Search Results (35)

The widespread use of single-use face masks during the recent epidemic has led to significant environmental challenges due to waste pollution. This study explores an innovative approach to address this issue by repurposing discarded face masks for hydrovoltaic energy harvesting. By coating the face masks with carbon black (CB) to enhance their hydrophilic properties, we developed mask-based hydrovoltaic power generators (MHPGs). These MHPGs were evaluated for their hydrovoltaic performance, revealing that different mask configurations and sizes affect their efficiency. The study found that MHPGs with smaller, more structured areas exhibited better energy output, with maximum open-circuit voltages (V_OC) reaching up to 0.39 V and short-circuit currents (I_SC) up to 65.6 μA. The integration of CB improved water absorption and transport, enhancing the hydrovoltaic performance. More specifically, MHPG-1 to MHPG-4, which represented different sizes and features, presented mean V_OC values of 0.32, 0.17, 0.19 and 0.05 V, as well as mean I_SC values of 16.57, 15.59, 47.43 and 3.02 μA, respectively. The findings highlight the feasibility of utilizing discarded masks in energy harvesting systems, offering both environmental benefits and a novel method for renewable energy generation. Therefore, this work provides a new paradigm for waste-to-energy (WTE) technologies and inspires further research into the use of unconventional waste materials for energy production. Full article

One of the environmental problems worldwide is the enormous number of surgical masks used during the COVID-19 pandemic due to the measures imposed by the World Health Organization on the mandatory use of masks in public spaces. The current study is a potential circular economy approach to recycling the surgical masks discarded into the environment during the COVID-19 pandemic for use in bituminous asphalt pavement. FTIR analysis showed that the surgical masks used were made from ethylene propylene diene monomer (EPDM) rubber modified with polypropylene. The effects of the addition of surgical masks in bituminous asphalt on the performance of the base course were demonstrated in this study. The morphology and elemental composition of the bituminous asphalt pavement samples with two ratios of surgical mask composition were investigated by SEM-EDX and the performance of the modified bituminous asphalt pavement was determined by Marshall stability, flow rate, solid–liquid ratio, apparent density, and water absorption. The study refers to the technological innovation of using surgical masks in the formulation of AB 31.5 bituminous asphalt base course, which brings tremendous benefits to the environment by reducing the damage caused by the COVID-19 pandemic. Full article

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSI_λ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSI_λ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSI_λ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers. Full article

In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most of the box proposals are suppressed and discarded in the Non-Maximum Suppression process. Additionally, for panoptic segmentation, it is a problem to properly fuse the semantic segmentation results with the Mask RCNN-produced instance segmentation results. To address these issues, we propose a new mask pyramid mechanism to distinguish objects and generate much fewer proposals by referring to existing segmented masks, so as to reduce computing resource consumption. The Mask-Pyramid Network generates object proposals and predicts masks from larger to smaller sizes. It records the pixel area occupied by the larger object masks, and then only generates proposals on the unoccupied areas. Each object mask is represented as a H × W × 1 logit, which fits well in format with the semantic segmentation logits. By applying SoftMax to the concatenated semantic and instance segmentation logits, it is easy and natural to fuse both segmentation results. We empirically demonstrate that the proposed Mask-Pyramid Network achieves comparable accuracy performance on the Cityscapes and COCO datasets. Furthermore, we demonstrate the computational efficiency of the proposed method and obtain competitive results. Full article

This paper is grounded on the following information: (1) Disposable masks primarily consist of polypropylene fiber, which exhibits excellent flexibility. (2) China has extensive coal gangue deposits that pose a significant environmental hazard. (3) Coal gangue concrete exhibits greater fragility compared to regular concrete and demonstrates reduced resistance to deformation. With the consideration of environmental conservation and resource reutilization, a preliminary concept suggests the conversion of discarded masks into fibers, which can be blended with coal gangue concrete to enhance its mechanical characteristics. In this paper, the stress–strain law of different mask fiber–doped coal gangue concrete (DMGC) under uniaxial compression is studied when the matrix strength is C20 and C30, and the effect of mask fiber content on the mechanical behavior and energy conversion relationship of coal gangue concrete is analyzed. The experimental results show that when the content of mask fiber is less than 1.5%, the strength, elastic modulus, deformation resistance, and energy dissipation of the concrete increase with mask fiber content. When the amount of mask fiber is more than 1.5%, because the tensile capacity and energy dissipation level of concrete produced by the mask fiber cannot compensate for the compression and deformation resistance of concrete of the same quantity and because excess fiber is difficult to evenly mix in the concrete, there are pore defects in concrete, which decreases the concrete strength due to the increase in mask fiber. Therefore, adding less than 1.5% mask fiber helps to improve the ductility, toughness, impermeability, and oxidation and control the cracking of coal gangue concrete. Based on Weibull theory, a constitutive model of DMGC is established, which fits well with the results of a uniaxial test, providing support for understanding the mechanical law of mask fiber–doped concrete. Full article

The COVID-19 pandemic led to a huge demand for disposable facemasks. Billions were manufactured from nonbiodegradable petroleum-derived polymers, and many were discarded in the environment where they contributed to plastic pollution. There is an urgent need for biobased and biodegradable facemasks to avoid environmental harm during future disease outbreaks. Melt electrospinning is a promising alternative technique for the manufacturing of filter layers using sub-microfibers prepared from biobased raw materials such as polybutylene succinate (PBS). However, it is not yet possible to produce sub-micrometer PBS fibers or uniform nonwoven-like samples at the pilot scale, which hinders their investigation as filter layers. Further optimization of pilot-scale PBS melt electrospinning is therefore required. Here, we tested the effect of different parameters such as electric field strength, nozzle-to-collector distance and throughput on the final fiber diameter and sample uniformity during PBS melt electrospinning on a pilot-scale device. We also studied the effect of a climate chamber and an additional infrared heater on the solidification of PBS fibers and their final diameter and uniformity. In addition, a post-processing step, including a hot air stream of 90 °C for 30 s has been studied and successfully lead to a nonwoven-like structure including filaments that weld together without changing their structure. The finest fibers (1.7 µm in diameter) were produced at an applied electric field strength of −40 kV, a nozzle-to-collector distance of 5.5 cm, and a spin pump speed of 2 rpm. Three uniform nonwoven-like samples were tested as filter layers in a medical face mask by measuring their ability to prevent the transfer of bacteria, but the pore size was too large for effective retention. Our results provide insight into the process parameters influencing the suitability of melt-electrospun nonwoven-like samples as biobased and biodegradable filter materials and offer guidance for further process optimization. Full article

Defects in wood growth affect the product’s quality and grade. At present, the research on texture defects of wood mainly focuses on defect localization, ignoring the splicing problem of maintaining texture consistency. In this paper, we designed the MRS-Transformer network and introduced image inpainting to the field of solid wood board splicing. First, we proposed an asymmetric encoder-decoder based on Vision Transformer, where the encoder uses a fixed mask(M) strategy, discarding the masked patches and using only the unmasked visual patches as input to reduce model calculations. Second, we designed a reverse Swin (RS) module with multi-scale characteristics as the decoder to adjust the divided image patches’ size and complete the restoration from coarse to fine. Finally, we proposed a weighted L2 loss (MSE, mean square error), which assigns different weights to the unmasked region according to the distance from the defective region, allowing the model to make full use of the effective pixels to repair the masked region. To demonstrate the effectiveness of the designed modules, we used MSE (mean square error), LPIPS (learned perceptual image patch similarity), PSNR (peak signal to noise ratio), SSIM (structural similarity), and FLOPs (floating point operations) to measure the quality of the model generated wood texture images and the model computational complexity, we designed relevant ablation experiments. The results show that the MSE, LPIPS, PSNR, and SSIM of the wood images restored by the MRS-Transformer reached 0.0003, 0.154, 40.12, 0.9173, and the GFLOPs is 20.18. Compared with images generated by the Vision Transformer, the MSE and LPIPS were reduced by 51.7% and 30%, PSNR and SSIM were improved by 12.2% and 7.5%, and the GFLOPs were reduced by 38%. To verify the superiority of MRS-Transformer, we compared the image inpainting algorithms with Deepfill v2 and TG-Net, respectively, in which the MSE was 47.0% and 66.9% lower; the LPIPS was 60.6% and 42.5% lower; the FLOPs was 70.6% and 53.5% lower; the PSNR was 16.1% and 26.2% higher; and the SSIM was 7.3% and 5.8% higher. MRS-Transformer repairs a single image in 0.05 s, nearly five times faster than Deepfill v2 and TG-Net. The experimental results demonstrate that the RSwin module effectively alleviates the sense of fragmentation caused by the division of images into patches, the proposed weighted L2 loss improves the semantic consistency of the edges of the missing regions and makes the generated wood texture more detailed and coherent, and the adopted asymmetric encoder-decoder effectively reduces the computational effort of the model and speeds up the training. Full article

Filter masks are disposable devices intended to be worn in order to reduce exposure to potentially harmful foreign agents of 0.1–10.0 microns. However, to perform their function correctly, these devices should be replaced after a few hours of use. Because of this, billions of non-biodegradable face masks are globally discarded every month (3 million/minute). The frequent renewal of masks, together with the strong environmental impact of non-biodegradable plastic-based mask materials, highlights the need to find a solution to this emerging ecological problem. One way to reduce the environmental impact of masks, decrease their turnover, and, at the same time, increase their safety level is to make them able to inhibit pathogen proliferation and vitality by adding antibacterial materials such as silver, copper, zinc, and graphene. Among these, silver and copper are the most widely used. In this study, with the aim of improving commercial devices’ efficacy and eco-sustainability, Ag-based and Cu-based antibacterial treatments were performed and characterized from morphological, compositional, chemical–physical, and microbiological points of view over time and compared with the antibacterial treatments of selected commercial products. The results demonstrated the good distribution of silver and copper particles onto the surface of the masks, along with almost 100% antibacterial capabilities of the coatings against both Gram-positive and Gram-negative bacteria, which were still confirmed even after several washing cycles, thus indicating the good potential of the developed prototypes for mask application. Full article

Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between different feature categories that are similar. In order to solve these problems, this paper proposes a novel approach to hyperspectral image classification using a multidimensional spectral transformer with channel-wise correlation. The proposed method consists of two key components: an input mask and a channel correlation block. The input mask is used to extract relevant spectral information from hyperspectral images and discard irrelevant information, reducing the dimensionality of the input data and improving classification accuracy. The channel correlation block captures the correlations between different spectral channels and is integrated into the transformer network to improve the model’s discrimination power. The experimental results demonstrate that the proposed method achieves great performance with several benchmark hyperspectral image datasets. The input mask and channel correlation block effectively improve classification accuracy and reduce computational complexity. Full article

Hypertension disorders during pregnancy has a wide range of severities, from a mild clinical condition to a life-threatening one. Currently, office BP is still the main method for the diagnosis of hypertension during pregnancy. Despite of the limitation these measurements, in clinical practice office BP of 140/90 mmHg cut point is used to simplify diagnosis and treatment decisions. The out-of-office BP evaluations are it comes to discarding white-coat hypertension with little utility in practice to rule out masked hypertension and nocturnal hypertension. In this revision, we analyzed the current evidence of the role of ABPM in diagnosing and managing pregnant women. ABPM has a defined role in the evaluation of BP levels in pregnant women, being appropriate performing an ABPM to classification of HDP before 20 weeks of gestation and second ABMP performed between 20–30 weeks of gestation to detected of women with a high risk of development of PE. Furthermore, we propose to, discarding white-coat hypertension and detecting masked chronic hypertension in pregnant women with office BP > 125/75 mmHg. Finally, in women who had PE, a third ABPM in the post-partum period could identify those with higher long-term cardiovascular risk related with masked hypertension. Full article

The carbon materials derived from discarded masks and lignin are used as adsorbent to remove two types of reactive dyes present in textile wastewater: anionic and cationic. This paper introduces the results of batch experiments where Congo red (CR) and Malachite green (MG) are removed from wastewater onto the carbon material. The relationship between adsorption time, initial concentration, temperature and pH value of reactive dyes was investigated by batch experiments. It is discovered that pH 5.0–7.0 leads to the maximum effectiveness of CR and MG removal. The equilibrium adsorption capacities of CR and MG are found to be 232.02 and 352.11 mg/g, respectively. The adsorption processes of CR and MG are consistent with the Freundlich and Langmuir adsorption models, respectively. The thermodynamic processing of the adsorption data reveals the exothermic properties of the adsorption of both dyes. The results show that the dye uptake processes follow secondary kinetics. The primary adsorption mechanisms of MG and CR dyes on sulfonated discarded masks and alkaline lignin (DMAL) include pore filling, electrostatic attraction, π-π interactions and the synergistic interactions between the sulphate and the dyes. The synthesized DMAL with high adsorption efficiency is promising as an effective recyclable adsorbent for adsorbing dyes, especially MG dyes, from wastewater. Full article

This research aims to develop a new strategy to valorize wasted COVID-19 masks based on chemical recycling by pyrolysis to convert them into useful products. First, surgical and filtering face piece masks, as defined in Europe by the EN 149 standard (FFP2), were thermally pyrolyzed at temperatures of 450, 500, and 550 °C, and the yields of valuable solid (biochar), liquid (biooil), and syngas products and their characteristics were determined. At low temperatures, biochar formation was favored over biooil and syngas production, while at high temperatures the syngas product yield was enhanced. The highest yield of biooil was found at a pyrolysis temperature of 500 °C, with both surgical and FFP2 masks achieving biooil yields of 59.08% and 58.86%, respectively. Then, the pyrolysis experiments were performed at 500 °C in a two-stage pyrolysis catalytic reactor using sepiolite as a catalyst. Sepiolite was characterized using nitrogen adsorption–desorption isotherms and Fourier-transform infrared spectroscopy. Results showed that the two-stage process increased the final yield of syngas product (43.89% against 39.52% for surgical masks and 50.53% against 39.41% for FFP2 masks). Furthermore, the composition of the biooils significantly changed, increasing the amount of 2,4-Dimethyl-1-heptene and other olefins, such as 3-Eicosene, (E)-, and 5-Eicosene, (E)-. Additionally, the methane and carbon dioxide content of the syngas product also increased in the two-stage experiments. Ultimately, the effect of sepiolite regeneration for its use in consecutive pyrolysis tests was examined. Characterization data showed that, the higher the use-regeneration of sepiolite, the higher the modification of textural properties, with mainly higher changes in its pore volume. The results indicated that the pyrolysis of face masks can be a good source of valuable products (especially from biooil and syngas products). Full article

The reuse of decontaminated disposable medical face masks can contribute to reducing the environmental burden of discarded masks. This research is focused on the effect of household and laboratory washing at 50 °C on the quality and functionality of the nonwoven structure of polypropylene medical masks by varying the washing procedure, bath composition, disinfectant agent, and number of washing cycles as a basis for reusability. The barrier properties of the medical mask were analyzed before and after the first and fifth washing cycle indirectly by measuring the contact angle of the liquid droplets with the front and back surface of the mask, further by measuring air permeability and determining antimicrobial resistance. Additional analysis included FTIR, pH of the material surface and aqueous extract, as well as the determination of residual substances—surfactants—in the aqueous extract of washed versus unwashed medical masks, while their aesthetic aspect was examined by measuring their spectral characteristics. The results showed that household washing had a stronger impact on the change of some functional properties, primarily air permeability, than laboratory washing. The addition of the disinfectant agent, didecyldimethylammonium chloride, contributes to the protective ability and supports the idea that washing of medical masks under controlled conditions can preserve barrier properties and enable reusability. Full article

Microplastics are small plastic particles that come from the degradation of plastics, ubiquitous in nature and therefore affect both wildlife and humans. They have been detected in many marine species, but also in drinking water and in numerous foods, such as salt, honey and marine organisms. Exposure to microplastics can also occur through inhaled air. Data from animal studies have shown that once absorbed, plastic micro- and nanoparticles can distribute to the liver, spleen, heart, lungs, thymus, reproductive organs, kidneys and even the brain (crosses the blood–brain barrier). In addition, microplastics are transport operators of persistent organic pollutants or heavy metals from invertebrate organisms to other higher trophic levels. After ingestion, the additives and monomers in their composition can interfere with important biological processes in the human body and can cause disruption of the endocrine, immune system; can have a negative impact on mobility, reproduction and development; and can cause carcinogenesis. The pandemic caused by COVID-19 has affected not only human health and national economies but also the environment, due to the large volume of waste in the form of discarded personal protective equipment. The remarkable increase in global use of face masks, which mainly contain polypropylene, and poor waste management have led to worsening microplastic pollution, and the long-term consequences can be extremely devastating if urgent action is not taken. Full article

A systemic study on improving particulate pollutant filtration efficiency through the combination of conventional fabrics is presented with the objective of finding comfortable, yet effective airway mask materials and products. Fabrics, nonwovens, and their combinations made of cotton, silk, wool, and synthetic fibers are examined on their filtration efficiency for aerosol particles with diameters ranging from 0.225 μm to 3.750 μm under industry-standard testing conditions. It is found that composite fabrics can improve filtration efficiency more than just layers of the same fabric, and the filtration quality factor of some of the fabric combinations can exceed that of the standard melt-blown materials. In addition, fabric friction and charging between the combined layers also improve filtration efficiency substantially. With a broader understanding of the fabric characteristics, we may design mask products with reduced facial skin discomfort, better aesthetics, as well as the ability to alleviate the environmental impact of discarded protective masks in the extended period of controlling the transmission of pollutants and viruses, such as during the COVID-19 pandemic. Full article