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Search Results (1,283)

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14 pages, 5954 KiB  
Article
Frost Resistance Differences of Concrete in Frequent Natural Freeze–Thaw versus Standard Rapid Method
by Changzhong Deng, Lei Yu, Haoyu Wang, Zhaolei Liu and Dongmei Fan
Buildings 2024, 14(8), 2489; https://doi.org/10.3390/buildings14082489 - 12 Aug 2024
Viewed by 212
Abstract
In order to find the anti-freezing durability differences between concrete in the frequent natural freeze–thaw conditions in the northwest of Sichuan Province, China, and concrete in the rapid freeze–thaw conditions of the standard rapid method, the typical temperature and humidity of the northwest [...] Read more.
In order to find the anti-freezing durability differences between concrete in the frequent natural freeze–thaw conditions in the northwest of Sichuan Province, China, and concrete in the rapid freeze–thaw conditions of the standard rapid method, the typical temperature and humidity of the northwest of Sichuan Province were simulated. The results showed that the average number of freeze–thaw cycles in the northwest of this province can reach up to 150 per year. The relative dynamic modulus of C30 ordinary concrete, which is 100% pre-saturated, still remained above 90% after 450 cycles in simulated environments. However, during the rapid freeze–thaw test, even the C30 air-entrained concrete failed after 425 cycles. Compared to the saturation degree of concrete itself, the continuous replenishment of external moisture during freeze–thaw cycles is a key factor affecting the frost resistance of concrete. Rapid freeze–thaw reduces the number of the most probable pore sizes in ordinary concrete, and the pore size distribution curve tends to flatten. The reduction rate of the surface porosity of air-entrained concrete before and after rapid freeze–thaw is only about one third of that of ordinary concrete. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>The procedure of the rapid freeze–thaw method.</p>
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<p>Daily temperature changes in Jiuzhi district (altitude 3628.5) within a year.</p>
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<p>Daily temperature changes in Hongyuan district (altitude 3492.8) within a year.</p>
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<p>Test machine: (<b>a</b>) test machine with a temperature that alternated between high and low; (<b>b</b>) rapid freeze–thaw test machine; (<b>c</b>) lateral fundamental frequency test machine.</p>
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<p>Specimen pretreatment: (<b>a</b>) immersion saturated; (<b>b</b>) sealing with cling film.</p>
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<p>The procedure of the simulated freeze–thaw test.</p>
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<p>Relative dynamic modulus of elasticity vs. cycles of freeze–thaw recycle: (<b>a</b>) C30 concretes; (<b>b</b>) C50 concretes.</p>
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<p>Appearance of the concretes after rapid freeze–thaw test: (<b>a</b>) C30 common concrete (50 cycles); (<b>b</b>) C30 air-entrained concrete (450 cycles); (<b>c</b>) C50 common concrete (50 cycles); (<b>d</b>) C50 air-entrained concrete (450 cycles). Note: the symbols on the specimens are the main information label for distinguishing the different specimens. For example, C50 means that the concrete compress strength level is C50. The 1# means it is the first specimen of this group because three are needed in one group test. C50-A means that this specimen is air-entrained concrete and its strength level is C50.</p>
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<p>Relative dynamic modulus of elasticity of concrete in simulated environment vs. cycle number.</p>
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<p>Appearance of different concretes after simulating the natural freeze–thaw recycle test: (<b>a</b>) common concrete; (<b>b</b>) air-entrained concrete. Note: the letters on the specimen are the marks for distinguishing the different ones. C30 or C50 means the strength level classification. The symbol A means this is air-entrained concrete. The number 100% means the degree of saturation in the concrete is 100%. The numbers 1 to 3 are the block numbers of different specimens to distinguish them in a group. For example, the C30A-100%-1 means this is an air-entrained concrete with C30 strength level classification and its degree of saturation is 100% and its serial number is 1 in the same group.</p>
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<p>The pore distribution of the concrete before and after the freeze–thaw recycle test: (<b>a</b>) rapid freeze–thaw test; (<b>b</b>) simulating the nature freeze–thaw test.</p>
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20 pages, 12655 KiB  
Article
Modeling of Explosive Pingo-like Structures and Fluid-Dynamic Processes in the Arctic Permafrost: Workflow Based on Integrated Geophysical, Geocryological, and Analytical Data
by Igor Buddo, Natalya Misyurkeeva, Ivan Shelokhov, Alexandr Shein, Vladimir Sankov, Artem Rybchenko, Anna Dobrynina, Alexey Nezhdanov, Anna Parfeevets, Marina Lebedeva, Alena Kadetova, Alexander Smirnov, Oxana Gutareva, Alexey Chernikh, Lyubov Shashkeeva and Gleb Kraev
Remote Sens. 2024, 16(16), 2948; https://doi.org/10.3390/rs16162948 - 12 Aug 2024
Viewed by 198
Abstract
Understanding the mechanisms responsible for the origin, evolution, and failure of pingos with explosive gas emissions and the formation of craters in the Arctic permafrost requires comprehensive studies in the context of fluid dynamic processes. Properly choosing modeling methods for the joint interpretation [...] Read more.
Understanding the mechanisms responsible for the origin, evolution, and failure of pingos with explosive gas emissions and the formation of craters in the Arctic permafrost requires comprehensive studies in the context of fluid dynamic processes. Properly choosing modeling methods for the joint interpretation of geophysical results and analytical data on core samples from suitable sites are prerequisites for predicting pending pingo failure hazards. We suggest an optimal theoretically grounded workflow for such studies, in a site where pingo collapse induced gas blowout and crater formation in the Yamal Peninsula. The site was chosen with reference to the classification of periglacial landforms and their relation to the local deformation pattern, according to deciphered satellite images and reconnaissance geophysical surveys. The deciphered satellite images and combined geophysical data from the site reveal a pattern of periglacial landforms matching the structural framework with uplifted stable permafrost blocks (polygons) bounded by eroded fractured zones (lineaments). Greater percentages of landforms associated with permafrost degradation fall within the lineaments. Resistivity anomalies beneath pingo-like mounds presumably trace deeply rooted fluid conduits. This distribution can be explained in terms of fluid dynamics. N–E and W–E faults, and especially their junctions with N–W structures, are potentially the most widely open conduits for gas and water which migrate into shallow sediments in the modern stress field of N–S (or rather NEN) extension and cause a warming effect on permafrost. The results obtained with a new workflow and joint interpretation of remote sensing, geophysical, and analytical data from the site of explosive gas emission in the Yamal Peninsula confirm the advantages of the suggested approach and its applicability for future integrated fluid dynamics research. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)
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<p>Site of planned studies. (<b>A</b>): Location map in the Yamal Peninsula; (<b>B)</b>: satellite image of the site and the road; (<b>C</b>,<b>D</b>): mounds detected in drone images (<b>C</b>) and found in the field (<b>D</b>). The photograph in panel C is by R. Iliyasov.</p>
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<p>Periglacial landforms revealed by deciphering a Jilin-1 image. (<b>A</b>): Flat watersheds; (<b>B</b>): water-logged depressions with small-polygon patterns; (<b>C</b>): drained thermokarst lakes (khasyreys); (<b>D</b>): watersheds with large-polygon patterns; (<b>E</b>): thermokarst lake; (<b>F</b>): mound.</p>
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<p>Neotectonic sketch of the study area, a fragment. (<b>A</b>): Southeastern slope of the Polar Ural uplift; (<b>B</b>): northwestern West Siberian Basin [<a href="#B58-remotesensing-16-02948" class="html-bibr">58</a>].</p>
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<p>Lineaments deciphered in Landsat-8 images: 1 = faults; 2 = regional-scale lineaments; 3 = domes; 4 = round lakes; 5, 6 = site contour and corners 1 to 4.</p>
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<p>Lineaments within the site deciphered in Jilin-1 images: 1 = site contour; 2 = potential frost-heaving zones; 3 = thermokarst lakes; 4 = large relatively uplifted blocks (domes); 5 = pingo-like mounds; 6, 7 = local (6) and regional-scale (7) lineaments.</p>
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<p>Rose diagrams of faults detectable in local (<b>A</b>) and regional (<b>B</b>) deformation patterns; red lines are major regional-scale lineaments.</p>
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<p>Geomorphology and periglacial landforms.</p>
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<p>Statistics of thermokarst lakes, large permafrost polygons, depression bottoms with patterned ground, and pingo-like structures within and outside the detected lineaments.</p>
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<p>Georadar image across a pingo-like mound. Red lines are inferred faults.</p>
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<p>ERT resistivity pattern to a depth of 20 m. Gray box in the background is a pingo zone.</p>
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<p>Workflow for acquisition and joint interpretation of geophysical and analytical data as a basis for pingo evolution modeling.</p>
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21 pages, 6416 KiB  
Article
Evaluation of Climate Suitability for Maize Production in Poland under Climate Change
by Aleksandra Król-Badziak, Jerzy Kozyra and Stelios Rozakis
Sustainability 2024, 16(16), 6896; https://doi.org/10.3390/su16166896 (registering DOI) - 11 Aug 2024
Viewed by 399
Abstract
Climatic conditions are the main factor influencing the suitability of agricultural land for crop production. Therefore, the evaluation of climate change impact on crop suitability using the best possible methods and data is needed for successful agricultural climate change adaptation. This study presents [...] Read more.
Climatic conditions are the main factor influencing the suitability of agricultural land for crop production. Therefore, the evaluation of climate change impact on crop suitability using the best possible methods and data is needed for successful agricultural climate change adaptation. This study presents the application of a multi-criteria evaluation approach to assess climate suitability for maize production in Poland, for a baseline period (BL, 1981–2010) and two future periods 2041–2070 (2050s) and 2071–2100 (2080s) under two RCP (Representative Concentration Pathways) scenarios: RCP4.5 and RCP8.5. The analyses incorporated expert knowledge using the Analytical Hierarchy Process (AHP) into the evaluation of criteria weights. The results showed that maturity and frost stress were the most limiting factors in assessing the climatic suitability of maize cultivation in Poland, with 30% and 11% of Poland classified as marginally suitable or not suitable for maize cultivation, respectively. In the future climate, the area limited by maturity and frost stress factors is projected to decrease, while the area of water stress and heat stress is projected to increase. For 2050 climate projections, water stress limitation areas occupy 7% and 8% of Poland for RCP4.5 and RCP8.5, respectively, while for 2080 projections, the same areas occupy 12% and 32% of the country, respectively. By 2080, heat stress will become a limiting factor for maize cultivation; according to our analysis, 3% of the Polish area under RCP8.5 will be marginally suitable for maize cultivation because of heat stress. The overall analyses showed that most of Poland in the BL climate is in the high suitability class (62%) and 38% is moderately suitable for maize cultivation. This situation will improves until 2050, but will worsen in the 2080s under the RCP8.5 scenario. Under RCP8.5, by the end of the century (2080s), the highly suitable area will decrease to 47% and the moderately suitable area will increase to 53%. Full article
(This article belongs to the Special Issue Sustainability of Agriculture: The Impact of Climate Change on Crops)
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<p>Methodology flowchart.</p>
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<p>The AHP methodology framework for assessing climate suitability for maize production.</p>
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<p>Multimodel ensemble mean of the date of beginning the growing season (11 °C) for the baseline (BL, 1981–2010) and projected according to the low emissions (LE, RCP4.5) and high emissions (HE, RCP8.5) scenario for the 2050s (2041–2070) and 2080s (2071–2100).</p>
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<p>Multimodel ensemble mean of the date of end of growing season (10 °C) for the baseline (BL, 1981–2010) and projected according to the low emissions (LE, RCP4.5) and high emissions (HE, RCP8.5) scenario for the 2050s (2041–2070) and 2080s (2071–2100).</p>
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<p>Multimodel ensemble mean of accumulated temperature sum of growing season [°C/yr] for the baseline (BL, 1981–2010) and projected according to the low emissions (LE, RCP4.5) and high emissions (HE, RCP8.5) scenario for the 2050s (2041–2070) and 2080s (2071–2100).</p>
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<p>Multimodel ensemble-mean of the percentage of years in the 30-year period analysed in which maize can reach maturity for baseline (1981–2010) and projected according to low-emissions (LE, RCP4.5) and high-emissions (HE, RCP8.5) scenario for 2050s (2041–2070) and 2080s (2071–2100). The probability of maturity is evaluated for early, medium-early and medium-late varieties.</p>
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<p>Multimodel ensemble-mean of number of years with frost stress occurrence for baseline (1981–2010) and projected according to low-emissions (LE, RCP4.5) and high-emissions (HE, RCP8.5) scenario for 2050s (2041–2070) and 2080s (2071–2100). Frost stress is defined as frequency of occurrence of at least one day between the beginning of growing season and the end of June with the minimum temperature falling below 0 °C during the 30 years analysed period.</p>
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<p>Multimodel ensemble-mean of number of years with heat stress occurrence for baseline (1981–2010) and projected according to low-emissions (LE, RCP4.5) and high-emissions (HE, RCP8.5) scenario for 2050s (2041–2070) and 2080s (2071–2100). Heat stress is defined as frequency of occurrence of 5 consecutive days with maximum temperature exceeding 35 °C, and at least one day with the maximum temperature greater than 40 °C during the 30 years analysed period.</p>
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<p>The relative importance (weights) for sub-criteria and main criteria according to pairwise comparisons.</p>
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<p>Percentages of different climate suitability levels (S1—highly suitable, S2—moderately suitable) for maize cultivation in Poland according to overall analyses for the baseline (1981–2010) and projected climate according to low emissions (LE, RCP4.5) and high emissions (HE, RCP8.5) scenarios for the 2050s (2041–2070) and 2080s (2071–2100).</p>
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<p>Average climate suitability for maize cultivation for baseline (1981–2010) and projected climate according to low-emissions (LE, RCP4.5) and high-emissions (HE, RCP8.5) scenario for 2050s (2041–2070) and 2080s (2071–2100).</p>
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15 pages, 4022 KiB  
Article
Genomic Characterization of Three Canadian Mumps Outbreaks Demonstrates Endemic Transmission in Canada
by Jasmine Rae Frost, Grace Eunchong Seo, Kerry Dust, Jared Bullard, Peter Daley, Jason J. LeBlanc, Joanne Hiebert, Elizabeth McLachlan and Alberto Severini
Viruses 2024, 16(8), 1280; https://doi.org/10.3390/v16081280 - 10 Aug 2024
Viewed by 365
Abstract
Despite the provision of a mumps vaccination program in Canada for over three decades, mumps has not reached elimination. Instead, a re-emergence has been observed in vaccinated populations, particularly in young adults. These outbreaks have been almost exclusively due to genotype G infections, [...] Read more.
Despite the provision of a mumps vaccination program in Canada for over three decades, mumps has not reached elimination. Instead, a re-emergence has been observed in vaccinated populations, particularly in young adults. These outbreaks have been almost exclusively due to genotype G infections, a trend that has been seen in other countries with high mumps vaccination rates. To characterize mumps outbreaks in Canada, genomes from samples from Manitoba (n = 209), Newfoundland (n = 25), and Nova Scotia (n = 48) were sequenced and analysed by Bayesian inference. Whole genome sequencing was shown to be highly discriminatory for outbreak investigations compared to traditional Sanger sequencing. The results showed that mumps virus genotype G most likely circulated endemically in Canada and between Canada and the US. Overall, this Canadian outbreak data from different provinces and ancestral strains demonstrates the benefits of molecular genomic data to better characterize mumps outbreaks, but also suggests genomics could further our understanding of the reasons for potential immune escape of mumps genotype G and evolution in highly vaccinated populations. With a possible endemic circulation of mumps genotype G and the remaining risk of new imported cases, increased surveillance and alternative vaccination strategies may be required for Canada to reach the current target for mumps or a future elimination status. Full article
(This article belongs to the Special Issue Molecular Epidemiology of Measles, Mumps, and Rubella)
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<p>Distribution of samples selected in Manitoba for WGS-t sequencing spanning the 2016–2018 outbreak. Samples were chosen by provincial regional health authority; a total of 10% of total outbreak samples were included in this data set. Successfully sequenced samples are shown in red, unsuccessful samples in grey.</p>
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<p>Likelihood mapping of MuV whole genome sequences (WGS-t) from the Manitoba, Newfoundland, and Nova Scotia outbreaks. IQ-TREE 2.1.3 was run with 5000 quartets indicated. The top triangle indicates the distribution of quartets. The left triangle indicates the percentage of quartets falling into one of the 3divided areas. Most importantly, the bottom right triangle shows how the samples were placed, with 85.2% being placed at the corners, indicating strong phylogenetic power.</p>
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<p>Maximum likelihood tree of mumps virus genome sequences. Illustrated are whole genomes from outbreaks in MB (pink), NL (green), and NS (blue). Samples from an outlier outbreak from BC was also included. This analysis was run using IQ-Tree with a bootstrap value of 1000 (bootstrap values are shown in teal to the left of the tree, those lower than 40 are not shown). Annotation of the tree was done using iTOL. The vaccination status (if known) is displayed on the outside of the tree. The tree can also be viewed at <a href="https://itol.embl.de/shared/18FQx6JYPbiX5" target="_blank">https://itol.embl.de/shared/18FQx6JYPbiX5</a>.</p>
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<p>Whole genome sequencing (WGS-t) BEAST tree of mumps virus sequences. Samples included 209 from MB (purple), 25 from NL (green) and from 48 NS (blue), archival Canadian sequences (black) along with samples from the US (orange), international (teal). A portion of Manitoba samples have been collapsed to better view the image. Additionally, redundant branch labels have been removed to allow for readability. Red arrows indicate divergent dates between provincial outbreaks.</p>
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<p>Mutational profile of successful mumps virus genome sequences. A representative sample of provincial sequences from 2018 were selected (ordered by date of receival at the NML), along with archival Canadian sequences with SH profiles similar to the WHO reference Sheffield strain, a selection of USA sequences: MF965313.1, MF965260.1, MF965258.1, MF965232.1, MT880127.1, MT880125.1, MG986426.1, MG986385.1, MG986417.1, MG986410.1 and international sequences representing countries that had publicly available genotype G whole genomes at the time of analysis: MG460606.1, MG765426.1, MH638235.1, MH638234.1, MT238687.1, MT238686.1, MT238683.1, MT238681.1. Nucleotide variations from the consensus are highlighted by the coloured dots. Groups are divided by a grey line.</p>
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<p>The mean relative evolutionary rate was determined for each nucleotide in the mumps virus WGS-t genome. Then the average mean relative evolutionary rate was taken for 100bp across the genome and this was plotted. Sites showing a rate lower than 1 are evolving slower than average, and those showing a rate above 1 are evolving faster than average. The analysis was run in MEGA-11 using the aligned dataset that included outbreak samples from MB, NL, and NS as well as archival outbreak samples as described above. Black bars at the top of the graph represent the coding region for each mumps virus gene.</p>
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22 pages, 8110 KiB  
Article
Investigation of Opening and Closing Water Boundary Conditions on Frost Damage Development in Concrete
by Wei Wang, Zhe Huang, Dian Zhi, Peng Xia, Fuyuan Gong and Peng Lin
Buildings 2024, 14(8), 2451; https://doi.org/10.3390/buildings14082451 - 8 Aug 2024
Viewed by 326
Abstract
Freeze–thaw damage significantly contributes to the degradation of concrete structures. A critical precondition for concrete to experience frost damage is reaching its critical saturation level. This study conducted freeze–thaw experiments on concrete specimens under both open and sealed moisture conditions to elucidate the [...] Read more.
Freeze–thaw damage significantly contributes to the degradation of concrete structures. A critical precondition for concrete to experience frost damage is reaching its critical saturation level. This study conducted freeze–thaw experiments on concrete specimens under both open and sealed moisture conditions to elucidate the mechanisms of freeze–thaw damage and the pivotal role of moisture. The research assessed concrete’s water absorption, ultrasonic pulse velocity, and compressive strength under restricted water conditions to study damage accumulation patterns. The findings indicate that implementing water limitation measures during freeze–thaw cycles can regulate concrete’s water absorption rate, reduce the loss of ultrasonic pulse velocity, and minimize strength degradation, with an observed strength increase of up to 36.22%. Consequently, these measures protect concrete materials from severe frost damage. Furthermore, a predictive model for concrete freeze–thaw deterioration was established based on regression analysis and relative dynamic modulus theory, confirming the critical role of water limitation in extending the service life of concrete structures in cold regions. Full article
(This article belongs to the Special Issue Intelligent Technologies in Concrete Engineering)
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<p>Diagram of moisture and crack development.</p>
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<p>The flowchart of the study.</p>
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<p>Specimen groups and treatment during FTCs.</p>
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<p>Specimen sealing process: (<b>a</b>) waterproof plastic sheet; (<b>b</b>) waterproof electrical tape; (<b>c</b>) waterproof plastic bag.</p>
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<p>Temperature changes in individual freeze−thaw cycle stages (5 cycles).</p>
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<p>Schematic diagram of ultrasonic pulse velocity testing method.</p>
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<p>Experiment apparatuses.</p>
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<p>Initial state specimen water absorption curve.</p>
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<p>Effect of different sealing times on specimen water absorption: (<b>a</b>) OF15; (<b>b</b>) OF15CF15; (<b>c</b>) OF15CF30; (<b>d</b>) OF30; (<b>e</b>) OF30CF15; (<b>f</b>) OF45. Note: “OF” denotes open FTCs; “CF” denotes closed FTCs.</p>
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<p>First stage water absorption times of specimens at different sealing stages.</p>
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<p>Ultrasonic velocity losses of concrete specimens during FTCs.</p>
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<p>Compressive strengths of concrete specimens at the end of FTCs. Note: “*” denotes <span class="html-italic">p</span>–value ≤ 0.05 (significant); “**” denotes <span class="html-italic">p</span>–value ≤ 0.01 (extreme significant).</p>
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<p>Calculation approach of freezing pressure due to ice.</p>
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<p>Difference in hydraulic pressure and water flow after cracking.</p>
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<p>Differences in concrete damages in open and closed environments. The blue arrow represents the direction of water flow.</p>
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<p>Prediction model of ultrasonic pulse velocity for frost-damaged concrete: (<b>a</b>) OF15CF60; (<b>b</b>) OF30CF45; (<b>c</b>) OF45CF30; (<b>d</b>) OF75.</p>
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<p>Fitting of relative strength and relative ultrasonic pulse velocity squared for frost-damaged concrete.</p>
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<p>Relationship between compressive strength and ultrasonic velocity.</p>
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<p>Damage variables of OF15CF60, OF30CF45, OF45CF30, and OF75.</p>
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14 pages, 2461 KiB  
Article
Heterogeneous IL-9 Production by Circulating Skin-Tropic and Extracutaneous Memory T Cells in Atopic Dermatitis Patients
by Irene García-Jiménez, Lídia Sans-de San Nicolás, Laia Curto-Barredo, Marta Bertolín-Colilla, Eloi Sensada-López, Ignasi Figueras-Nart, Montserrat Bonfill-Ortí, Antonio Guilabert-Vidal, Anna Ryzhkova, Marta Ferran, Giovanni Damiani, Tali Czarnowicki, Ramon M. Pujol and Luis F. Santamaria-Babí
Int. J. Mol. Sci. 2024, 25(16), 8569; https://doi.org/10.3390/ijms25168569 - 6 Aug 2024
Viewed by 377
Abstract
Interleukin (IL)-9 is present in atopic dermatitis (AD) lesions and is considered to be mainly produced by skin-homing T cells expressing the cutaneous lymphocyte-associated antigen (CLA). However, its induction by AD-associated triggers remains unexplored. Circulating skin-tropic CLA+ and extracutaneous/systemic CLA memory [...] Read more.
Interleukin (IL)-9 is present in atopic dermatitis (AD) lesions and is considered to be mainly produced by skin-homing T cells expressing the cutaneous lymphocyte-associated antigen (CLA). However, its induction by AD-associated triggers remains unexplored. Circulating skin-tropic CLA+ and extracutaneous/systemic CLA memory T cells cocultured with autologous lesional epidermal cells from AD patients were activated with house dust mite (HDM) and staphylococcal enterotoxin B (SEB). Levels of AD-related mediators in response to both stimuli were measured in supernatants, and the cytokine response was associated with different clinical characteristics. Both HDM and SEB triggered heterogeneous IL-9 production by CLA+ and CLA T cells in a clinically homogenous group of AD patients, which enabled patient stratification into IL-9 producers and non-producers, with the former group exhibiting heightened HDM-specific and total IgE levels. Upon allergen exposure, IL-9 production depended on the contribution of epidermal cells and class II-mediated presentation; it was the greatest cytokine produced and correlated with HDM-specific IgE levels, whereas SEB mildly induced its release. This study demonstrates that both skin-tropic and extracutaneous memory T cells produce IL-9 and suggests that the degree of allergen sensitization reflects the varied IL-9 responses in vitro, which may allow for patient stratification in a clinically homogenous population. Full article
(This article belongs to the Special Issue Skin Diseases: From Molecular Mechanisms to Pathology)
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<p><b>HDM induces IL-9 production by both skin-tropic CLA<sup>+</sup> and non-tropic CLA<sup>−</sup> memory T cells at higher level than other proinflammatory cytokines, which correlates with HDM-specific IgE plasma levels.</b> (<b>A</b>) IL-9 (pg/mL) produced in basal conditions (M) or stimulated with HDM in AD- (n = 52) and C-derived (n = 12) CLA<sup>+</sup>/Epi and CLA<sup>−</sup>/Epi cocultures after 5 days. IL-4, IL-5, IL-9, IL-13, IL-17A, IL-31, IFN-γ (n = 52), IL-21 (n = 46), and IL-22 (n = 45) cytokines were simultaneously quantified in (<b>B</b>) CLA<sup>+</sup>/Epi and (<b>C</b>) CLA<sup>−</sup>/Epi AD cocultures. Cytokines were sorted according to the median production and compared in relation to IL-9 production levels. Red line indicates the median. Correlations between IL-9 (pg/mL) and (<b>D</b>) HDM-specific, and (<b>E</b>) total IgE plasma levels (n = 51). AD, atopic dermatitis; C, control subjects; CLA, cutaneous lymphocyte-associated antigen; Epi, epidermal cells; HDM, house dust mite; M, untreated; OD, optical density. ns: <span class="html-italic">p</span> &gt; 0.05; * <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; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><b>HDM-activated memory T cells require cell-to-cell contact with epidermal cells for IL-9 production, which primarily depends on HLA class II molecules.</b> (<b>A</b>) Epidermal cells, CLA<sup>+/−</sup> T cells, and CLA<sup>+/−</sup>/Epi cocultures were left untreated or stimulated with HDM, and IL-9 (pg/mL) was measured at day 5. (<b>B</b>) HDM-stimulated (n = 4) CLA<sup>+/−</sup> T cells and epidermal cells were added in the upper and lower chamber of the transwell plate (right) or cocultured together (left), and IL-9 (pg/mL) was quantified on day 5. (<b>C</b>) HLA class I and (<b>D</b>) class II molecules were neutralized in HDM-activated CLA<sup>+/−</sup>/Epi cocultures on day 0, and IL-9 levels (pg/mL) were compared to isotype values on day 5 (n = 8). Cytokine content under HDM stimulation without neutralizing antibodies (M) are shown. CLA<sup>+/−</sup>/Epi cocultures were polyclonally activated with (<b>E</b>) PMA/ION and (<b>F</b>) anti-CD3/CD28 beads, and IL-9 (pg/mL) was measured on day 5 (n = 6). Red line indicates the median. Cl, class; CLA, cutaneous lymphocyte-associated antigen; Epi, epidermal cells; HDM, house dust mite; HLA, human leukocyte antigens; ION, ionomycin; M, untreated; PMA, phorbol myristate acetate. ns: <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p><b>Patients with HDM-induced CLA<sup>+</sup> T cell IL-9 response have elevated specific and total IgE plasma levels; their IL-9 levels decrease over the course of the disease and, within the CLA<sup>+</sup> subset, IL-9 correlates with cytokines of Th2- and Th17/22-induced inflammation.</b> (<b>A</b>) AD patients were stratified according to IL-9 production by HDM-stimulated CLA<sup>+</sup> T (producers n = 30, non-producers n = 22) and CLA<sup>−</sup> T cells (producers n = 27, non-producers n = 25). (<b>B</b>) Specific IgE (OD) and (<b>C</b>) total IgE (kU/L) plasma levels were compared between IL-9 producers and non-producers in the CLA<sup>+</sup> (n = 28 and 26, respectively) and CLA<sup>−</sup> (n = 21 and 23, respectively) subsets in AD patients, and control volunteers (n = 25). Red lines indicate the median. Correlation between IL-9 (pg/mL) produced by HDM-activated CLA<sup>+/−</sup> T cell cocultures and (<b>D</b>) years since diagnosis and (<b>E</b>) IL-13, IL-4, IL-5, IL-31, IL-17A, IL-22, IL-21, IFN-γ, CCL17, and CCL22 (pg/mL) within CLA<sup>+</sup> (n = 11–30) and CLA<sup>−</sup> (n = 12–27) IL-9 producers. Bold values indicate significant data. AD, atopic dermatitis; C, control subjects; CLA, cutaneous lymphocyte-associated antigen; Epi, epidermal cells; HDM, house dust mite; M, untreated; OD, optical density. ns: <span class="html-italic">p</span> &gt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><b>SEB triggers mild IL-9 production in both CLA<sup>+</sup> and CLA<sup>−</sup> memory T cells.</b> (<b>A</b>) IL-9 (pg/mL) produced in basal conditions (M) or stimulated with SEB in AD- (n = 50) and C-derived (n = 17) CLA<sup>+</sup>/Epi and CLA<sup>−</sup>/Epi cocultures after 24 h. AD patients were stratified according to SEB-induced IL-9 response. Blue and green boxes show those AD patients producing IL-9 by CLA<sup>+</sup> (n = 35) and CLA<sup>−</sup> (n = 17) T cells, respectively. (<b>B</b>) In CLA<sup>+</sup>/Epi and (<b>C</b>) CLA<sup>−</sup>/Epi AD cocultures IL-4, IL-5, IL-9, IL-13, IL-17A, IL-31, IFN-γ (n = 52), IL-21 (n = 46), and IL-22 (n = 45) levels were simultaneously quantified. Cytokines were sorted according to the median production and compared to IL-9 production levels. Red lines indicate the median. Correlations of IL-9 levels (pg/mL) in HDM-activated CLA<sup>+</sup> and CLA<sup>−</sup> T cell cocultures and (<b>D</b>) years since diagnosis (n = 33 for CLA<sup>+</sup>, n = 16 for CLA<sup>−</sup>), (<b>E</b>) IL-13, IL-4, IL-5, IL-31, IL-17A, IL-22, IL-21, IFN-γ, CCL17, and CCL22 (pg/mL) within patients with IL-9 production. Bold values indicate significant data. AD, atopic dermatitis; C, control subjects; CLA, cutaneous lymphocyte-associated antigen; Epi, epidermal cells; M, untreated; SEB, staphylococcal enterotoxin B. ns: <span class="html-italic">p</span> &gt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><b>Workflow of the study design.</b> AD, atopic dermatitis; CLA, cutaneous lymphocyte-associated antigen; Epi, epidermal cells; HDM, house dust mite; PBMCs, peripheral blood mononuclear cells; SEB, staphylococcal enterotoxin B; Tm, memory T cells. This figure was created using images under a Creative Commons license.</p>
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15 pages, 6175 KiB  
Article
Study on the Coupling of Air-Source Heat Pumps (ASHPs) and Passive Heating in Cold Regions
by Feipeng Jiao, Guopeng Li, Chunjie Zhang and Jiyuan Liu
Buildings 2024, 14(8), 2410; https://doi.org/10.3390/buildings14082410 - 5 Aug 2024
Viewed by 526
Abstract
Air-source heat pumps (ASHPs), as an active device, are widely used in building heating and cooling processes. However, in severe cold regions, they face reduced heating efficiency and frosting problems in winter. This paper proposes a new heating solution by coupling an ASHP [...] Read more.
Air-source heat pumps (ASHPs), as an active device, are widely used in building heating and cooling processes. However, in severe cold regions, they face reduced heating efficiency and frosting problems in winter. This paper proposes a new heating solution by coupling an ASHP with passive heating systems. It combines an ASHP with passive sunrooms and heat storage systems for heating. Through software simulations and mathematical modeling, the new scheme is compared and analyzed against traditional ASHP solutions to explore the performance of this scheme in rural houses in severe cold regions of China during winter. According to simulation and calculation analysis, on the coldest day of winter, the coupling scheme can provide approximately 99.41 kWh of heat to the indoors, which exceeds the 86.67 kWh required to maintain an indoor temperature of 20 °C. The system’s power consumption is 36.96 kWh, which is 66.88% lower than that of traditional heat pump heating. The study shows that the coupling system of an ASHP and passive heating has a good heating effect in severe cold regions. For the situation of insufficient solar energy at night, the design of phase-change materials and heat storage media can meet heating needs throughout the day. Full article
(This article belongs to the Special Issue Energy Performance in Sustainable Architecture Design)
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<p>Air-source heat pump coupled with passive heating system.</p>
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<p>Harbin Region typical farmhouse: (<b>a</b>) southwest view; (<b>b</b>) south elevation.</p>
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<p>Floor plan of the typical farmhouse in the Harbin Region.</p>
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<p>Typical rural residential building model.</p>
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<p>Hourly dry-bulb temperature graph for a typical year in Harbin.</p>
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<p>Hourly heating load graph for typical rural residential buildings.</p>
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<p>Simulation model parameters of the air-source heat pump.</p>
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<p>Air-source heat pump simulation model.</p>
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<p>Hourly temperature comparison between the heated room and outdoor temperature.</p>
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<p>Relationship curve between COP of air-source heat pump and ambient temperature.</p>
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<p>Simulation results of passive solar sunroom internal temperature on January 1.</p>
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<p>Hourly solar energy gain curve for the sunroom.</p>
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<p>Heat Transfer in Mode 2.</p>
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<p>Co-location of PCM and heating equipment: (<b>a</b>) application during winter day; (<b>b</b>) application during winter night.</p>
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<p>Application of PCM in building enclosures of heating spaces: (<b>a</b>) application during winter day; (<b>b</b>) application during winter night.</p>
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<p>Incorporation of PCM in the floor: (<b>a</b>) application during winter day; (<b>b</b>) application during winter night.</p>
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28 pages, 17468 KiB  
Article
Characterisation of Large-Sized REBaCuO Bulks for Application in Flux Modulation Machines
by Quentin Nouailhetas, Yiteng Xing, Rémi Dorget, Walid Dirahoui, Santiago Guijosa, Frederic Trillaud, Jean Lévêque, Jacques Guillaume Noudem, Julien Labbé and Kévin Berger
Materials 2024, 17(15), 3827; https://doi.org/10.3390/ma17153827 - 2 Aug 2024
Viewed by 304
Abstract
High temperature superconductors (HTSs) are enablers of extensive electrification for aircraft propulsion. Indeed, if used in electrical machines, HTS materials can drastically improve their performance in terms of the power-to-weight ratio. Among the different topologies of superconducting electrical machines, a flux modulation machine [...] Read more.
High temperature superconductors (HTSs) are enablers of extensive electrification for aircraft propulsion. Indeed, if used in electrical machines, HTS materials can drastically improve their performance in terms of the power-to-weight ratio. Among the different topologies of superconducting electrical machines, a flux modulation machine based on HTS bulks is of interest for its compactness and light weight. Such a machine is proposed in the FROST (Flux-barrier Rotating Superconducting Topology) project led by Airbus to develop new technologies as part of their decarbonization goals driven by international policies. The rotor of the machine will house large ring-segment-shaped HTS bulks in order to increase the output power. However, the properties of those bulks are scarcely known and have barely been investigated in the literature. In this context, the present work aims to fill out partially this scarcity within the framework of FROST. Thus, a thorough characterisation of the performances and homogeneity of 11 large REBaCuO bulks was carried out. Ten of the bulks are to be utilized in the machine prototype, originally keeping the eleventh bulk as a spare. A first set of characterisation was conducted on the eleven bulks. For this set, the trapped field mapping and the critical current were estimated. Then, a series of in-depth characterisations on the eleventh bulk followed. It included critical current measurement, X-ray diffraction, and scanning electron microscopy on different millimetre-size samples cut out from the bulk at various locations. The X-ray diffraction and scanning electron microscopy showed weakly oxygenated regions inside the bulk explaining the local drop or loss in superconducting properties. The objective was to determine the causes of the inhomogeneities found in the trapped field measured on all the bulks, sacrificing one of them, here the spare one. To help obtain a clearer picture, a numerical model was then elaborated to reproduce the field map of the eleventh bulk using the experimental data obtained from the characterisation of its various small samples. It is concluded that further characterisations, including the statistics on various bulks, are still needed to understand the underlying reasons for inhomogeneity in the trapped field. Nonetheless, all the bulks presented enough current density to be usable in the construction of the proposed machine. Full article
(This article belongs to the Special Issue Characterization and Application of Superconducting Materials)
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<p>Exploded-view drawing of the active components of the superconducting axial-flux modulation machine using HTS bulks in its rotor for the FROST project.</p>
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<p>(<b>a</b>) GdBaCuO disk-shaped single-domain bulk (100 mm in diameter and 10 mm in height). (<b>b</b>) GdBaCuO single-domain bulk machined as ring segment. (<b>c</b>) The 10 GdBaCuO ring segment bulks after laser cutting the edges.</p>
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<p>(<b>a</b>) The 9 T external superconducting magnet with a 150 mm warm bore; (<b>b</b>) the 3-axis table equipped with a hall probe showing the dismantled sample holder; (<b>c</b>) bulk installed in the sample holder. The sample holder can be moved around to be able to map the trapped field on the 3-axis table so that the bulk remains in the liquid nitrogen at all times.</p>
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<p>Trapped field distributions for S1 to S10 resulting from a 3 T Field Cooling (FC) magnetization process at 77 K. The bulks were previously laser-cut.</p>
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<p>Trapped field distribution of S11 bulk measured at 77 K following a Field Cooling (FC) magnetization using a permanent magnet with a remanent magnetic flux density of 0.3 T.</p>
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<p>Picture of bulk S11 indicating the locations of the small samples to be characterised. G1 to G4 refer to samples along the growth sector region, while B2 to B4 refer to samples along the growth sector boundary. These samples were taken every centimetre from the seed location. D1 has been cut at a location where a defect has been identified. For each position indicated, three samples are taken from the thickness of bulk S11. This gives a total of 24 samples.</p>
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<p>Critical current density <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> at 77 K obtained at 1 T and 2 T according to the position of the 24 different samples in the bulk. The black and red horizontal lines represent the respective mean values at 1 T (14.36 ± 3.71 kA·cm<sup>−2</sup>) and 2 T (15.12 ± 4.19 kA·cm<sup>−2</sup>). The statistics do not include G4T and D1T, as they do not show any superconductivity. The vertical dashed lines indicate the different zones in the bulk resulting from the manufacturing process.</p>
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<p>Critical current densities <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> as a function of the magnetic field for G1T (right below the seed), G4T (4 cm away from the seed in the growth sector on top of the bulk), and D1T (weak trapped field region on top of the bulk). The magnetic moment as a function of temperature is also displayed for G1T and G4T using a 1 mT background field. The curves of G4T and D1T overlap.</p>
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<p>Critical current density <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> as a function of the magnetic flux density for the sample G1T (right below the seed), G3T (3 cm away from the seed in the growth sector at the top of the bulk) and B3B (3 cm away from the seed in the grain boundary at bottom of the bulk). <math display="inline"><semantics> <msub> <mi>J</mi> <mrow> <mi mathvariant="normal">c</mi> <mo>,</mo> <mi>hollow</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>J</mi> <mrow> <mi mathvariant="normal">c</mi> <mo>,</mo> <mi>hill</mi> </mrow> </msub> </semantics></math> indicate the local minima and maxima describing the fishtail effect.</p>
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<p>Fishtail strength <math display="inline"><semantics> <msub> <mo>Δ</mo> <mrow> <mi>F</mi> <mi>E</mi> </mrow> </msub> </semantics></math> at 77 K in blue triangle for the 24 superconducting samples. Only one of the superconducting samples did not show a fishtail effect, sample G2B, and is therefore not shown in the figure. The horizontal blue line indicates the mean value at 8.18%. The standard deviation is 4.03%.</p>
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<p>Critical current density <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> as a function of the magnetic flux density from magnetic measurements taken at 77 K in samples (<b>a</b>) along the growth sector region, (<b>b</b>) along the growth sector boundary, and (<b>c</b>) in the weakly superconducting area D1 with G1 as a reference value.</p>
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<p>Scaling behaviours of the normalized volume pinning force <span class="html-italic">f</span> vs. reduced magnetic field <span class="html-italic">h</span> at 77 K for all locations averaged over the bulk height. The dashed lines represent the fitted curves with a Dew–Hughes’ pinning function <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mi>A</mi> <mspace width="0.166667em"/> <msup> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> <mi>p</mi> </msup> <mspace width="0.166667em"/> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>h</mi> <mo>)</mo> </mrow> <mi>q</mi> </msup> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Critical current density <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> at 40 K obtained at 4 T and 8 T, for each location except D1. The black and red horizontal lines represent the mean values at 4 T (172.43 kA·cm<sup>−2</sup>) and 8 T (178.74 kA·cm<sup>−2</sup>), respectively. The non-superconducting samples are not included in the statistics. (<b>b</b>) Amplitude of the fishtail effect at 40 K in blue triangle. The horizontal blue line indicates the mean value equal to 18.31 ± 3.73%.</p>
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<p>Critical current density <math display="inline"><semantics> <msub> <mi>J</mi> <mi>c</mi> </msub> </semantics></math> as a function of the applied magnetic flux density <span class="html-italic">B</span> at 40 K (<b>a</b>) along the growth sector region and (<b>b</b>) along the growth sector boundary.</p>
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<p>X-ray diffraction pattern of G1T, G4T and D1T samples with a normalised intensity within (<b>a</b>) the corresponding phase orientation indicated right above each major peak and (<b>b</b>) an emphasis on the (003) diffraction peak.</p>
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<p>X-ray diffraction pattern of G1T, G3T and B3B samples with a normalised intensity. (<b>a</b>) The corresponding phase orientation is indicated right above each major peak. (<b>b</b>) Zoom on the (003) diffraction peak.</p>
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<p>Pictures of the surface of the samples: (<b>a</b>) G1T, (<b>b</b>) G3T, and (<b>c</b>) B3B made by Scanning Electron Microscopy (SEM). The pictures are accompanied by an EDS image of an area to determine its atomic composition. The colours correspond to <span style="color: #00FF00">•</span> Silver <span style="color: #00AEEF">•</span> Gadolinium <span style="color: #EC008C">•</span> Barium.</p>
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<p>(<b>a</b>) Scanning Electron Microscopy picture of the G3T sample, and (<b>b</b>) analysis of the oxygen content of G3T using the SEM-EDS method. The red dots <span style="color: #FF0000">•</span> indicate the presence of oxygen atoms.</p>
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<p>Geometry used to simulate the S11 inhomogeneous bulk with homothetic areas (R1–4) of the various critical current densities extracted from measurements (G1, B2–4), weakly superconducting areas (R5 and R8–10 using respectively G4 and D1 measured <math display="inline"><semantics> <mrow> <msub> <mi>J</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>), and non-superconducting areas (R6–7, considered as air).</p>
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<p>Computed trapped field distribution of a ring-segment-shaped bulk representing the configuration of S11 bulk, (<b>a</b>) with the average <math display="inline"><semantics> <mrow> <msub> <mi>J</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> measured on the cutout samples of S11 bulk, and (<b>b</b>) with adjusted values of average <math display="inline"><semantics> <mrow> <msub> <mi>J</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the weakly and non-superconducting areas. The magnetic field is computed 2 mm above the bulk surface, as was performed experimentally (see <a href="#materials-17-03827-f005" class="html-fig">Figure 5</a>).</p>
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29 pages, 10006 KiB  
Article
The Impacts of Frozen Material-Other-Than-Grapes (MOG) on Aroma Compounds of Cabernet Franc and Cabernet Sauvignon
by Yibin Lan, Xiaoyu Xu, Jiaming Wang, Emily Aubie, Marnie Crombleholme and Andrew Reynolds
Beverages 2024, 10(3), 68; https://doi.org/10.3390/beverages10030068 - 2 Aug 2024
Viewed by 341
Abstract
An undesirable sensory attribute (“floral taint”) has recently been detected in red wines from some winegrowing jurisdictions in North America (e.g., Ontario, British Columbia, Washington), caused by the introduction of frost-killed leaves and petioles [materials-other-than-grapes (MOG)] during mechanical harvest and winemaking. It was [...] Read more.
An undesirable sensory attribute (“floral taint”) has recently been detected in red wines from some winegrowing jurisdictions in North America (e.g., Ontario, British Columbia, Washington), caused by the introduction of frost-killed leaves and petioles [materials-other-than-grapes (MOG)] during mechanical harvest and winemaking. It was hypothesized that terpenes, norisoprenoids, and higher alcohols would be the main responsible compounds. The objectives were to investigate the causative volatile compounds for floral taint and explore threshold concentrations for this problem. Commercial wines displaying varying intensities of floral taint were subjected to GC-MS and sensory analysis. Several odor-active compounds were higher in floral-tainted wines, including terpenes (geraniol, citronellol, cis- and trans-rose oxide), norisoprenoids (β-damascenone, β-ionone), five ethyl esters, and three alcohols. Thereafter, fermentations of Cabernet Franc (CF) and Cabernet Sauvignon (CS) (2016, 2017) were conducted. MOG treatments were (w/w): 0, 0.5%, 1%, 2%, and 5% petioles, and 0, 0.25%, 0.5%, 1%, and 2% leaf blades. Terpenes (linalool, geraniol, nerol, nerolidol, citronellol, citral, cis- and trans-rose oxides, eugenol, myrcene), norisoprenoids (α- and β-ionone), and others (e.g., hexanol, octanol, methyl and ethyl salicylate) increased linearly/quadratically with increasing MOG levels in both cultivars. Principal components analysis separated MOG treatments from the controls, with 5% petioles and 2% leaves as extremes. Increasing MOG levels in CF wines increased floral aroma intensity, primarily associated with terpenes, higher alcohols, and salicylates. Increased leaf levels in CF were associated with higher vegetal and earthy attributes. Increased petioles in CS were not correlated with floral aromas, but increased leaves increased floral, vegetal, and herbaceous attributes. Overall, petioles contributed more to floral taint than leaves through increased terpenes and salicylates (floral notes), while leaves predominantly contributed norisoprenoids and C6 alcohols (green notes). Full article
(This article belongs to the Special Issue Wine and Spirits)
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<p>Relationships between several frozen leaf levels (N = 30) added to Ontario Cabernet Franc wine fermentations vs. aroma compound concentrations, 2016. *, **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest leaf treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S4</a>.</p>
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<p>Relationships between several frozen petiole levels (N = 30) added to Ontario Cabernet Franc wine fermentations vs. aroma compound concentrations, 2016. **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest petiole treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S5</a>.</p>
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<p>Relationships between several frozen leaf levels (N = 15) added to Ontario Cabernet Sauvignon wine fermentations vs. aroma compound concentrations, 2016. **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest leaf treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S6</a>.</p>
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<p>Relationships between several frozen petiole levels (N = 15) added to Ontario Cabernet Sauvignon wine fermentations vs. aroma compound concentrations, 2016. *, **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest petiole treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S7</a>.</p>
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<p>Relationships between several frozen leaf levels (N = 15) added to Ontario Cabernet Sauvignon wine fermentations vs. aroma compound concentrations, 2017. *, **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest leaf treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S10</a>.</p>
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<p>Relationships between several frozen petiole levels (N = 15) added to Ontario Cabernet Sauvignon wine fermentations vs. aroma compound concentrations, 2017. **, ***, ****, NS: Significant at <span class="html-italic">p</span> ≤ 0.01, 0.001, 0.0001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively. <sup>a</sup> Odor-active for at least the highest petiole treatment. Information on other compounds is in <a href="#app1-beverages-10-00068" class="html-app">Supplemental Table S11</a>.</p>
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<p>Principal components analysis of aroma compounds of: Cabernet Franc wines, Ontario, Canada, 2017. Abbreviations in lower figure: Control (0 MOG addition); other treatments refer to % <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen leaves or petioles.</p>
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<p>Principal components analysis of aroma compounds of Cabernet Sauvignon wines, Ontario, Canada, 2017. Abbreviations in lower figure: Control (0 MOG addition); other treatments refer to % <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen leaves or petioles. Compound abbreviations in upper figure: DS: diethyl succinate; EN: ethyl nonanoate; IA: isoamyl acetate; PA: phenylethyl acetate; PAol: phenylethyl alcohol.</p>
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<p>Sensory response of Ontario Cabernet Franc wines in relation to frozen leaf (<b>A</b>–<b>G</b>) and petiole (<b>H</b>–<b>N</b>) additions, 2016. *, ***, NS: Significant at <span class="html-italic">p</span> ≤ 0.05, 0.001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively.</p>
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<p>Sensory response of Ontario Cabernet Sauvignon wines in relation to frozen leaf (<b>A</b>–<b>G</b>) and petiole (<b>H</b>–<b>N</b>) additions, 2016. *, **, ***, NS: Significant at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, or not significant, respectively. L, Q: Linear or quadratic trends, respectively.</p>
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<p>Principal components analysis of sensory data of: (<b>A</b>): Cabernet Franc and (<b>B</b>): Cabernet Sauvignon, Ontario, Canada, 2016. Abbreviations: Control: 0 MOG addition; R1, R2, R3: Replicates 1, 2, and 3, respectively. (<b>B</b>): CTL: 0 MOG addition; 0.25L, 0.5L, 1L, 2L: 0.25, 0.5, 1, and 2% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen leaves; 0.5P, 1P, 2P, 5P: 0.5, 1, 2, and 5% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen petioles. Uppercase and lowercase descriptors refer to orthonasal and taste/retronasal descriptors, respectively.</p>
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<p>Partial least squares analysis of sensory data of Cabernet Franc, Ontario, Canada, 2016. Variability of X: 45.2%, Y: 23.3%. (<b>A</b>): Aroma compounds and sensory descriptors; (<b>B</b>): Aroma compounds, sensory descriptors, and treatments. Abbreviations: CTL: 0 MOG addition; R1, R2, R3: Replicates 1, 2, and 3, respectively; 0.25L, 0.5L, 1L, 2L: 0.25, 0.5, 1, and 2% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen leaves; 0.5P, 1P, 2P, 5P: 0.5, 1, 2, and 5% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen petioles. Uppercase and lowercase descriptors refer to orthonasal and taste/retronasal descriptors, respectively.</p>
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<p>Partial least squares analysis of sensory data of Cabernet Sauvignon, Ontario, Canada, 2016. Variability of X: 43.1%, Y: 27.4%. (<b>A</b>): Aroma compounds and sensory descriptors; (<b>B</b>): Aroma compounds, sensory descriptors, and treatments. Abbreviations: CTL: 0 MOG addition; R1, R2, R3: Replicates 1, 2, and 3, respectively; 0.25L, 0.5L, 1L, 2L: 0.25, 0.5, 1, and 2% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen leaves; 0.5P, 1P, 2P, 5P: 0.5, 1, 2, and 5% <span class="html-italic">w</span>/<span class="html-italic">w</span> addition of frozen petioles.</p>
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22 pages, 12110 KiB  
Article
Mechanical Properties of Cement Concrete with Waste Rubber Powder
by Junqin Liu, Jiyue Li, Yanwei Xu and Shibin Ma
Appl. Sci. 2024, 14(15), 6636; https://doi.org/10.3390/app14156636 - 30 Jul 2024
Viewed by 642
Abstract
To investigate the mechanical properties of cement concrete incorporating waste rubber powder, the response surface methodology was employed. The Box–Behnken central composite design was applied to analyze the three primary factors influencing the road performance of cement concrete containing waste rubber powder: the [...] Read more.
To investigate the mechanical properties of cement concrete incorporating waste rubber powder, the response surface methodology was employed. The Box–Behnken central composite design was applied to analyze the three primary factors influencing the road performance of cement concrete containing waste rubber powder: the water–cement ratio, sand ratio, and waste rubber powder content. The study determined the impact of these factors on the flexural strength of waste rubber powder cement concrete at both 7 and 28 days. Additionally, the effects of the water–cement ratio, sand ratio, and waste rubber powder content on the performance of cement concrete were analyzed. To investigate the impact of waste rubber powder on cement concrete, various mechanical property tests were conducted, including compressive, flexural, dynamic elastic modulus, and impact performance tests. Furthermore, the study explored the influence of waste rubber powder on the noise reduction capacity of cement concrete using both the rubber ball impact method and ultrasonic method. Lastly, the durability of cement concrete with added rubber powder was assessed through shrinkage tests, frost resistance tests, and chloride ion penetration tests. Full article
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<p>Waste tire rubber powder.</p>
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<p>The grain size distribution curve of the coarse aggregate.</p>
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<p>The grain size distribution curve of the medium sand.</p>
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<p>Schematic diagram of hammer impact test device.</p>
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<p>Rubber ball impact test.</p>
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<p>Drying shrinkage test of waste rubber powder cement concrete.</p>
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<p>Vacuum saturation device.</p>
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<p>Measurement device of chloride ion electrical flux of waste rubber powder concrete.</p>
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<p>Schematic diagram of chloride ion penetration test device (1—direct current stabilized voltage power supply; 2—test flume; 3—copper electrode; 4—concrete sample; 5—3.0%NaCl solution; 6—0.3%mol/L NaOH solution; 7—direct current digital voltmeter; 8—specimen washer).</p>
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<p>Anti-freezing test of waste rubber powder cement concrete.</p>
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<p>Residual plot of test (<b>a</b>) residual plot of 7 d flexural strength and (<b>b</b>) residual plot of 28 d flexural strength.</p>
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<p>Predicted versus actual (<b>a</b>) ratio of 7 d predicted flexural strength to actual flexural strength and (<b>b</b>) ratio of 28 d predicted flexural strength to actual flexural strength.</p>
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<p>3D plots illustrating the impact of the water–cement ratio and sand ratio on 7 d flexural strength.</p>
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<p>3D plots illustrating the impact of the water–cement ratio and sand ratio on 28 d flexural strength.</p>
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<p>3D plots illustrating the impact of the water–cement ratio and 10-mesh waste rubber powder content on 7 d flexural strength.</p>
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<p>3D plots illustrating the impact of the water–cement ratio and 10-mesh waste rubber powder content on 28 d flexural strength.</p>
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<p>3D plots illustrating the impact of the sand ratio and 10-mesh waste rubber powder content on 7 d flexural strength.</p>
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<p>3D plots illustrating the impact of the sand ratio and 10-mesh waste rubber powder content on 28 d flexural strength.</p>
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<p>The results of the compressive strength test (<b>a</b>) 3 d compressive strength, (<b>b</b>) 7 d compressive strength, (<b>c</b>) 14 d compressive strength, and (<b>d</b>) 28 d compressive strength.</p>
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<p>The results of the flexural strength test (<b>a</b>) 3 d flexural strength, (<b>b</b>) 7 d flexural strength, (<b>c</b>) 14 d flexural strength, and (<b>d</b>) 28 d flexural strength.</p>
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<p>The results of the dynamic modulus test (<b>a</b>) 3 d dynamic modulus, (<b>b</b>) 7 d dynamic modulus, (<b>c</b>) 14 d dynamic modulus, and (<b>d</b>) 28 d dynamic modulus.</p>
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<p>The results of the hammer impact test: (<b>a</b>) the initial crack energy absorption and (<b>b</b>) the final crack energy absorption.</p>
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<p>The results of the rubber ball impact test.</p>
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<p>The results of the drying shrinkage test (<b>a</b>) the shrinkage rate of 10-mesh waste rubber powder cement, (<b>b</b>) the shrinkage rate of 20-mesh waste rubber powder cement, (<b>c</b>) the shrinkage rate of 30-mesh waste rubber powder cement, and (<b>d</b>) the shrinkage rate of 10-mesh waste rubber powder cement.</p>
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<p>The results of electrical flux test.</p>
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<p>The results of the drying shrinkage test (<b>a</b>) the mass loss rate of 10-mesh waste rubber powder cement, (<b>b</b>) the mass loss rate of 20-mesh waste rubber powder cement, (<b>c</b>) the mass loss rate of 30-mesh waste rubber powder cement, and (<b>d</b>) the mass loss rate of 10-mesh waste rubber powder cement.</p>
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<p>The results of the drying shrinkage test (<b>a</b>) the mass loss rate of 10-mesh waste rubber powder cement, (<b>b</b>) the mass loss rate of 20-mesh waste rubber powder cement, (<b>c</b>) the mass loss rate of 30-mesh waste rubber powder cement, and (<b>d</b>) the mass loss rate of 10-mesh waste rubber powder cement.</p>
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16 pages, 9567 KiB  
Article
Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard
by Su Hyun Kim, Seung-Min Lee and Seung-Jae Lee
Atmosphere 2024, 15(8), 906; https://doi.org/10.3390/atmos15080906 - 29 Jul 2024
Viewed by 336
Abstract
Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to [...] Read more.
Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics. Full article
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<p>Study area and observation system installation site: (<b>a</b>) Location of the observation site and surrounding terrain. (<b>b</b>) Multiple-sensor-based frost observation system (MFOS) field site.</p>
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<p>Multiple-sensor-based frost observation system (MFOS) version 3. The previous version was MFOS v2 (refer to [<a href="#B1-atmosphere-15-00906" class="html-bibr">1</a>,<a href="#B11-atmosphere-15-00906" class="html-bibr">11</a>,<a href="#B12-atmosphere-15-00906" class="html-bibr">12</a>]) and meteorological sensor data is transmitted to the NCAM PC or server through the wireless communication function of the MFOS data logger.</p>
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<p>Post-processing workflow for detecting apple objects with a pre-trained model.</p>
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<p>Extracting the fruit surface temperature (°C) from a multiple-sensor-based frost observation system (MFOS) infrared image produced in an apple farm. The left side shows the RGB camera image and the right shows the thermal camera image. We applied the bounding box extracted from the RGB camera on to the thermal image and calculated the 9-point average temperatures.</p>
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<p>Comparison of the surface temperatures of apples and the leaf wetness sensor (LWS) for the period of 17:00 LST 2 August 2022 to 20:00 LST 8 August 2022. The surface temperature data were extracted from a thermal infrared camera image according to [<a href="#B11-atmosphere-15-00906" class="html-bibr">11</a>].</p>
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<p>Correlation analysis of the surface temperatures of apples and the leaf wetness sensor (LWS) for the period of 27 July 2022 to 28 October 2022. The surface temperature data were extracted from a thermal infrared camera image according to [<a href="#B11-atmosphere-15-00906" class="html-bibr">11</a>]. (R: correlation coefficient, RMSD: Root-Mean-Squared Deviation, y: regression equation, N: number of samples.)</p>
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<p>The process of extracting the leaf wetness sensor’s surface temperature.</p>
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<p>Comparison of surface temperatures (<b>a</b>) between apple leaves and the leaf wetness sensor (LWS), (<b>b</b>) between apple fruit and the LWS, and (<b>c</b>) between apple fruit and apple leaves in the orchard during the day on 5 October 2022. The surface temperature data were extracted from a thermal infrared camera image [<a href="#B11-atmosphere-15-00906" class="html-bibr">11</a>].</p>
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<p>RGB camera images from 04:00 LST to 09:00 LST on 18 October 2022 (Case 1).</p>
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<p>Comparison of observations and model predictions for a frost event (Case 1) from 12:00 LST on 17 October 2022 to 12:00 LST on 18 October 2022. The observations were made at an altitude of approximately 2 m, and the model predictions were also set at 2 m. pts1, pst2, and pst3 refer to the grid points closest, second closest, and third closest to the observations in two domains (d03 and d04), respectively. (TA<sub>observed</sub>: observed air temperature, TA<sub>predicted</sub>: air temperature according to the numerical model, TD<sub>observed</sub>: observed dew-point temperature, TD<sub>predicted</sub>: dew-point temperature according to the numerical model, TS<sub>LWS</sub>: surface temperature according to the leaf wetness sensor (LWS). The blue range shows the section where the LWS surface was classified as ice in the observation through the algorithm).</p>
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<p>RGB camera images from 04:00 LST to 07:00 LST on 24 October 2022 (Case 2).</p>
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<p>Same as <a href="#atmosphere-15-00906-f010" class="html-fig">Figure 10</a> but for the Case 2 from 12:00 LST on 23 October 2022 to 12:00 LST on 24 October 2022.</p>
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20 pages, 12121 KiB  
Article
Simulation of Frost-Heave Failure of Air-Entrained Concrete Based on Thermal–Hydraulic–Mechanical Coupling Model
by Xinmiao Wang, Feng Xue, Xin Gu and Xiaozhou Xia
Materials 2024, 17(15), 3727; https://doi.org/10.3390/ma17153727 - 27 Jul 2024
Viewed by 650
Abstract
The internal pore structural characteristics and microbubble distribution features of concrete have a significant impact on its frost resistance, but their size is relatively small compared to aggregates, making them difficult to visually represent in the mesoscopic numerical model of concrete. Therefore, based [...] Read more.
The internal pore structural characteristics and microbubble distribution features of concrete have a significant impact on its frost resistance, but their size is relatively small compared to aggregates, making them difficult to visually represent in the mesoscopic numerical model of concrete. Therefore, based on the ice-crystal phase transition mechanism of pore water and the theory of fine-scale inclusions, this paper establishes an estimation model for effective thermal conductivity and permeability coefficients that can reflect the distribution characteristics of the internal pore size and the content of microbubbles in porous media and explores the evolution mechanism of effective thermal conductivity and permeability coefficients during the freezing process. The segmented Gaussian integration method is adopted for the calculation of integrals involving pore size distribution curves. In addition, based on the concept that the fracture phase represents continuous damage, a switching model for the permeability coefficient is proposed to address the fundamental impact of frost cracking on permeability. Finally, the proposed estimation models for thermal conductivity and permeability are applied to the cement mortar and the interface transition zone (ITZ), and a thermal–hydraulic–mechanical coupling finite element model of concrete specimens at the mesoscale based on the fracture phase-field method is established. After that, the frost-cracking mechanism in ordinary concrete samples during the freezing process is explored, as well as the mechanism of microbubbles in relieving pore pressure and the adverse effect of accelerated cooling on frost cracking. The results show that the cracks first occurred near the aggregate on the concrete sample surface and then extended inward along the interface transition zone, which is consistent with the frost-cracking scenario of concrete structures in cold regions. Full article
(This article belongs to the Section Mechanics of Materials)
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<p>The effective thermal conductivity coefficient vs. microbubble occupancy for different degrees of saturation of ice.</p>
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<p>The effective thermal conductivity coefficient vs. the degree of saturation of ice for different microbubble occupancies.</p>
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<p>The pore size distribution curve of concrete.</p>
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<p>The pore size distribution fitting curves of concrete with different air-entraining contents.</p>
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<p>The effective permeability coefficient vs. the temperature.</p>
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<p>The evolution of pressure <math display="inline"><semantics> <mrow> <mi>X</mi> <mo stretchy="false">(</mo> <mi>θ</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> during freezing for three types of concrete.</p>
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<p>The FEM mesh of a concrete sample with randomly distributed aggregates.</p>
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<p>The frost damage distribution during freezing. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mrow> <mn>0</mn> <mo> </mo> </mrow> <mrow> <mi mathvariant="normal">°</mi> </mrow> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>, <span class="html-italic">t</span> = 2040 s. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mo>-</mo> <mrow> <mn>5</mn> </mrow> <mo> </mo> <mrow> <mi mathvariant="normal">°</mi> </mrow> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>, <span class="html-italic">t</span> = 3840 s. (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mo>-</mo> <mn>1</mn> <mrow> <mn>0</mn> </mrow> <mo> </mo> <mrow> <mi mathvariant="normal">°</mi> </mrow> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>, <span class="html-italic">t</span> = 5640 s.</p>
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<p>The frost damage distribution in concrete with different microbubble contents at a freezing temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mo>−</mo> <mn>10</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>. (<b>a</b>) c = 3%. (<b>b</b>) c = 6%. (<b>c</b>) c = 9%. (<b>d</b>) c = 10%.</p>
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<p>The frost damage distribution at different cooling rates when the temperature drops to −10 <math display="inline"><semantics> <mrow> <mo>°</mo> <mi mathvariant="normal">C</mi> <mo>.</mo> </mrow> </semantics></math> (<b>a</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>θ</mi> </mrow> <mo>˙</mo> </mover> <mo>=</mo> <mn>5</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>θ</mi> </mrow> <mo>˙</mo> </mover> <mo>=</mo> <mn>10</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>θ</mi> </mrow> <mo>˙</mo> </mover> <mo>=</mo> <mn>20</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>.</p>
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<p>Fully automatic rapid freeze–thaw testing machine.</p>
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<p>Damage to concrete specimens in freeze–thaw experiments.</p>
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16 pages, 2918 KiB  
Article
Experiment on Freeze–Thaw Resistance of Tunnel Portal-Lining Concrete with Silicone Coating in Cold Regions
by Yuanfu Zhou, Jinlong Zheng, Bo Zheng, Feng Yang, Rui Guo and Hongyu Huang
Buildings 2024, 14(8), 2330; https://doi.org/10.3390/buildings14082330 - 27 Jul 2024
Viewed by 397
Abstract
The freeze–thaw effect has a significant impact on the strength deterioration of tunnel-lining concrete in cold regions. Therefore, the strength deterioration characteristics of concrete in a tunnel were studied, and silicone coating materials were used to improve its frost resistance and durability under [...] Read more.
The freeze–thaw effect has a significant impact on the strength deterioration of tunnel-lining concrete in cold regions. Therefore, the strength deterioration characteristics of concrete in a tunnel were studied, and silicone coating materials were used to improve its frost resistance and durability under freeze–thaw cycles. Freeze–thaw cycle tests were conducted on concrete specimens with different coatings. The freeze–thaw damage phenomenon, dynamic elastic modulus, and mass loss of the specimens were used to evaluate the freeze–thaw durability of concrete strengthened with coatings. The results demonstrated that silicone coatings effectively prevented moisture and corrosive substances from infiltrating the concrete, thereby enhancing its durability; the silicone–polyether hybrid had the most significant frost resistance at 500 g/m2 and silane type III at 300 g/m2, with freezing resistance times of 175 and 300, respectively. During the freeze–thaw process, the strength reduction rate of specimens was much greater than the mass loss rate of concrete. Taking into account the water environment surrounding the lining concrete and the site temperature, an equivalent indoor freeze–thaw cycle conversion model was established. The results can provide an experimental basis for selecting better frost-resistant materials for tunnel concrete in cold regions. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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<p>Lining strength of each tunnel.</p>
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<p>Photos of the specimens after the maximum freeze–thaw cycles.</p>
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<p>Relative dynamic elastic modulus curves of silane antifreeze specimens.</p>
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<p>Relative dynamic elastic modulus curves of silane impermeable antifreeze specimens.</p>
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<p>Relative dynamic elastic modulus curve of silicone antifreeze specimen.</p>
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<p>Mass loss ratio curve of silane antifreeze specimen.</p>
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<p>Mass loss ratio curves of silane impermeability antifreeze agent specimens.</p>
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<p>Mass loss ratio curves of silicone antifreeze specimens.</p>
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14 pages, 5052 KiB  
Article
Effects of Lime Powder on the Properties of Portland Cement–Sulphoaluminate Cement Composite System at Low Temperature
by Ge Zhang, Bei Zhang, Yixin Hao, Qianbiao Pang, Lei Tian, Ruyan Ding, Lin Ma and Hui Wang
Materials 2024, 17(15), 3658; https://doi.org/10.3390/ma17153658 - 24 Jul 2024
Viewed by 404
Abstract
In order to reduce the risk of early freezing damage to cement-based materials in winter construction, lime powder was used to improve the properties of the Portland cement–sulphoaluminate cement (PC–CSA) composite system at low temperatures. In this study, the effects of lime powder [...] Read more.
In order to reduce the risk of early freezing damage to cement-based materials in winter construction, lime powder was used to improve the properties of the Portland cement–sulphoaluminate cement (PC–CSA) composite system at low temperatures. In this study, the effects of lime powder dosage on the properties of a PC–CSA blended system with two proportions (PC:CSA = 9:1 and 7:3) at −10 °C were investigated, and the mechanisms of improvement were revealed. The results showed that the compressive strength of the PC–CSA composite system was effectively improved, and the setting time was shortened by the addition of lime powder. Lime powder could effectively act as an early heating source in the PC–CSA composite system, as the maximum temperature of samples exposed to sub-zero temperatures was increased and the time before dropping to 0 °C was prolonged by the addition of lime powder. The extra CH generated by the hydration of lime powder provided an added hydration path for C4A3S¯, which accelerated the formation of AFt at each stage. Frozen water as well as the early frost damage were effectively decreased by lime powder because of the faster consumption of free water at an early stage. The modification of the hydration products also contributed to the denseness of the microstructure. Full article
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<p>The effects of lime powder dosage on the compressive strength of (<b>a</b>) S10 and (<b>b</b>) S30.</p>
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<p>The effects of lime powder addition (5 wt. %) on the temperature curve of samples.</p>
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<p>The effects of lime powder addition (5 wt. %) on the hydration heat of samples. (<b>a</b>) The first peak; (<b>b</b>) The second and third peak.</p>
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<p>The effects of lime powder addition (5 wt. %) on the XRD patterns of (<b>a</b>) S10 and (<b>b</b>) S30.</p>
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<p>BSE images of S10 cement paste at −7 d. (<b>a</b>) Without lime powder; (<b>b</b>) with 5 wt. % lime powder.</p>
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<p>BSE images of S30 cement paste at −7 d. (<b>a</b>) Without lime powder; (<b>b</b>) with 5 wt. % lime powder.</p>
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<p>SEM micrographs of S10 cement paste at −7d. (<b>a</b>) Without lime powder; (<b>b</b>) with 5 wt. % lime powder addition.</p>
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<p>SEM micrographs of S30 cement paste at −7 d. (<b>a</b>) Without lime powder; (<b>b</b>) with 5 wt. % lime powder addition.</p>
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<p>SEM micrographs of CH at −28 d. (<b>a</b>) Without lime powder; (<b>b</b>) with lime powder addition.</p>
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<p>Schematic diagram of the mechanism.</p>
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21 pages, 2797 KiB  
Review
A Review of Gene–Property Mapping of Cementitious Materials from the Perspective of Material Genome Approach
by Fei Li and Yan Zhong
Materials 2024, 17(15), 3640; https://doi.org/10.3390/ma17153640 - 23 Jul 2024
Viewed by 481
Abstract
As an important gelling material, cementitious materials are widely used in civil engineering construction. Currently, research on these materials is conducted using experimental and numerical image processing methods, which enable the observation and analysis of structural changes and mechanical properties. These methods are [...] Read more.
As an important gelling material, cementitious materials are widely used in civil engineering construction. Currently, research on these materials is conducted using experimental and numerical image processing methods, which enable the observation and analysis of structural changes and mechanical properties. These methods are instrumental in designing cementitious materials with specific performance criteria, despite their resource-intensive nature. The material genome approach represents a novel trend in material research and development. The establishment of a material gene database facilitates the rapid and precise determination of relationships between characteristic genes and performance, enabling the bidirectional design of cementitious materials’ composition and properties. This paper reviews the characteristic genes of cementitious materials from nano-, micro-, and macro-scale perspectives. It summarizes the characteristic genes, analyzes expression parameters at various scales, and concludes regarding their relationship to mechanical properties. On the nanoscale, calcium hydrated silicate (C-S-H) is identified as the most important characteristic gene, with the calcium–silicon ratio being the key parameter describing its structure. On the microscale, the pore structure and bubble system are key characteristics, with parameters such as porosity, pore size distribution, pore shape, air content, and the bubble spacing coefficient directly affecting properties like frost resistance, permeability, and compressive strength. On the macroscale, the aggregate emerges as the most important component of cementitious materials. Its shape, angularity, surface texture (grain), crushing index, and water absorption are the main characteristics influencing properties such as chloride ion penetration resistance, viscosity, fluidity, and strength. By analyzing and mapping the relationship between these genes and properties across different scales, this paper offers new insights and establishes a reference framework for the targeted design of cementitious material properties. Full article
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<p>Expression of major characteristic genes corresponding to different scales.</p>
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<p>Structure of C-S-H compositions of two different densities.</p>
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<p>Schematic structure of calcium–silica layer.</p>
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<p>Simple diagram of the side of hydrated calcium silicate.</p>
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<p>Schematic representation of the morphology of hydrated calcium silicate at different Ca/Si ratios [<a href="#B22-materials-17-03640" class="html-bibr">22</a>]. (<b>a</b>) C-S-H is agglomerated, granular, and flaky at 1.5 Ca/Si ratio, (<b>b</b>) C-S-H is in the form of short and thick acicular rods and longer network fibers at 1.5–2.0 Ca/Si ratio, (<b>c</b>) C-S-H is in the form of long and thin acicular rods and longer network fibers at 1.5–2.0 Ca/Si ratio.</p>
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<p>Schematic representation of capillary pores, gel pores, and stomata in cementitious materials [<a href="#B31-materials-17-03640" class="html-bibr">31</a>,<a href="#B32-materials-17-03640" class="html-bibr">32</a>]: (<b>a</b>) capillary pores, (<b>b</b>) gel pores, (<b>c</b>) stomata.</p>
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<p>Convex polygon method schematic.</p>
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<p>Positive and negative correlation between calcium–silicon ratio and performance.</p>
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