“…This is because the properties of the particles were not considered. Currently, numerous studies are being conducted on process technologies involving the nanoparticle characteristics of tungsten particles in radiation shields [39].…”
The use of disposable containers and packaging materials has increased due to the recent COVID-19 pandemic. Thus, the generation of plastic waste is also increasing, and research on recycling such waste is being actively conducted. In this study, an X-ray-shielding film for medical diagnosis was manufactured by mixing a radiation-shielding material and a plastic waste-based polymer material and its effectiveness was evaluated. The film, which is intended as a fabric for a shielding garment, consists of barium sulfate (BaSO4) shielding nanoparticles embedded in a matrix of polyethylene terephthalate (PET), a commonly available waste plastic material. A particle-dispersing technology, which can improve the ratio between the shielding and matrix materials while maintaining the tensile strength of the film, was studied. Therefore, to increase the content of the barium sulfate (BaSO4) nanoparticles used as the shielding material, this multi-carrier method—under which the particles are dispersed in units of time—was developed to improve the shielding performance. Compared with the effectiveness of lead (Pb) shielding film, the 3 mm barium sulfate film developed in this study satisfies the lead equivalent of 0.150 mmPb when stacked in two layers. Therefore, a shielding film was successfully manufactured by using plastic waste as a polymer resin and barium sulfate, an eco-friendly radiation-shielding material, instead of lead.
“…This is because the properties of the particles were not considered. Currently, numerous studies are being conducted on process technologies involving the nanoparticle characteristics of tungsten particles in radiation shields [39].…”
The use of disposable containers and packaging materials has increased due to the recent COVID-19 pandemic. Thus, the generation of plastic waste is also increasing, and research on recycling such waste is being actively conducted. In this study, an X-ray-shielding film for medical diagnosis was manufactured by mixing a radiation-shielding material and a plastic waste-based polymer material and its effectiveness was evaluated. The film, which is intended as a fabric for a shielding garment, consists of barium sulfate (BaSO4) shielding nanoparticles embedded in a matrix of polyethylene terephthalate (PET), a commonly available waste plastic material. A particle-dispersing technology, which can improve the ratio between the shielding and matrix materials while maintaining the tensile strength of the film, was studied. Therefore, to increase the content of the barium sulfate (BaSO4) nanoparticles used as the shielding material, this multi-carrier method—under which the particles are dispersed in units of time—was developed to improve the shielding performance. Compared with the effectiveness of lead (Pb) shielding film, the 3 mm barium sulfate film developed in this study satisfies the lead equivalent of 0.150 mmPb when stacked in two layers. Therefore, a shielding film was successfully manufactured by using plastic waste as a polymer resin and barium sulfate, an eco-friendly radiation-shielding material, instead of lead.
“…The characteristic peak in each spectrum was amplified, then analyzed by win TMC software. The linear attenuation coefficient μ is one of the main parameters for estimating the impact interaction of gamma radiation with shielding material which calculated according to Beer–Lambert low 19 – 23 . Figure 2 The arrangement of the radiating system.…”
In this work, morphological and attenuation parameters of gamma ray protection were studied. Dimethyl polysiloxane (Silicon Rubber) Mixed with micro-size and nano-size lead oxide particles at different weight percentage were prepared. The morphological structure of PbO/SR composites was investigated by SEM test, according to SEM images the nano PbO particles are more uniform micro PbO particles. The radiation attenuation test was carried out using 3ʺ × 3ʺ NaI (TI) detector for (Am-241), (Cs-137), (Co-60), (Ba-133), and (Eu-152). The effect on attenuation property of SR-PbO shown that the increase of PbO filler significantly increases the linear attenuation coefficient and improve the other radiation protection parameters especially at low gamma energy. It's found that a significant agreement between the experimental result and theoretical result from Xcom program. In this study it's found matrix filled with nano-PbO have higher gamma shielding ability compared to micro-PbO matrix at the same filler concentration. It can say that SR-nano PbO has a higher radiation protection than SR-micro PbO compositions.
“…The convolution process for a point (𝑖, 𝑗) in the image is mathematically expressed in Equation (6).…”
Section: 𝐺(𝑥 𝑦mentioning
confidence: 99%
“…Generated digital Xray radiography images are typically rendered in shades of gray, having gray values ranging from 0 to 65535 for 16-bit images or 0 to 255 gray values for 8-bit images. The principal factor influencing the noticeable gray value difference within acquired radiographic images is X-ray beam attenuation [5], which is affected by factors such as differences in material thickness, density, foreign material inclusions, and the geometry of the components being inspected, among others [6]. The seemingly simple process of X-ray image acquisition conceals the intricate physics and engineering that underpin the technique.…”
Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.
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