Aggregation Concentration Research Articles

Improving the Performance of Porous Concrete by Utilizing the Pumice Aggregate

The performance of porous concrete, widely used for storm-water management, can be compromised due to the limitations of conventional aggregates. This research investigates the potential of pumice aggregate, a lightweight volcanic material, to enhance the mechanical strength, durability, and permeability of porous concrete. A series of experimental tests were conducted to evaluate the effects of varying pumice aggregate content on the properties of porous concrete. The results demonstrate that the inclusion of pumice aggregate significantly improves the compressive strength, flexural strength, and freeze-thaw resistance of porous concrete. Moreover, the permeability of the concrete mixtures is enhanced, allowing for better storm-water infiltration. The favorable porous characteristics of pumice aggregate contribute to increased void content within the concrete, enhancing its water storage capacity and reducing runoff. These findings suggest that utilizing pumice aggregate can lead to the development of more sustainable and efficient porous concrete systems for storm-water management. The incorporation of pumice aggregate not only improves the mechanical and hydraulic performance of porous concrete but also offers environmental benefits by utilizing a lightweight and natural material. The findings of this research have significant implications for sustainable construction practices and the effective management of storm-water runoff.

Study on the influence of coarse aggregate distribution uniformity on the compaction characteristics of gap-graded gravels

There has yet to be a quantitatively characterised study that relates the degree of uniformity of aggregate distribution to the compaction of gap-graded gravels. The particle tracking technique was used to calibrate the vibrational compaction discrete element model, which was used to simulate the vibratory compaction of gap-graded aggregates at varying coarse aggregate contents (25 %, 50 %, and 75 %) and different coarse aggregate distribution uniformities ranging between 0.0 and 1.0. The results showed that with an increase in the coarse aggregate distribution uniformity, the amount of void ratio of gap-graded gravels under three coarse aggregate contents decreased significantly. The mechanism of variation in the effect of gap-graded aggregates with coarse aggregate distribution uniformity was found to be due to the variation of the local coarse content between layers: the disparity in the depth range of coordination number, stress transfer, distribution of strong and weak force chains and interlayer void ratio decreases with increasing coarse aggregate distribution uniformity. Hence, it improves the overall compaction effect and the homogeneity of the specimen. Local variations in coarse content between layers are considered to be the main cause of this effect.

Influence of cationic dimeric and trimeric-type surfactants on the aggregation behavior of water-soluble phthalocyanine: A combined experimental and computational study

The aggregation behavior of the water-soluble phthalocyanine, namely zinc 2,9,16,23–tetra(tert–butyl)–29H,31H–phthalocyanine tetrasulphonic acids (ZnPcS4), has been examined in the presence of dimeric and trimeric-type cationic surfactants and their conventional counterpart cetyltrimethylammonium bromide (CTAB). The absorption and fluorescence studies of ZnPcS4 in water showed a different feature than that in methanol suggesting the formation of aggregated ZnPcS4 in an aqueous medium. The fluorescence studies of ZnPcS4 in the presence of examined dimeric-type cationic surfactants showed lower critical aggregation concentration (CAC = 0.62–13.6 µM) compared to that of CTAB (CAC = 16 µM). It was found that the position of the substituents affected the critical micelle concentration (CMC) value, and the ortho-dimeric surfactant had a lower CMC value compared to those surfactants with meta, and para-dimeric surfactant. It was interesting to see that the CMC for the dimeric surfactant with two groups at the para position (13.6 µM) decreased significantly (0.04 µM) by removing the (O-CH2) groups attached to the benzene which can be explained by structural parameters determined by theoretical calculations. By changing the surfactants from dimers to cationic trimers surfactants, a significant decrease in the CAC values was observed (0.04 µM for Trimer-12 with two dodecyl groups and 0.39 µM for Trimer-8 with two octyl groups). This finding indicates that the increase in tail length by four methylene groups of Trimer-12 compared to Trimer-8 results in a decrease in CAC by about one order of magnitude. These new findings suggest that the trimeric-type cationic surfactants are remarkably superior to conventional and dimeric-type surfactants, which could have a profound impact on many applications of ZnPCS4 including, but not limited to, medical, organic electronics, light harvesting, photonics, and optical sensors.

A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete

Incorporating steel fibers into concrete effectively enhances its tensile strength by managing crack formation, increasing toughness and ductility, and reducing brittleness, making it a valuable choice for various applications, including bridge decks. Thus, the objective of this research is to employ the machine learning (ML) technique to estimate the splitting tensile strength (STS) of steel fiber-reinforced concrete (SFRC) that incorporates hooked industrial steel fibers (ISF) using SFRC mixtures collected from various research papers in this field. The dataset comprises 116 data points from various research papers. Utilizing the recursive feature elimination with cross-validation and leave-one-out (RFECV-LOO) method, the most influential parameters, including ISF content, water-to-cement ratio (W/C), cement content (C), fine aggregate content (FA), superplasticizer (SP), and fly ash, were identified to train the ML models. Eight statistical metrics, including the root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE), mean bias error (MBE), statistical test (Tstat), and scatter index (SI) were employed as metrics to assess model performance. Various ML models including multiple linear regression (MLR), ridge regression (Ridge), lasso regression (Lasso), elastic net regression (ElasticNet), k-nearest neighbor (KNN), support vector regression (SVR), categorical boosting (CatBoost), light gradient boosting model (LightGBM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) were employed to predict the SFRC’s STS. The Python programming language, TensorFlow framework as well as the Scikit-learn packages, were used to run the models. Among the various ML methods, SVR demonstrated higher accuracy, achieving an R2 of 0.930, RMSE of 0.502, and MAE of 0.330. In contrast, linear regression models, especially Lasso with an R2 of 0.631, RMSE of 0.854, and MAE of 0.710, exhibited the least effective performance when it came to predicting the STS of SFRC. Moreover, the performance of the developed ML models was approved through sensitivity analysis as well as parametric analysis.

Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms

This study introduces a novel methodology for enhancing the compressive strength of self-compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a sophisticated and interpretable machine learning algorithm. It presents a data-driven model that aims to accurately predict the strength of SCC by considering the intricate interactions among its various elements. Additionally, the model provides insights into the variables that influence SCC's compressive strength. By using EBM in conjunction with XGBoost and CatBoost algorithms, this study conducts a comparative examination of predictive abilities using datasets related to composition and rheology. The findings reveal that CatBoost has greater predictive performance using rheology dataset, as shown by an R2 value of 0.977. Conversely, XGBoost exhibits a higher predictive capability using the composition dataset, as indicated by an R2 value of 0.947. The EBM can provide comprehensive explanations at both global and local levels. It effectively identifies the key factors that have a significant influence on compressive strength. These factors include the coarse aggregate content, cement content, water content, viscosity, and V-funnel flow time. The study findings provide more evidence to support the notion that including rheological data into the model leads to a notable improvement in its accuracy. This outcome further confirms the existence of a direct correlation between rheological properties and compressive strength. The explanatory insights provided by EBM give practical instructions for customising SCC mixes to attain desired strengths. This facilitates quality control and enables personalised concrete design in the field of construction. This study highlights the potential of interpretable machine learning algorithms in improving the predictive modelling of SCC features. This advancement may lead to the development of more durable, efficient, and customised building materials.

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

To overcome limitations inherent in existing mechanical performance prediction models for pervious concrete, including material constraints, limited applicability, and inadequate accuracy, this study employs a deep learning approach to construct a Convolutional Neural Network (CNN) model with three convolutional modules. The primary objective of the model is to precisely predict the 28-day compressive strength of pervious concrete. Eight input variables, encompassing coarse and fine aggregate content, water content, admixture content, cement content, fly ash content, and silica fume content, were selected for the model. The dataset utilized for both model training and testing consists of 111 sample sets. To ensure the model’s coverage within the practical range of pervious concrete strength and to enhance its robustness in real-world applications, an additional 12 sets of experimental data were incorporated for training and testing. The research findings indicate that, in comparison to the conventional machine learning method of Backpropagation (BP) neural networks, the developed CNN prediction model in this paper demonstrates a higher coefficient of determination, reaching 0.938, on the test dataset. The mean absolute percentage error is 9.13%, signifying that the proposed prediction model exhibits notable accuracy and universality in predicting the 28-day compressive strength of pervious concrete, regardless of the materials used in its preparation.

Numerical and Analytical Simulation of the Growth of Amyloid-β Plaques.

Numerical and analytical solutions were employed to calculate the radius of an amyloid-β (Aβ) plaque over time. To the author's knowledge, this study presents the first model simulating the growth of Aβ plaques. Findings indicate that the plaque can attain a diameter of 50 μm after 20 years of growth, provided the Aβ monomer degradation machinery is malfunctioning. A mathematical model incorporates nucleation and autocatalytic growth processes using the Finke-Watzky model. The resulting system of ordinary differential equations was solved numerically, and for the simplified case of infinitely long Aβ monomer half-life, an analytical solution was found. Assuming that Aβ aggregates stick together and using the distance between the plaques as an input parameter of the model, it was possible to calculate the plaque radius from the concentration of Aβ aggregates. This led to the "cube root hypothesis," positing that Aβ plaque size increases proportionally to the cube root of time. This hypothesis helps explain why larger plaques grow more slowly. Furthermore, the obtained results suggest that the plaque size is independent of the kinetic constants governing Aβ plaque agglomeration, indicating that the kinetics of Aβ plaque agglomeration is not a limiting factor for plaque growth. Instead, the plaque growth rate is limited by the rates of Aβ monomer production and degradation.

Synthesis of PEG-Polycycloether Block Copolymers: Poloxamer Mimics Containing a Rigid Helical Block.

Poloxamers are amphiphilic block copolymers consisting of poly(ethylene glycol) (PEG) and poly(propylene glycol) segments. Their self-assembly and interfacial properties are tied to the relative hydrophilicity and hydrophobicity of each block and can therefore be adjusted by changing block lengths. Here, a series of PEG-polycycloether block copolymers is synthesized that have the same structure as a poloxamer, but they encompass a rigid polycyclic backbone as the hydrophobic block. A variety of polymer structures are synthesized, for example diblock or triblock architectures, with/without olefinic units, atactic or isotactic backbone, and different block lengths. Due to their amphiphilicity, self-assembly into spherical aggregates (diameters ranging from 64 to 132nm) at low concentrations (critical aggregation concentration as low as 0.04mgmL-1) is observed in water. Low surface tensions (as low as 26.7 mNm-1) are observed as well as the formation of stable high internal phase emulsions (HIPEs) irrespective of the oil/water ratio. This contrasts with the properties of the commonly used poloxamers P188 or P407 and illustrates the significance of the rigid polycycloether block. These new colloidal properties offer new prospects for applications in emulsion formulations for biomedicine, cosmetics, and the food industry.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

A Review of Durability of New and Old Concrete Joints

The purpose of this study is to explore the durability of the new and old concrete joints, including chloride ion erosion, freeze-thaw phenomena, and the influence of aggregates. Firstly, taking into account the difference in material properties between the old and new concrete, this study analyzes the chloride ion transport characteristics of the joints in detail, focusing on the key areas of the joints, that is, the areas where the old concrete may have been eroded by chloride ions. Secondly, this study explores the change in the properties of the joints under freeze-thaw conditions, and the influence of different interface treatment methods on the properties of the joints. Finally, this study examines the influence of the type and content of aggregates on the durability of the joints. Through the above research, this paper aims to provide some useful guidance and suggestions for the durability design of the old and new concrete structures.

Macroscopic and Microscopic Investigation of Gypsum Slag Cement-Stabilized Recycled Aggregate Base Layers.

The purpose of this study is to investigate the macro and micro properties of stabilized recycled aggregate base layers using gypsum slag cement (GSC) and compare them with ordinary Portland cement (OPC). To achieve this, four levels of recycled aggregate content (0%, 50%, 60%, 70%) and three levels of binder materials (3.5%, 4.5%, 5.5%) were designed, where the binding materials included OPC and GSC. When GSC is used as the binding material with 0% recycled content, two scenarios for the ratio of slag to activator are considered: 4:1 and 4:2. For recycled content of 50%, 60%, and 70%, only the 4:1 ratio is considered. The macro-mechanical properties of the composite material were studied through compaction tests, unconfined compressive strength tests, and indirect tensile strength tests. Microscopic properties were investigated through X-ray diffraction (XRD) and scanning electron microscopy (SEM). Macroscopic test results indicate that, at an equal binder content, GSC exhibits a higher moisture content and maximum dry density compared to OPC. Moreover, the unconfined compressive strength and indirect tensile strength of GSC are higher than those of OPC. Microscopic test results reveal that the hydration products of both binding materials are essentially similar; however, under identical curing conditions, the hydration products of GSC are more abundant than those of OPC.

New strategy for closing the plastic loop: Lightweight concrete by the waste of recycled synthetic fibers

Environmental protection related to the mismanagement of plastic waste and rational consumption of natural raw materials is an essential goal of the building materials industry and the production of structural lightweight concrete. Plastic waste is not a degradable polymer and increases severe environmental burdens each moment due to its significant quantity. In addition, the recycling of plastic materials is lower than that of other materials. It is usually described as an inefficient and complex process. The textile industry offers an eco-green, effective part of the recycling loop of plastics. Therefore, many textile mills consider recycled plastics as synthetic fiber raw material, particularly for household applications. Unfortunately, such a process results again in another waste known as popcorn. This study aims to introduce an end-of-life management option for the waste of synthetic textile fibers manufactured from recycled plastic materials in construction. Furthermore, the study presents a novel approach to develop an eco-green lightweight structural concrete by incorporating a unique type of lightweight aggregate (popcorn aggregate) as a 100% environmentally friendly product. To achieve that, fourteen mixes of fine and coarse popcorn aggregate contents (25%, 50%, and 100%) are considered to replace the fine and coarse natural aggregate by volume. In addition, the workability, compressive, splitting tensile, flexural strength, microstructure analysis, and Energy Dispersive X-ray Spectrometry are assessed. The results revealed that the higher the substitution ratio of ordinary aggregates by popcorn aggregates, the higher the slump value. Meanwhile, the unit weight of hardened concrete exhibited a tangible decrease at a 100% replacement ratio of fine and coarse natural aggregates; a 56% reduction achieved a dry density of 1059 kg/m³. Moreover, the compressive strength results indicated a decrease of 32% for mixing 25% fine e popcorn aggregate with 100% coarse popcorn aggregate, resulting in a compressive strength of 45 MPa. Fine ally, no evidence of chemical interactions between the popcorn aggregate and the cementitious element during hydration is found.

A comparative environmental life cycle assessment of road asphalt pavement solutions made up of artificial aggregates

The construction and maintenance of road pavements entail detrimental impacts on the consumption of resources and damage to the natural environment but also make up an opportunity for the large-scale application of circular economy principles and innovative waste valorisation paths. The present study focuses on developing a comprehensive procedure to evaluate the technical and environmental sustainability of replacing high percentage of limestone aggregates with artificial aggregates from municipal solid waste incineration (MSWI) into hot or cold recycled asphalt mixtures for asphalt pavements.The technical feasibility of the designed mixtures was investigated in terms of the main physical and mechanical properties of both the raw materials and the asphalt mixtures with content of artificial aggregates or sand in the range 25–40 % by mass. The environmental feasibility of the asphalt mixtures was evaluated through the SEM-EDS technique, the analysis of the eluate of the leaching test and the ecotoxicity for living organisms.Afterwards, the life cycle assessment (LCA) was applied to detect the critical spots of the life cycle of 1 m2 of a 6 cm-thick binder layer with high percentage of artificial aggregates or sand built and maintained through 30 years analysis period according to 18 impact category indicators.The main results show that, recycling the artificial aggregates into hot asphalt mixtures has on average a negligible effect on the overall environmental performance of the life cycle, and appears to be detrimental only for the consumption of fossil resources due to the higher optimum bitumen content. Looking at the results for cold mixes, the introduction of the artificial aggregates has an effect on the predicted durability of the asphalt layers, which is maximized in the case of coarse artificial aggregates. Consequent environmental benefits regard the global warming potential, fossil resource scarcity and freshwater eutrophication indicators.

Unraveling gluten protein compositions of einkorn, emmer, and spelt grown in the Lyon region in France

AbstractBackground and ObjectivesMinor species of wheat, including einkorn (Ei), emmer (Em), and spelt (Sp), are believed to have different gluten compositions compared to common wheat (CW). Given the limited research on these resources, and as a result of a collaboration with a collective of farmers, we aimed to provide insights into their gluten compositions. We focused on varieties readily accessible to farmers and bakers within the region of Lyon, France.FindingsWe compared gluten compositions in 31 wheat varieties, revealing significantly lower glutenin and protein aggregate contents in Ei and Em than in CW. Similar trends, though milder, were observed for Sp. Additionally, we observed intra‐species variability, especially for Em, and identified varieties within each species with noteworthy protein profiles.ConclusionsEach species showed characteristic protein contents, yet with intraspecies variability. Utilizing an innovative approach to analyze the full information provided by chromatograms, we successfully identified varieties with unusual characteristics.Significance and NoveltyThe specific protein compositions of Ei and Em, and to a lower extent Sp, may impact their processability and digestibility. Our study also highlights that considering complete chromatographic profiles can reveal differences that remain unnoticed when using traditional analytical approaches.

Environmental colloid behaviors of humic acid - Cadmium nanoparticles in aquatic environments

Humic acid (HA), a principal constituent of natural organic matter (NOM), manifests ubiquitously across diverse ecosystems and can significantly influence the environmental behaviors of Cd(II) in aquatic systems. Previous studies on NOM-Cd(II) interactions have primarily focused on the immobilization of Cd(II) solids, but little is known about the colloidal stability of organically complexed Cd(II) particles in the environment. In this study, we investigated the formation of HA-Cd(II) colloids and quantified their aggregation, stability, and transport behaviors in a saturated porous media representative of typical subsurface conditions. Results from batch experiments indicated that the relative quantity of HA-Cd(II) colloids increased with increasing C/Cd molar ratio and that the carboxyl functional groups of HA dominated the stability of HA-Cd(II) colloids. The results of correlation analysis between particle size, critical aggregation concentration (CCC), and zeta potential indicated that both Derjaguin-Landau-Verwey-Overbeek (DLVO) and non-DLVO interactions contributed to the enhanced colloidal stability of HA-Cd(II) colloids. Column results further confirmed that the stable HA-Cd(II) colloid can transport fast in a saturated media composed of clean sand. Together, this study provides new knowledge of the colloidal behaviors of NOM-Cd(II) nanoparticles, which is important for better understanding the ultimate cycling of Cd(II) in aquatic systems.

Probing berberine syringate-bovine serum albumin interactions by multi-spectroscopic, conductimetric, volumetric and molecular docking methods

In this paper, a new berberine-based salt berberine syringate ([BBR][SA]) was synthesized and [BBR][SA]-bovine serum albumin (BSA) interactions were investigated by various techniques like UV–visible absorption, fluorescence spectroscopy, Fourier transform infrared spectroscopy (FT-IR), volumetry and conductimetry. Using the experimental values of density and conductivity, the specific volume of BSA and the critical aggregation concentration of [BBR][SA] were derived. From the variation trends of specific volume for BSA with [BBR][SA] concentrations, it was observed the predominate ionic interaction/dehydration effect at small [BBR][SA] concentration and the non-polar interaction/hydration phenomenon at higher [BBR][SA] concentration for the binding of [BBR][SA] with BSA, as well as hydration shell structure changes at different temperatures in the medium with different pH values. Aggregation of [BBR][SA] is slowed by increasing concentration of BSA and temperature. The thermodynamic parameters of aggregation process have been exploited to gain more information on the interaction of [BBR][SA] with BSA. UV–vis absorption, multiple fluorescence spectroscopy (steady-state spectroscopy, synchronous fluorescence, three-dimensional fluorescence), and FT-IR have been utilized to understand the interaction mechanisms and binding strength between the two molecules. [BBR][SA] quenches the fluorescence of BSA through static quenching mechanism. The 1:1 complex between [BBR][SA] and BSA with a strong affinity was formed. The changes in microenvironment and conformation of BSA caused by [BBR][SA] were revealed by synchronous fluorescence spectra, 3D fluorescence maps and FT-IR. The binding average distance from [BBR][SA] to BSA (2.83 nm) was obtained based on the theory of Förster energy transfer. Competitive binding experiment confirms [BBR][SA] binding to Sudlow site I (sub-domain IIA) of BSA. In addition, molecular docking was generated to provide valuable information about the available binding sites of [BBR][SA] on BSA and the types of binding forces.

Modeling the impact of supplementary cementitious materials on compressive strength of recycled aggregate concrete forest-random approach

Recycled concrete aggregates (RCAs) and supplementary cementitious materials (SCMs) may substitute some cement and natural aggregates (NA) in concrete manufacturing. However, their effects on recycled aggregate concrete (RAC) compressive strength are difficult to model. Reactivity, silica, and alumina modulus were examined for cementitious materials' chemical complexity. Random Forest approaches were developed to predict and analyze RAC compressive strength. Even with RCAs and SCMs, the RF model accurately estimated concrete compressive strength. The Variable Importance (VI) research examined how input factors affected RAC compressive strength. VI indicated that silica fume contributes most to RAC compressive strength, followed by cementitious materials' reactivity modulus, cement content, silica modulus, fine natural aggregate content, and coarse natural aggregate dosage. The water dosage, water/binder ratio, and RCA content lower the RAC compressive strength. As a result, to highlight, the amount of SCM was not significant, but its nature was (i.e., hydraulic, silica pozzolanic, or alumina pozzolanic).

Dynamic Interfacial Properties and Foamability of DoTAB/SiO2 Mixtures

The interaction between nanoparticles and cationic surfactants is an exciting and emerging field in interfacial science. This area of research holds significant promise, linking fundamental principles to practical applications in a variety of industries, including chemical processes, biomedical applications and the petroleum industry. This study explores the interaction between dodecyltrimethylammonium bromide (DoTAB) and silica (SiO2) nanoparticles, investigating their influence on dynamic interfacial properties and foam characteristics. Through equilibrium and dynamic surface tension measurements, along with examining the dilational visco-elasticity behavior, this research reveals the complex surface behavior of DoTAB/SiO2 mixtures compared to individual surfactant solutions. The foamability and stability experiments indicate that the addition of SiO2 significantly improves the foam stability. Notably, stable foams are achieved at low SiO2 concentrations, suggesting a cost-effective approach to enhancing the foam stability. This study identifies the optimal stability conditions for 12 mM DoTAB solutions, emphasizing the crucial role of the critical aggregation concentration region. These findings offer valuable insights for designing surfactant-nanoparticle formulations to enhance foam performance in various industrial applications.

The aggregation behaviors of organic radicals in polar fluorinated arenes

AbstractPolar fluorinated arenes can promote organic free radical reactions, which have attracted scientists’ interest in recent years. However, it is still unknown how these solvents interact weakly with organic radical molecules to influence their reactivity. In this study, we investigated how organic free radicals aggregate in five polar fluorocarbon solvents, and demonstrated that different substituents can influence their aggregation behaviors. In these solvents, small organic radicals with simple substituents maintain a homogeneous solution; however, radicals with substituents that form intermolecular hydrogen bonds or with long‐chain aliphatic hydrocarbons tend to aggregate in them, whereas substituents of long‐chain aliphatic hydrocarbons tend to promote aggregation better. The critical aggregation concentrations of these aggregates are measured by concentration‐dependent UV–visible spectroscopy. Their topological morphologies are all spherical based on TEM. The compactness and rotational motivation speed of radical molecules within these aggregates are determined by EPR spectroscopy. The particle sizes of these aggregates are determined by analyzing their cyclic voltammograms. Most excitingly, electrochemical experiments reveal that the aggregation behaviors of free radical molecules with intermolecular hydrogen bonds can significantly increase their catalytic rate for electro‐oxidizing benzyl alcohol in such a solvent. The results of this study indicate that in polar fluorinated arenes organic radical molecules’ aggregation behaviors are related to their structures. This may provide guidelines for regulating organic radical reactivity in these solvents in the future.

Effects of Continuous Annual Crop Rotation and Fallow on Soil Aggregate Stability and Organic Carbon

In order to explore the effects of continuous annual crop rotation and fallow on aggregate stability and organic carbon content in red soil, the red soil in sloping farmland was taken as the research object, and the water-stable aggregates and organic carbon content were determined using the wet sieve method and potassium dichromate-concentrated sulfuric acid external heating method, respectively. The changes in soil aggregate stability and organic carbon content under the four treatments of maize-vetch-maize rotation (M-V-M), maize-pea-maize rotation (M-P-M), maize-fallow-maize (M-F-M), and annual fallow (F-F-F) from 2020 to 2022 and the relationships between them were analyzed. The results showed that in 2021 and 2022, the contents of > 2 mm aggregates treated with F-F-F, M-V-M, and M-P-M were significantly increased by 67.01%-100.92%, 29.71%-33.67%, and 29.68%-38.07%, respectively, compared with that treated with M-F-M. In 2021 and 2022, the stability parameters of F-F-F and M-V-M were significantly higher than those of M-F-M (P < 0.05). The content of > 2 mm aggregates, geometric mean diameter (GMD), and mean weight diameter (MWD) under the M-V-M treatment and R0.25 (> 0.25 mm aggregate contents), MWD and > 2 mm aggregate contents under the F-F-F treatment increased with the increase in fallow years, whereas the content of 1-2 mm and < 0.25 mm under the F-F-F treatment decreased with the increase in fallow years. Both green manure rotation and fallow treatment could increase the SOC content, and the SOC content of F-F-F and M-V-M treatment increased with the extension in age. Correlation analysis showed that SOC content was significantly positively correlated with R0.25 and GMD under all treatments. R0.25 and GMD under the F-F-F treatment and GMD and MWD under M-V-M were significantly positively correlated with SOC content. The results showed that continuous annual crop rotation and fallow was beneficial to improve the content of soil macro-aggregates, aggregate stability, and SOC content, which could provide theoretical basis for the implementation of reasonable continuous annual crop rotation and fallow patterns and soil erosion control in red soil areas of sloping farmland in southern China.