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Keywords = red wall tile

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16 pages, 7939 KiB  
Article
Assessment of the Valorization Potential of Municipal Sewage Treatment Plant (STP) Sludge to Produce Red-Firing Wall Tiles
by Isabela Oliveira Rangel Areias, Felipe Sardinha Maciel and José Nilson França Holanda
Minerals 2025, 15(8), 879; https://doi.org/10.3390/min15080879 - 21 Aug 2025
Viewed by 37
Abstract
Municipal sewage treatment plants generate significant amounts of polluting sludge, which demands innovative valorization approaches to support its sustainable recycling. This work aimed to evaluate the valorization potential of sludge from a municipal sewage treatment plant (STP) as an alternative raw material to [...] Read more.
Municipal sewage treatment plants generate significant amounts of polluting sludge, which demands innovative valorization approaches to support its sustainable recycling. This work aimed to evaluate the valorization potential of sludge from a municipal sewage treatment plant (STP) as an alternative raw material to traditional limestone in red wall tile formulations. For this purpose, four red wall tile formulations were performed with 0%, 5%, 10%, and 15% weight of STP sludge replacing traditional limestone. The tile formulations prepared by the dry process were characterized to determine their chemical and mineral compositions, thermal analysis, and sintering behavior. The red wall tile pieces were manufactured by pressing and firing at temperatures ranging from 1150 °C to 1180 °C. The effects of STP sludge incorporation and firing temperature on the densification behavior and technological properties were investigated. The results indicated that the STP sludge exhibited good chemical compatibility for use in red wall tile formulations. Water absorption values varied between 16.52% and 19.70%, indicating compliance with the red wall tile production (BIII group). These findings demonstrate the valorization potential of STP sludge in red wall tiles, which offers a relevant recycling option for the sanitation sector and the circular economy. Full article
(This article belongs to the Special Issue From Clay Minerals to Ceramics: Progress and Challenges)
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20 pages, 1788 KiB  
Article
Hyperspectral Imaging for Bloodstain Identification
by Maheen Zulfiqar, Muhammad Ahmad, Ahmed Sohaib, Manuel Mazzara and Salvatore Distefano
Sensors 2021, 21(9), 3045; https://doi.org/10.3390/s21093045 - 27 Apr 2021
Cited by 43 | Viewed by 6891
Abstract
Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this [...] Read more.
Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods. Full article
(This article belongs to the Special Issue Recent Advances in Multi- and Hyperspectral Image Analysis)
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