Research on the aggregation phenomena in moraine soil: a new approach to disperse them through a particle size analysis

Research on the aggregation phenomena in moraine soil: a new approach to disperse them through a particle size analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the aggregation phenomena in moraine soil: a new approach to disperse them through a particle size analysis Tuo Lu, Yongbo Tie, Yaming Tang, Zhijie Ning, Lingfeng Gong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3945900/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Moraine soils exposed to the moraine landscape surface due to glaciers retreating and global warming have been the subject of interest for researchers. The particle size distribution (PSD) of moraine soils is essential to understanding their engineering properties and soil characteristics. However, the wide aggregation phenomena observed in these soils introduce difficulties in particle size analysis. To overcome this, an experimental group test (EGT) and control groups test (CGT) were designed, which included a series of pretreatment methods to disperse particles, such as grain sieving test, laser diffraction test, microscopic observation, and scanning electron microscope. The tests were performed to record the aggregation phenomena and perform particle size test analysis. The results revealed widespread evidence of aggregation phenomena in each grain group (ranging from < 0.075 mm to 60 mm ~ 40 mm), encapsulated by three basic conceptual models (Core-coating, Core(s)-coat, and Coat-coring) for moraine soil. A new pretreatment method was developed, which involved the addition of 0.5% polycarboxylate in optimal ultrasonic dispersion to disperse aggregation phenomena before the sieving and laser diffraction tests. This method proved to be effective in removing the aggregation phenomena, which significantly impacted on the particle size distribution of moraine soils. The aggregation phenomena caused an overestimation of the content of the gravel group (about 7.91%) and an underestimation of the content of the silt-clay group (about 5.07%). After effectively removing the aggregation phenomena, many samples collected from poorly graded gravelly soils (GP) became silty sand soil (SM) and silty gravel soil (GM). Additionally, the average particle size was reduced by approximately 40%. Moraine soil Aggregation phenomena Pretreatments Particle size distribution Polycarboxylate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction The Earth’s cryosphere is shrinking rapidly due to global climate warming. This is causing the exposure of moraine soil in high latitudes and permafrost landscapes to increase at a fast pace [ 1 – 6 ]. Moraine soil's particle size characteristics are crucial to understanding the geography, geology, and ecology of moraine landscapes. The size of the particles and how they aggregate are essential indicators of exogenic forces that have shaped the Earth's surface over time [ 7 , 8 ]. Additionally, the mechanical strength, permeability, and water retention capacity of the soil largely depend on its particle size distribution [ 9 – 15 ]. Previous studies have shed some light on moraine soil's physical and mechanical properties. Moraine soil generally refers to poorly graded gravelly soils that have a high natural density ranging from 1.7 g·cm − 3 to 2.3 g·cm − 3 and a low to medium moisture content of 0–15% [ 16 – 22 ]. This type of soil is often characterized by a wide range of particle sizes with low roundness and is composed of gravel (60 mm ~ 2 mm, nearly 50%), sand, and silt-clay particles [ 16 , 23 – 28 ]. The particle size distribution (PSD) of moraine soil is usually divided into two sections around 1mm, known as the "dual grain group" characteristic [ 29 , 30 ]. However, most of the studies above did not systematically consider the impact of aggregation phenomena on the particle size distribution. Soil particle size analysis faces significant challenges due to the aggregation phenomena, where fine particles stick to coarser particles and small grains clump together [ 31 – 33 ]. This phenomenon is commonly observed in fine sandy, silty, and clayey soils. Complicated geological depositional processes can cause the precipitation of soluble salts, forming calcium carbonate and clay minerals that can cement and aggregate particles, especially in well-consolidated soils. For instance, Zheng, Li, Wang, Li, Shi and Bi [ 34 ] concluded that aggregation happens in loess and proposed the “core-coating” concept. Some pretreatments, such as water distillation, the use of dispersants ((NaPO 3 ) 6 , H 2 O 2 , HCl chemicals, etc.), and the application of the ultrasonic method, are considered effective in dispersing soil aggregation phenomena in fine sands, silts, loess, coals, and clays[ 33 , 35 – 38 ]. These pretreatments are based on several mechanisms such as vibration dispersion, electrostatic repulsion, lubrication, steric hindrance, dissolution of part of the cement [ 31 – 33 ]. It is possible to achieve proper dispersion of aggregate particles by adding dispersants into ultrasonic dispersion. But, it is important to note that the aggregation phenomena in fine-grained soils are quite different from those in moraine soil. Moreover, polycarboxylates are widely used in the dispersion of concrete materials [ 39 , 40 ], and they may be instructive for the dispersion of moraine soils. For this study, soil samples weighing around 270 kg were collected from Bengga Valley, which hosts modern glaciers southeast of the Qinghai-Tibet Plateau. These samples were divided into experimental groups test (EGT) and control groups test (CGT). The study aimed to investigate the different aggregation phenomena present in moraine soil and develop a novel pretreatment method to remove them effectively. Various pretreatment methods were applied to the soil samples, including using different combinations of dispersants and ultrasound. The samples were then subjected to particle size analysis and a series of observation techniques such as exceptional magnification photography using a smartphone, transmitted-reflected metallographic microscope, scanning electron microscopy [ 41 ], grain sieving test, and grain laser diffraction test. The goals of this study are (1) to discuss the types of moraine soil’s aggregation phenomena and models, (2) to develop a novel pretreatment method to remove aggregation phenomena effectively in particle size analysis, and (3) to quantify the impact of aggregation phenomena on the particle size distribution for moraine soil. 2. Materials and methods 2.1 Site investigation and soil sample collection Widely distributed moraine soils can be found along the Sichuan–Tibet Railway in the southeast part of the Qinghai-Tibet Plateau, with a significant concentration stretching from Bomi County to Linzhi City [ 28 ] (Fig. 1 b). The Bengga Valley, which spans an area of approximately 17.30 km 2 , is situated on the east bank of the Yarlung Tsangpo River southeast of Linzhi city. The valley is divided into left and right branches, with the main right channel measuring around 6.6 km, and the left branch around 6.7 km. The valley entrance sits at an elevation of approximately 2925 m, while the highest point reaches about 5378 m, resulting in a relative elevation difference of about 2453 m. Modern glaciers can be found in the upper reaches of the left branch at around 4450 m and upstream of the right branch at an elevation exceeding 5000 m (Fig. 1 c). The accelerated melting of glaciers leads to increased exposure of moraine soils and eluvium gravels, which can be used as loose solid deposits for agricultural and forestry purposes. However, these loose deposits can also pose a hidden danger as solid material sources of geologic hazards, such as glacier debris flows, landslides, and ice lake outbursts [ 42 ]. Therefore, investigating the particle size characteristics of moraine soils in Bengga Valley under global warming is highly significant. The sampling strategy and the environment description of the study area were designed based on the observations made through multiple techniques such as surface survey, glacial till investigation, unmanned aerial vehicle (UAV) inspection, remote sensing interpretation using Gaofen-2 satellite imagery, site earth temperature testing, on-site moisture content test, natural density testing, and particle size test. All these investigations were conducted in the Bengga Valley (Fig. 1 c- 1 d) during March and August 2022, February and October 2023. The remote sensing data analysis revealed that the moraine soil is primarily concentrated in the upstream region of elevations above 3790 m to 5000 m (Fig. 1 c). Moreover, the UAV investigation and the surface survey showed that some typical moraine soil profiles were distributed in the downstream and the middle stream regions (Fig. 1 d) at approximately 3026 m to 3539 m. Also, the gravel layer is predominantly distributed downstream. Four typical moraine outcrop profiles mainly formed by the retreat of Quaternary glaciations were found at elevations of 3026 m (Profile 1), 3338 m (Profile 2), 3408 m (Profile 3), and 3539 m (Profile 4) (Fig. 1 d). The top of these profiles was covered with dense vegetation (Fig. 1 a). Notably, the loose moraine deposits were found on the side of the Bengga valley, which led to several landslides. Hence, it is necessary to investigate moraine soil particle size data for the unstable slopes, soil permeability, retention capacity, etc. However, it is worth noting that aggregation phenomena in moraine soils interfered with particle size analysis (Fig. 1 a, Fig. 2 ). Experimental groups test (EGT) and control groups test (CGT) were conducted to analyze particle size and aggregate phenomena in moraine soil. For the EGT, 170 kg of samples (removed topsoil) were collected from different moraine soil profiles (profiles 1 ~ 4). The samples were categorized based on particle size: 60 mm ~ 20 mm particles (55.0 kg), 20 mm ~ 10 mm particles (30.0 kg), 10 mm ~ 5 mm particles (15.0 kg), 5 mm ~ 2 mm particles (20.0 kg), 2 mm ~ 0.5 mm particles (15.0 kg), 0.5 mm ~ 0.25 mm particles (15.0 kg), 0.25 mm ~ 0.075 mm particles (15.0 kg), and < 0.075 mm particles (5.0 kg). For the CGT, a total of 23 samples were collected, each weighing approximately 4.0 ~ 4.5 kg (in accordance with the geotechnical test standard GB/T 50123 − 2019, 2019. China code), from two moraine soil profiles (profiles 3 and 4), also with the topsoil removed. Overall, a total of 270 kg of samples were collected for this study. 2.2 Test method The aggregation phenomena were observed using a smartphone with a magnifying device and scanning electron microscope (JSM-7500, JEOL Company, Japan), as well as a transmitted-reflected metallographic microscope (JX-2503, Chengdu Jingxin Powder Testing Equipment Company, China), as shown in Fig. 3b 3 and Fig. 4b 5 . The major mineralogical constituents of grain group samples were tested using a Powder X-ray diffractometer (XPert Pro, PANalytical B.V. Company, Netherlands). To develop a new effective pretreatment method in particle size analysis and to discuss aggregation phenomena that impact on the particle size distribution for moraine soil, an experimental groups test (EGT) and control groups test (CGT) were designed before the grain sieve and grain laser diffraction (Fig. 3b 2 and Fig. 4b 4 ). Specifically, the goal of EGT was to select an appropriate pretreatment method by considering ultrasonic time, the chemical dispersant (NaPO 3 ) 6 , H 2 O 2 , Na 2 SiO 3 ·5H 2 O, Polycarboxylate, HCl, to disperse aggregation moraine soil particles effectively. On the other hand, CGT aimed to check the difference in results between an appropriate pretreatment method determined by EGT and no pretreatment. For EGT, the samples were prepared as follows (Table 1 , Table 2 , Figs. 3 a- 3 b, and Figs. 4 a- 4 b): 2000 g ~ 2100 g per sample for the 60 mm ~ 20 mm grain group. The weights of the single sample on site ranged from 4000 g to 4500 g, and the weights of the 60 − 20 mm grain group by primary sieving were 800 g ~ 2000 kg. Each 20 mm ~ 10 mm grain group sample weighted 1000 g ~ 1030 g. Each 10 mm ~ 5 mm grain group sample weight was 400 g ~ 410 g. Each 5 mm ~ 2 mm grain group sample was 400 g ~ 405 g. Each 2 mm ~ 0.5 mm grain group sample was 200 g ~ 201 g. Each 0.5 mm ~ 0.25 mm grain group sample was 200 g ~ 201. Each 0.25 mm ~ 0.075 mm grain group sample was 200.0 g ~ 200.1 g. Lastly, each < 0.075 mm grain group sample weighted 0.20 g ~ 0.21 g. This information is based on the geotechnical test standard GB/T 50123 − 2019. In this study, three different pretreatment methods were applied (Fig. 3 a, Fig. 4 a). The first method, method A, involved ultrasonic dispersion (40 kHz) of the samples (> 0.075 mm) in hot water (80 ℃) for different cumulative ultrasonic times ranging from 1 to 90 minutes. This method was mainly applied to gravel and sand groups (Fig. 3 a, Fig. 3b 1 ). The second method, method B, involved adding the dispersant, such as (NaPO 3 ) 6 , H 2 O 2 , Na 2 SiO 3 ·5H 2 O, and polycarboxylate into optimal ultrasonic dispersion (80 ℃ water), as determined by pretreatment method A (Fig. 3 a, Fig. 4 a). The dispersant concentration was designed to be 0%, 0.5%, 1%, and 2% (percentage of quality). The third method, method C, was only applied to the silt-clay grain group (< 0.075 mm) to investigate whether dilute hydrochloric acid (HCl) reduced or destroyed the silt-clay particles (Fig. 4 a). The concentration of dispersant HCl was also designed to be 0%, 0.5%, 1%, and 2%. Table 1 The samples for EGT Grain group (mm) Single weight (g) Number of Samples Pretreatment Notes 60 − 20 2000–2100 5*5 A, B1, B2, B3, B4 A: Ultrasonic; B1:(NaPO 3 ) 6 + Optimal ultrasonic time; B2: H 2 O 2 + Optimal ultrasonic time; B3:Na 2 SiO 3 ·5H 2 O + Optimal ultrasonic time; B4: Polycarboxylate + Optimal ultrasonic time. 20 − 10 1000–1030 5*5 A, B1, B2, B3, B4 10 − 5 400–410 5*5 A, B1, B2, B3, B4 5 − 2 400–405 5*8 A, B1, B2, B3, B4 2-0.5 200–201 5*15 A, B1, B2, B3, B4 0.5 − 0.25 200–201 5*15 A, B1, B2, B3, B4 0.25 − 0.075 200.0-200.1 5*15 A, B1, B2, B3, B4 0.075 mm) was as follows. Firstly, the samples were oven-dried for 24 h and sieved (Fig. 3b 1 -3b 2 ). Secondly, the different grain group particles were observed under the microscope (Fig. 3b 3 ). After this, these samples underwent pretreatments (Fig. 3b 4 ) and were left to settle in water settling for 24 h (Fig. 3b 5 ). Finally, the samples were dried again and sieved (Fig. 3b 2 ), and the above steps were repeated. The EGT test procedure for the silt-clay group samples (< 0.075 mm) was conducted in the following manner. First, the samples were soaked in deionized water for 24 hours (Fig. 4b 1 ). Then, pretreatments were carried out and the samples were left to settle for 24 h (Figs. 4b 2 -4b 3 ). Finally, particle size data was obtained using a laser diffraction particle size analyzer before 10 minutes of ultrasonic vibration (Fig. 4b 4 ). It is worth noting that prolonged ultrasonic vibration can damage the particles [ 31 – 33 ]. Therefore, these samples were further observed under microscopes (Fig. 4b 5 ) and were also prepared for scanning electron microscope analysis. The sample preparation and procedure for CGT involved the following steps. Firstly, 23 samples were subjected to grain sieving and laser diffraction tests. After that, EGT determined an appropriate pretreatment method (M) for these 23 samples to perform the particle size test again (Table 2 ). Table 2 The samples for CGT. Samples Altitude Single weight (g) Number of samples Pretreatment Notes S1-S14 3408 m 4100 ~ 4500 15(Profiles 3) M (?% dispersant) Determined by EGT S15-S23 3539 m 4100 ~ 4500 8 (Profiles 4) 3. Results 3.1 Results of aggregation phenomena observation and mineral composition Tables 3 and 4 illustrate the primary mineralogical components found in the grain group samples. Quartz (24.4% to 35.6%), Potassium feldspar (4.6% to 36.3%), and Plagioclase (16.6% to 37.5%) were the most abundant minerals identified in all samples analyzed. Some grain group samples also contained a certain amount of calcite and hornblende. It was found that the fine sand had the highest clay mineral content (9.6% and 12.0%), and interestingly, the clay mineral content increased gradually as the grain class size decreased. Figs. 5 and 6 show the outcomes of observing images through a smartphone with a magnifier and microscopes. In moraine soil, aggregation phenomena were frequently observed, ranging from large gravel to smaller sand and silt-clay particles. The patterns of these phenomena are still complex and poorly understood. Table 3. The mineral composition of grain group samples (from Profiles 3) Grain groups (mm) 60-40 40-20 20-10 10-5 5-2 2-1 1-0.5 0.5-0.25 0.25-0.075 <0.075 Quartz (%) 31.7 34.1 30.9 29.3 28.5 19.7 35.6 31.1 26.7 24.4 Potassium feldspar (%) 4.6 5.6 16.0 11.3 18.1 19.2 6.1 19.6 36.3 6.9 Plagioclase (%) 35.2 18.4 28.4 33.9 23.6 37.5 32.9 30.0 16.6 28.7 Calcite (%) 11.5 22.3 5.7 - 4.4 4.1 - 3.6 - 7.6 Dolomite (%) 8.5 14.6 2.5 3.2 5.7 - - 2.4 0.3 2.5 Pyrite (%) 0.1 - - - - - - - - - Hematite (%) - - 0.4 0.3 0.1 - 0.5 - 0.1 0.1 Hornblende (%) 3.6 0.7 8.8 14.4 9.5 16.6 21.1 8.4 8.0 24.0 Clay mineral (%) 4.8 4.3 7.3 7.6 10.1 2.9 3.8 4.9 12.0 5.8 Table 4. The mineral composition of grain group samples (from Profiles 4) Grain groups (mm) 60-40 40-20 20-10 10-5 5-2 2-1 1-0.5 0.5-0.25 0.25-0.075 <0.075 Quartz (%) 35.2 28.7 34.0 27.5 26.4 24.4 29.2 28.4 25.9 26.6 Potassium feldspar (%) 4.7 6.3 12.6 8.0 13.4 14.2 6.3 15.3 29.2 6.1 Plagioclase (%) 30.3 27.6 29.5 34.3 35.1 40.0 31.4 33.3 25.4 28.1 Calcite (%) 9.1 13.0 4.0 5.3 4.2 4.1 9.8 3.6 - 7.6 Dolomite (%) 4.9 8.3 3.2 2.0 2.9 - - 2.4 2.2 1.5 Pyrite (%) 0.1 - 0.0 - - 1.1 - - - 0.6 Hematite (%) 0.0 1.5 0.4 0.3 1.2 - 0.5 0.0 0.1 0.1 Hornblende (%) 10.6 12.5 8.6 14.3 10.5 13.7 19.7 9.7 7.8 24.2 Clay mineral (%) 5.3 2.2 7.8 8.4 6.6 2.6 3.3 7.5 9.6 5.3 For samples belonging to the 60 mm ~ 20 mm grain group, some fine sand and silt particles were found to be adhered to the surface of coarse gravel grains. These particles are referred to as “coats” in this study and their thickness could be thick, medium, or thin. The distribution of these coats was random, with some covering half or more of the surface area of the gravel particles and others covering less than one-fourth (Fig. 5). In the case of samples in the 20 mm ~ 5 mm grain group, some of the gravels were entirely coated by silt-clay grains. Additionally, several fine gravels were grouped together by silt-clay grains and some silt-clay grains as if they were gravel (Fig. 5). The thickness of these coat layers could be thick, medium, or thin, and their distribution was also random. For samples in the 5 mm ~ 2 mm grain group, the coats were less frequently found in depressions or crevices on the surface area of the fine gravel particles. Also, several fine gravels and silt-clay grains were grouped, and some silt-clay grains were grouped as gravel. For samples belonging to the 2 mm ~ 0.5 mm grain group, the “coats” were still attached to the coarse sand surface in clusters. These clusters were randomly distributed and occupied half or more of the total particle’s surface area, while some covered less than half. In some cases, they were spread over fewer surfaces, appearing to be point-like shape (Fig. 6). However, some fine or medium sands were clustered together with silt-clay grains (Fig. 6). The “coats” could also observed in noticeable depressions or crevices in the coarse sands. For the 0.5 mm ~ 0.25 mm groups, flaky silt-clay particles were observed, and several fine gravels were clustered together with silt-clay grains (Fig. 6). The thickness of these “coats” layers could also be thick, medium, and thin, with a random distribution for irregular sands. Lastly, for 0.25 mm~0.075 mm grain groups, some fine sands were entirely coated by the silt-clay grains, while some “coats” were still not thick, similar to a point-like shape (Fig. 6). Finally, for grain group samples with a diameter less than 0.075 mm, clay particles, being mainly cohesive, tend to cluster together on the surface of silt particles (as shown in Fig. 6). Likewise, clay grains could also bind silt and fine sand particles to form “coats,” which play a crucial role in the aggregation phenomena observed in moraine soil. 3.2 Results of experimental groups test (EGT) 3.2.1 Ultrasonic dispersion time Fig. 7 depicts the impact of different cumulative ultrasonic dispersion time conditions on the quality of various grain group samples. In general, all the test samples displayed similar behavior. The quality either remained stable or decreased gradually, followed by a sharp decline (Fig. 7). The quality of gravel remained unchanged for around 5 minutes, whereas sand maintained its quality for between 15 and 30 minutes. Fig. 7a shows that the quality of gravel grain group samples diminishes as the ultrasonic dispersion time increases. This decline in quality is more noticeable in fine gravels (60 mm to 2 mm) than in coarse gravels. Although the mass of some grain group samples remained unchanged after 3 minutes, overall, the mass of all grain group samples stopped declining after 5 minutes of ultrasonic dispersion (Fig. 7a). Therefore, it can be concluded that 5 minutes of ultrasonic dispersion is the optimal ultrasonic dispersion time (A opt ) for gravels. In Fig. 7b, it is shown that the quality of sand grain groups decreases with increasing ultrasonic dispersion time. Interestingly, the extent of reducing quality was less for coarse sands than for fine sands (Fig. 7b). After 15 minutes, the mass of coarse sand remained constant, while the mass of medium and fine sand remained constant after 20 and 30 minutes, respectively (Fig. 7b). This suggests that the optimal ultrasonic dispersion (Aopt) for coarse, medium, and fine sands in this study was 15, 20, and 30 minutes, respectively. Thus, it can be inferred that the dispersion of aggregation phenomena is more challenging in smaller sand grain sizes than in larger sizes. 3.2.2 Different dispersants Adding dispersant in the optimal ultrasonic dispersion may be helpful. Although the effects of adding dispersants in ultrasonic dispersion versus not adding them are less noticeable for gravels (Fig. 8), they work well for sands and silt-clay grains (Fig. 8), especially polycarboxylate. Some parameters described in Fig.8 are explained as follows: m after is the quality of the sample after pretreatment B or C (B1~B4, C) in the original size class. m <0.075 is the quality of the detached silt-clay after pretreatment B or C (B1~B4, C). m o is no pretreatment sample quality. A opt is the optimal ultrasonic dispersion (without any dispersants). U: no any pretreatment. Most dispersants had little effect on the ultrasonic dispersion for most gravels (Fig. 8a). However, polycarboxylate seemed to work for fine gravel because its mass ratio ( m after / m o ) in pretreatment B4 (Table 1, Section 2.2, paragraph 3) was the lowest (Fig. 8a). The dispersing effect of the dispersant becomes more pronounced as the grain size class decreases (Fig. 8a). But when the concentration of dispersants was increased to 1% and 2% respectively, hardly any decline in the mass ratio ( m after / m o ) was observed (Fig. 8a). The addition of dilute hydrochloric acid (in pretreatment C) resulted in the reduction or elimination of aggregation, destabilization of complexes, and removal of susceptible clay minerals, which caused a significant decrease in the mass ratio ( m clay / m silt ) and a considerable increase in median particle size (d 50 ) (Figs. 8b-8c). Notably, an analogous behavior was observed in the silt-clay group grain samples (except for pretreatment C, which involved adding hydrochloric acid during 10 minutes of ultrasonic dispersion). However, even though the concentration of dispersants was increased to 1% and 2%, the mass ratio ( m after / m o ) did not increase, and in some cases even decreased (Figs. 8b-8c). It is worth noting that the pretreatment B4 (adding polycarboxylate during 10 minutes of ultrasonic dispersion, described in section 2.2) performed better than the other pretreatments due to having the largest mass ratio ( m clay / m silt ) and the smallest median particle size (d 50 ) (Figs. 8b-8c). The mass ratios ( m after / m o ) of two different types of samples were compared. The first group of samples was made using ultrasonic dispersion and labeled as A opt . The second group was treated with four different pretreatments (B1~B4, 0.5% concentration). After pretreatments, the mass ratio ( m after / m o ) ranged from 99.1% to 74.75%, showing a decrease (Fig. 8d). This reduction in mass ratio continued up to the 0.5 mm ~ 0.25 mm grain group (Fig. 8d). On the other hand, the mass ratio ( m clay / m silt ) increased, varying from 0.31% to 23.88% (Fig. 8e). 3.3 Results of control groups test (CGT) After determining that the pretreatment B4 (adding 0.5% polycarboxylate in optimal ultrasonic dispersion) provides more accurate measurements of PSD, it is essential to examine this pretreatment in practice. Fig. 9 presents the grain size distribution before and after pretreatment B4, and the attached Tables T1 and T2 (Supplementary materials) show the corresponding grain grading parameters, such as average particle size (`d), median particle size (d 50 ), skewness (SK), kurtosis (K), coefficient of nonuniformity (C u ), gravel, sand, silt-clay content, etc. in detail. Remarkably, it was found that traditional grain sieving overestimated gravel fractions in most of the samples, except for S4 and S8 (Fig. 9). However, the reduction in gravel fraction was minimal. After pretreatment B4, the average gravel fraction decreased from 60.55% (standard deviation of 9.54%) to 52.64% (standard deviation of 10.12%). At the same time, the average sand fraction increased from 32.59% (with a standard deviation of 8.88%) to 35.43% (with a standard deviation of 10.22%). The average silt-clay fraction also showed an increase from 6.86% (with a standard deviation of 3.71%) to 11.93% (with a standard deviation of 4.59%). The silt-clay and fine sand fraction values of the samples displayed a marked rise after pretreatment B4, especially the silt-clay fraction. The difference in the content before and after pretreatment B4 for the gravel group ranged from 2.53% to 18.27%, with a mean of 7.91% and a standard deviation of 4.23%. For the sand group, the difference ranged from -13.81% to 5.10%, with a mean and a standard deviation of -2.84% and 4.50%, respectively. The increasing extent of the fine sand group ranged from -9.59% to 2.01%, with a mean and a standard deviation of -3.85% and 3.41%, respectively. The silt-clay group had a difference ranging from -9.03% to -2.58%, with a mean of -5.07% and a standard deviation of 1.70%. In Fig. 9, it can be clearly seen the low content and consistent presence of the 1 mm ~ 2 mm grain group (red line in Fig. 9). These coarse sands accounted for approximately 0.47 to 6.70% of the total weight before pretreatment B4 and 0.41 to 5.64% after the treatment. The red line serves as a boundary between coarse and fine-grain systems, which is a characteristic of moraine soil in terms of its particle size distribution. After pretreatment B4, the percentage of silt and clay in laser diffraction was found to be different, despite a rapid increase in silt-clay grains. According to Fig. 9, the silt percentage ranged from 77.15% to 86.42%, while the clay percentage varied from 3.80% to 10.76%. 4. Discussion 4.1 Aggregation phenomena and models In a recent study, Zheng, Li, Wang, Li, Shi and Bi [34] proposed the core-coating concept in loess. However, aggregation patterns in moraine soil differ significantly from those in loess. As a result, this study has developed three basic concepts of core and coat in moraine soil, along with nine detailed models (Fig. 10). According to the core-coating concept (Fig. 10a), some fine sand and silt-clay particles exist as aggregates or attachments to the surfaces of gravel particles. These aggregates and attachments form a thin “coat.” Clay plays a crucial role in forming the “coat”, and acts as an adhesive to bind other particles together. The gravel particle acts as the “core” and is not separated in ultrasonic dispersion or pretreatments. The “core” particle is dominant, while the “coat” occupies less than half of the surface of the “core.” This phenomenon is similar to the process of adding a coating to the “core.” The detailed aggregation models according to this concept are shown in Fig. 10b (①~⑥). The core(s)-coat concept (Fig. 10a) states that the fine gravel or sand particles function as the “core” and may appear in multiple “cores.” The “core” particles still dominate, but the “coat” covers almost half of the surface of the “core” and is thick. The detailed aggregation models according to this concept are shown in Fig. 10b (⑦~⑧). The coat-coring concept, as depicted in Fig. 10a, proposes that the silt-clay or partial sand forms the “core” of a particle, but this core is losing its dominance even in cases where multiple cores are present. Instead, the “coat” covers almost the entire surface of the “core”, and can even exist without the core. The detailed aggregation models according to this concept are shown in Fig. 10b (⑨ and partial ⑦ in sand or ⑧). The formation of this “coat” is related to the presence of clay minerals. These minerals are found in higher concentrations in the fine gravel group and are highest in the fine sand and silt-clay groups (Table 3, Table 4). Hence, it is only in the core-coating concept that some fine gravel acts as a coating component. The silt-clay particles serve as the primary ingredients in the “coat” and are even dominant in the Core(s)-coat concept and Coat-coring concepts. 4.2 Statistical analysis One way to evaluate the effectiveness of different pretreatment methods in the EGT is by conducting a significance test. To do this, Δm 1 and Δm 2 were used as measurement values, which denote the mass ratio difference between adding dispersants in ultrasonic and ultrasound-only treatments, respectively. These values are obtained from grain group samples that have undergone different pretreatments, ranging from B1 to B4 and C. For 5 mm ~ 2 mm grain group samples, Δm 1 and Δm 2 can be calculated using the formula Δm 1 = B 1~4 (m after /m o ) - `A opt (m after /m o ). Here, B1 to B4 represent the mass ratios after each pretreatment, while Aopt is the average mass ratio after pretreatment A. On the other hand, for grain group samples smaller than 0.075 mm, the formula Δm 2 = C(m after /m o ) - `A(m after /m o ), or Δm 2 = B 1~4 (m after /m o ) - `A opt (m after /m o ) can be used. In this case, C(m after /m o ) represents the mass ratio after pretreatment C. A normal distribution test was performed by using Δm 1 and Δm 2 parameters (Fig. 11). When the results revealed a normal distribution, the mean of the measurement data between the two groups was compared with the t-test. This study used the Jarque-Bera test to calculate p-values using Monte Carlo simulation, and the Lilliefors test to detect whether the statistics conformed to a normal distribution. Considering that the statistics Δm 1 and Δm 2 are consistent with (or close to) a normal distribution (Fig.11), t-tests can be performed representing the different pretreatment methods. For samples belonging to the 5 mm ~ 0.075 mm grain group, the pretreatment B4 is statistically significant in one sample, based on the results obtained through the t-test (Fig.12 (a)). Unpaired t-tests showed that the B4-pretreated samples are significantly different from the samples of the other three pretreatment methods (B1, B2, and B3) (Fig.12 (a)). After the application of pretreatment methods B1 to B4, the aggregation particles in the sample were effectively dispersed, reducing the original particle mass. Therefore, the m after /m o value displayed a decreasing trend, and the associated statistic Δm 1 was less than 0 (Fig.12 (a)). This study examined whether Δm 1 was significantly less than -2%. For samples with particle diameters smaller than 0.075 mm, the pretreatment B4 and B2 are statistically significant in one sample, based on the results obtained through the t-test (Fig.12 (b)). Unpaired t-tests indicate that the B4-pretreated samples are only significantly different from the samples of the B3. The C-pretreated samples are significantly different from the samples subjected to the other four pretreatment methods (from B1 to B4) (Fig.12 (a)). After applying various pretreatment methods (B1 to B4), the aggregated particles were effectively dispersed. This led to a reduction in silt and an increase in clay. Hence, the m clay /m silt value should exhibit an increasing trend, and the associated statistic Δm 2 should be greater than 0 (Fig.12 (a)). This study examined whether Δm 2 was significantly greater than 0%, indicating a more effective dispersion without destroying the particles. In Fig. 12(b), it can be observed that pretreatment has a significant difference from the others, but its values are less than zero percent. This suggests that the HCl undergoes a chemical reaction with the clay minerals, which has been reported in previous studies [32, 33, 36]. Results of the t-test for particles <0.075 mm show that pretreatment B4 and B2 have similar significance. 4.3 Effectiveness of pretreatment B4 and its dispersion mechanism After confirming the statistical significance of the pretreatment B4%, the results suggest that this method produces more precise particle size distribution (PSD) measurements. Therefore, it is important to discuss the dispersion mechanism of this pretreatment method further (Fig.13). Fig. 13(a) depicts the particle changes before and after pretreatment B4. The particles were cleaner and brighter after this pretreatment. This suggests that the separation of “coat” and “core” was effective. Polycarboxylate works as a dispersant for pretreatment B4. Polycarboxylate and its derivatives were widely used in concrete production because it has the advantages of molecular designability, high water reduction, and good slump retention [39, 40]. The polycarboxylate dispersant adsorbs the silt-clay particles and separates them from agglomeration under ultrasonic cavitation, separating the “coat” from the “core.” The polycarboxylate adsorb particles into the surface of the silt-clay through electrostatic attraction and intermolecular forces (Fig. 13(b)). The primary and side chains prevent the particles from reaggregating by providing resistance to steric hindrance. Due to gravity, larger particles settle first in the lower layers, while smaller particles remain suspended in water or slowly settle, resulting in effective stratification. Finally, the particles achieve dispersed state through the steric hindrance effect, lubricating effect, and DLVE effect (an electrostatic repulsive effect) [43-46]. Meanwhile, pretreatment C (dispersant is dilute hydrochloric acid) has an evident chemical reaction with the sample, destroying the particles compared to pretreatment B4 (Fig. 13(c)). Although the polycarboxylate works well at separating the “coat” from the “core,” it is ineffective in excess and is likely to have side effects. For instance, they unite different clays due to functional groups and side-chain effects. 4.4 The impact of the aggregation phenomena Soil texture can be classified based on the percentage of three classes - gravel, sand, and silt-clay. However, grain grading parameters, such as average particle size and median particle size, were significant indicators of the mechanical and hydrological properties of the soil. Pedologists, geologists, and soil scientists use these features to quickly assess soil texture and properties in moraine landscapes by manually inspecting samples [37]. Soil texture triangles were plotted according to three main particle size classes: gravel (60 mm ~ 2 mm), sand (2 mm ~ 0.075 mm), and silt-clay (<0.075 mm). The common classification system used in soil science, which is the geotechnical test standard GB/T 50123-2019 (China code), was used to plot these triangles Fig. 14(a) shows that the untreated soil samples were mostly poorly graded gravelly soils (GP) and gravelly soils that included fine particles [47]. However, after the B4, more than 50% of the samples turned out to be sandy soil, including fine particles (SF), silty sand soil (SM), and silty gravel soil (GM). Furthermore, the kernel density core of the soil texture triangle shifted to the upper left. To compare differences among samples in grain grading parameters (section 3.3), a normalization method was applied. With that aim, the formula X′=(X−X min )/(X max −X min ) was used, where X is a soil grain grading parameter, such as `d (the average particle size, mm), σ (the standard deviation of the particle size, mm), d 50 (the median particle size, mm), C u (the coefficient of nonuniformity), C c (the coefficient of curvature), K (kurtosis) and SK (skewness) (attached table T1 and T2 in Supplementary materials). Fig. 14(b) depicts that the aggregation phenomena have a primary impact on the average particle size (`d) and the standard deviation of the particle size (σ). After the pretreatment B4, the mean value of all samples' average particle sizes (`d) decreased from 2.61 mm (with a standard deviation of 1.36 mm) to 1.56 mm (with a standard deviation of 0.76). This represents a reduction of around 40% (Fig. 14(b)). The standard deviation of the particle size (σ) has increased from 10.27 mm (with a standard deviation of 2.78 mm) to 13.46 mm (with a standard deviation of 3.50), with an increase of roughly 30%. The impact of the aggregation phenomena is quite different for the different grain groups, probably due to their different amounts of clay minerals (section 3.1). Fig. 14(c) illustrates a decrease in the grain group content of 2 mm to 10 mm and 0.0 mm to 0.25 mm. The mean value of 0.0 mm to 0.25 mm grain group contents has increased from 18.73% (with a standard deviation of 7.82%) to 27.02% (with a standard deviation of 8.83%), revealing an increase of 8.29% after B4. Among them, the mean value of silt-clay content has increased from 6.86% (with a standard deviation of 3.71%) to 11.93% (with a standard deviation of 4.59%), with an increase of 5.07% after B4. 5. Conclusion This study focuses on the introduction of uncertainty in particle size analysis of moraine soil due to the occurrence of aggregation phenomena. The research observed that the moraine soil often exhibited aggregation phenomena, and models were developed to understand them. Then, experimental and control group tests (EGT and CGT) were designed to compare the effectiveness of different pretreatment methods (five to six) in removing aggregation phenomena during particle size analysis. Finally, the impact of aggregation phenomena on particle size distribution and soil texture assessment is discussed. The main conclusions are as follows: Widespread and complex aggregation phenomena were observed in moraine soil, and are present in every grain group (from < 0.075 mm to 60 mm ~ 40 mm). The study identifies three basic conceptual models of the aggregation phenomena: ① Core-coating concept, ② Core(s)-coat concept, ③ Coat-coring concept, along with nine detailed models. As a result, the problem of pretreatment methods in particle size analysis is reduced to separating the “coat” from the “core.” A new pretreatment method called “B4” has been proposed to disperse the aggregation phenomena of moraine soil before conducting grain sieving and laser diffraction tests. This method involves adding 0.5% polycarboxylate to achieve optimal ultrasonic dispersion. The results obtained through this method have been found to be significant in comparison to other pretreatment methods. The dispersion mechanism of this method has been discussed in detail, including the role of the dispersant polycarboxylate and the comprehensive dispersion mechanism, which includes ultrasonic cavitation, steric hindrance effect, lubricating effect, and DLVE effect (an electrostatic repulsive effect). However, for particles smaller than 0.075 mm, there is little difference between the pretreatments B2 (adding H2O2 in optimal ultrasonic dispersion) and B4. It is important to note that dilute hydrochloric acid destroys clay and is not recommended. The aggregation phenomena have an impact on particle size analysis for moraine soils. It leads to an overestimation of the content of the gravel group (about 7.91%) and an underestimation of the content of the silt-clay group (about 5.07%). The aggregation phenomena mainly affect the particle size ranges of 2 mm ~ 10 mm and 0.0 mm ~ 0.25 mm, which is related to the content of clay minerals contained in these grain groups. Moreover, the aggregation phenomena have an impact on the moraine soil texture triangles, where nearly 50% of the samples changed from GP and GF to SF, SM, and GM. The average particle size and the standard deviation of the particle size also changed significantly, with roughly a 40% reduction and a 30% increase, respectively. Declarations Acknowledgments This research work is supported by the National Natural Science Foundation of China (Grant No. 41772324), the China Geological Survey Project (DD20221746), and the Natural Science Foundation of Sichuan Province (No.2023NSFSC2086). Author contributions Tuo Lu and Yongbo Tie designed the research. Tuo Lu and Yaming Tang processed the corresponding data. Zhijie Ning and Lingfeng Gong help with site investigation and soil sample collection. Tuo Lu wrote the draft of the manuscript. Yongbo Tie helped to revise. Conflict of interest Tuo Lu, Yongbo Tie, Yaming Tang, Lingfeng Gong, and Zhijie Ning declare no conflict of interest. References OBU J, WESTERMANN S, BARTSCH A, et al. (2019) Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth-Science Reviews, 193: 299-316. https://doi.org/10.1016/j.earscirev.2019.04.023 MEKONNEN Z A, RILEY W J, GRANT R F, et al. (2021) Changes in precipitation and air temperature contribute comparably to permafrost degradation in a warmer climate. Environmental Research Letters, 16(2): 024008. https://doi.org/10.1088/1748-9326/abc444 LI C, WEI Y, LIU Y, et al. (2022) Active Layer Thickness in the Northern Hemisphere: Changes From 2000 to 2018 and Future Simulations. Journal of Geophysical Research: Atmospheres, 127(12): e2022JD036785. https://doi.org/10.1029/2022JD036785 FANG X, WANG A, LYU S, et al. (2023) Dynamics of Freezing/Thawing Indices and Frozen Ground from 1961 to 2010 on the Qinghai-Tibet Plateau. Remote Sensing, 15(14). https://doi.org/10.3390/rs15143478 WANG L, LIU G, MA P, et al. (2023) Effects of thaw slump on soil bacterial communities on the Qinghai-Tibet Plateau. CATENA, 232: 107342. https://doi.org/10.1016/j.catena.2023.107342 CMA CLIMATE CHANGE CENTRE. (2023) Blue Book on Climate Change in China (2023). Science Press, Beijing, China JR S B (2018) Principles of Sedimentology and Stratigraphy. Pearson, Upper Saddle River, New Jersey, USA LUTGENS F K, TARBUCK E J, TASA D G (2017) Essentials of Geology. Pearson, Boston, MA, USA SPRINGMAN S M, JOMMI C, TEYSSEIRE P (2003) Instabilities on moraine slopes induced by loss of suction: a case history. Géotechnique, 53(1): 3-10. https://doi.org/10.1680/geot.2003.53.1.3 BITTELLI M, CAMPBELL G S, TOMEI F (2015) Soil Physics with Python. Transport in the Soil-Plant-Atmosphere Continuum. Oxford University Press, Oxford, United Kingdom. HARTMANN A, WEILER M, BLUME T (2020) The impact of landscape evolution on soil physics: evolution of soil physical and hydraulic properties along two chronosequences of proglacial moraines. Earth Syst Sci Data, 12(4): 3189-204. https://doi.org/10.5194/essd-12-3189-2020 KUST P, MAKEEV A, LESSOVAIA S, et al. (2022) Polygenetic features in Retisols, formed in Moscow (Late Saalian) glacial till. CATENA, 214: 106245. https://doi.org/10.1016/j.catena.2022.106245 SANTOS J B, VIEIRA G, SANTOS-GONZáLEZ J, et al. (2022) Macrofabric and grain size analysis of moraines and other till deposits in the Serra da Estrela Mountains, central Portugal. Physical Geography, 43(2): 238-64. https://doi.org/10.1080/02723646.2020.1838136 OSTROWSKI P, FALKOWSKI T, KOCHANEK K (2023) Reconstructing parameters of the Holocene paleofloods in alluvial lowland river valleys – An example from the Bug valley (East Poland). Journal of Hydrology, 624: 129930. https://doi.org/10.1016/j.jhydrol.2023.129930 THOMAS M, MONHONVAL A, HIRST C, et al. (2023) Evidence for preservation of organic carbon interacting with iron in material displaced from retrogressive thaw slumps: Case study in Peel Plateau, western Canadian Arctic. Geoderma, 433: 116443. https://doi.org/10.1016/j.geoderma.2023.116443 ÅSTRöM M, BJöRKLUND A (1997) Geochemistry and acidity of sulphide-bearing postglacial sediments of western Finland. Environmental Geochemistry and Health, 19(4): 155-64. https://doi.org/10.1023/A:1018462824486 XIE C Q (2002) Research on the Engineering Performance of Moraine Soil. Journal of Mountain Science, S1: 129-32 (in Chinese). https://doi.org/10.16089/j.cnki.1008-2786.2002.s1.020 MACDONALD A M, MAURICE L, DOBBS M R, et al. (2012) Relating in situ hydraulic conductivity, particle size and relative density of superficial deposits in a heterogeneous catchment. Journal of Hydrology, 434-435: 130-41. https://doi.org/10.1016/j.jhydrol.2012.01.018 STUMPP C, HENDRY M J (2012) Spatial and temporal dynamics of water flow and solute transport in a heterogeneous glacial till: The application of high-resolution profiles of δ18O and δ2H in pore waters. Journal of Hydrology, 438-439: 203-14. https://doi.org/10.1016/j.jhydrol.2012.03.024 FANG X, HUANG R (2013) PHYSICAL AND MECHANICAL PROPERTIES OF TYPICAL MORAINE SOIL ON THE QINGHAI-TIBET PLATEAU. Journal of Engineering Geology 21(01): 123-8 (in Chinese). https://doi.org/1004-9665/2013/21(1)-0123-06 G C B (2018) The engineering properties of glacial tills. Geotechnical Research, 5(4): 262-77. https://doi.org/10.1680/jgere.18.00020 ZHOU G G D, CHEN L-L, MU Q-Y, et al. (2019) Effects of water content on the shear behavior and critical state of glacial till in Tianmo Gully of Tibet, China. Journal of Mountain Science, 16(8): 1743-59. https://doi.org/10.1007/s11629-019-5440-9 MCGOWN A, MCARTHUR A A (1971) MORAINIC SOIL DEPOSITS AND THEIR USE IN LOWER COST ROADS. Roads & Road Construction, London /UK/, 49(587): p. 388-96. SHELP G S, NICHOL I (1987) Distribution and dispersion of gold in glacial till associated with gold mineralization in the Canadian shield. Journal of Geochemical Exploration, 28(1): 315-36. https://doi.org/10.1016/0375-6742(87)90055-0 HU G, YI C-L, ZHANG J-F, et al. (2015) Luminescence dating of glacial deposits near the eastern Himalayan syntaxis using different grain-size fractions. Quaternary Science Reviews, 124: 124-44. https://doi.org/10.1016/j.quascirev.2015.07.018 GAO J, BAI R, MA Q (2018) Experimental study on freezing of unsaturated coarse-grained soils. Journal of Glaciology and Geocryology, 40(1): 94-101. WANG J, CUI P, WANG H, et al. (2022) Insight into geotechnical properties of glacial tills in a periglacial area, Southeast Tibet. Bulletin of Engineering Geology and the Environment, 81(8): 303. https://doi.org/10.1007/s10064-022-02803-y LI C, WANG R, GU D, et al. (2022) Temperature and ice form effects on mechanical behaviors of ice-richmoraine soil of Tianmo valley nearby the Sichuan-Tibet Railway. Engineering Geology, 305: 106713. https://doi.org/10.1016/j.enggeo.2022.106713 BEAUMONT P (1971) BREAK OF SLOPE IN PARTICLE-SIZE CURVES OF GLACIAL TILLS. Sedimentology, 16(1-2): 125-8. https://doi.org/10.1111/j.1365-3091.1971.tb00224.x YANJUN S, XIN W, LEI Z, et al. (2022) Hydrothermal migration of moraine soil and the mechanism of ice accumulation and frost swelling in Alpine-cold mountain region. Journal of Engineering Geology, 30(5): 1450-65. https://doi.org/10.13544/j.cnki.jeg.2022-0449. LU H, AN Z (1998) Pretreated methods on loess-palaeosol samples granulometry. Chinese Science Bulletin, 43(3): 237-40. https://doi.org/10.1007/BF02898920 SCHULTE P, LEHMKUHL F, STEININGER F, et al. (2016) Influence of HCl pretreatment and organo-mineral complexes on laser diffraction measurement of loess–paleosol-sequences. CATENA, 137: 392-405. https://doi.org/10.1016/j.catena.2015.10.015 KOZA M, SCHMIDT G, BONDAROVICH A, et al. (2021) Consequences of chemical pretreatments in particle size analysis for modelling wind erosion. Geoderma, 396: 115073. https://doi.org/10.1016/j.geoderma.2021.115073 ZHENG Z, LI X-A, WANG L, et al. (2021) A new approach to evaluation of loess collapsibility based on quantitative analyses of colloid-clay coating with statistical methods. Engineering Geology, 288: 106167. https://doi.org/10.1016/j.enggeo.2021.106167 KONERT M, VANDENBERGHE J (1997) Comparison of laser grain size analysis with pipette and sieve analysis: a solution for the underestimation of the clay fraction. Sedimentology, 44(3): 523-35. https://doi.org/10.1046/j.1365-3091.1997.d01-38.x LU R, LIANG R, DUAN W (2021) Dispersion and stability of fine-grained soils under different pretreatments based on the particle size distribution. Journal of Soils and Sediments, 21(1): 96-105. https://doi.org/10.1007/s11368-020-02689-x BITTELLI M, PELLEGRINI S, OLMI R, et al. (2022) Experimental evidence of laser diffraction accuracy for particle size analysis. Geoderma, 409: 115627. https://doi.org/10.1016/j.geoderma.2021.115627 SVENSSON D N, MESSING I, BARRON J (2022) An investigation in laser diffraction soil particle size distribution analysis to obtain compatible results with sieve and pipette method. Soil and Tillage Research, 223: 105450. https://doi.org/10.1016/j.still.2022.105450 BOUHAMED H, BOUFI S, MAGNIN A (2007) Dispersion of alumina suspension using comb-like and diblock copolymers produced by RAFT polymerization of AMPS and MPEG. Journal of Colloid and Interface Science, 312(2): 279-91. https://doi.org/10.1016/j.jcis.2007.03.060 LIU J, RAN Q, MIAO C, et al. (2011) Synthesis and Characterization of Comb-Like Copolymer Dispersant with Methoxy Poly (Ethylene Oxide) Side Chains. Polymer-Plastics Technology and Engineering, 50(1): 59-66. https://doi.org/10.1080/03602559.2010.512351 BENN D I, BOLCH T, HANDS K, et al. (2012) Response of debris-covered glaciers in the Mount Everest region to recent warming, and implications for outburst flood hazards. Earth-Science Reviews, 114(1): 156-74. https://doi.org/10.1016/j.earscirev.2012.03.008 BARAL P, ALLEN S, STEINER J F, et al. (2023) Climate change impacts and adaptation to permafrost change in High Mountain Asia: a comprehensive review. Environmental Research Letters, 18(9): 093005. https://dx.doi.org/10.1088/1748-9326/acf1b4 SHUI L, SUN Z, YANG H, et al. (2016) Experimental evidence for a possible dispersion mechanism of polycarboxylate-type superplasticisers. Advances in Cement Research, 28(5): 287-97. https://doi.org/10.1680/jadcr.15.00070 CONTE T, PLANK J (2019) Impact of molecular structure and composition of polycarboxylate comb polymers on the flow properties of alkali-activated slag. Cement and Concrete Research, 116: 95-101. https://doi.org/10.1016/j.cemconres.2018.11.014 STECHER J, PLANK J (2019) Novel concrete superplasticizers based on phosphate esters. Cement and Concrete Research, 119: 36-43. https://doi.org/10.1016/j.cemconres.2019.01.006 MA Y, SHI C, LEI L, et al. (2020) Research progress on polycarboxylate based superplasticizers with tolerance to clays - A review. Construction and Building Materials, 255: 119386. https://doi.org/10.1016/j.conbuildmat.2020.119386 SHI L, WANG P, HU G, et al. (2023) Sedimentation and geomorphology of the Ruoergai Basin outlet reach at the source of the Yellow River: Response to the late quaternary glacial debris flow damming events. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.1017597 Additional Declarations No competing interests reported. Supplementary Files 03.Supplimentalmater.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3945900","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273901196,"identity":"560adeb4-f3fa-45ce-9858-3ad0c8060593","order_by":0,"name":"Tuo Lu","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Lu","suffix":""},{"id":273901197,"identity":"91ab49d0-6539-4bee-a162-dcb7ac4ed8f6","order_by":1,"name":"Yongbo Tie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie2NP4vCMBiH33DgLUIcU+T0K5wUOgn9KglCuug3iBKXuvgtnF2LYyCQWwKuDje0S2dvONDNWBRd2qvbDXmG9x/vww/A4/mvIAkDV5eq1Xf3poTX+prCpOvtlJhMTPG7GyfblXYpYh4D3hgC4rs+hfAk/LB8llnmFPPFJCk5AVM2KNOoH6R6limnIGkoEBt+Iqn/VJJoX1RK3EoJflJNo0OVIpDE61HeqNiS95Hlo+zgUqhRLCUdllNTr7yvJiY478bDaJ8U+VEsYoy1UkdRr0CPwlv3vlDQ0CH0OjSAFaDTY11UF4/H4/E8cwFjzVzguM79fAAAAABJRU5ErkJggg==","orcid":"","institution":"China Geological Survey (Geosciences Innovation Center of Southwest China)","correspondingAuthor":true,"prefix":"","firstName":"Yongbo","middleName":"","lastName":"Tie","suffix":""},{"id":273901198,"identity":"fa6984d5-d1ec-495e-b117-4981d12018a6","order_by":2,"name":"Yaming Tang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yaming","middleName":"","lastName":"Tang","suffix":""},{"id":273901199,"identity":"f64e5f0a-add9-419e-aca8-7e59c4db3250","order_by":3,"name":"Zhijie Ning","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Ning","suffix":""},{"id":273901200,"identity":"72aceac4-029a-41e4-b50e-01ca8e46c6b8","order_by":4,"name":"Lingfeng Gong","email":"","orcid":"","institution":"China Geological Survey (Geosciences Innovation Center of Southwest China)","correspondingAuthor":false,"prefix":"","firstName":"Lingfeng","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2024-02-10 12:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3945900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3945900/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51545086,"identity":"94349eb5-ea9f-4cae-a816-a7f3ca7b2d3a","added_by":"auto","created_at":"2024-02-23 12:49:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15880467,"visible":true,"origin":"","legend":"\u003cp\u003eSampling and investigation site at the Bengga Valley. (a) The typical moraine outcrop profile (profile 3) and sampling. (b) A detailed surface survey. (c) The moraine soil outcrop profile investigation. (d) The location of the study area.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/0835341333d050a29e94b2c7.jpg"},{"id":51545084,"identity":"32ad8dcb-dc27-48bd-97d5-4fa5b099ad9f","added_by":"auto","created_at":"2024-02-23 12:49:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":659796,"visible":true,"origin":"","legend":"\u003cp\u003eAggregation phenomena in moraine soil.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/3b447074e157b51a8ce69a33.jpg"},{"id":51545582,"identity":"386df9d0-8ca6-435d-b6fa-21948fa76ce7","added_by":"auto","created_at":"2024-02-23 12:57:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1673660,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental procedure for pretreatment of sand \u0026amp; gravel in the sample and particle size analysis. (a) Flowchart of sand and gravel grain pretreatments. (b) Experimental procedure in detail.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/ee6c3e0ed457459179a4a90a.jpg"},{"id":51545090,"identity":"dd23b7bf-2d5a-4917-8e51-ee35480997b5","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1090842,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental procedure for pretreatment of silt-clay grains in the sample and particle size analysis. (a) Flowchart of silt-clay grain pretreatments. (b) Experimental procedure in detail.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/a3e1a270c9a156c6a769ebb0.jpg"},{"id":51545088,"identity":"3fe5867a-a986-4a1f-a2b7-cec6a2aec091","added_by":"auto","created_at":"2024-02-23 12:49:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2816498,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative aggregation phenomena in gravel groups under the smartphone (with magnifier) and transmitted-reflected metallographic microscope. ① ~ ⑨are aggregation models in this study (seen in Section 4.1).\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/135196822fcda365125638b0.jpg"},{"id":51545583,"identity":"3397d77e-f068-4be2-9836-0c66e73224b0","added_by":"auto","created_at":"2024-02-23 12:57:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2252852,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative aggregation phenomena in the sand and silt-clay groups under the smartphone (with magnifier) and scanning electron microscope. ① ~ ⑨are aggregation models in this study (seen in Section 4.1).\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/eede8b4dc5984cd4b02121a8.jpg"},{"id":51545087,"identity":"f03e2dad-58bc-45bd-ad01-9682df6f3f57","added_by":"auto","created_at":"2024-02-23 12:49:21","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":761039,"visible":true,"origin":"","legend":"\u003cp\u003eThe ultrasonic dispersion time of different representative grain group samples. (a) The ultrasonic dispersion time of gravel groups. (b) The ultrasonic dispersion time of sand groups. mafter is the quality of the sample after pretreatment A (ultrasonic dispersion) in the original size class. mo is no pretreatment sample quality. Aopt is the optimal ultrasonic dispersion.\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/f35a1cc074b5e491f33cff3a.jpg"},{"id":51545094,"identity":"f503afe4-c036-48f1-a0e0-acb48cb87a56","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1141942,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent representative grain group samples respond to different pretreatments. (a) Comparing the pretreatments of varying dispersant concentrations (10 mm ~ 0.075 mm). (b) Comparing the pretreatments of different dispersant concentrations (\u0026lt;0.075 mm). (c) The impact of varying dispersant concentration pretreatments on the median particle size (\u0026lt;0.075 mm). (d) The mass ratio results from different pretreatments (dispersants are 0.5%). (e) Silt-clay ratio under the different pretreatments (dispersants are 0.5%).\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/262e48c9d71d4d7e43edf8d5.jpg"},{"id":51545093,"identity":"6a4431c2-12ca-48c8-a253-d0eb15f1c7f5","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":444983,"visible":true,"origin":"","legend":"\u003cp\u003eThe representative samples’ particle size distribution of no pretreatment vs. pretreatment B4.\u003c/p\u003e","description":"","filename":"Fig.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/7af77c5f0db101d3e54aedfa.jpg"},{"id":51545095,"identity":"252df33b-d147-4721-aac0-a101f7c8a5a3","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":637931,"visible":true,"origin":"","legend":"\u003cp\u003eAggregation models of moraine soil (gravel \u0026amp; sand \u0026amp; silt). (a) Core and coat concept model in this study. (b) Detailed aggregation models, ① ~ ⑨, are aggregation model types in this study.\u003c/p\u003e","description":"","filename":"Fig.10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/1223fa4b774dba670c48840a.jpg"},{"id":51545089,"identity":"cd4ad416-7aa7-46be-a386-294cab1b15e0","added_by":"auto","created_at":"2024-02-23 12:49:21","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":281045,"visible":true,"origin":"","legend":"\u003cp\u003eNormal distribution test. * represents significance level 0.05. ** represents significance level 0.01. Δm1 and Δm2 seen in section 4.2, paragraph 1.\u003c/p\u003e","description":"","filename":"Fig.11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/8c8075f84bcbf5b52198660f.jpg"},{"id":51545091,"identity":"a2fdaacb-fd37-4ecf-b812-f5865318e5a2","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":335361,"visible":true,"origin":"","legend":"\u003cp\u003eT-tests. (a) T-tests for 5 mm ~ 0.075 mm grain group samples, * and **, represent significance levels 0.05 and 0.01 in unpaired t-tests, respectively. ※※ represents a significance level of 0.01 in one sample t-test, which aims to determine whether the means of Δm1 are significantly less than -2%. (b) T-tests for \u0026lt;0.075 mm grain group samples. ※ represent a significance level 0.05 in one sample t-test, aiming to determine whether the means of Δm2 are significantly greater than 0%.\u003c/p\u003e","description":"","filename":"Fig.12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/1c000d689cf4cf9f6eb131a5.jpg"},{"id":51545584,"identity":"b2ea9667-c27b-4689-bb7e-a2f6a442437e","added_by":"auto","created_at":"2024-02-23 12:57:22","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2530778,"visible":true,"origin":"","legend":"\u003cp\u003eEffectiveness of pretreatment B4 and its dispersion mechanism. (a) 60 mm ~ \u0026lt;0.075 mm grains before and after pretreatment B4. (b) Dispersion mechanism. (c). \u0026lt;0.075 mm particles after pretreatment B4 and C under the SEM.\u003c/p\u003e","description":"","filename":"Fig.13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/121c9ad43de2c308b9805c5c.jpg"},{"id":51545098,"identity":"0edfc743-0cab-4fb8-8ea4-ae19ba403ee7","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":878593,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of aggregation phenomena on the particle size distribution and grain grading parameters. (a) The soil texture triangles, SF, SM, GP, GF, GM seen in section 4.4, paragraph 2. (b) Some grain grading parameters, ▢d, d50, Cu, SK, etc., seen in section 4.4, paragraph 3. (c) The particle size distribution.\u003c/p\u003e","description":"","filename":"Fig.14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/7c9ed67a35c92238befb860f.jpg"},{"id":55565122,"identity":"ce2eee5c-2412-4808-8d35-2ef6f4de949c","added_by":"auto","created_at":"2024-04-30 03:49:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3442664,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/6964aab1-0b58-420c-81b0-c05a44345c40.pdf"},{"id":51545097,"identity":"e7728af1-cf56-40d2-afd2-caa1a8df84b3","added_by":"auto","created_at":"2024-02-23 12:49:22","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":203619,"visible":true,"origin":"","legend":"","description":"","filename":"03.Supplimentalmater.docx","url":"https://assets-eu.researchsquare.com/files/rs-3945900/v1/7970b34bbd24d533c55e4dae.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the aggregation phenomena in moraine soil: a new approach to disperse them through a particle size analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Earth\u0026rsquo;s cryosphere is shrinking rapidly due to global climate warming. This is causing the exposure of moraine soil in high latitudes and permafrost landscapes to increase at a fast pace [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moraine soil's particle size characteristics are crucial to understanding the geography, geology, and ecology of moraine landscapes. The size of the particles and how they aggregate are essential indicators of exogenic forces that have shaped the Earth's surface over time [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, the mechanical strength, permeability, and water retention capacity of the soil largely depend on its particle size distribution [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shed some light on moraine soil's physical and mechanical properties. Moraine soil generally refers to poorly graded gravelly soils that have a high natural density ranging from 1.7 g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 2.3 g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and a low to medium moisture content of 0\u0026ndash;15% [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This type of soil is often characterized by a wide range of particle sizes with low roundness and is composed of gravel (60 mm\u0026thinsp;~\u0026thinsp;2 mm, nearly 50%), sand, and silt-clay particles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The particle size distribution (PSD) of moraine soil is usually divided into two sections around 1mm, known as the \"dual grain group\" characteristic [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, most of the studies above did not systematically consider the impact of aggregation phenomena on the particle size distribution.\u003c/p\u003e \u003cp\u003eSoil particle size analysis faces significant challenges due to the aggregation phenomena, where fine particles stick to coarser particles and small grains clump together [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This phenomenon is commonly observed in fine sandy, silty, and clayey soils. Complicated geological depositional processes can cause the precipitation of soluble salts, forming calcium carbonate and clay minerals that can cement and aggregate particles, especially in well-consolidated soils. For instance, Zheng, Li, Wang, Li, Shi and Bi [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] concluded that aggregation happens in loess and proposed the \u0026ldquo;core-coating\u0026rdquo; concept.\u003c/p\u003e \u003cp\u003eSome pretreatments, such as water distillation, the use of dispersants ((NaPO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e6\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, HCl chemicals, etc.), and the application of the ultrasonic method, are considered effective in dispersing soil aggregation phenomena in fine sands, silts, loess, coals, and clays[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These pretreatments are based on several mechanisms such as vibration dispersion, electrostatic repulsion, lubrication, steric hindrance, dissolution of part of the cement [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is possible to achieve proper dispersion of aggregate particles by adding dispersants into ultrasonic dispersion. But, it is important to note that the aggregation phenomena in fine-grained soils are quite different from those in moraine soil. Moreover, polycarboxylates are widely used in the dispersion of concrete materials [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and they may be instructive for the dispersion of moraine soils.\u003c/p\u003e \u003cp\u003eFor this study, soil samples weighing around 270 kg were collected from Bengga Valley, which hosts modern glaciers southeast of the Qinghai-Tibet Plateau. These samples were divided into experimental groups test (EGT) and control groups test (CGT). The study aimed to investigate the different aggregation phenomena present in moraine soil and develop a novel pretreatment method to remove them effectively. Various pretreatment methods were applied to the soil samples, including using different combinations of dispersants and ultrasound. The samples were then subjected to particle size analysis and a series of observation techniques such as exceptional magnification photography using a smartphone, transmitted-reflected metallographic microscope, scanning electron microscopy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], grain sieving test, and grain laser diffraction test. The goals of this study are (1) to discuss the types of moraine soil\u0026rsquo;s aggregation phenomena and models, (2) to develop a novel pretreatment method to remove aggregation phenomena effectively in particle size analysis, and (3) to quantify the impact of aggregation phenomena on the particle size distribution for moraine soil.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Site investigation and soil sample collection\u003c/h2\u003e \u003cp\u003eWidely distributed moraine soils can be found along the Sichuan\u0026ndash;Tibet Railway in the southeast part of the Qinghai-Tibet Plateau, with a significant concentration stretching from Bomi County to Linzhi City [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The Bengga Valley, which spans an area of approximately 17.30 km\u003csup\u003e2\u003c/sup\u003e, is situated on the east bank of the Yarlung Tsangpo River southeast of Linzhi city. The valley is divided into left and right branches, with the main right channel measuring around 6.6 km, and the left branch around 6.7 km. The valley entrance sits at an elevation of approximately 2925 m, while the highest point reaches about 5378 m, resulting in a relative elevation difference of about 2453 m. Modern glaciers can be found in the upper reaches of the left branch at around 4450 m and upstream of the right branch at an elevation exceeding 5000 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The accelerated melting of glaciers leads to increased exposure of moraine soils and eluvium gravels, which can be used as loose solid deposits for agricultural and forestry purposes. However, these loose deposits can also pose a hidden danger as solid material sources of geologic hazards, such as glacier debris flows, landslides, and ice lake outbursts [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, investigating the particle size characteristics of moraine soils in Bengga Valley under global warming is highly significant.\u003c/p\u003e \u003cp\u003eThe sampling strategy and the environment description of the study area were designed based on the observations made through multiple techniques such as surface survey, glacial till investigation, unmanned aerial vehicle (UAV) inspection, remote sensing interpretation using Gaofen-2 satellite imagery, site earth temperature testing, on-site moisture content test, natural density testing, and particle size test. All these investigations were conducted in the Bengga Valley (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) during March and August 2022, February and October 2023.\u003c/p\u003e \u003cp\u003eThe remote sensing data analysis revealed that the moraine soil is primarily concentrated in the upstream region of elevations above 3790 m to 5000 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Moreover, the UAV investigation and the surface survey showed that some typical moraine soil profiles were distributed in the downstream and the middle stream regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) at approximately 3026 m to 3539 m. Also, the gravel layer is predominantly distributed downstream. Four typical moraine outcrop profiles mainly formed by the retreat of Quaternary glaciations were found at elevations of 3026 m (Profile 1), 3338 m (Profile 2), 3408 m (Profile 3), and 3539 m (Profile 4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The top of these profiles was covered with dense vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Notably, the loose moraine deposits were found on the side of the Bengga valley, which led to several landslides. Hence, it is necessary to investigate moraine soil particle size data for the unstable slopes, soil permeability, retention capacity, etc. However, it is worth noting that aggregation phenomena in moraine soils interfered with particle size analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExperimental groups test (EGT) and control groups test (CGT) were conducted to analyze particle size and aggregate phenomena in moraine soil. For the EGT, 170 kg of samples (removed topsoil) were collected from different moraine soil profiles (profiles 1\u0026thinsp;~\u0026thinsp;4). The samples were categorized based on particle size: 60 mm\u0026thinsp;~\u0026thinsp;20 mm particles (55.0 kg), 20 mm\u0026thinsp;~\u0026thinsp;10 mm particles (30.0 kg), 10 mm\u0026thinsp;~\u0026thinsp;5 mm particles (15.0 kg), 5 mm\u0026thinsp;~\u0026thinsp;2 mm particles (20.0 kg), 2 mm\u0026thinsp;~\u0026thinsp;0.5 mm particles (15.0 kg), 0.5 mm\u0026thinsp;~\u0026thinsp;0.25 mm particles (15.0 kg), 0.25 mm\u0026thinsp;~\u0026thinsp;0.075 mm particles (15.0 kg), and \u0026lt;\u0026thinsp;0.075 mm particles (5.0 kg). For the CGT, a total of 23 samples were collected, each weighing approximately 4.0\u0026thinsp;~\u0026thinsp;4.5 kg (in accordance with the geotechnical test standard GB/T 50123\u0026thinsp;\u0026minus;\u0026thinsp;2019, 2019. China code), from two moraine soil profiles (profiles 3 and 4), also with the topsoil removed. Overall, a total of 270 kg of samples were collected for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Test method\u003c/h2\u003e \u003cp\u003eThe aggregation phenomena were observed using a smartphone with a magnifying device and scanning electron microscope (JSM-7500, JEOL Company, Japan), as well as a transmitted-reflected metallographic microscope (JX-2503, Chengdu Jingxin Powder Testing Equipment Company, China), as shown in Fig.\u0026nbsp;3b\u003csub\u003e3\u003c/sub\u003e and Fig.\u0026nbsp;4b\u003csub\u003e5\u003c/sub\u003e. The major mineralogical constituents of grain group samples were tested using a Powder X-ray diffractometer (XPert Pro, PANalytical B.V. Company, Netherlands). To develop a new effective pretreatment method in particle size analysis and to discuss aggregation phenomena that impact on the particle size distribution for moraine soil, an experimental groups test (EGT) and control groups test (CGT) were designed before the grain sieve and grain laser diffraction (Fig.\u0026nbsp;3b\u003csub\u003e2\u003c/sub\u003e and Fig.\u0026nbsp;4b\u003csub\u003e4\u003c/sub\u003e). Specifically, the goal of EGT was to select an appropriate pretreatment method by considering ultrasonic time, the chemical dispersant (NaPO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e6\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, Na\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e3\u003c/sub\u003e\u0026middot;5H\u003csub\u003e2\u003c/sub\u003eO, Polycarboxylate, HCl, to disperse aggregation moraine soil particles effectively. On the other hand, CGT aimed to check the difference in results between an appropriate pretreatment method determined by EGT and no pretreatment.\u003c/p\u003e \u003cp\u003eFor EGT, the samples were prepared as follows (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, and Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb): 2000 g\u0026thinsp;~\u0026thinsp;2100 g per sample for the 60 mm\u0026thinsp;~\u0026thinsp;20 mm grain group. The weights of the single sample on site ranged from 4000 g to 4500 g, and the weights of the 60\u0026thinsp;\u0026minus;\u0026thinsp;20 mm grain group by primary sieving were 800 g\u0026thinsp;~\u0026thinsp;2000 kg. Each 20 mm\u0026thinsp;~\u0026thinsp;10 mm grain group sample weighted 1000 g\u0026thinsp;~\u0026thinsp;1030 g. Each 10 mm\u0026thinsp;~\u0026thinsp;5 mm grain group sample weight was 400 g\u0026thinsp;~\u0026thinsp;410 g. Each 5 mm\u0026thinsp;~\u0026thinsp;2 mm grain group sample was 400 g\u0026thinsp;~\u0026thinsp;405 g. Each 2 mm\u0026thinsp;~\u0026thinsp;0.5 mm grain group sample was 200 g\u0026thinsp;~\u0026thinsp;201 g. Each 0.5 mm\u0026thinsp;~\u0026thinsp;0.25 mm grain group sample was 200 g\u0026thinsp;~\u0026thinsp;201. Each 0.25 mm\u0026thinsp;~\u0026thinsp;0.075 mm grain group sample was 200.0 g\u0026thinsp;~\u0026thinsp;200.1 g. Lastly, each \u0026lt;\u0026thinsp;0.075 mm grain group sample weighted 0.20 g\u0026thinsp;~\u0026thinsp;0.21 g. This information is based on the geotechnical test standard GB/T 50123\u0026thinsp;\u0026minus;\u0026thinsp;2019.\u003c/p\u003e \u003cp\u003eIn this study, three different pretreatment methods were applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The first method, method A, involved ultrasonic dispersion (40 kHz) of the samples (\u0026gt;\u0026thinsp;0.075 mm) in hot water (80 ℃) for different cumulative ultrasonic times ranging from 1 to 90 minutes. This method was mainly applied to gravel and sand groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Fig.\u0026nbsp;3b\u003csub\u003e1\u003c/sub\u003e). The second method, method B, involved adding the dispersant, such as (NaPO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e6\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, Na\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e3\u003c/sub\u003e\u0026middot;5H\u003csub\u003e2\u003c/sub\u003eO, and polycarboxylate into optimal ultrasonic dispersion (80 ℃ water), as determined by pretreatment method A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The dispersant concentration was designed to be 0%, 0.5%, 1%, and 2% (percentage of quality). The third method, method C, was only applied to the silt-clay grain group (\u0026lt;\u0026thinsp;0.075 mm) to investigate whether dilute hydrochloric acid (HCl) reduced or destroyed the silt-clay particles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The concentration of dispersant HCl was also designed to be 0%, 0.5%, 1%, and 2%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe samples for EGT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain group (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePretreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eA: Ultrasonic;\u003c/p\u003e \u003cp\u003eB1:(NaPO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e6\u003c/sub\u003e+ Optimal ultrasonic time;\u003c/p\u003e \u003cp\u003eB2: H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Optimal ultrasonic time;\u003c/p\u003e \u003cp\u003eB3:Na\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e3\u003c/sub\u003e\u0026middot;5H\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;Optimal ultrasonic time;\u003c/p\u003e \u003cp\u003eB4: Polycarboxylate\u0026thinsp;+\u0026thinsp;Optimal ultrasonic time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026ndash;1030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026minus;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026minus;\u0026thinsp;0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.0-200.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5*15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026ndash;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6*25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B1, B2, B3, B4, C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLaser diffraction\u003c/p\u003e \u003cp\u003eC: Hcl\u0026thinsp;+\u0026thinsp;10 min ultrasonic.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe EGT procedure for testing gravel and sand group samples (\u0026gt;\u0026thinsp;0.075 mm) was as follows. Firstly, the samples were oven-dried for 24 h and sieved (Fig.\u0026nbsp;3b\u003csub\u003e1\u003c/sub\u003e-3b\u003csub\u003e2\u003c/sub\u003e). Secondly, the different grain group particles were observed under the microscope (Fig.\u0026nbsp;3b\u003csub\u003e3\u003c/sub\u003e). After this, these samples underwent pretreatments (Fig.\u0026nbsp;3b\u003csub\u003e4\u003c/sub\u003e) and were left to settle in water settling for 24 h (Fig.\u0026nbsp;3b\u003csub\u003e5\u003c/sub\u003e). Finally, the samples were dried again and sieved (Fig.\u0026nbsp;3b\u003csub\u003e2\u003c/sub\u003e), and the above steps were repeated.\u003c/p\u003e \u003cp\u003eThe EGT test procedure for the silt-clay group samples (\u0026lt;\u0026thinsp;0.075 mm) was conducted in the following manner. First, the samples were soaked in deionized water for 24 hours (Fig.\u0026nbsp;4b\u003csub\u003e1\u003c/sub\u003e). Then, pretreatments were carried out and the samples were left to settle for 24 h (Figs.\u0026nbsp;4b\u003csub\u003e2\u003c/sub\u003e-4b\u003csub\u003e3\u003c/sub\u003e). Finally, particle size data was obtained using a laser diffraction particle size analyzer before 10 minutes of ultrasonic vibration (Fig.\u0026nbsp;4b\u003csub\u003e4\u003c/sub\u003e). It is worth noting that prolonged ultrasonic vibration can damage the particles [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, these samples were further observed under microscopes (Fig.\u0026nbsp;4b\u003csub\u003e5\u003c/sub\u003e) and were also prepared for scanning electron microscope analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sample preparation and procedure for CGT involved the following steps. Firstly, 23 samples were subjected to grain sieving and laser diffraction tests. After that, EGT determined an appropriate pretreatment method (M) for these 23 samples to perform the particle size test again (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe samples for CGT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003esamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePretreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1-S14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3408 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4100\u0026thinsp;~\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(Profiles 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eM (?% dispersant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetermined by EGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS15-S23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3539 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4100\u0026thinsp;~\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (Profiles 4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Results of aggregation phenomena observation and mineral composition\u003c/h2\u003e\n\u003cp\u003eTables 3 and 4 illustrate the primary mineralogical components found in the grain group samples. Quartz (24.4% to 35.6%), Potassium feldspar (4.6% to 36.3%), and Plagioclase (16.6% to 37.5%) were the most abundant minerals identified in all samples analyzed. Some grain group samples also contained a certain amount of calcite and hornblende. It was found that the fine sand had the highest clay mineral content (9.6% and 12.0%), and interestingly, the clay mineral content increased gradually as the grain class size decreased.\u003c/p\u003e\n\u003cp\u003eFigs. 5 and 6 show the outcomes of observing images through a smartphone with a magnifier and microscopes. In moraine soil, aggregation phenomena were frequently observed, ranging from large gravel to smaller sand and silt-clay particles. The patterns of these phenomena are still complex and poorly understood.\u003c/p\u003e\n\u003cp\u003eTable 3. The mineral composition of grain group samples (from\u0026nbsp;Profiles 3)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eGrain groups\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e60-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e40-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e20-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e10-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e5-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e2-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e1-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.5-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e0.25-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u0026lt;0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eQuartz (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003cp\u003efeldspar (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003ePlagioclase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eCalcite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eDolomite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003ePyrite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eHematite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eHornblende (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eClay mineral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. The mineral composition of grain group samples (from\u0026nbsp;Profiles 4)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eGrain groups\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e60-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e40-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e20-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e10-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e5-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e2-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e0.5-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.25-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e\u0026lt;0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eQuartz (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003cp\u003efeldspar (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003ePlagioclase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eCalcite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eDolomite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003ePyrite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eHematite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eHornblende (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eClay mineral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor samples belonging to the 60 mm ~ 20 mm grain group, some fine sand and silt particles were found to be adhered to the surface of coarse gravel grains. These particles are referred to as \u0026ldquo;coats\u0026rdquo; in this study and their thickness could be thick, medium, or thin. The distribution of these coats was random, with some covering half or more of the surface area of the gravel particles and others covering less than one-fourth (Fig. 5). In the case of samples in the 20 mm ~ 5 mm grain group, some of the gravels were entirely coated by silt-clay grains. Additionally, several fine gravels were grouped together by silt-clay grains and some silt-clay grains as if they were gravel (Fig. 5). The thickness of these coat layers could be thick, medium, or thin, and their distribution was also random. For samples in the 5 mm ~ 2 mm grain group, the coats were less frequently found in depressions or crevices on the surface area of the fine gravel particles. Also, several fine gravels and silt-clay grains were grouped, and some silt-clay grains were grouped as gravel.\u003c/p\u003e\n\u003cp\u003eFor samples belonging to the 2 mm ~ 0.5 mm grain group, the \u0026ldquo;coats\u0026rdquo; were still attached to the coarse sand surface in clusters. These clusters were randomly distributed and occupied half or more of the total particle\u0026rsquo;s surface area, while some covered less than half. In some cases, they were spread over fewer surfaces, appearing to be point-like shape (Fig. 6). However, some fine or medium sands were clustered together with silt-clay grains (Fig. 6). The \u0026ldquo;coats\u0026rdquo; could also observed in noticeable depressions or crevices in the coarse sands. For the 0.5 mm ~ 0.25 mm groups, flaky silt-clay particles were observed, and several fine gravels were clustered together with silt-clay grains (Fig. 6). The thickness of these \u0026ldquo;coats\u0026rdquo; layers could also be thick, medium, and thin, with a random distribution for irregular sands. Lastly, for 0.25 mm~0.075 mm grain groups, some fine sands were entirely coated by the silt-clay grains, while some \u0026ldquo;coats\u0026rdquo; were still not thick, similar to a point-like shape (Fig. 6).\u003c/p\u003e\n\u003cp\u003eFinally, for grain group samples with a diameter less than 0.075 mm, clay particles, being mainly cohesive, tend to cluster together on the surface of silt particles (as shown in Fig. 6). Likewise, clay grains could also bind silt and fine sand particles to form \u0026ldquo;coats,\u0026rdquo; which play a crucial role in the aggregation phenomena observed in moraine soil.\u003c/p\u003e\n\u003ch2\u003e3.2 Results of experimental groups test (EGT)\u003c/h2\u003e\n\u003ch3\u003e3.2.1 Ultrasonic dispersion time\u003c/h3\u003e\n\u003cp\u003eFig. 7 depicts the impact of different cumulative ultrasonic dispersion time conditions on the quality of various grain group samples. In general, all the test samples displayed similar behavior. The quality either remained stable or decreased gradually, followed by a sharp decline (Fig. 7). The quality of gravel remained unchanged for around 5 minutes, whereas sand maintained its quality for between 15 and 30 minutes.\u003c/p\u003e\n\u003cp\u003eFig. 7a shows that the quality of gravel grain group samples diminishes as the ultrasonic dispersion time increases. This decline in quality is more noticeable in fine gravels (60 mm to 2 mm) than in coarse gravels. Although the mass of some grain group samples remained unchanged after 3 minutes, overall, the mass of all grain group samples stopped declining after 5 minutes of ultrasonic dispersion (Fig. 7a). Therefore, it can be concluded that 5 minutes of ultrasonic dispersion is the optimal ultrasonic dispersion time (A\u003csub\u003eopt\u003c/sub\u003e) for gravels.\u003c/p\u003e\n\u003cp\u003eIn Fig. 7b, it is shown that the quality of sand grain groups decreases with increasing ultrasonic dispersion time. Interestingly, the extent of reducing quality was less for coarse sands than for fine sands (Fig. 7b). After 15 minutes, the mass of coarse sand remained constant, while the mass of medium and fine sand remained constant after 20 and 30 minutes, respectively (Fig. 7b). This suggests that the optimal ultrasonic dispersion (Aopt) for coarse, medium, and fine sands in this study was 15, 20, and 30 minutes, respectively. Thus, it can be inferred that the dispersion of aggregation phenomena is more challenging in smaller sand grain sizes than in larger sizes.\u003c/p\u003e\n\u003ch3\u003e3.2.2 Different dispersants\u003c/h3\u003e\n\u003cp\u003eAdding dispersant in the optimal ultrasonic dispersion may be helpful. Although the effects of adding dispersants in ultrasonic dispersion versus not adding them are less noticeable for gravels (Fig. 8), they work well for sands and silt-clay grains (Fig. 8), especially polycarboxylate.\u003c/p\u003e\n\u003cp\u003eSome parameters described in Fig.8 are explained as follows: m\u003csub\u003eafter\u003c/sub\u003e is the quality of the sample after pretreatment B or C (B1~B4, C) in the original size class. m\u003csub\u003e\u0026lt;0.075\u003c/sub\u003e is the quality of the detached silt-clay after pretreatment B or C (B1~B4, C). m\u003csub\u003eo\u003c/sub\u003e is no pretreatment sample quality. A\u003csub\u003eopt\u0026nbsp;\u003c/sub\u003eis the optimal ultrasonic dispersion (without any dispersants). U: no any pretreatment.\u003c/p\u003e\n\u003cp\u003eMost dispersants had little effect on the ultrasonic dispersion for most gravels (Fig. 8a). However, polycarboxylate seemed to work for fine gravel because its mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eafter\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) in pretreatment B4 (Table 1, Section 2.2, paragraph 3) was the lowest (Fig. 8a). The dispersing effect of the dispersant becomes more pronounced as the grain size class decreases (Fig. 8a). But when the concentration of dispersants was increased to 1% and 2% respectively, hardly any decline in the mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eafter\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) was observed (Fig. 8a).\u003c/p\u003e\n\u003cp\u003eThe addition of dilute hydrochloric acid (in pretreatment C) resulted in the reduction or elimination of aggregation, destabilization of complexes, and removal of susceptible clay minerals, which caused a significant decrease in the mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eclay\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003esilt\u003c/sub\u003e) and a considerable increase in median particle size (d\u003csub\u003e50\u003c/sub\u003e) (Figs. 8b-8c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, an analogous behavior was observed in the silt-clay group grain samples (except for pretreatment C, which involved adding hydrochloric acid during 10 minutes of ultrasonic dispersion). However, even though the concentration of dispersants was increased to 1% and 2%, the mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eafter\u003c/sub\u003e/\u003cem\u003e\u0026nbsp;m\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) did not increase, and in some cases even decreased (Figs. 8b-8c). It is worth noting that the pretreatment B4 (adding polycarboxylate during 10 minutes of ultrasonic dispersion, described in section 2.2) performed better than the other pretreatments due to having the largest mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eclay\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003esilt\u003c/sub\u003e) and the smallest median particle size (d\u003csub\u003e50\u003c/sub\u003e) (Figs. 8b-8c).\u003c/p\u003e\n\u003cp\u003eThe mass ratios (\u003cem\u003em\u003c/em\u003e\u003csub\u003eafter\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) of two different types of samples were compared. The first group of samples was made using ultrasonic dispersion and labeled as A\u003csub\u003eopt\u003c/sub\u003e. The second group was treated with four different pretreatments (B1~B4, 0.5% concentration). After pretreatments, the mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eafter\u003c/sub\u003e/\u003cem\u003e\u0026nbsp;m\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) ranged from 99.1% to 74.75%, showing a decrease (Fig. 8d). This reduction in mass ratio continued up to the 0.5 mm ~ 0.25 mm grain group (Fig. 8d). On the other hand, the mass ratio (\u003cem\u003em\u003c/em\u003e\u003csub\u003eclay\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003esilt\u003c/sub\u003e) increased, varying from 0.31% to 23.88% (Fig. 8e).\u003c/p\u003e\n\u003ch2\u003e3.3 Results of control groups test (CGT)\u003c/h2\u003e\n\u003cp\u003eAfter determining that the pretreatment B4 (adding 0.5% polycarboxylate in optimal ultrasonic dispersion) provides more accurate measurements of PSD, it is essential to examine this pretreatment in practice. Fig. 9 presents the grain size distribution before and after pretreatment B4, and the attached Tables T1 and T2 (Supplementary materials) show the corresponding grain grading parameters, such as average particle size (`d), median particle size (d\u003csub\u003e50\u003c/sub\u003e), skewness (SK), kurtosis (K), coefficient of nonuniformity (C\u003csub\u003eu\u003c/sub\u003e), gravel, sand, silt-clay content, etc. in detail.\u003c/p\u003e\n\u003cp\u003eRemarkably, it was found that traditional grain sieving overestimated gravel fractions in most of the samples, except for S4 and S8 (Fig. 9). However, the reduction in gravel fraction was minimal. After pretreatment B4, the average gravel fraction decreased from 60.55% (standard deviation of 9.54%) to 52.64% (standard deviation of 10.12%). At the same time, the average sand fraction increased from 32.59% (with a standard deviation of 8.88%) to 35.43% (with a standard deviation of 10.22%). The average silt-clay fraction also showed an increase from 6.86% (with a standard deviation of 3.71%) to 11.93% (with a standard deviation of 4.59%). The silt-clay and fine sand fraction values of the samples displayed a marked rise after pretreatment B4, especially the silt-clay fraction.\u003c/p\u003e\n\u003cp\u003eThe difference in the content before and after pretreatment B4 for the gravel group ranged from 2.53% to 18.27%, with a mean of 7.91% and a standard deviation of 4.23%. For the sand group, the difference ranged from -13.81% to 5.10%, with a mean and a standard deviation of -2.84% and 4.50%, respectively. The increasing extent of the fine sand group ranged from -9.59% to 2.01%, with a mean and a standard deviation of -3.85% and 3.41%, respectively. The silt-clay group had a difference ranging from -9.03% to -2.58%, with a mean of -5.07% and a standard deviation of 1.70%.\u003c/p\u003e\n\u003cp\u003eIn Fig. 9, it can be clearly seen the low content and consistent presence of the 1 mm ~ 2 mm grain group (red line in Fig. 9). These coarse sands accounted for approximately 0.47 to 6.70% of the total weight before pretreatment B4 and 0.41 to 5.64% after the treatment. The red line serves as a boundary between coarse and fine-grain systems, which is a characteristic of moraine soil in terms of its particle size distribution.\u003c/p\u003e\n\u003cp\u003eAfter pretreatment B4, the percentage of silt and clay in laser diffraction was found to be different, despite a rapid increase in silt-clay grains. According to Fig. 9, the silt percentage ranged from 77.15% to 86.42%, while the clay percentage varied from 3.80% to 10.76%.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003ch2\u003e4.1 Aggregation phenomena and models\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn a recent study, Zheng, Li, Wang, Li, Shi and Bi [34] proposed the core-coating concept in loess. However, aggregation patterns in moraine soil differ significantly from those in loess. As a result, this study has developed three basic concepts of core and coat in moraine soil, along with nine detailed models (Fig. 10).\u003c/p\u003e\n\u003cp\u003eAccording to the core-coating concept (Fig. 10a), some fine sand and silt-clay particles exist as aggregates or attachments to the surfaces of gravel particles. These aggregates and attachments form a thin \u0026ldquo;coat.\u0026rdquo; Clay plays a crucial role in forming the \u0026ldquo;coat\u0026rdquo;, and acts as an adhesive to bind other particles together. The gravel particle acts as the \u0026ldquo;core\u0026rdquo; and is not separated in ultrasonic dispersion or pretreatments. The \u0026ldquo;core\u0026rdquo; particle is dominant, while the \u0026ldquo;coat\u0026rdquo; occupies less than half of the surface of the \u0026ldquo;core.\u0026rdquo; This phenomenon is similar to the process of adding a coating to the \u0026ldquo;core.\u0026rdquo; The detailed aggregation models according to this concept are shown in Fig. 10b (①~⑥).\u003c/p\u003e\n\u003cp\u003eThe core(s)-coat concept (Fig. 10a) states that the fine gravel or sand particles function as the \u0026ldquo;core\u0026rdquo; and may appear in multiple \u0026ldquo;cores.\u0026rdquo; The \u0026ldquo;core\u0026rdquo; particles still dominate, but the \u0026ldquo;coat\u0026rdquo; covers almost half of the surface of the \u0026ldquo;core\u0026rdquo; and is thick. The detailed aggregation models according to this concept are shown in Fig. 10b (⑦~⑧).\u003c/p\u003e\n\u003cp\u003eThe coat-coring concept, as depicted in Fig. 10a, proposes that the silt-clay or partial sand forms the \u0026ldquo;core\u0026rdquo; of a particle, but this core is losing its dominance even in cases where multiple cores are present. Instead, the \u0026ldquo;coat\u0026rdquo; covers almost the entire surface of the \u0026ldquo;core\u0026rdquo;, and can even exist without the core. The detailed aggregation models according to this concept are shown in Fig. 10b (⑨ and partial ⑦ in sand or ⑧).\u003c/p\u003e\n\u003cp\u003eThe formation of this \u0026ldquo;coat\u0026rdquo; is related to the presence of clay minerals. These minerals are found in higher concentrations in the fine gravel group and are highest in the fine sand and silt-clay groups (Table 3, Table 4). Hence, it is only in the core-coating concept that some fine gravel acts as a coating component. The silt-clay particles serve as the primary ingredients in the \u0026ldquo;coat\u0026rdquo; and are even dominant in the Core(s)-coat concept and Coat-coring concepts.\u003c/p\u003e\n\u003ch2\u003e4.2 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eOne way to evaluate the effectiveness of different pretreatment methods in the EGT is by conducting a significance test. To do this, \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e and \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e were used as measurement values, which denote the mass ratio difference between adding dispersants in ultrasonic and ultrasound-only treatments, respectively. These values are obtained from grain group samples that have undergone different pretreatments, ranging from B1 to B4 and C. For 5 mm ~ 2 mm grain group samples, \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e and \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e can be calculated using the formula \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e = B\u003csub\u003e1~4\u003c/sub\u003e(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e) -\u0026nbsp;`A\u003csub\u003eopt\u003c/sub\u003e(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e). Here, B1 to B4 represent the mass ratios after each pretreatment, while Aopt is the average mass ratio after pretreatment A. On the other hand, for grain group samples smaller than 0.075 mm, the formula \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e = C(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e) -\u0026nbsp;`A(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e), or \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e = B\u003csub\u003e1~4\u003c/sub\u003e(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e) -\u0026nbsp;`A\u003csub\u003eopt\u003c/sub\u003e(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e) can be used. In this case, C(m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e) represents the mass ratio after pretreatment C.\u003c/p\u003e\n\u003cp\u003eA normal distribution test was performed by using \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e and \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e parameters (Fig. 11). When the results revealed a normal distribution, the mean of the measurement data between the two groups was compared with the t-test. This study used the Jarque-Bera test to calculate p-values using Monte Carlo simulation, and the Lilliefors test to detect whether the statistics conformed to a normal distribution. Considering that the statistics \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e and \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e are consistent with (or close to) a normal distribution (Fig.11), t-tests can be performed representing the different pretreatment methods.\u003c/p\u003e\n\u003cp\u003eFor samples belonging to the 5 mm ~ 0.075 mm grain group, the pretreatment B4 is statistically significant in one sample, based on the results obtained through the t-test (Fig.12 (a)). Unpaired t-tests showed that the B4-pretreated samples are significantly different from the samples of the other three pretreatment methods (B1, B2, and B3) (Fig.12 (a)). After the application of pretreatment methods B1 to B4, the aggregation particles in the sample were effectively dispersed, reducing the original particle mass. Therefore, the m\u003csub\u003eafter\u003c/sub\u003e/m\u003csub\u003eo\u003c/sub\u003e value displayed a decreasing trend, and the associated statistic \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e was less than 0 (Fig.12 (a)). This study examined whether \u0026Delta;m\u003csub\u003e1\u003c/sub\u003e was significantly less than -2%.\u003c/p\u003e\n\u003cp\u003eFor samples with particle diameters smaller than 0.075 mm, the pretreatment B4 and B2 are statistically significant in one sample, based on the results obtained through the t-test (Fig.12 (b)). Unpaired t-tests indicate that the B4-pretreated samples are only significantly different from the samples of the B3. The C-pretreated samples are significantly different from the samples subjected to the other four pretreatment methods (from B1 to B4) (Fig.12 (a)). After applying various pretreatment methods (B1 to B4), the aggregated particles were effectively dispersed. This led to a reduction in silt and an increase in clay. Hence, the m\u003csub\u003eclay\u003c/sub\u003e/m\u003csub\u003esilt\u003c/sub\u003e value should exhibit an increasing trend, and the associated statistic \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e should be greater than 0 (Fig.12 (a)). This study examined whether \u0026Delta;m\u003csub\u003e2\u003c/sub\u003e was significantly greater than 0%, indicating a more effective dispersion without destroying the particles.\u003c/p\u003e\n\u003cp\u003eIn Fig. 12(b), it can be observed that pretreatment has a significant difference from the others, but its values are less than zero percent. This suggests that the HCl undergoes a chemical reaction with the clay minerals, which has been reported in previous studies [32, 33, 36]. Results of the t-test for particles \u0026lt;0.075 mm show that pretreatment B4 and B2 have similar significance.\u003c/p\u003e\n\u003ch2\u003e4.3 Effectiveness of pretreatment B4 and its dispersion mechanism\u003c/h2\u003e\n\u003cp\u003eAfter confirming the statistical significance of the pretreatment B4%, the results suggest that this method produces more precise particle size distribution (PSD) measurements. Therefore, it is important to discuss the dispersion mechanism of this pretreatment method further (Fig.13).\u003c/p\u003e\n\u003cp\u003eFig. 13(a) depicts the particle changes before and after pretreatment B4. The particles were cleaner and brighter after this pretreatment. This suggests that the separation of \u0026ldquo;coat\u0026rdquo; and \u0026ldquo;core\u0026rdquo; was effective. Polycarboxylate works as a dispersant for pretreatment B4. Polycarboxylate and its derivatives were widely used in concrete production because it has the advantages of molecular designability, high water reduction, and good slump retention [39, 40].\u003c/p\u003e\n\u003cp\u003eThe polycarboxylate dispersant adsorbs the silt-clay particles and separates them from agglomeration under ultrasonic cavitation, separating the \u0026ldquo;coat\u0026rdquo; from the \u0026ldquo;core.\u0026rdquo; The polycarboxylate adsorb particles into the surface of the silt-clay through electrostatic attraction and intermolecular forces (Fig. 13(b)). The primary and side chains prevent the particles from reaggregating by providing resistance to steric hindrance. Due to gravity, larger particles settle first in the lower layers, while smaller particles remain suspended in water or slowly settle, resulting in effective stratification. Finally, the particles achieve dispersed state through the steric hindrance effect, lubricating effect, and DLVE effect (an electrostatic repulsive effect) [43-46].\u003c/p\u003e\n\u003cp\u003eMeanwhile, pretreatment C (dispersant is dilute hydrochloric acid) has an evident chemical reaction with the sample, destroying the particles compared to pretreatment B4 (Fig. 13(c)).\u003c/p\u003e\n\u003cp\u003eAlthough the polycarboxylate works well at separating the \u0026ldquo;coat\u0026rdquo; from the \u0026ldquo;core,\u0026rdquo; it is ineffective in excess and is likely to have side effects. For instance, they unite different clays due to functional groups and side-chain effects.\u003c/p\u003e\n\u003ch2\u003e4.4 The impact of the aggregation phenomena\u003c/h2\u003e\n\u003cp\u003eSoil texture can be classified based on the percentage of three classes - gravel, sand, and silt-clay. However, grain grading parameters, such as average particle size and median particle size, were significant indicators of the mechanical and hydrological properties of the soil. Pedologists, geologists, and soil scientists use these features to quickly assess soil texture and properties in moraine landscapes by manually inspecting samples [37].\u003c/p\u003e\n\u003cp\u003eSoil texture triangles were plotted according to three main particle size classes: gravel (60 mm ~ 2 mm), sand (2 mm ~ 0.075 mm), and silt-clay (\u0026lt;0.075 mm). The common classification system used in soil science, which is the geotechnical test standard GB/T 50123-2019 (China code), was used to plot these triangles Fig. 14(a) shows that the untreated soil samples were mostly poorly graded gravelly soils (GP) and gravelly soils that included fine particles [47]. However, after the B4, more than 50% of the samples turned out to be sandy soil, including fine particles (SF), silty sand soil (SM), and silty gravel soil (GM). Furthermore, the kernel density core of the soil texture triangle shifted to the upper left.\u003c/p\u003e\n\u003cp\u003eTo compare differences among samples in grain grading parameters (section 3.3), a normalization method was applied. With that aim, the formula X\u0026prime;=(X\u0026minus;X\u003csub\u003emin\u003c/sub\u003e)/(X\u003csub\u003emax\u003c/sub\u003e\u0026minus;X\u003csub\u003emin\u003c/sub\u003e) was used, where X is a soil grain grading parameter, such as\u0026nbsp;`d (the average particle size, mm), \u0026sigma; (the standard deviation of the particle size, mm), d\u003csub\u003e50\u0026nbsp;\u003c/sub\u003e(the median particle size, mm), C\u003csub\u003eu\u003c/sub\u003e (the coefficient of nonuniformity), C\u003csub\u003ec\u0026nbsp;\u003c/sub\u003e(the coefficient of curvature), K (kurtosis) and SK (skewness) (attached table T1 and T2 in Supplementary materials). Fig. 14(b) depicts that the aggregation phenomena have a primary impact on the average particle size (`d) and the standard deviation of the particle size (\u0026sigma;). After the pretreatment B4, the mean value of all samples\u0026apos; average particle sizes (`d) decreased from 2.61 mm (with a standard deviation of 1.36 mm) to 1.56 mm (with a standard deviation of 0.76). This represents a reduction of around 40% (Fig. 14(b)). The standard deviation of the particle size (\u0026sigma;) has increased from 10.27 mm (with a standard deviation of 2.78 mm) to 13.46 mm (with a standard deviation of 3.50), with an increase of roughly 30%.\u003c/p\u003e\n\u003cp\u003eThe impact of the aggregation phenomena is quite different for the different grain groups, probably due to their different amounts of clay minerals (section 3.1). Fig. 14(c) illustrates a decrease in the grain group content of 2 mm to 10 mm and 0.0 mm to 0.25 mm. The mean value of 0.0 mm to 0.25 mm grain group contents has increased from 18.73% (with a standard deviation of 7.82%) to 27.02% (with a standard deviation of 8.83%), revealing an increase of 8.29% after B4. Among them, the mean value of silt-clay content has increased from 6.86% (with a standard deviation of 3.71%) to 11.93% (with a standard deviation of 4.59%), with an increase of 5.07% after B4.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study focuses on the introduction of uncertainty in particle size analysis of moraine soil due to the occurrence of aggregation phenomena. The research observed that the moraine soil often exhibited aggregation phenomena, and models were developed to understand them. Then, experimental and control group tests (EGT and CGT) were designed to compare the effectiveness of different pretreatment methods (five to six) in removing aggregation phenomena during particle size analysis. Finally, the impact of aggregation phenomena on particle size distribution and soil texture assessment is discussed. The main conclusions are as follows:\u003c/p\u003e \u003cp\u003eWidespread and complex aggregation phenomena were observed in moraine soil, and are present in every grain group (from \u0026lt;\u0026thinsp;0.075 mm to 60 mm\u0026thinsp;~\u0026thinsp;40 mm). The study identifies three basic conceptual models of the aggregation phenomena: ① Core-coating concept, ② Core(s)-coat concept, ③ Coat-coring concept, along with nine detailed models. As a result, the problem of pretreatment methods in particle size analysis is reduced to separating the \u0026ldquo;coat\u0026rdquo; from the \u0026ldquo;core.\u0026rdquo;\u003c/p\u003e \u003cp\u003eA new pretreatment method called \u0026ldquo;B4\u0026rdquo; has been proposed to disperse the aggregation phenomena of moraine soil before conducting grain sieving and laser diffraction tests. This method involves adding 0.5% polycarboxylate to achieve optimal ultrasonic dispersion. The results obtained through this method have been found to be significant in comparison to other pretreatment methods. The dispersion mechanism of this method has been discussed in detail, including the role of the dispersant polycarboxylate and the comprehensive dispersion mechanism, which includes ultrasonic cavitation, steric hindrance effect, lubricating effect, and DLVE effect (an electrostatic repulsive effect). However, for particles smaller than 0.075 mm, there is little difference between the pretreatments B2 (adding H2O2 in optimal ultrasonic dispersion) and B4. It is important to note that dilute hydrochloric acid destroys clay and is not recommended.\u003c/p\u003e \u003cp\u003eThe aggregation phenomena have an impact on particle size analysis for moraine soils. It leads to an overestimation of the content of the gravel group (about 7.91%) and an underestimation of the content of the silt-clay group (about 5.07%). The aggregation phenomena mainly affect the particle size ranges of 2 mm\u0026thinsp;~\u0026thinsp;10 mm and 0.0 mm\u0026thinsp;~\u0026thinsp;0.25 mm, which is related to the content of clay minerals contained in these grain groups. Moreover, the aggregation phenomena have an impact on the moraine soil texture triangles, where nearly 50% of the samples changed from GP and GF to SF, SM, and GM. The average particle size and the standard deviation of the particle size also changed significantly, with roughly a 40% reduction and a 30% increase, respectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work is supported by the National Natural Science Foundation of China (Grant No. 41772324), the China Geological Survey Project (DD20221746), and the Natural Science Foundation of Sichuan Province (No.2023NSFSC2086).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTuo Lu and Yongbo Tie designed the research. Tuo Lu and Yaming Tang processed the corresponding data. Zhijie Ning and Lingfeng Gong help with site investigation and soil sample collection. Tuo Lu wrote the draft of the manuscript. Yongbo Tie helped to revise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTuo Lu, Yongbo Tie, Yaming Tang, Lingfeng Gong, and Zhijie Ning declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOBU J, WESTERMANN S, BARTSCH A, et al. (2019) Northern Hemisphere permafrost map based on TTOP modelling for 2000\u0026ndash;2016 at 1 km2 scale. Earth-Science Reviews, 193: 299-316. https://doi.org/10.1016/j.earscirev.2019.04.023\u003c/li\u003e\n\u003cli\u003eMEKONNEN Z A, RILEY W J, GRANT R F, et al. (2021) Changes in precipitation and air temperature contribute comparably to permafrost degradation in a warmer climate. Environmental Research Letters, 16(2): 024008. https://doi.org/10.1088/1748-9326/abc444\u003c/li\u003e\n\u003cli\u003eLI C, WEI Y, LIU Y, et al. (2022) Active Layer Thickness in the Northern Hemisphere: Changes From 2000 to 2018 and Future Simulations. Journal of Geophysical Research: Atmospheres, 127(12): e2022JD036785. https://doi.org/10.1029/2022JD036785\u003c/li\u003e\n\u003cli\u003eFANG X, WANG A, LYU S, et al. (2023) Dynamics of Freezing/Thawing Indices and Frozen Ground from 1961 to 2010 on the Qinghai-Tibet Plateau. Remote Sensing, 15(14). https://doi.org/10.3390/rs15143478\u003c/li\u003e\n\u003cli\u003eWANG L, LIU G, MA P, et al. (2023) Effects of thaw slump on soil bacterial communities on the Qinghai-Tibet Plateau. CATENA, 232: 107342. https://doi.org/10.1016/j.catena.2023.107342\u003c/li\u003e\n\u003cli\u003eCMA CLIMATE CHANGE CENTRE. (2023) Blue Book on Climate Change in China (2023). Science Press, Beijing, China\u003c/li\u003e\n\u003cli\u003eJR S B (2018) Principles of Sedimentology and Stratigraphy. Pearson, Upper Saddle River, New Jersey, USA\u003c/li\u003e\n\u003cli\u003eLUTGENS F K, TARBUCK E J, TASA D G (2017) Essentials of Geology. Pearson, Boston, MA, USA\u003c/li\u003e\n\u003cli\u003eSPRINGMAN S M, JOMMI C, TEYSSEIRE P (2003) Instabilities on moraine slopes induced by loss of suction: a case history. G\u0026eacute;otechnique, 53(1): 3-10. https://doi.org/10.1680/geot.2003.53.1.3\u003c/li\u003e\n\u003cli\u003eBITTELLI M, CAMPBELL G S, TOMEI F (2015) Soil Physics with Python. Transport in the Soil-Plant-Atmosphere Continuum. Oxford University Press, Oxford, United Kingdom.\u003c/li\u003e\n\u003cli\u003eHARTMANN A, WEILER M, BLUME T (2020) The impact of landscape evolution on soil physics: evolution of soil physical and hydraulic properties along two chronosequences of proglacial moraines. Earth Syst Sci Data, 12(4): 3189-204. https://doi.org/10.5194/essd-12-3189-2020\u003c/li\u003e\n\u003cli\u003eKUST P, MAKEEV A, LESSOVAIA S, et al. (2022) Polygenetic features in Retisols, formed in Moscow (Late Saalian) glacial till. CATENA, 214: 106245. https://doi.org/10.1016/j.catena.2022.106245\u003c/li\u003e\n\u003cli\u003eSANTOS J B, VIEIRA G, SANTOS-GONZ\u0026aacute;LEZ J, et al. (2022) Macrofabric and grain size analysis of moraines and other till deposits in the Serra da Estrela Mountains, central Portugal. Physical Geography, 43(2): 238-64. https://doi.org/10.1080/02723646.2020.1838136\u003c/li\u003e\n\u003cli\u003eOSTROWSKI P, FALKOWSKI T, KOCHANEK K (2023) Reconstructing parameters of the Holocene paleofloods in alluvial lowland river valleys \u0026ndash; An example from the Bug valley (East Poland). Journal of Hydrology, 624: 129930. https://doi.org/10.1016/j.jhydrol.2023.129930\u003c/li\u003e\n\u003cli\u003eTHOMAS M, MONHONVAL A, HIRST C, et al. (2023) Evidence for preservation of organic carbon interacting with iron in material displaced from retrogressive thaw slumps: Case study in Peel Plateau, western Canadian Arctic. Geoderma, 433: 116443. https://doi.org/10.1016/j.geoderma.2023.116443\u003c/li\u003e\n\u003cli\u003e\u0026Aring;STR\u0026ouml;M M, BJ\u0026ouml;RKLUND A (1997) Geochemistry and acidity of sulphide-bearing postglacial sediments of western Finland. Environmental Geochemistry and Health, 19(4): 155-64. https://doi.org/10.1023/A:1018462824486\u003c/li\u003e\n\u003cli\u003eXIE C Q (2002) Research on the Engineering Performance of Moraine Soil. Journal of Mountain Science, S1: 129-32 (in Chinese). https://doi.org/10.16089/j.cnki.1008-2786.2002.s1.020\u003c/li\u003e\n\u003cli\u003eMACDONALD A M, MAURICE L, DOBBS M R, et al. (2012) Relating in situ hydraulic conductivity, particle size and relative density of superficial deposits in a heterogeneous catchment. Journal of Hydrology, 434-435: 130-41. https://doi.org/10.1016/j.jhydrol.2012.01.018\u003c/li\u003e\n\u003cli\u003eSTUMPP C, HENDRY M J (2012) Spatial and temporal dynamics of water flow and solute transport in a heterogeneous glacial till: The application of high-resolution profiles of \u0026delta;18O and \u0026delta;2H in pore waters. Journal of Hydrology, 438-439: 203-14. https://doi.org/10.1016/j.jhydrol.2012.03.024\u003c/li\u003e\n\u003cli\u003eFANG X, HUANG R (2013) PHYSICAL AND MECHANICAL PROPERTIES OF TYPICAL MORAINE SOIL ON THE QINGHAI-TIBET PLATEAU. Journal of Engineering Geology 21(01): 123-8 (in Chinese). https://doi.org/1004-9665/2013/21(1)-0123-06\u003c/li\u003e\n\u003cli\u003eG C B (2018) The engineering properties of glacial tills. Geotechnical Research, 5(4): 262-77. https://doi.org/10.1680/jgere.18.00020\u003c/li\u003e\n\u003cli\u003eZHOU G G D, CHEN L-L, MU Q-Y, et al. (2019) Effects of water content on the shear behavior and critical state of glacial till in Tianmo Gully of Tibet, China. Journal of Mountain Science, 16(8): 1743-59. https://doi.org/10.1007/s11629-019-5440-9\u003c/li\u003e\n\u003cli\u003eMCGOWN A, MCARTHUR A A (1971) MORAINIC SOIL DEPOSITS AND THEIR USE IN LOWER COST ROADS. Roads \u0026amp; Road Construction, London /UK/, 49(587): p. 388-96. \u003c/li\u003e\n\u003cli\u003eSHELP G S, NICHOL I (1987) Distribution and dispersion of gold in glacial till associated with gold mineralization in the Canadian shield. Journal of Geochemical Exploration, 28(1): 315-36. https://doi.org/10.1016/0375-6742(87)90055-0\u003c/li\u003e\n\u003cli\u003eHU G, YI C-L, ZHANG J-F, et al. (2015) Luminescence dating of glacial deposits near the eastern Himalayan syntaxis using different grain-size fractions. Quaternary Science Reviews, 124: 124-44. https://doi.org/10.1016/j.quascirev.2015.07.018\u003c/li\u003e\n\u003cli\u003eGAO J, BAI R, MA Q (2018) Experimental study on freezing of unsaturated coarse-grained soils. Journal of Glaciology and Geocryology, 40(1): 94-101. \u003c/li\u003e\n\u003cli\u003eWANG J, CUI P, WANG H, et al. (2022) Insight into geotechnical properties of glacial tills in a periglacial area, Southeast Tibet. Bulletin of Engineering Geology and the Environment, 81(8): 303. https://doi.org/10.1007/s10064-022-02803-y\u003c/li\u003e\n\u003cli\u003eLI C, WANG R, GU D, et al. (2022) Temperature and ice form effects on mechanical behaviors of ice-richmoraine soil of Tianmo valley nearby the Sichuan-Tibet Railway. Engineering Geology, 305: 106713. https://doi.org/10.1016/j.enggeo.2022.106713\u003c/li\u003e\n\u003cli\u003eBEAUMONT P (1971) BREAK OF SLOPE IN PARTICLE-SIZE CURVES OF GLACIAL TILLS. Sedimentology, 16(1-2): 125-8. https://doi.org/10.1111/j.1365-3091.1971.tb00224.x\u003c/li\u003e\n\u003cli\u003eYANJUN S, XIN W, LEI Z, et al. (2022) Hydrothermal migration of moraine soil and the mechanism of ice accumulation and frost swelling in Alpine-cold mountain region. Journal of Engineering Geology, 30(5): 1450-65. https://doi.org/10.13544/j.cnki.jeg.2022-0449.\u003c/li\u003e\n\u003cli\u003eLU H, AN Z (1998) Pretreated methods on loess-palaeosol samples granulometry. Chinese Science Bulletin, 43(3): 237-40. https://doi.org/10.1007/BF02898920\u003c/li\u003e\n\u003cli\u003eSCHULTE P, LEHMKUHL F, STEININGER F, et al. (2016) Influence of HCl pretreatment and organo-mineral complexes on laser diffraction measurement of loess\u0026ndash;paleosol-sequences. CATENA, 137: 392-405. https://doi.org/10.1016/j.catena.2015.10.015\u003c/li\u003e\n\u003cli\u003eKOZA M, SCHMIDT G, BONDAROVICH A, et al. (2021) Consequences of chemical pretreatments in particle size analysis for modelling wind erosion. Geoderma, 396: 115073. https://doi.org/10.1016/j.geoderma.2021.115073\u003c/li\u003e\n\u003cli\u003eZHENG Z, LI X-A, WANG L, et al. (2021) A new approach to evaluation of loess collapsibility based on quantitative analyses of colloid-clay coating with statistical methods. Engineering Geology, 288: 106167. https://doi.org/10.1016/j.enggeo.2021.106167\u003c/li\u003e\n\u003cli\u003eKONERT M, VANDENBERGHE J (1997) Comparison of laser grain size analysis with pipette and sieve analysis: a solution for the underestimation of the clay fraction. Sedimentology, 44(3): 523-35. https://doi.org/10.1046/j.1365-3091.1997.d01-38.x\u003c/li\u003e\n\u003cli\u003eLU R, LIANG R, DUAN W (2021) Dispersion and stability of fine-grained soils under different pretreatments based on the particle size distribution. Journal of Soils and Sediments, 21(1): 96-105. https://doi.org/10.1007/s11368-020-02689-x\u003c/li\u003e\n\u003cli\u003eBITTELLI M, PELLEGRINI S, OLMI R, et al. (2022) Experimental evidence of laser diffraction accuracy for particle size analysis. Geoderma, 409: 115627. https://doi.org/10.1016/j.geoderma.2021.115627\u003c/li\u003e\n\u003cli\u003eSVENSSON D N, MESSING I, BARRON J (2022) An investigation in laser diffraction soil particle size distribution analysis to obtain compatible results with sieve and pipette method. Soil and Tillage Research, 223: 105450. https://doi.org/10.1016/j.still.2022.105450\u003c/li\u003e\n\u003cli\u003eBOUHAMED H, BOUFI S, MAGNIN A (2007) Dispersion of alumina suspension using comb-like and diblock copolymers produced by RAFT polymerization of AMPS and MPEG. Journal of Colloid and Interface Science, 312(2): 279-91. https://doi.org/10.1016/j.jcis.2007.03.060\u003c/li\u003e\n\u003cli\u003eLIU J, RAN Q, MIAO C, et al. (2011) Synthesis and Characterization of Comb-Like Copolymer Dispersant with Methoxy Poly (Ethylene Oxide) Side Chains. Polymer-Plastics Technology and Engineering, 50(1): 59-66. https://doi.org/10.1080/03602559.2010.512351\u003c/li\u003e\n\u003cli\u003eBENN D I, BOLCH T, HANDS K, et al. (2012) Response of debris-covered glaciers in the Mount Everest region to recent warming, and implications for outburst flood hazards. Earth-Science Reviews, 114(1): 156-74. https://doi.org/10.1016/j.earscirev.2012.03.008\u003c/li\u003e\n\u003cli\u003eBARAL P, ALLEN S, STEINER J F, et al. (2023) Climate change impacts and adaptation to permafrost change in High Mountain Asia: a comprehensive review. Environmental Research Letters, 18(9): 093005. https://dx.doi.org/10.1088/1748-9326/acf1b4\u003c/li\u003e\n\u003cli\u003eSHUI L, SUN Z, YANG H, et al. (2016) Experimental evidence for a possible dispersion mechanism of polycarboxylate-type superplasticisers. Advances in Cement Research, 28(5): 287-97. https://doi.org/10.1680/jadcr.15.00070\u003c/li\u003e\n\u003cli\u003eCONTE T, PLANK J (2019) Impact of molecular structure and composition of polycarboxylate comb polymers on the flow properties of alkali-activated slag. Cement and Concrete Research, 116: 95-101. https://doi.org/10.1016/j.cemconres.2018.11.014\u003c/li\u003e\n\u003cli\u003eSTECHER J, PLANK J (2019) Novel concrete superplasticizers based on phosphate esters. Cement and Concrete Research, 119: 36-43. https://doi.org/10.1016/j.cemconres.2019.01.006\u003c/li\u003e\n\u003cli\u003eMA Y, SHI C, LEI L, et al. (2020) Research progress on polycarboxylate based superplasticizers with tolerance to clays - A review. Construction and Building Materials, 255: 119386. https://doi.org/10.1016/j.conbuildmat.2020.119386\u003c/li\u003e\n\u003cli\u003eSHI L, WANG P, HU G, et al. (2023) Sedimentation and geomorphology of the Ruoergai Basin outlet reach at the source of the Yellow River: Response to the late quaternary glacial debris flow damming events. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.1017597\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Moraine soil, Aggregation phenomena, Pretreatments, Particle size distribution, Polycarboxylate","lastPublishedDoi":"10.21203/rs.3.rs-3945900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3945900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMoraine soils exposed to the moraine landscape surface due to glaciers retreating and global warming have been the subject of interest for researchers. The particle size distribution (PSD) of moraine soils is essential to understanding their engineering properties and soil characteristics. However, the wide aggregation phenomena observed in these soils introduce difficulties in particle size analysis. To overcome this, an experimental group test (EGT) and control groups test (CGT) were designed, which included a series of pretreatment methods to disperse particles, such as grain sieving test, laser diffraction test, microscopic observation, and scanning electron microscope. The tests were performed to record the aggregation phenomena and perform particle size test analysis. The results revealed widespread evidence of aggregation phenomena in each grain group (ranging from \u0026lt;\u0026thinsp;0.075 mm to 60 mm\u0026thinsp;~\u0026thinsp;40 mm), encapsulated by three basic conceptual models (Core-coating, Core(s)-coat, and Coat-coring) for moraine soil. A new pretreatment method was developed, which involved the addition of 0.5% polycarboxylate in optimal ultrasonic dispersion to disperse aggregation phenomena before the sieving and laser diffraction tests. This method proved to be effective in removing the aggregation phenomena, which significantly impacted on the particle size distribution of moraine soils. The aggregation phenomena caused an overestimation of the content of the gravel group (about 7.91%) and an underestimation of the content of the silt-clay group (about 5.07%). After effectively removing the aggregation phenomena, many samples collected from poorly graded gravelly soils (GP) became silty sand soil (SM) and silty gravel soil (GM). Additionally, the average particle size was reduced by approximately 40%.\u003c/p\u003e","manuscriptTitle":"Research on the aggregation phenomena in moraine soil: a new approach to disperse them through a particle size analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 12:49:16","doi":"10.21203/rs.3.rs-3945900/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c646193d-7a78-49a4-a727-2ee2ae01da89","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-30T03:40:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 12:49:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3945900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3945900","identity":"rs-3945900","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}