| 1. |
- Albolafio, Sofia, et al.
(author)
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Potential of Wastewater Valorization after Wet Extraction of Proteins from Faba Bean and Pea Flours
- 2021
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In: Recent Progress in Materials. - : Lidsen Publ Inc. - 2689-5846. ; 3:2
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Journal article (peer-reviewed)abstract
- The present study aimed to characterize wastewater fractions obtained after the wet extraction of proteins from legumes. In addition, the suitability of wastewater fractions for the potential recovery of high value-added compounds was also examined, and consequently, the prevention of the environmental impact of these wastes was explored. Similar to the industrial production of proteins, wet alkaline and acidic extractions of proteins from faba bean and pea flours were performed in two stages of extraction. The different wastewater fractions were characterized by measuring their organic matter content, total solids (TS), total dissolved solids (TDS), electrical conductivity (EC), pH, and turbidity. The value-added compounds from these wastewater fractions were quantified, which included the protein content, carbohydrate content, phenolic content, and antioxidant activity. In addition, the phenolic compounds in these factions were identified and quantified. It was observed that the fractions obtained in the first extraction stage had 60%–90% higher organic matter content, measured as the chemical oxygen demand (COD), compared to the second fractions, indicating a higher environmental impact of the former in case of disposal. The results obtained for COD, TS, TDS, EC, pH, and turbidity demonstrated that microfiltration reduced only the turbidity (85%), and consequently, a decrease was observed in the particulate matter, while there was a practically negligible reduction in the soluble matter. Wastewater from faba exhibited the highest polyphenol content and antioxidant activity, and was, therefore, considered the most valuable fraction for potential valorization.
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| 2. |
- Vestin Fredriksson, Malin, 1977-, et al.
(author)
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Construction and evaluation of a modular anthropomorphic phantom of the skull with an exchangeable specimen jar to optimize the radiological examination of temporal bone pathology
- 2024
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In: Recent Progress in Materials. - : Lidsen Publishing. - 2689-5846. ; 06:03
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Journal article (peer-reviewed)abstract
- To develop a modular anthropomorphic phantom to evaluate the performance of radiological techniques for detecting pathologies in the temporal bone region. A phantom was constructed using a human skull, temporal bone specimen, and 3D-printed contour of a human skull. The human skull was embedded in tissue-equivalent plastic, with a cavity to hold the plastic jars containing the exchangeable freshly frozen human temporal bones. Subsequently, stepwise introduction and examination of different clinicopathological scenarios were conducted. Radiological images were nearly identical to those acquired from patients using computed tomography (CT) and cone beam computed tomography (CBCT). The radiological attenuation of polyurethane plastic (PUR) and alginate were similar to those of the soft tissues of living human patients. The mean Hounsfield unit values of the CT slices representing tissue at the brain and temporal bone level were 184 and 171 in the phantom and patient groups, respectively. The modular phantom developed in this study can evaluate radiological techniques and diagnostic possibilities without exposing patients to radiation. To our knowledge, no such modular phantom has been reported in the literature or made available commercially.
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| 3. |
- Wu, Pei-Yu, et al.
(author)
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Machine Learning in Hazardous Building Material Management : Research Status and Applications
- 2021
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In: Recent Progress in Materials. - : Lidsen Publ.. - 2689-5846. ; 3:2
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Journal article (peer-reviewed)abstract
- Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction
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