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10 pages, 1433 KiB  
Article
Ion Beam-Induced Luminescence (IBIL) for Studying Manufacturing Conditions in Ceramics: An Application to Ceramic Body Tiles
by Victoria Corregidor, José Luis Ruvalcaba-Sil, Maria Isabel Prudêncio, Maria Isabel Dias and Luís C. Alves
Materials 2024, 17(20), 5075; https://doi.org/10.3390/ma17205075 (registering DOI) - 17 Oct 2024
Abstract
The first experimental results obtained by the ion beam-induced luminescence technique from the ceramic bodies of ancient tiles are reported in this work. The photon emission from the ceramic bodies is related to the starting minerals and the manufacturing conditions, particularly the firing [...] Read more.
The first experimental results obtained by the ion beam-induced luminescence technique from the ceramic bodies of ancient tiles are reported in this work. The photon emission from the ceramic bodies is related to the starting minerals and the manufacturing conditions, particularly the firing temperature and cooling processes. Moreover, the results indicate that this non-destructive technique, performed under a helium-rich atmosphere instead of an in-vacuum setup and with acquisition times of only a few seconds, presents a promising alternative to traditional, often destructive, compositional characterisation methods. Additionally, by adding other ion beam-based techniques such as PIXE (Particle-Induced X-ray Emission) and PIGE (Particle-Induced Gamma-ray Emission), compositional information from light elements such as Na can also be inferred, helping to also identify the raw materials used. Full article
(This article belongs to the Section Advanced Materials Characterization)
15 pages, 6629 KiB  
Article
The Contribution of Carbonaceous Material to Gold Mineralization in the Huangjindong Deposit, Central Jiangnan Orogen, China
by Yueqiang Zhou, Zhilin Wen, Yongjun Liu, Jun Wu, Baoliang Huang, Hengcheng He, Yuxiang Luo, Peng Fan, Xiang Wang, Xiaojun Liu, Teng Deng, Ming Zhong, Shengwei Zhang and Mei Xiao
Minerals 2024, 14(10), 1042; https://doi.org/10.3390/min14101042 (registering DOI) - 17 Oct 2024
Abstract
The Huangjindong gold deposit in northeastern Hunan is one of the most representative gold deposits in the Jiangnan Orogenic Belt. The orebodies are mainly hosted in the Neoproterozoic Lengjiaxi Group, which comprises carbonaceous slates. Abundant carbonaceous material (CM) can be found in the [...] Read more.
The Huangjindong gold deposit in northeastern Hunan is one of the most representative gold deposits in the Jiangnan Orogenic Belt. The orebodies are mainly hosted in the Neoproterozoic Lengjiaxi Group, which comprises carbonaceous slates. Abundant carbonaceous material (CM) can be found in the host rocks and ore-bearing quartz veins, but its geological characteristics and genesis, as well as its association with gold mineralization, are still unclear. Systematic petrographic observation demonstrated two types of CM in host rocks and ores, i.e., CM1 and CM2. Among them, CM1 is the predominant type and mainly occurs in the layered carbonaceous slates, while CM2 is mostly present in quartz veins and mineralized host rocks. Laser Raman spectroscopic analyses of CM1 were performed at higher temperatures (376–504 °C), and CM2 was generated at similar temperatures (255–435 °C) to gold mineralization. Combined with previous studies, we can conclude that CM1 was produced by Neoproterozoic to early Paleozoic metamorphism before gold mineralization, while CM2 is of hydrothermal origin. Geochemical modeling indicates that CM1 could promote gold precipitation through reduction, as well as facilitate structure deformation and metal absorption as previously proposed. However, hydrothermal CM2 is favorable for gold mineralization because it triggers sulfidation, similar to other Fe-bearing minerals (such as siderite) in the host rocks. Consequently, both types of CM in the Huangjindong deposit are favorable for gold mineralization and carbonaceous slates could be important gold-bearing units for future ore prospecting in the Jiangnan Orogen as well as other places in South China. Full article
(This article belongs to the Special Issue Microanalysis Applied to Mineral Deposits)
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Figure 1
<p>(<b>a</b>) Simplified tectonic map of South China showing the location of the Jiangnan Orogen (Modified after Sun et al., 2012 [<a href="#B22-minerals-14-01042" class="html-bibr">22</a>]); (<b>b</b>) Geological map of eastern Hunan showing the distribution of structures, lithologies, and major intrusions of different ages, and different types of ore deposits (Modified after Mao et al., 1997 [<a href="#B23-minerals-14-01042" class="html-bibr">23</a>]).</p>
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<p>(<b>a</b>) Geological map of the Huangjindong gold deposit and (<b>b</b>) a cross-section showing ore geological features and related host rocks of the deposit (modified from [<a href="#B31-minerals-14-01042" class="html-bibr">31</a>]).</p>
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<p>Photographs of orebodies and host rocks in the field from the Jinshan gold deposit. (<b>a</b>) carbonaceous slate; (<b>b</b>) bleaching and unaltered carbonaceous slate; (<b>c</b>) CM in the quartz-carbonate veins; (<b>d</b>) CM is associated with sulfides.</p>
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<p>Photomicrographs of ore bodies and host rocks in thin sections from the Huangjindong gold deposit. (<b>a</b>) CM, quartz, sericite, and siderite in the carbonaceous slate; (<b>b</b>,<b>c</b>) CM1 is closely associated with arsenopyrite, pyrite, sphalerite, siderite, and rutile; (<b>d</b>) locally occurring CM in quartz–carbonate veins; (<b>e</b>) the schematic section showing the relationship of the veins and CM. Q, quartz; Ser, sericite; Sd, siderite; Rt, rutile; Py, pyrite; Ccp, chalcopyrite; Sp, sphalerite; Gn, galena.</p>
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<p>(<b>a</b>) The quartz (Q1)–carbonate veins; (<b>b</b>) ankerite in the quartz (Q1)–carbonate veins; (<b>c</b>) scheelite-quartz (Q2) vein is crosscut by the arsenopyrite–pyrite–quartz (Q3) vein; (<b>d</b>) CM2 is closely associated with pyrite and arsenopyrite; (<b>e</b>) polysulphides cut through pyrite and arsenopyrite; (<b>f</b>) quartz-calcite veins. Apy, arsenopyrite; Ank, ankerite.</p>
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<p>Mineralization stages and the paragenesis of the Huangjindong gold deposit.</p>
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<p>(<b>a</b>,<b>b</b>) Raman spectra of CM1; (<b>c</b>,<b>d</b>) Raman spectra of CM2.</p>
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<p>(<b>a</b>) Au solubility diagrams in pH-log <span class="html-italic">f</span>O<sub>2</sub>(g) coordinates at 250 °C with a distribution of Au-bearing aqueous species and Fe-bearing minerals at S = 0.005 mol/kg; (<b>b</b>) mineral precipitation and changes of pH, log <span class="html-italic">f</span>O<sub>2</sub>(g) and S concentrations in fluids at 250 °C when reacting with graphite.</p>
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<p>Mineralization model for the Huangjindong gold deposit. CM1 promotes gold precipitation through reduction, while CM2 favors mineralization by triggering sulfidation. Sd, siderite; CM, Carbonaceous matter; Au, native gold.</p>
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15 pages, 3214 KiB  
Article
Influence of Particle Size on Flotation Separation of Ilmenite and Forsterite
by Senpeng Zhang, Yaohui Yang, Donghui Wang, Weiping Yan and Weishi Li
Minerals 2024, 14(10), 1041; https://doi.org/10.3390/min14101041 - 17 Oct 2024
Abstract
In addition to bubble–particle interaction, particle–particle interaction also has a significant influence on mineral flotation. Fine particles that coat the mineral surface prevent direct contact with collectors and/or air bubbles, thereby lowering flotation recovery. Calculating the particle interaction energy can help in evaluating [...] Read more.
In addition to bubble–particle interaction, particle–particle interaction also has a significant influence on mineral flotation. Fine particles that coat the mineral surface prevent direct contact with collectors and/or air bubbles, thereby lowering flotation recovery. Calculating the particle interaction energy can help in evaluating the interaction behavior of particles. In this study, the floatability of coarse ilmenite (−151+74 μm) and different particle sizes (−45+25, −25+19, −19 μm) of forsterite with NaOL as a collector was investigated. The results showed that forsterite sizes of −45+25 and −25+19 μm had no effect on the ilmenite floatability, whereas −19 μm forsterite significantly reduced ilmenite floatability. A particle size analysis of artificially mixed minerals and a scanning electron microscopy (SEM) analysis of the flotation products showed that heterogeneous aggregation occurred between ilmenite and −19 μm forsterite particles. The extended DLVO (Derjaguin–Landau–Verwey–Overbeek) theory was applied to calculate the interaction energy between mineral particles using data from zeta potential and contact angle measurements. The results showed that the interaction barriers between ilmenite (−151+74 μm) and forsterite (−45+25, −25+19, and −19 μm) were 11.94 × 103 kT, 8.23 × 103 kT and 4.09 × 103 kT, respectively. Additionally, the interaction barrier between forsterite particles smaller than 19 μm was 0.51 × 103 kT. The strength of the barrier decreased as the size of the forsterite decreased. Therefore, fine forsterite particles and aggregated forsterite can easily overcome the energy barrier, coating the ilmenite particle surface. This explains the effect of different forsterite sizes on the floatability of ilmenite and the underlying mechanism of particle interaction. Full article
19 pages, 1840 KiB  
Article
Dietary Additive Combination for Dairy Calves After Weaning Has a Modulating Effect on the Profile of Short-Chain Fatty Acids in the Rumen and Fecal Microbiota
by Tainara Leticia Dos Santos, Jorge Augusto Rosina Favaretto, Andrei Lucas Rebelatto Brunetto, Emerson Zatti, Maiara Sulzbach Marchiori, Wanderson Adriano Biscola Pereira, Miklos Maximiliano Bajay and Aleksandro S. Da Silva
Fermentation 2024, 10(10), 528; https://doi.org/10.3390/fermentation10100528 - 17 Oct 2024
Abstract
Background: This study aimed to verify whether adding a combination of additives (blend) to the diet of dairy calves after weaning can improve animal performance and health and influence the profile of ruminal short-chain fatty acids and intestinal microbiota. Methods: We used 35 [...] Read more.
Background: This study aimed to verify whether adding a combination of additives (blend) to the diet of dairy calves after weaning can improve animal performance and health and influence the profile of ruminal short-chain fatty acids and intestinal microbiota. Methods: We used 35 Holstein calves, males, with an average age of 70 days and an average body weight of 68 kg. The treatments used were negative control (T-0: without additive), positive control (T-Control: flavomycin + monensin), T-500 (500 g blend/ton), T-1000 (1000 g blend/ton), and T-1500 (1500 g blend/ton). The additives were classified as zootechnical (probiotics, prebiotics, and essential oils of cinnamon and oregano) and nutritional additive (minerals). Results: Weight gain and daily weight gain were higher for calves in the T-Control, T-500, and T-1000 groups. The concentration of heavy-chain immunoglobulins was higher in the blood of calves in the T-Control and T-500 groups when compared to the other groups. In the T-1500 groups, higher levels of reactive oxygen species were observed, while, in the T-0 and T-1500 groups, higher levels of TBARS and glutathione S-transferase activity were detected. The 15 abundant microorganisms in the calves’ feces, regardless of treatment, were Treponema suis, Treponema saccharophilum, Faecalibacterium prausnitzii, Pseudoflavonifractor sp., Roseburia faecis, Rikenellaceae, Enterobacteriaceae_f, Clostridium sp., Roseburia intestinalis, Aeromonadales_o, Prevotella copri, Treponema succinifaciens, Eubacterium sp., Treponema porcium, and Succinivibrio sp. The T-1000 group showed greater alpha diversity for the intestinal microbiota than T-Control, T-0, and T-500. The additive combination (T-1000) increased the bacterial activity in the ruminal fluid, and the animals of T-1000 had a higher concentration of short-chain fatty acids compared to T-0 and T-1500; this difference is because, in these calves, the production of acetic, butyric, and propionic acid increased. Conclusions: The combination of additives had positive effects on animal health, ruminal volatile fatty acid production, and intestinal microbiota, resulting in animals with more significant weight gain and feed efficiency. Full article
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<p>Regression analysis demonstrates the quadrative effect of additive consumption on weight gain during the experimental period (days 1–60).</p>
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<p>Relative abundance of the 15 most frequent microorganisms in the feces of calves fed with the additive combination (T-500, T-1000, and T-1500) compared to negative control (T-0) and positive control (T-Control: monensin and virginicin) groups: data by period (day 30 and 60) and by experimental group.</p>
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<p>Alpha diversity (Simpson’s inverse) and beta diversity (Pco2) of the intestinal microbiota of calves fed additive combination (T-500, T-1000, and T-1500) compared to negative control (T-0) and positive control (T-Control: monensin and virginicin) groups. Different letters between groups in alpha diversity represent statistical differences between groups (<span class="html-italic">p</span> &lt; 0.05), as well as compared to day 1 (collection performed before starting additive consumption).</p>
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<p>Correlation between relative abundance of microorganisms versus weight gain on days 30 and 60 of the experiment (* = <span class="html-italic">p</span> &lt; 0.05; ** = <span class="html-italic">p</span> &lt; 0.01; *** = <span class="html-italic">p</span> &lt; 0.001).</p>
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13 pages, 694 KiB  
Article
A Clinical Evaluation of the Role of Autoimmunity in the Relation Between Erosions and Bone Mineral Density in Rheumatoid Arthritis
by Margaux Moret, Caroline Morizot, Marcelo de Carvalho Bittencourt, Edem Allado, Isabelle Chary-Valckenaere and Damien Loeuille
Biomedicines 2024, 12(10), 2376; https://doi.org/10.3390/biomedicines12102376 - 17 Oct 2024
Abstract
Background/objectives: Both erosions and osteoporosis in rheumatoid arthritis (RA) have common mechanisms. The aim of this study was to evaluate the relationship between erosion and bone mineral density (BMD) in RA and whether it can be driven by autoimmunity. Methods: Patients fulfilling the [...] Read more.
Background/objectives: Both erosions and osteoporosis in rheumatoid arthritis (RA) have common mechanisms. The aim of this study was to evaluate the relationship between erosion and bone mineral density (BMD) in RA and whether it can be driven by autoimmunity. Methods: Patients fulfilling the ACR 1987- or ACR/EULAR 2010-criteriae for RA. performed radiographs (erosions evaluated by the modified Sharp/van der Heidje erosion score) and biology for anti-citrullinated peptide antibodies (ACPAs), rheumatoid factors (RFs) and anti-nuclear antibodies (ANAs) at intervals of less than 2 years from dual-energy X-ray absorptiometry (DXA) for BMD assessment. Results: A total of 149 patients were included, (75.8% women, mean age of 62 y.o (SD 9.61) and a median disease duration of 132 months [60; 240]). A total of 61.1% patients were ACPA positive, 79.9% were erosive and 10.7% had a hip or spine T-score ≤ −2.5. A higher erosion score was associated with a lower BMD (value: −0.222; p = 0.009) and T-score (value −0.397; p < 0.0001) in the hip. ACPA status was associated with a higher erosion score (63.0 (53.2) vs. 45.5 (44.1) for ACPA- (p = 0.04)). ACPA titers were associated with a lower BMD in the hip (value −0.216; p = 0.01). In linear regression, erosion and BMD were still associated, but this association is not driven by ACPA status or titer. Conclusions: In RA patients, erosions and BMD are inversely associated but this relationship does not seem to be driven by autoimmunity only. However, the presence of ACPA or erosion should lead to osteoporosis screening. Full article
(This article belongs to the Special Issue Molecular Research on Osteoarthritis and Osteoporosis)
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<p>Flowchart of the study.</p>
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<p><b>Variables associated with SHSe total score, HipBMD and Hip T-score on multivariate analysis presented in forest plots.</b> (<b>A</b>) SHSe total score and associated variables with hip BMD included in the multivariate analysis. (<b>B</b>) SHSe total score and associated variables with hip T-score included in the multivariate analysis. (<b>C</b>) Hip T-score and associated variables. (<b>D</b>) Hip BMD and associated variables. SHSe: modified Sharp/van der Heijde erosion score, BMI: body mass index, BMD: bone mineral density, ACPA: anti-cyclic citrullinated peptide antibody, RF: rheumatoid factor. Linear regression was used. All variables included in the multivariate presented a <span class="html-italic">p</span>-value &lt; 0.1 in the univariate analysis. The x-axis represents the estimated coefficient of regression.</p>
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21 pages, 8416 KiB  
Article
Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India
by Pravat Ranjan Dixit, Muhammad Saeed Akhtar, Rakesh Ranjan Thakur, Partha Chattopadhyay, Biswabandita Kar, Dillip Kumar Bera, Sasmita Chand and Muhammad Kashif Shahid
Water 2024, 16(20), 2961; https://doi.org/10.3390/w16202961 - 17 Oct 2024
Abstract
Marine pollution poses significant risks to both human and marine health. This investigation explores the limnological status of the Odisha and West Bengal coasts during the annual cruise program, focusing on the influence of riverine inputs on coastal marine waters. To assess this [...] Read more.
Marine pollution poses significant risks to both human and marine health. This investigation explores the limnological status of the Odisha and West Bengal coasts during the annual cruise program, focusing on the influence of riverine inputs on coastal marine waters. To assess this impact, physicochemical parameters such as pH, salinity, total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), and dissolved nutrients (NO2-N, NO3-N, NH4-N, PO4-P, SiO4-Si, total-N, and total-P) were analyzed from samples collected along 11 transects. Multivariate statistics and principal component analysis (PCA) were applied to the datasets, revealing four key factors that account for over 70.09% of the total variance in water quality parameters, specifically 25.01% for PC1, 21.94% for PC2, 13.13% for PC3, and 9.99% for PC4. The results indicate that the increase in nutrient and suspended solid concentrations in coastal waters primarily arises from weathering and riverine transport from natural sources, with nitrate sources linked to the decomposition of organic materials. Coastal Odisha was found to be rich in phosphorus-based nutrients, particularly from industrial effluents in Paradip and the Mahanadi, while ammonia levels were attributed to municipal waste in Puri. In contrast, the West Bengal coast exhibited higher levels of nitrogenous nutrients alongside elevated pH and DO values. These findings provide a comprehensive understanding of the seasonal dynamics and anthropogenic influences on coastal water quality in Odisha and West Bengal, highlighting the need for targeted conservation and management efforts. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Sampling location of 11 different transects along Odisha and West Bengal Coasts.</p>
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<p>Contours showing variation in salinity in PSU unit.</p>
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<p>Contours showing variation in dissolved oxygen in mg/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in BOD in mg/L by SURFER Analysis.</p>
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<p>Contours showing variation in dissolved nitrite (µmol/L) by SURFER Analysis method.</p>
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<p>Contours showing variation in nitrate in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in ammonia in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in total nitrogenin µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in Inorganic phosphatein µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in total phosphorous in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in silicate in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in Chlorophyll-a in mg/L unit by SURFER Analysis method.</p>
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<p>Scree plot for components with its eigenvalue.</p>
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<p>Linear regression analysis: (<b>a</b>) Suspended solids and Chl-a; (<b>b</b>) BOD and Salinity; (<b>c</b>) Suspended solids and Salinity; (<b>d</b>) Nitrite and Salinity; (<b>e</b>) Chl-a and Salinity; (<b>f</b>) Nitrate and Salinity; (<b>g</b>) DO and Salinity; (<b>h</b>) Ammonia and Salinity; (<b>i</b>) Inorganic Phosphate and Salinity; (<b>j</b>) Silicate and Salinity.</p>
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23 pages, 7325 KiB  
Article
Dissolution of Volcanic Ash in Alkaline Environment for Cold Consolidation of Inorganic Binders
by Giovanni Dal Poggetto, Philippe Douwe, Antonio Stroscio, Elie Kamseu, Isabella Lancellotti, Antoine Elimbi and Cristina Leonelli
Materials 2024, 17(20), 5068; https://doi.org/10.3390/ma17205068 - 17 Oct 2024
Abstract
A systematic study on the dissolution in concentrated alkali of two volcanic ashes from Cameroon, denoted as DAR and VN, is presented here. One volcanic ash, DAR, was 2 wt% richer in Fe and Ca and 4 wt% lower in Si than the [...] Read more.
A systematic study on the dissolution in concentrated alkali of two volcanic ashes from Cameroon, denoted as DAR and VN, is presented here. One volcanic ash, DAR, was 2 wt% richer in Fe and Ca and 4 wt% lower in Si than the other, designated as VN. Such natural raw materials are complex mixtures of aluminosilicate minerals (kaersutite, plagioclase, magnetite, diopside, thenardite, forsterite, hematite, and goethite) with a good proportion of amorphous phase (52 and 74 wt% for DAR and VN, respectively), which is more reactive than the crystalline phase in alkaline environments. Dissolution in NaOH + sodium silicate solution is the first step in the geopolymerisation process, which, after hardening at room temperature, results in solid and resistant building blocks. According to XRD, the VN finer ash powders showed a higher reactivity of Al-bearing soluble amorphous phases, releasing Al cations in NaOH, as indicated by IPC-MS. In general, dissolution in a strong alkaline environment did not seem to be affected by the NaOH concentration, provided that it was kept higher than 8 M, or by the powder size, remaining below 75 µm, while it was affected by time. However, in the time range studied, 1–120 min, the maximum element release was reached at about 100 min, when an equilibrium was reached. The hardened alkali activated materials show a good reticulation, as indicated by the low weight loss in water (10 wt%) when a hardening temperature of 25 °C was assumed. The same advantage was found for of the room-temperature consolidated specimens’ mechanical performance in terms of resistance to compression (4–6 MPa). The study of the alkaline dissolution of volcanic ash is, therefore, an interesting way of predicting and optimising the reactivity of the phases of which it is composed, especially the amorphous ones. Full article
(This article belongs to the Special Issue Advances in Natural Building and Construction Materials)
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Graphical abstract

Graphical abstract
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<p>Comparison of particle size distribution curves of pure DAR (orange) and pure VN (blue) volcanic ash powders.</p>
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<p>Chemical composition of the 3 different fractions of each volcanic ash: DAR (red colours) and VN (green colours). Numerical values are given in <a href="#materials-17-05068-t001" class="html-table">Table 1</a>.</p>
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<p>FT-IR spectra of DAR volcanic ash: as-ground (blue) and after immersion in NaOH 12 M for 2 h (red).</p>
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<p>FT-IR spectra of VN volcanic ash: as-ground (blue) and after immersion in NaOH 12 M for 2 h (red).</p>
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<p>Leaching of metals of volcanic ash powder, DAR, after 8, 10, and 12 M after 120 min. The grain size used for the test is 5–75 µm.</p>
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<p>Leaching of metals of volcanic ash powder, VN, after 8, 10, and 12 M after 120 min. The grain size used for the test is 5–75 µm.</p>
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<p>Leaching of metals of volcanic ash powders, DAR and VN, after immersion in NaOH 12 M as a function of time. The grain size used for the test is 5–75 µm.</p>
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<p>Al released from DAR volcanic ash at different grain size after NaOH 12 M over a period of 2 h.</p>
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<p>Al released from VN volcanic ash at different grain size after NaOH 12 M over a period of 2 h.</p>
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<p>Si released from DAR volcanic ash at different grain size after NaOH 12 M over a period of 2 h.</p>
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<p>Si released from VN volcanic ash at different grain size after NaOH 12 M over a period of 2 h.</p>
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<p>XRD patterns of DAR powders before and after immersion in NaOH at different molar ratios for 120 min. di = diopside (PDF: 19–0239); hem = hematite (PDF: 89–2810); An = Anorthite (PDF: 71–0748); Ka = Kaersutite (PDF: 44–1450); gibb = gibbsite (PDF: 96–101–1082); ght = goethite (PDF: 81–0462); Mg = Magnetite (PDF: 89–3854); Fo = Forsterite (PDF: 87–0619); Au = Augite (PDF: 71–1070); * = zincite (standard).</p>
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<p>XRD patterns of VN powders before and after immersion in NaOH at different molar ratios for 120 min. di = diopside (PDF: 19–0239); hem = hematite (PDF: 89–2810); An = Anorthite (PDF: 71–0748); Ka = Kaersutite (PDF: 44–1450); gibb = gibbsite (PDF: 96–101–1082); ght = goethite (PDF: 81–0462); Mg = Magnetite (PDF: 89–3854); Fo = Forsterite (PDF: 87–0619); Au = Augite (PDF: 71–1070); * = zincite (standard).</p>
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<p>Weight loss of GP-DAR and GP-VN geopolymers cured for 24 h at two different temperatures (25 °C and 45 °C).</p>
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<p>Ionic conductivity of geopolymer with DAR and VN made with different NaOH concentrations (8, 10, and 12 M), cured at 25 °C.</p>
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<p>ESEM backscattered images of freshly fractured samples: (<b>A</b>) GPDAR 12 T25 and (<b>B</b>) GPVN 12 T25.</p>
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<p>Comparison of mechanical properties of GPDAR samples made at different NaOH concentrations and different temperatures after 28 days of curing.</p>
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<p>Comparison of mechanical properties of GPVN samples made at different NaOH concentrations and different temperatures after 28 days of curing.</p>
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20 pages, 11405 KiB  
Article
Characterization of Three-Dimensional Strong Force Chain Properties of Mineral Aggregate Mixtures Based on the Discrete Element Method
by Yuan Gao, Guoqiang Liu and Nan Jiang
Buildings 2024, 14(10), 3289; https://doi.org/10.3390/buildings14103289 - 17 Oct 2024
Abstract
The skeleton structure composed of mineral aggregates is the main body to bear and transfer external loading in asphalt mixtures. To investigate the loading transfer mechanism of the mineral aggregate skeleton, the uniaxial penetration test and Discrete Element Method (DEM) were conducted for [...] Read more.
The skeleton structure composed of mineral aggregates is the main body to bear and transfer external loading in asphalt mixtures. To investigate the loading transfer mechanism of the mineral aggregate skeleton, the uniaxial penetration test and Discrete Element Method (DEM) were conducted for the Mineral Aggregate Mixture (MAM) to analyze its mechanical behavior. The three-dimensional strong force chain (SFC) was identified and evaluated based on the proposed recognition criterion and evaluation indices. The results indicate that 4.75 mm should be the boundary to distinguish the coarse and fine aggregates. The skeleton composed of aggregates located on SFCs has better bearing and transferring loading capacity due to its SFC number, average length, and total length decreasing with an increase in the aggregate size. Compared to SMA-16 and OGFC-16, AC-16 exhibits a higher number and total length of its SFC, a smaller average length of its SFC, and a lower average strength of its SFC. Consequently, AC-16 has a lower bearing and transferring loading capacity than that of SMA-16 and OGFC-16. In addition, approximately 90% of SFCs can only transfer external loading downward through 3–5 aggregates. The average direction angle of the SFC formed by fine aggregates is significantly higher than those formed by coarse aggregates. This indicates that the load transfer range of MAM composed of fine aggregates is noticeably larger, leading to lower loading transfer efficiency. Full article
(This article belongs to the Special Issue Advances in Performance-Based Asphalt and Asphalt Mixtures)
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<p>Gradation curves of different MAM.</p>
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<p>Different DEM specimens of MAMs: (<b>a</b>) 9.50–13.2 mm; (<b>b</b>) 4.75–9.50 mm; (<b>c</b>) 2.36–4.75 mm (<b>d</b>) AC-16; (<b>e</b>) SMA-16; (<b>f</b>) OGFC-16.</p>
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<p>Schematic diagram of linear contact model.</p>
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<p>Different single-sized MAMs: (<b>a</b>) 19.0–26.5 mm; (<b>b</b>) 16.0–19.0 mm; (<b>c</b>) 13.2–16.0 mm (<b>d</b>) 9.50–13.2 mm; (<b>e</b>) 4.75–9.50 mm; (<b>f</b>) 2.36–4.75 mm.</p>
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<p>Experimental instruments: (<b>a</b>) test cylinder size diagram; (<b>b</b>) penetration head; (<b>c</b>) pavement strength tester, which can load the specimen at a constant speed of 1.25 mm/min.</p>
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<p>Penetration resistance curve of SMAMs: (<b>a</b>) coarse aggregates; (<b>b</b>) fine aggregates.</p>
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<p>Penetration resistance curve of DEM specimens: (<b>a</b>) SMAMs; (<b>b</b>) GMAMs.</p>
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<p>The virtual uniaxial penetration test program.</p>
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<p>Contact angle threshold.</p>
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<p>“Crescent-shaped” SFC.</p>
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<p>Force-extension γ diagram.</p>
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<p>Program of SFC recognition algorithm: (<b>a</b>) SFC recognition algorithm; (<b>b</b>) repeat SFC recognition algorithm.</p>
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<p>Mineral skeleton diagram of SMAM with 2.36–4.75 mm.</p>
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<p>The linearity of SFCs.</p>
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<p>The orientation angle of an SFC.</p>
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<p>Identified aggregates located on SFC of SMAMs: (<b>a</b>) 19.0–26.5 mm; (<b>b</b>) 16.0–19.0 mm; (<b>c</b>) 13.2–16.0 mm (<b>d</b>) 9.50–13.2 mm; (<b>e</b>) 4.75–9.50 mm; (<b>f</b>) 2.36–4.75 mm.</p>
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<p>SFC characteristics of SMAMs: (<b>a</b>) the number of SFCs; (<b>b</b>) the average length of the SFCs; (<b>c</b>) the total length of the SFCs; (<b>d</b>) the average particle number in an SFC; (<b>e</b>) particle number distribution; (<b>f</b>) the average linearity; (<b>g</b>) the average orientation angle; (<b>h</b>) the average strength.</p>
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<p>SFC characteristics of SMAMs: (<b>a</b>) the number of SFCs; (<b>b</b>) the average length of the SFCs; (<b>c</b>) the total length of the SFCs; (<b>d</b>) the average particle number in an SFC; (<b>e</b>) particle number distribution; (<b>f</b>) the average linearity; (<b>g</b>) the average orientation angle; (<b>h</b>) the average strength.</p>
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<p>Identified aggregates located on SFCs of different GMAMs: (<b>a</b>) AC-16; (<b>b</b>) SMA-16; (<b>c</b>) OGFC-16.</p>
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<p>The length of the SFC of different GMAMs: (<b>a</b>) the average length of the SFC; (<b>b</b>) the total length of the SFC; (<b>c</b>) the length distribution of SFC.</p>
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<p>The number of particles in SFCs of different GMAMs: (<b>a</b>) average particle number in an SFC; (<b>b</b>) particle number distribution in SFCs.</p>
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<p>The linearity of SFCs of different GMAMs: (<b>a</b>) the average linearity of SFCs; (<b>b</b>) the linearity distribution of SFCs.</p>
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<p>The orientation angle of SFCs of different GMAMs: (<b>a</b>) the average orientation angle of SFCs; (<b>b</b>) the orientation angle distribution of SFCs.</p>
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<p>The strength of SFCs of different GMAMs: (<b>a</b>) the average strength of SFCs; (<b>b</b>) the strength distribution of SFCs.</p>
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15 pages, 3814 KiB  
Article
Implementing Antimony Supply and Sustainability Measures via Extraction as a By-Product in Skarn Deposits: The Case of the Chalkidiki Pb-Zn-Au Mines
by Micol Bussolesi, Alessandro Cavallo, Vithleem Gazea, Evangelos Tzamos and Giovanni Grieco
Sustainability 2024, 16(20), 8991; https://doi.org/10.3390/su16208991 - 17 Oct 2024
Abstract
Antimony is one of the world’s scarcest metals and is listed as a Critical Raw Material (CRM) for the European Union. To meet the increasing demand for metals in a sustainable way, one of the strategies that could be implemented would be the [...] Read more.
Antimony is one of the world’s scarcest metals and is listed as a Critical Raw Material (CRM) for the European Union. To meet the increasing demand for metals in a sustainable way, one of the strategies that could be implemented would be the recovery of metals as by-products. This would decrease the amount of hazardous materials filling mining dumps. The present study investigates the potential for producing antimony as a by-product at the Olympias separation plant in Northern Greece. This plant works a skarn mineralization that shows interesting amounts of Sb. Boulangerite (Pb5Sb4S11) reports on Pb concentrate levels reached 8% in the analyzed product. This pre-enrichment is favorable in terms of boulangerite recovery since it can be separated from galena through froth flotation. Boulangerite distribution in the primary ore is quite heterogeneous in terms of the inclusion relationships and grain size. However, a qualitative assessment shows that the current Pb concentrate grain size is too coarse to successfully liberate a good amount of boulangerite. The use of image analysis and textural assessments is pivotal in determining shape factors and crystal size, which is essential for the targeting of flotation parameters during separation. The extraction of antimony as a by-product is possible through a two-step process; namely, (i) the preliminary concentration of boulangerite, followed by (ii) the hydrometallurgical extraction of the antimony from the boulangerite concentrate. The Olympias enrichment plant could therefore set a positive example by promoting the benefits of targeted Sb extraction as a by-product within similar sulfide deposits within the European territory. Full article
(This article belongs to the Special Issue Sustainable Mining and Circular Economy)
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<p>(<b>a</b>) simplified geological map of the Kassandra mining district; modified after Högdahl et al. [<a href="#B14-sustainability-16-08991" class="html-bibr">14</a>]; (<b>b</b>) simplified flotation plant; sample tags are reported as numbers.</p>
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<p>Grain size distribution of Pb (<b>a</b>), Zn (<b>c</b>) and Au (<b>e</b>) concentrates and of the different final tailings (<b>b</b>,<b>d</b>,<b>f</b>) produced during the flotation process.</p>
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<p>Texture and optical features of Stratoni ore minerals; (<b>a</b>) pyrite, galena and sphalerite with chalcopyrite disease (optical microscope reflected light); (<b>b</b>) boulangerite replacing galena and arsenopyrite (BSE image); (<b>c</b>) acicular boulangerite crystals within carbonate gangue and arsenopyrite (BSE image); (<b>d</b>) pyrrhotite, pyrite, chalcopyrite and galena crystals (optical microscope, reflected light).</p>
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<p>XRD patterns and mineral modal contents (relative abundance %) of initial feed (<b>a</b>) Pb concentrate; (<b>b</b>) Pb tailing acting as Zn feed (<b>c</b>); Zn concentrate; (<b>d</b>) Zn tailing acting as Au feed; (<b>e</b>) Au concentrate; (<b>f</b>) final tailing (<b>g</b>).</p>
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<p>Examples of boulangerite distribution in selected samples; (<b>a</b>) rare boulangerite included within pyrite, sample A; (<b>b</b>) widespread boulangerite within pyrite, sample C1; (<b>c</b>) boulangerite included within galena and pyrite, sample C1; (<b>d</b>) boulangerite crystals crosscutting gangue and ore minerals, sample C1.</p>
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<p>Particle size distribution of boulangerite in eight representative BSE images; cumulative areas of boulangerite particles and frequency of distribution histograms are also reported; ore sample A-5 area a (<b>a</b>), ore sample C1, BSE image C1-30 area a (<b>b</b>), ore sample C1, BSE image C1-30 area b (<b>c</b>) and ore sample C1, BSE image C1-30 area c (<b>d</b>), ore sample C1, BSE image C1-3 area a (<b>e</b>), ore sample C1, BSE image C1-6 area a (<b>f</b>), ore sample C1, BSE image C1-12 area a (<b>g</b>), ore sample C1, BSE image C1-32 (<b>h</b>).</p>
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17 pages, 2864 KiB  
Article
Organic Mulching Versus Soil Conventional Practices in Vineyards: A Comprehensive Study on Plant Physiology, Agronomic, and Grape Quality Effects
by Andreu Mairata, David Labarga, Miguel Puelles, Luis Rivacoba, Javier Portu and Alicia Pou
Agronomy 2024, 14(10), 2404; https://doi.org/10.3390/agronomy14102404 - 17 Oct 2024
Abstract
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost [...] Read more.
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost (SMC), straw (STR), and grapevine pruning debris (GPD)) and two conventional soil practices (herbicide application (HERB) and tillage (TILL)) on grapevine physiology, agronomy, and grape quality parameters over three years. SMC mulch enhanced soil moisture and nutrient concentration. However, its mineral composition increased soil electrical conductivity (0.78 dS m⁻1) and induced grapevine water stress due to osmotic effects without significantly affecting yield plant development. Only minor differences in leaf physiological parameters were observed during the growing season. However, straw (STR) mulch reduced water stress and increased photosynthetic capacity, resulting in higher pruning weights. Organic mulches, particularly SMC and STR, increased grape pH, potassium, malic acid, and tartaric acid levels, while reducing yeast assimilable nitrogen. The effect of organic mulching on grapevine development depends mainly on soil and mulch properties, soil water availability, and environmental conditions. This research highlights the importance of previous soil and organic mulch analysis to detect vineyard requirements and select the most appropriate soil management treatment. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p>Summary of field climatic conditions. (<b>A</b>) Monthly accumulated precipitation (mm) in 2020, 2021, and 2022 and average monthly precipitation in 2005–2019. (<b>B</b>) Annual accumulated precipitation (P, mm), reference evapotranspiration (ET0, mm), and growing degree days (GDDs, °C day<sup>−1</sup>).</p>
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<p>Average soil volumetric water content (VWC, %) and standard deviations of the soil under SMC (<span class="html-fig-inline" id="agronomy-14-02404-i001"><img alt="Agronomy 14 02404 i001" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i001.png"/></span>), STR (<span class="html-fig-inline" id="agronomy-14-02404-i002"><img alt="Agronomy 14 02404 i002" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i002.png"/></span>), GPD (<span class="html-fig-inline" id="agronomy-14-02404-i003"><img alt="Agronomy 14 02404 i003" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i003.png"/></span>), HERB (<span class="html-fig-inline" id="agronomy-14-02404-i004"><img alt="Agronomy 14 02404 i004" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i004.png"/></span>), and TILL (<span class="html-fig-inline" id="agronomy-14-02404-i005"><img alt="Agronomy 14 02404 i005" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i005.png"/></span>) through the day of year (DOY) of the vine vegetative cycle in 2021 (<b>A1</b>–<b>A3</b>) and 2022 (<b>B1</b>–<b>B3</b>) at three soil depths: 5 (1), 15 (2), and 25 (3) cm. Precipitation events (mm) are represented by blue columns (<span class="html-fig-inline" id="agronomy-14-02404-i006"><img alt="Agronomy 14 02404 i006" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i006.png"/></span>). Black lines indicate the phenology stages of flowering (F), fruit set (S), veraison (V), and grape maturity (M) and the day when leaf gas exchange parameters and water potentials were recorded. The treatment soil water content was monitored with three field devices.</p>
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<p>The mean and standard deviation of grape carbon isotope discrimination (δ<sup>13</sup>C) of the soil management treatments studied. Statistical differences indicated by letters were accepted when <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Must and grape physical-chemical parameters of the five soil management treatments from 2020 to 2022: (<b>A</b>) total soluble solids (Brix), (<b>B</b>) pH, (<b>C</b>) total acidity (TA), (<b>D</b>) tartaric acid, (<b>E</b>) malic acid, (<b>F</b>) potassium, (<b>G</b>) yeast assimilable nitrogen (YAN), (<b>H</b>) berry weight, (<b>I</b>) anthocyanins, and (<b>J</b>) total polyphenol index at 280 nm (TPI). Significant differences between soil management treatments are represented by letters when <span class="html-italic">p</span> &lt; 0.05 (n.s. = non-significant differences).</p>
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14 pages, 3485 KiB  
Article
Strengthening Transformer Tank Structural Integrity through Economic Stiffener Design Configurations Using Computational Analysis
by Md Milon Hasan, Arafater Rahman, Asif Islam and Mohammad Abu Hasan Khondoker
Appl. Mech. 2024, 5(4), 717-730; https://doi.org/10.3390/applmech5040039 - 17 Oct 2024
Abstract
Power transformers play a vital role in adjusting voltage levels during transmission. This study focuses on optimizing the structural design of power transformer tanks, particularly high-voltage (HV) tank walls, to enhance their mechanical robustness, performance, and operational reliability. This research investigates various stiffener [...] Read more.
Power transformers play a vital role in adjusting voltage levels during transmission. This study focuses on optimizing the structural design of power transformer tanks, particularly high-voltage (HV) tank walls, to enhance their mechanical robustness, performance, and operational reliability. This research investigates various stiffener designs and their impact on stress distribution and deformation through finite element analysis (FEA). Ten different configurations of stiffeners, including thickness, width, type, and position variations, were evaluated to identify the optimal design that minimizes stress and deflection while considering weight constraints. The results indicate that specific configurations, particularly those incorporating 16 mm thick H beams, significantly enhance structural integrity. Experimental validation through pressure testing corroborated the simulation findings, ensuring the practical applicability of the optimized designs. This study’s findings have implications for enhancing the longevity and reliability of power transformers, ultimately contributing to more efficient and resilient power transmission systems. Full article
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<p>A sequential procedure for optimizing a power transformer’s HV (high-voltage) tank wall. CAD: computer-aided design; ANSYS: a commercial computational software for simulation.</p>
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<p>(<b>a</b>) Power transformer whole tank design and (<b>b</b>) power transformer HV tank wall design.</p>
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<p>Flowchart optimizes the HV tank wall in ANSYS static structure simulation.</p>
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<p>CAD Models of (<b>a</b>) conventional design; (<b>b</b>) stiffeners with width changed to 300 mm; (<b>c</b>) stiffeners with thickness changed to 30 mm; (<b>d</b>) body plate changed to 12 mm; (<b>e</b>) supports added to both sides of each stiffener; (<b>f</b>) 12 mm thick H beam added at center; (<b>g</b>) 16 mm thick H beam added at center; (<b>h</b>) three 40 mm thick stiffeners added at the center; (<b>i</b>) 40 mm thick stiffeners added at positions 3, 5, 6, and 8; (<b>j</b>) 40 mm thick stiffeners added at positions 2, 4, 6, and 8.</p>
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<p>Mesh dependency test for analyzing dependent and sensitive element sizes in the designs of a 120 MVA power transformer HV tank wall. (<b>a</b>) Von Mises stress vs. element size. (<b>b</b>) Deflection vs. element size. (<b>c</b>) Boundary condition and unstructured meshed body.</p>
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<p>(<b>a</b>) Experimental setup for pressure testing of 120 MVA 132/33 kV power transformer tank. (<b>b</b>) Set up the pressure gauge meter; (<b>c</b>) Supports are installed on both sides of the stiffeners.</p>
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<p>CAD model of HV tank wall showing various stiffener positions.</p>
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<p>ANSYS simulation of deformation data for various design modifications: (<b>a</b>) original design; (<b>b</b>) stiffener width changed to 300 mm; (<b>c</b>) stiffener thickness changed to 30 mm; (<b>d</b>) body plate thickness changed to 12 mm; (<b>e</b>) supports added to both sides of each stiffener; (<b>f</b>) 12 mm thick H beam added at center; (<b>g</b>) 16 mm thick H beam added at center; (<b>h</b>) three 40 mm thick stiffeners added at the center; (<b>i</b>) 40 mm thick stiffeners added at positions 3, 5, 6, and 8; (<b>j</b>) 40 mm thick stiffeners added at positions 2, 4, 6, and 8.</p>
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<p>Von Mises stress for various design modifications: (<b>a</b>) original design; (<b>b</b>) stiffener width changed to 300 mm; (<b>c</b>) stiffener thickness changed to 30 mm; (<b>d</b>) body plate thickness changed to 12 mm; (<b>e</b>) supports added to both sides of each stiffener; (<b>f</b>) 12 mm thick H beam added at center; (<b>g</b>) 16 mm thick H beam added at center; (<b>h</b>) three 40 mm thick stiffeners added at the center; (<b>i</b>) 40 mm thick stiffeners added at positions 3, 5, 6, and 8; (<b>j</b>) 40 mm thick stiffeners were added at positions 2, 4, 6, and 8, with an H-beam added at the center.</p>
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16 pages, 2439 KiB  
Article
Enhancing Mango Productivity with Wood Vinegar, Humic Acid, and Seaweed Extract Applications as an Environmentally Friendly Strategy
by Mahmoud Abdel-Sattar, Laila Y. Mostafa and Hail Z. Rihan
Sustainability 2024, 16(20), 8986; https://doi.org/10.3390/su16208986 - 17 Oct 2024
Abstract
Although chemical fertilization has gained a lot of attention due to its ability to increase the yield of fruit trees, it has been known to cause numerous environmental problems such as soil deterioration, alleviating beneficial microorganisms, and reducing fruit quality and safety. Hence, [...] Read more.
Although chemical fertilization has gained a lot of attention due to its ability to increase the yield of fruit trees, it has been known to cause numerous environmental problems such as soil deterioration, alleviating beneficial microorganisms, and reducing fruit quality and safety. Hence, today, we aim to reduce these problems by using eco-friendly and sustainable biostimulants to promote nutritional status, yield, and quality. The effect of wood vinegar (WV) on mango production has yet to be investigated. Therefore, a field trial was conducted during the 2023 and 2024 seasons to evaluate the regulatory effect of individual and combined application of wood vinegar (WV), seaweed extract (SW), and humic acid (HA) on the performance of mango (Mangifera indica L.) cv. Ewais. The results revealed that all treatments had a pronounced effect and significantly improved the total chlorophyll content (107.7 and 106.6%), leaf N (2.02 and 2.23%), P (0.38 and 0.4), and K (1.07 and 1.13%), as well as enhancing the quality of mango fruits by increasing fruit length (11.68 and 12.38 cm), fruit width (7.8 and 8.59 cm), total sugars (40 and 37.3%), and TSS (21.9 and 20.8%) while reducing the total acidity (64.3 and 69.0%) in the 2023 and 2024 seasons, respectively, compared with the control. Based on this study, the treatment of 2 L/ha seaweed + 2 L/ha humic acid + 2 L/ha wood vinegar combined had the greatest effect on enhancing Ewais mango fruit yield by up-regulating leaf mineral acquisition, antioxidant response, and sugar accumulation. This study supports the application of HA and SW in combination with WV to improve mango fruit yield and quality. Full article
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<p>The impact of humic acid (HA), seaweed extract (SW), and wood vinegar (WV) applied to leaf chlorophyll (chlorophyll a (<b>A</b>), chlorophyll b (<b>B</b>), total chlorophyll (<b>C</b>), and total carbohydrate (<b>D</b>) content of Ewais mango trees assessed for the 2023 and 2024 seasons. Data are presented as means ± SE.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on the yield of Ewais mango trees in the 2023 and 2024 seasons. Data are presented as means ± SE.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on TSS (<b>A</b>), acidity (<b>B</b>), TSS/acidity ratio (<b>C</b>), and vitamin C (<b>D</b>) of Ewais mango trees in the 2023 and 2024 seasons.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on total sugar (<b>A</b>), reducing sugar (<b>B</b>), non-reducing sugar (<b>C</b>), and total carotenoids (<b>D</b>) of Ewais mango trees in the 2023 and 2024 seasons.</p>
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42 pages, 113259 KiB  
Article
Hypogene Alteration of Base–Metal Mineralization at the Václav Vein (Březové Hory Deposit, Příbram, Czech Republic): The Result of Recurrent Infiltration of Oxidized Fluids
by Zdeněk Dolníček, Jiří Sejkora and Pavel Škácha
Minerals 2024, 14(10), 1038; https://doi.org/10.3390/min14101038 - 17 Oct 2024
Abstract
The Václav vein (Březové Hory deposit, Příbram ore area, Czech Republic) is a base–metal vein containing minor Cu-Zn-Pb-Ag-Sb sulfidic mineralization in a usually hematitized gangue. A detailed mineralogical study using an electron microprobe revealed a complicated multistage evolution of the vein. Early siderite [...] Read more.
The Václav vein (Březové Hory deposit, Příbram ore area, Czech Republic) is a base–metal vein containing minor Cu-Zn-Pb-Ag-Sb sulfidic mineralization in a usually hematitized gangue. A detailed mineralogical study using an electron microprobe revealed a complicated multistage evolution of the vein. Early siderite and Fe-rich dolomite were strongly replaced by assemblages of hematite+rhodochrosite and hematite+kutnohorite/Mn-rich dolomite, respectively. In addition, siderite also experienced strong silicification. These changes were associated with the dissolution of associated sulfides (sphalerite, galena). The following portion of the vein contains low-Mn dolomite and calcite gangue with Zn-rich chlorite, wittichenite, tetrahedrite-group minerals, chalcopyrite, bornite, and djurleite, again showing common replacement textures in case of sulfides. The latest stage was characterized by the input of Ag and Hg, giving rise to Ag-Cu sulfides, native silver (partly Hg-rich), balkanite, and (meta)cinnabar. We explain the formation of hematite-bearing oxidized assemblages at the expense of pre-existing “normal” Příbram mineralization due to repeated episodic infiltration of oxygenated surface waters during the vein evolution. Episodic mixing of ore fluids with surface waters was suggested from previous stable isotope and fluid inclusion studies in the Příbram ore area. Our mineralogical study thus strengthens this genetic scenario, illustrates the dynamics of fluid movement during the evolution of a distinct ore vein structure, and shows that the low content of ore minerals cannot be necessarily a primary feature of a vein. Full article
(This article belongs to the Special Issue Mineralogy and Geochemistry of Polymetallic Ore Deposits)
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<p>The youngest mineral assemblage of a drusy cavity in sample P1N 9430. (<b>a</b>) A top view across the whole drusy cavity. (<b>b</b>) Calcite crystals with acicular ore aggregates. (<b>c</b>) Smooth and finely wrinkled surface of acicular ore aggregates. (<b>d</b>) Acicular ore aggregates enclosed in a transparent crystal of calcite.</p>
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<p>Geological position of the Březové Hory deposit in the Příbram ore area (modified from [<a href="#B1-minerals-14-01038" class="html-bibr">1</a>]). BHD—Březové Hory base–metal district, PUD—Příbram uranium district. Positions of some other sites mentioned in the text are also indicated.</p>
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<p>Position of the Václav vein in geological cross-section through the Březové Hory ore district (modified from [<a href="#B9-minerals-14-01038" class="html-bibr">9</a>]). The Anna shaft is situated north of the Prokop shaft, out of the section line.</p>
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<p>The macroscopic appearance of the sample P1N 9430 with marked zones A–E and sites, from which samples for preparation of polished sections were cut off. The left part of the figure illustrates the distribution of selected mineral phases. Sample width is 4.5 cm.</p>
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<p>Mineral assemblage and textures of the Václav vein in the BSE images. (<b>a</b>) A slightly zoned relic of siderite replaced by surrounding quartz, carbonates of the dolomite-kutnohorite series, hematite, and tetrahedrite. Right part is wall rock. Zone A, sample VA-1. (<b>b</b>) Relic of siderite strongly replaced by hematite, rhodochrosite, and zoned carbonates of the dolomite series. Zone A, sample VA-2. (<b>c</b>) Relic of siderite rimmed by rhodochrosite and hematite. Zone A, sample VA-2. (<b>d</b>) Euhedral rhodochrosite crystal enclosed in hematite in the proximity of a relic of siderite, which is strongly replaced by the rhodochrosite rim and carbonates of the dolomite group. Zone A, sample VA-2. (<b>e</b>) Boundary between Zone A (hematite-rich on the left) and Zone B (hematite-poor on the right) separated by quartz crystals. Replacement of Do-I by Do-II is observed in the right part. Sample VA-5. (<b>f</b>) Detailed view on replacement of Do-I by zoned Do-II containing inclusions of hematite. Zone B, sample VA-5.</p>
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<p>Variations in the chemical composition of siderite, rhodochrosite, and calcite from the Václav vein in comparison with published data. (<b>a</b>) Siderite and rhodochrosite in the classification diagram by [<a href="#B27-minerals-14-01038" class="html-bibr">27</a>]. (<b>b</b>) Fe vs. Mn and Fe vs. Mg plots for calcite. Comparative data for other deposits of the Příbram uranium and base metal district are from [<a href="#B5-minerals-14-01038" class="html-bibr">5</a>,<a href="#B6-minerals-14-01038" class="html-bibr">6</a>,<a href="#B7-minerals-14-01038" class="html-bibr">7</a>,<a href="#B28-minerals-14-01038" class="html-bibr">28</a>].</p>
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<p>Variations in the chemical composition of carbonates of the dolomite-ankerite series from the Václav vein in comparison with published data. (<b>a</b>) All data in the classification diagram by [<a href="#B27-minerals-14-01038" class="html-bibr">27</a>]. (<b>b</b>) Data sorted according to Zones. (<b>c</b>) Data arbitrarily grouped according to compositional similarities. Comparative data for other deposits of the Příbram uranium and base metal district are from [<a href="#B5-minerals-14-01038" class="html-bibr">5</a>,<a href="#B6-minerals-14-01038" class="html-bibr">6</a>,<a href="#B7-minerals-14-01038" class="html-bibr">7</a>,<a href="#B28-minerals-14-01038" class="html-bibr">28</a>]. PUD–average dolomite from the Příbram uranium and base–metal district according to wet-chemical analyses by [<a href="#B29-minerals-14-01038" class="html-bibr">29</a>].</p>
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<p>Mineral assemblage and textures of the Václav vein. (<b>a</b>) Contact between Do-II and Do-III dolomites. Note the corrosion of Do-II by Do-III just along the contact. Zone C, sample VA-7. (<b>b</b>) Zoned crystals of carbonates of the dolomite group (bright Do-II is overgrown by darker Do-III) growing over lenticular hematite crystals. Residual vug was later filled up by calcite with aggregates of tetrahedrite and chalcopyrite. Zone A, black domain, sample VA-6. (<b>c</b>) The latest Do-III dolomite crystals overgrown by chalcopyrite-bornite aggregates and calcite. Zone D, sample VA-9. (<b>d</b>) Aggregates of Zn-rich chlorite filling together with calcite residual cavities in the vein composed of euhedral lenticular hematite crystals, sulfidic aggregates, and Do-II+Do-III carbonates. Zone A, black domain, sample VA-6. (<b>e</b>) Two generations of hematite strongly differing in the quality of the polished surface. Fine-grained early hemispherical aggregates are poorly polished, whereas the latest hematite preceding crystallization of Do-II carbonate followed by sulfides is well polished. Sulfide aggregate is composed of sphalerite, chalcopyrite, tetrahedrite-(Zn) (Ttd-I), and an unknown reddish AgCu<sub>6</sub>Fe<sub>2</sub>S<sub>8</sub> phase. Zone A, sample VA-1. (<b>f</b>) The central area of Figure (<b>e</b>) in BSE image. Note the zonality of hematite and Do-II carbonate. Zone A, sample VA-1. Figure (<b>e</b>) is taken in plane-polarized reflected light, whereas the other pictures are BSE images.</p>
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<p>Variations in the chemical composition of chlorite from the Václav vein and comparison with published data. (<b>a</b>) A Fe-Mg-Zn plot. (<b>b</b>) The Ca vs. Si plot. The comparative data from the Jerusalem deposit (Příbram uranium district) are from [<a href="#B5-minerals-14-01038" class="html-bibr">5</a>].</p>
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<p>Mineral assemblage and textures of the Václav vein. (<b>a</b>) Concentric zonation of hematite. Zone A, sample VA-2. (<b>b</b>) Patchy zonation of hematite caused by variable Sb contents. The youngest part of Zone A, sample VA-3. (<b>c</b>) The strongly corroded cassiterite hosted by sphalerite (partly Cu,Sn-enriched) replaced by hematite+Do-II aggregate. Zone A, sample VA-4. (<b>d</b>) Finely porous and non-porous bornite and chalcopyrite. Note a bluish tint of a part of porous bornite. Sample Dy-817. (<b>e</b>) Finely porous and non-porous bornite and chalcopyrite, with a short veinlet of tetrahedrite. Note the compositional homogeneity of bornite. Sample Dy-817. (<b>f</b>) An acicular polymineral aggregate composed of bornite, covellite, and Ag-Cu sulfides (Ag-Cu-S) with thick symmetrical rims of chalcopyrite I (Cpy-I). Zone E, sample Dy-973. Figures (<b>d</b>,<b>f</b>) are taken in plane-polarized reflected light, whereas the other pictures are BSE images.</p>
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<p>Variations in the chemical composition of hematite from the Václav vein. (<b>a</b>) The Si vs. Al plot. (<b>b</b>) The Si vs. Sb plot. (<b>c</b>) The Me<sup>2+</sup> vs. Sb plot. (<b>d</b>) The Pb vs. Sb plot.</p>
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<p>Mineral assemblage and textures of the Václav vein. (<b>a</b>) A crust formed by chalcopyrite (Cpy-I), partly filled and enclosed by bornite and overgrown by a tetrahedrite (Ttd-III) crystal. Part of the pores in bornite was filled by covellite and Ag-Cu sulfides. Bornite contains ribbons of chalcopyrite (Cpy-II). Zone E, sample Dy-973. (<b>b</b>) Three morphological forms of chalcopyrite, crust (Cpy-I), ribbon (Cpy-II), and symplectite with mckinstryite (Symplectite), hosted by bornite with late rims and fillings of covellite and Ag-Cu(-Hg)-S phases. The black rectangle shows the area of <a href="#minerals-14-01038-f012" class="html-fig">Figure 12</a>c. Zone E, sample Dy-973. (<b>c</b>) BSE detail of the central part of Figure (c) showing the nature of Ag-Cu(-Hg)-S phases: fine intergrowths of stromeyerite and mckinstryite are partly overgrown by balkanite. (<b>d</b>) Sphalerite grains are rimmed by bornite and chalcopyrite. Note the intense corrosion of both earlier sulfide phases by later ones. Zone A, sample VA-1. (<b>e</b>) Sphalerite rimmed by bornite, tetrahedrite (Ttd-I), and two generations of chalcopyrite differing in porosity. Note early porous chalcopyrite Cpy-I is replaced by Ttd-I. Zone D, sample VA-9. (<b>f</b>) Bright Ag-enriched zone in chalcopyrite. Zone D, sample VA-8. Figures (<b>c</b>,<b>f</b>) are BSE images; the other pictures are taken in plane-polarized reflected light.</p>
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<p>Variations in the chemical composition of some ore minerals from the Václav vein in comparison with published data. (<b>a</b>) Graph Ag versus Sb for chalcopyrite. (<b>b</b>) Graph Ag versus Cu for bornite. (<b>c</b>) Graph Sn versus Cu for sphalerite. (<b>d</b>) Graph Fe versus Cd for sphalerite. (<b>e</b>) Graph Ag versus Cu+Fe+Cd for mckinstryite. (<b>f</b>) Graph Ag versus Cu for balkanite. Comparative data for sphalerite from the Háje deposit (Příbram uranium district) are from [<a href="#B7-minerals-14-01038" class="html-bibr">7</a>], for mckinstryite from Milín from [<a href="#B31-minerals-14-01038" class="html-bibr">31</a>], for other mckinstryite data from [<a href="#B32-minerals-14-01038" class="html-bibr">32</a>], for danielsite from [<a href="#B33-minerals-14-01038" class="html-bibr">33</a>], and for published balkanite data from [<a href="#B5-minerals-14-01038" class="html-bibr">5</a>,<a href="#B34-minerals-14-01038" class="html-bibr">34</a>,<a href="#B35-minerals-14-01038" class="html-bibr">35</a>,<a href="#B36-minerals-14-01038" class="html-bibr">36</a>,<a href="#B37-minerals-14-01038" class="html-bibr">37</a>].</p>
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<p>Mineral assemblage and textures of the Václav vein. (<b>a</b>) Irregular aggregate of djurleite partly replaced by bornite, which is rimmed by a non-continuous zone of chalcopyrite and small grains of Ag-Cu-Hg-S phases. Note the abundant ribbons of chalcopyrite in the outer part of bornite adjacent to the chalcopyrite rim. Sample Dy-816. (<b>b</b>) Sphalerite rimmed by bornite (with brighter Ag-enriched domains) and then by chalcopyrite. Late microfractures contain Ag-Cu-S phases. Zone D, sample VA-9. (<b>c</b>) The brighter Cu,Sn-enriched domains in sphalerite in the vicinity of strongly corroded grains of cassiterite. Zone A, sample VA-4. (<b>d</b>) Sphalerite with brighter Cd-enriched domains (in the lower part of the grain), rimmed by chalcopyrite and enclosing grains of wittichenite and Bi-enriched tetrahedrite. Zone A, sample VA-4. Inset–Oscillatory zoning of a sphalerite grain due to changing Cd contents. Zone C, sample VA-7. (<b>e</b>) Mn-enriched sphalerite and hematite in the residual cavity in Mn-rich dolomite Do-II replacing Fe-rich dolomite Do-I. Zone B, sample VA-5. (<b>f</b>) Tetrahedrite Ttd-I is cut by veinlets of bornite and chalcopyrite Cpy-II. Zone D, sample VA-9. Figures (<b>a</b>,<b>f</b>) are taken in plane-polarized reflected light; the other pictures are BSE images.</p>
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<p>Mineral assemblage and textures of the Václav vein in BSE images. (<b>a</b>) Zonality of a tetrahedrite Ttd-I aggregate is largely caused by Fe-Zn substitution and also, exceptionally, by high Cd (arrowed). The Ttd-I is cut by veinlets of Bi-bearing Ttd-II and a narrow overgrowth of Ttd-III is observed in the lower part of the photograph. Zone D, sample VA-9. (<b>b</b>) Oscillatory zoned Bi-bearing Ttd-II rimming a grain of chalcopyrite. Zone A, sample VA-6. (<b>c</b>) Patchy zoning of Ttd-II. The brightest domain already corresponds to annivite-(Zn). Zone A, sample VA-6. (<b>d</b>) Zoned Bi-bearing tetrahedrite Ttd-II rimming and cutting tetrahedrite Ttd-I. The brightest domain corresponds to <span class="html-italic">annivite-</span>(<span class="html-italic">Cu</span>). Surrounding sphalerite encloses wittichenite. Zone A, sample VA-3. (<b>e</b>) Nature of Ag-Cu sulfides in chalcopyrite-hosted “acicular” polymineral aggregate from <a href="#minerals-14-01038-f010" class="html-fig">Figure 10</a>f: small domains of jalpaite are hosted by the mckinstryite matrix. Zone E, sample Dy-973. (<b>f</b>) A finely porous aggregate of Hg-absent native silver partly rimmed by stromeyerite. Zone E, sample Dy-973.</p>
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<p>Variations in the chemical composition of tetrahedrite-group minerals from the Václav vein (data points) in comparison with published data (outlined). (<b>a</b>) Graph Fe-Zn-Cd. (<b>b</b>) Graph As-Sb-Bi. Data in at. %. Comparative data for Jáchymov and Hřebečná sites are from [<a href="#B41-minerals-14-01038" class="html-bibr">41</a>]. Notably, data from the Příbram ore area are not visualized as they exhibit Fe-Zn and Sb-As substitutions only.</p>
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<p>Mineral assemblage and textures of the Václav vein in BSE images. (<b>a</b>) Balkanite-like phase rimming grains of probable (meta)cinnabar. Zone A, sample VA-1. (<b>b</b>) Grains of unspecified Ag-Cu-Hg-S phases (white) with variable compositions growing on a chalcopyrite finger-like aggregate. Zone E, sample Dy-973. (<b>c</b>) Individual grains of likely galena (GA) and unspecified Ag-Cu-Hg-S phases with variable compositions growing on a djurleite-bornite-chalcopyrite aggregate. Sample Dy-816. (<b>d</b>) An unknown (Cu,Ag)<sub>4</sub>FeS<sub>4</sub> phase associated with covellite in a sphalerite-chalcopyrite-hematite aggregate. Zone A, sample VA-3.</p>
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<p>Variations in the chemical composition of some ore minerals from the Václav vein in comparison with published data. (<b>a</b>) Graph Cu/Ag versus Hg for balkanite. (<b>b</b>) Graph Ag versus Cu or Hg for balkanite and possible intergrowths of Ag-Cu-Hg phases. (<b>c</b>) Graph Hg versus Cu for balkanite and possible intergrowths of Ag-Cu-Hg phases. (<b>d</b>) Graph Hg versus Ag+Cu+Fe for balkanite and possible intergrowths of Ag-Cu-Hg phases. Comparative published balkanite data are from [<a href="#B5-minerals-14-01038" class="html-bibr">5</a>,<a href="#B34-minerals-14-01038" class="html-bibr">34</a>,<a href="#B35-minerals-14-01038" class="html-bibr">35</a>,<a href="#B36-minerals-14-01038" class="html-bibr">36</a>,<a href="#B37-minerals-14-01038" class="html-bibr">37</a>], and data for danielsite are from [<a href="#B33-minerals-14-01038" class="html-bibr">33</a>].</p>
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<p>A sketch showing the interpreted textural evolution of the late ore assemblage from a drusy cavity of the sample P1N 9430. The crystallization of an acicular mineral (<b>a</b>) was followed by the deposition of a continuous layer of early chalcopyrite Cpy-I on its crystals (<b>b</b>). Then, the dissolution of acicular mineral took place (<b>c</b>), followed by the crystallization of bornite inside and outside of Cpy-I crusts (<b>d</b>). The crystallization of late chalcopyrite Cpy-II and tetrahedrite Ttd-III (<b>e</b>) was followed by minor fracturing of early ores and partial healing of the residual porosity by the latest ore minerals including covellite and Ag-Cu(-Hg) sulfides (<b>f</b>).</p>
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<p>The simplified paragenetic scheme of the Václav vein. Note that the position of some mineral phases is questionable (marked by ?); more problematic phases are missing.</p>
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<p>The interpreted scenario of the origin of the studied mineralization from the Václav vein. (<b>a</b>) Early stage characterized by the escape of “deep” fluids. (<b>b</b>) Late stage involving the circulation of basinal fluids—Scenario I. (<b>c</b>) Late stage involving the circulation of basinal fluids–Scenario II. Arrows characterize the direction of fluid movement.</p>
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21 pages, 1883 KiB  
Article
Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
by Ammar Alsheghri, Yoan Ladini, Golriz Hosseinimanesh, Imane Chafi, Julia Keren, Farida Cheriet and François Guibault
Appl. Sci. 2024, 14(20), 9486; https://doi.org/10.3390/app14209486 - 17 Oct 2024
Abstract
During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin [...] Read more.
During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field. Full article
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<p>Converting die meshes to point clouds and downsampling the point clouds to 10,000 points.</p>
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<p>Extracting ground truth margin lines. A crown bottom is first extracted from a crown designed by a dental technician. The internal edge of crown bottom lower horizontal thickness coincides with the margin line on the dental preparation. The internal points are extracted, projected on the die, and augmented to represent the margin line.</p>
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<p>AdaPoinTr architecture showing the forward pass in blue arrows and backpropagation pass in red.</p>
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<p>One case augmented 20 times.</p>
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<p>Identifying outliers with (<b>a</b>) local density only; (<b>b</b>) with local density and PCA; (<b>c</b>) first component of PCA. Purple represents outliers in (<b>a</b>,<b>b</b>). With both local density and PCA, less outliers are observed.</p>
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<p>Illustration of the post-processing procedures to remove outliers.</p>
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<p>Predicted margin line point clouds of four test cases of different positions compared with ground truth. Red is the prediction, green is the ground truth.</p>
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<p>Qualitative comparison of margin lines obtained using the proposed framework showing the predicted points with outliers highlighted, the predicted margin line splines with outliers (baseline), the predicted splines without outliers improvement, and the ground truth margin lines. The chamfer distance and confidence metric are also presented for each test case.</p>
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<p>Qualitative and quantitative results comparing the margin line predictions using our proposed model with the ground truth.</p>
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<p>Challenging test case showing margin line prediction (dotted) compared with ground truth (solid), both overlaid on the die. The contours of the mean curvatures values of the die mesh are shown. Blue represents high curvature and red represents low curvature. The die geometry is also shown without contours.</p>
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<p>Worst margin line point cloud prediction recorded on a test case considered as a special case.</p>
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<p>Representative frequencies of (<b>a</b>) CD values; (<b>b</b>) percentage of outliers, for the test set obtained using fold 2 model. CD values start from 0.062 mm because the prediction never matches the ground truth exactly.</p>
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<p>Representative CD training and validation loss curves.</p>
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<p>Ordering point cloud using travel sales person algorithm. The 10 lasts points of the point cloud are shown in red, and the first 10 are shown in blue. Notice that one red point is far from where it is supposed to be.</p>
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13 pages, 2645 KiB  
Article
Assessing the Effectiveness of Turf Transplantation and Artificial Replanting in Restoring Abandoned Mining Areas
by Amannisa Kuerban, Guankui Gao, Abdul Waheed, Hailiang Xu, Shuyu Wang and Zewen Tong
Sustainability 2024, 16(20), 8977; https://doi.org/10.3390/su16208977 - 17 Oct 2024
Viewed by 187
Abstract
Long-term and extensive mineral mining in the Kuermutu mine section of the Two Rivers Nature Reserve in the Altai region has disrupted the ecological balance between soil and vegetation. To assess the effectiveness of various restoration measures in this abandoned mine area, we [...] Read more.
Long-term and extensive mineral mining in the Kuermutu mine section of the Two Rivers Nature Reserve in the Altai region has disrupted the ecological balance between soil and vegetation. To assess the effectiveness of various restoration measures in this abandoned mine area, we compared two restoration approaches—natural turf transplantation (NTT) and replanted economic crop grassland (ARGC)—against an unaltered control (original grassland). We employed 11 evaluation indices to conduct soil and vegetation surveys. We developed a comprehensive evaluation model using the Analytic Hierarchy Process (AHP) to assess restoration outcomes for each grassland type. Our findings indicate that both NTT and ARGC significantly improved ecological conditions, such as reducing soil fine particulate matter loss and restoring vegetation cover. This brought these areas closer to their original grassland state. The species composition and community structure of the NTT and ARGC vegetation communities improved relative to the original grassland. This was due to a noticeable increase in dominant species’ importance value. Vegetation cover averaged higher scores in NTT, while the average height was greater in ARGC. The soil water content and soil organic carbon (SOC) varied significantly with depth (p < 0.05), following a general ‘V’ pattern. NTT positively impacted soil moisture content (SMC) at the surface, whereas ARGC influenced SMC in deeper layers, with the 40–50 cm soil layer achieving 48.13% of the original grassland’s SMC. SOC levels were highest in the control (original grassland), followed by ARGC and NTT, with ARGC showing the greatest organic carbon content at 20–30 cm depths. A comprehensive AHP ecological-economic evaluation revealed that restoration effectiveness scores were 0.594 for NTT and 0.669 for ARGC, translating to 59.4% and 66.9%, respectively. ARGC restoration was found to be more effective than NTT. These results provide valuable insights into ecological restoration practices for abandoned mines in Xinjiang and can guide future effectiveness evaluations. Full article
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<p>Study area.</p>
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<p>Vegetation coverage (<b>a</b>) and mean height (<b>b</b>) of different types of grasslands. Note: Different lowercase letters indicate significant differences between different types of grasslands at the 0.05 level. NG, natural grassland; NTT, natural turf transplantation; ARCG, artificial replanting of cash crop grassland; this is applicable for the following figures as well.</p>
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<p>Changes in diversity indices of vegetation communities in different grassland types (<b>a</b>–<b>d</b>). “NS” indicated that the grassland type diversity indices of the restored NTT and ARCG were not significantly different from those of the original grassland, illustrating that the restoration was effective.</p>
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<p>Effect of restoration of natural turf-transplanted grassland and replanted cash crop blackcurrant grassland.</p>
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