to CG (p < 0.05). CA and SM growth was inhibited by the pure unpolymerized VP resin (48 h). EG-50 and EG-100 recorded a greater irregularity, higher surface roughness, and decreased CA and SM biofilm formation over the CG.

Super fine cerium hydroxide abrasives for SiO2 film chemical mechanical planarization performing scratch free

Face-centered-cubic crystallized super-fine (~ 2 nm in size) wet-ceria-abrasives are synthesized using a novel wet precipitation process that comprises a Ce4+ precursor, C3H4N2 catalyst, and NaOH titrant for a synthesized termination process at temperature of at temperature of 25 °C. This process overcomes the limitations of chemical–mechanical-planarization (CMP)-induced scratches from conventional dry ceria abrasives with irregular surfaces or wet ceria abrasives with crystalline facets in nanoscale semiconductor devices. The chemical composition of super-fine wet ceria abrasives depends on the synthesis termination pH, that is, Ce(OH)4 abrasives at a pH of 4.0–5.0 and a mixture of CeO2 and Ce(OH)4 abrasives at a pH of 5.5–6.5. The Ce(OH)4 abrasives demonstrate better abrasive stability in the SiO2-film CMP slurry than the CeO2 abrasives and produce a minimum abrasive zeta potential (~ 12 mV) and a minimum secondary abrasive size (~ 130 nm) at the synthesis termination pH of 5.0. Additionally, the abrasive stability of the SiO2-film CMP slurry that includes super-fine wet ceria abrasives is notably sensitive to the CMP slurry pH; the best abrasive stability (i.e., a minimum secondary abrasive size of ~ 130 nm) is observed at a specific pH (6.0). As a result, a maximum SiO2-film polishing rate (~ 524 nm/min) is achieved at pH 6.0, and the surface is free of stick-and-slip type scratches.

Development of Hexapod Robot with Computer Vision

Nowadays many robotic systems are developed with lot of innovation, seeking to get flexibility and efficiency of biological systems. Hexapod Robot is the best example for such robots, it is a six-legged robot whose walking movements try to imitate the movements of the insects, it has two sets of three legs alternatively which is used to walk, this will provide stability, flexibility and mobility to travel on irregular surfaces. With these attributes the hexapod robots can be used to explore irregular surfaces, inhospitable places, or places which are difficult for humans to access. This paper involves the development of hexapod robot with digital image processing implemented on Raspberry Pi, to study in the areas of robotic systems with legged locomotion and robotic vision. This paper is an integration of a robotic system and an embedded system of digital image processing, programmed in high level language using Python. It is equipped with a camera to capture real time video and uses a distance sensor that allow the robot to detect obstacles. The Robot is Self-Stabilizing and can detect corners. The robot has 3 degrees of freedom in each six legs thus making a 18 DOF robotic movement. The use of multiple degrees of freedom at the joints of the legs allows the legged robots to change their movement direction without slippage. Additionally, it is possible to change the height from the ground, introducing a damping and a decoupling between the terrain irregularities and the body of the robot servo motors. Keywords: Hexapod, Raspberry Pi, Computer vision, Object detection, Yolo, Servo Motor, OpevCV.

Physical Properties of Newly Developed Resin Modified Glass Ionomer Cement with Synthesised Coumarin Derivatives

The aim of this study was to determine the effects of two types of coumarin derivatives, namely, 3-acetylcoumarin (AC) and coumarin thiosemicarbazone (CT) on surface characteristics such as the roughness, hardness, and morphology of resin-modified glass-ionomer cement (RMGIC). The release of coumarin from the fabricated RMGIC was also investigated. AC and CT at 1.0% (w/w) concentration were added into 0.3 g of RMGIC powder and mixed with 0.1 g of polyacrylic acid. The fabricated RMGIC-AC and RMGIC-CT were evaluated for surface characteristics such as roughness, hardness and topography. The coumarin release of AC and CT from RMGIC was also determined. The RMGIC-CT demonstrated a decreased roughness value among the materials. The surface roughness exhibited by the RMGIC was statistically higher (p < 0.05) than that of fabricated materials. The RMGIC-AC was observed to have the highest hardness value compared to the RMGIC-CT and RMGIC, and this value was significantly higher (p < 0.05). The surface morphologies of the RMGIC-AC and RMGIC-CT showed a number of pores and irregular surfaces. Meanwhile, the surface roughness value of the RMGIC was statistically higher (p < 0.05) than both RMGIC-AC and RMGIC-CT. In conclusion, the large-sized AC particles significantly increased the surface hardness of the fabricated RMGIC. Conversely, the particle size of both coumarins were not influenced the surface roughness value and the coumarin release profile of fabricated RMGICs.

Improving the robustness of DI and PVI further using fast guided filter on radiographic images

To further improve the robustness of the weld defect index (DI) and peak-valley index (PVI), which are key indices for detecting weld defects in radiographic testing (RT) images accurately and reliably, a robust improvement method is proposed, in which a fast guided filter (Fast-GF) is introduced and its effect on the DI and PVI is analysed. In this paper, the principle of the proposed robust improvement method, the related theory of Fast-GF, the definition and the calculational method of the DI and PVI are systematically analysed. Taking some practical RT images from industrial welding as an example, smoothing experiments with different filters and comparative computational experiments for the DI and PVI both with and without Fast-GF are carried out. The experimental results show that the robustness of the DI and PVI is further improved by the proposed robust improvement method, which is a desirable outcome. More specifically, the values of the DI and PVI are computed accurately and reliably regardless of some non-uniform distribution of grey levels, noise, irregular surfaces and artefacts in the RT images.

Learning to Recognize Irregular Features on Leather Surfaces

As part of industrial quality control in the leather industry, it is important to identify the abnormal features in wet-blue leather samples. Manual inspection of leather samples is the current norm in industrial settings. To comply with the current industrial standards that advocate large-scale automation, visual inspection based leather processing is imperative. Visual inspection of irregular surfaces is a challenging problem as the characteristics of the abnormalities can take a variety of shape and color variations. The aim of this work is to automatically categorize leather images into normal or abnormal by visual analysis of the surfaces. To achieve this aim, a deep learning based approach is devised that learns to recognize regular and irregular leather surfaces and categorize leather images on its basis. To this end, we propose an ensemble of multiple convolutional neural networks for classifying leather images. The proposed ensemble network exhibited competitive performance obtaining 92.68% test accuracy on our own curated leather images dataset.

A Universal Strategy for Organic Fluid Phosphorescence Materials

<b>It has become an accepted approach to construct room-temperature phosphorescence (RTP) materials by suppressing the non-radiative decay process. However, there is limited success in developing fluid phosphorescence materials due to the ultrafast non-radiation relaxation of vibration and collision of molecules in fluid matrixes. In this study, a universal strategy was proposed for pure organic phosphorescent fluid materials that are able to generate effective phosphorescent emissions at both room temperature (Φ_{RTP, 293 K} ~ 30%) and <a>even higher temperature (Φ_{RTP, 358 K} ~ 4.53%)</a>. Based on these findings, a qualitative analytical method was developed for leak detection and a quantitative analytical technique was further validated to help visually identify the heat distribution of irregular surfaces. This advancement greatly empowers the current organic phosphorescent system offering an alternative approach to determine moisture and heat from non-invasive photoluminescence emission colors.</b>

A Universal Strategy for Organic Fluid Phosphorescence Materials

<b>It has become an accepted approach to construct room-temperature phosphorescence (RTP) materials by suppressing the non-radiative decay process. However, there is limited success in developing fluid phosphorescence materials due to the ultrafast non-radiation relaxation of vibration and collision of molecules in fluid matrixes. In this study, a universal strategy was proposed for pure organic phosphorescent fluid materials that are able to generate effective phosphorescent emissions at both room temperature (Φ_{RTP, 293 K} ~ 30%) and <a>even higher temperature (Φ_{RTP, 358 K} ~ 4.53%)</a>. Based on these findings, a qualitative analytical method was developed for leak detection and a quantitative analytical technique was further validated to help visually identify the heat distribution of irregular surfaces. This advancement greatly empowers the current organic phosphorescent system offering an alternative approach to determine moisture and heat from non-invasive photoluminescence emission colors.</b>