Higher Entropy Research Articles

High Entropy Helps Na4Fe3(PO4)2P2O7 Improve Its Sodium Storage Performance

AbstractRecently, a high‐entropy strategy has attracted extensive attention and is applied to the preparation of electrode materials for energy storage batteries, aiming to improve electrochemical performance. It is found that adjusting the conformational entropy of the material can significantly enhance ion and electron transport efficiency, as well as improve the structural stability of the host material. However, there still remains a lack of deep understanding into the high‐entropy strategy, specifically regarding how this approach can alter the intrinsic properties of the material. In this work, the Na4Fe3(PO4)2P2O7 is designed and prepared as a model material with higher entropy, and ultimately, an optimized sample of Na3.9Fe2.6V0.1Mn0.1Cu0.1Mg0.1(PO4)2(P2O7) is achieved. The results indicate that increasing the entropy value of the material notably enhances its crystal structure, diffusion kinetics, and interfacial stability. Consequently, this optimized sample demonstrates deep insertion/extraction of 2.8 Na+, yielding an impressively high capacity of 122.3 mAh g−1 at 0.1C, alongside an ultra‐high rate capability of 100C. Remarkably, it also sustains performance over 14 000 cycles at 50C. This study underscores a method for fabricating high‐performance electrode materials through the implementation of the entropy strategy.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

The effect of entropy on the structure and aqueous leaching resistance of nano monazite-type phosphates

Monazite, with its unique chemical composition and stable crystal structure, shows significant potential for immobilizing high-level radioactive waste. In this study, a series of monazite ceramics were synthesized to study the effect of entropy change on their structure and aqueous durability. The synthesized ceramics exhibit a monoclinic structure (P21/n), with an average grain size below 80 nm. MCC-1 leaching tests reveal that the normalized leaching rate decreases over time but increases with higher entropy. The five-element high-entropy ceramics have an average leaching rate of 4.2 × 10⁻³ g·m⁻2 d⁻1. Notably, the higher leaching rate in high-entropy nanocrystalline monazites is likely attributed to the reduced grain size. The presence of numerous grain boundaries tends to mitigate the precipitation inhibition effect typically associated with high-entropy ceramics. These findings offer insights into the role of entropy and grain size in ceramic materials for radioactive waste immobilization.

Wodyetia bifurcata (foxtail palm tree) leaves as a super-augmented instantaneous methylene blue remover from simulated water and wastewater.

Wodyetia bifurcata, also known as foxtail palm tree leaves, was tested for highly effective methylene blue (MB) removal from commercial and artificial effluent. BET surface area measurement, FESEM, FTIR, and pHzpc were used to get information on the shape and structure of the particles. Several important factors were used to determine its adsorption activity, including intake concentration, contact duration, and pH level. Accelerated adsorption is seen in the experimental results, with more than 94% adsorption occurring successfully in the initial 12min and reaching equilibrium within 15min (% removal = 97.45%) at neutral pH. It was discovered that the maximum adsorption capacity was 58.74mgg-1 at 308K. The adsorption procedure confirms an active adsorption process of linear and non-linear kinetics of pseudo-second order, and the adsorption path is well addressed by the Freundlich model both in linear and non-linear form, having an R2 value close to unity. Thermodynamic characteristics point to an exothermic, viable, spontaneous reaction with higher entropy. Utilizing a 1:1 MeOH/H2O ratio, spent adsorbent may be readily regenerated by as much as 75% with a possible three-cycle usage. The practical application of biosorbents was confirmed by real-time effectiveness testing using MB-carrying industrial wastewater, and up to 45.75% adsorption was shown. A relative standard deviation confirmed statistical dependability. All things considered, the current material provides a clean and environmentally friendly way to remove MB dye from various wastewater types.

Associative study of maternal genetic variations with preeclampsia in Russian population: SNP-SNP interactions and haplotypes association

Preeclampsia is a pregnancy-associated hypertensive pathology that affects maternal and fetal quality of life. It was linked to several environmental and genetic factors including genetic polymorphisms. This study aimed to study the association, SNP-SNP interactions and haplotypes associations of thrombophilia, folate cycle and hypertension maternal genetic variations with preeclampsia risk in Russian women from Rostov region. 53 pregnant women diagnosed with preeclampsia were compared with 3108 women with relatively healthy pregnancy. DNA was extracted from blood samples and real time allele specific PCR was used for genotyping. MDR analysis was used for SNP-SNP interactions study. According to our data, F5(G1691A) and ITGB3(T1565C) mutant homozygotes were associated with higher risk of preeclampsia. MTRR (A66G) mutant allele (G) was shown as significant preeclampsia risk factor. ADD1 (G1378T) showed significantly higher frequency of mutant allele (T) and mutant homozygote genotype (TT) in preeclampsia patients comparing to control group. MDR showed that this polymorphism has the higher entropy among targeted variations which was confirmed by binary haplotypes association study. We have also examined linkage disequilibrium showing two haploblocks in between the studied genetic variations included MTHFR (C677T) and MTHFR (A1298C) block and AGT (T704C) with AGT (C521T) block. Results suggest several genetic variations and haplotypes as perspective maternal marker for preeclampsia which will contribute to improve prognosis tools and accordingly treatment and prevention possibilities.

The rising entropy of English in the attention economy

We present evidence that the word entropy of American English has been rising steadily since around 1900. We also find differences in word entropy between media categories, with short-form media such as news and magazines having higher entropy than long-form media, and social media feeds having higher entropy still. To explain these results we develop an ecological model of the attention economy that combines ideas from Zipf’s law and information foraging. In this model, media consumers maximize information utility rate taking into account the costs of information search, while media producers adapt to technologies that reduce search costs, driving them to generate higher entropy content in increasingly shorter formats.

Low-Cost Self-Reconstructed High Entropy Oxide as an Ultra-Durable OER Electrocatalyst for Anion Exchange Membrane Water Electrolyzer.

Future energy loss can be minimized to a greater extent via developing highly active electrocatalysts for alkaline water electrolyzers. Incorporating an innovative design like high entropy oxides, dealloying, structural reconstruction, in situ activation can potentially reduce the energy barriers between practical and theoretical potentials. Here, a Fd-3m spinel group high entropy oxide is developed via a simple solvothermal and calcination approach. The developed (FeCoMnZnMg)3O4 electrocatalyst shows a near equimolar distribution of all the metal elements resulting in higher entropy (ΔS≈1.61R) and higher surface area. The self-reconstructed spinel high entropy oxide (S-HEO) catalyst exhibited a lower overpotential of 240mV to reach 10mA cm-2 and enhanced reaction kinetics (59mV dec-1). Noticeably, the S-HEO displayed an outstanding durability of 1000h without any potential loss, significantly outperforming most of the reported OER electrocatalysts. Further, S-HEO is evaluated as the anode catalyst for an anion exchange membrane water electrolyzer (AEMWE) in 1m, 0.1m KOH, and DI water at 20 and 60°C. These results demonstrate that S-HEO is a highly attractive, non-noble class of materials for high active oxygen evolution reaction (OER) electrocatalysts allowing fine-tuning beyond the limits of bi- or trimetallic oxides.

Metal-water interface formation: Thermodynamics from abinitio molecular dynamics simulations.

Metal-water interfaces are central to many electrochemical, (electro)catalytic, and materials science processes and systems. However, our current understanding of their thermodynamic properties is limited by the scarcity of accurate experimental and computational data and procedures. In this work, thermodynamic quantities for metal-water interface formation are computed for a range of FCC(111) surfaces (Pd, Pt, Au, Ag, Rh, and PdAu) through extensive density functional theory based molecular dynamics and the two-phase entropy model. We show that metal-water interface formation is thermodynamically favorable and that most metal surfaces studied in this work are completely wettable, i.e., have contact angles of zero. Interfacial water has higher entropy than bulk water due to the increased population of low-frequency translational modes. The entropic contributions also correlate with the orientational water density, and the highest solvation entropies are observed for interfaces with a moderately ordered first water layer; the entropic contributions account for up to ∼25% of the formation free energy. Water adsorption energy correlates with the water orientation and structure and is found to be a good descriptor of the internal energy part of the interface formation free energy, but it alone cannot satisfactorily explain the interfacial thermodynamics; the interface formation is driven by the competition between energetic and entropic contributions. The obtained results and insight can be used to develop, parameterize, and benchmark theoretical and computational methods for studying metal-water interfaces. Overall, our study yields benchmark-quality data and fundamental insight into the thermodynamic forces driving metal-water interface formation.

Do I sound anxious? Emotional arousal is linked to changes in vocalisations in domestic chicks (Gallus gallus dom.)

A major goal in animal welfare science is the development of methods that quantify or ‘read-out’ current affective states in freely behaving animals. In mammalian models, changes in acoustic parameters within vocalisations have been linked to differences in emotional arousal, although there are very few studies showing changes in vocalisations that act as indicators of valence (positive or negative affect). Currently, there are very few studies on vocal indicators of emotion in birds. To determine the link between acoustic parameters within vocalisations and emotion in domestic chicks (Gallus gallus dom.), we used a well-validated paradigm that purports to elicit two distinct, negative emotional states: anxiety and depression. In this paradigm, chicks in social isolation initially show high rates of distress calls (anxiety-like phase: high arousal/negative affect) that decrease over the next 20–30 minutes to a depression-like phase (low arousal/negative affect). We analysed acoustic parameters of distress vocalisations from Legbar chicks (4–7 days old) that were placed in social isolation for 30 minutes, either with or without a mirror (Isolated: n=18; Mirror: n=16), (a mirror is known to reduce the negative behavioural and physiological indicators of isolation). Chicks in the Isolated condition produced louder calls, of a higher frequency, and calls were individually longer, and more ‘degraded’ (higher entropy and lower harmonics-to-noise ratio) compared to the Mirror condition. However, the call rate did not drop below 50 % of the initial rate in either condition, indicating the chicks in this study did not reach the criteria for the depression-like phase of the paradigm. To investigate further we analysed vocalisations at times during a trial when calls were at a high rate per minute (indicating a higher level of emotional arousal at that point in time) and we found those vocalisations were louder, more ‘degraded’, longer and higher in frequency across both conditions. These results are consistent with findings across several species of mammal, indicating that changes in emotional arousal in negatively valanced situations give rise to consistent changes in vocal parameters across a wide range of species. Indeed, further work could look to see if the same links are found in groups such as chelonians, crocodilians and geckos, as their vocal production mechanisms share some similarities. There could also be scope to use these changes to automatically detect shifts in emotion in captive animals.

Unravelling the thermodynamic properties of soil ecosystems in mature beech forests

Thermodynamics is a vast area of knowledge with a debatable role in explaining the evolution of ecosystems. In the case of soil ecosystems, this role is still unclear due to difficulties in determining the thermodynamic functions that are involved in the survival and evolution of soils as living systems. The existing knowledge is largely based on theoretical approaches and has never been applied to soils using thermodynamic functions that have been experimentally determined. In this study, we present a method for the complete experimental thermodynamic characterization of soil organic matter. This method quantifies all the thermodynamic functions for combustion and formation reactions which are involved in the thermodynamic principles governing the evolution of the universe. We applied them to track the progress of soil organic matter with soil depth in mature beech forests. Our results show that soil organic matter evolves to a higher degree of reduction as it is mineralized, yielding products with lower carbon but higher energy content than the original organic matter used as reference. These products have higher entropy than the original one, demonstrating how the soil ecosystem evolves with depth, in accordance with the second law of thermodynamics. The results were sensitive to soil organic matter transformation in forests under different management, indicating potential applicability in elucidating the energy strategies for evolution and survival of soil systems as well as in settling their evolutionary states.

The FLAMINGO project: Galaxy clusters in comparison to X-ray observations

Abstract Galaxy clusters are important probes for both cosmology and galaxy formation physics. We test the cosmological, hydrodynamical FLAMINGO simulations by comparing to observations of the gaseous properties of clusters measured from X-ray observations. FLAMINGO contains unprecedented numbers of massive galaxy groups (>106) and clusters (>105) and includes variations in both cosmology and galaxy formation physics. We predict the evolution of cluster scaling relations as well as radial profiles of the temperature, density, pressure, entropy, and metallicity for different masses and redshifts. We show that the differences between volume-, and X-ray-weighting of particles in the simulations, and between cool-core non cool-core samples, are similar in size as the differences between simulations for which the stellar and AGN feedback has been calibrated to produce significantly different gas fractions. Compared to thermally-driven AGN feedback, kinetic jet feedback calibrated to produce the same gas fraction at R500c yields a hotter core with higher entropies and lower densities, which translates into a smaller fraction of cool-core clusters. Stronger feedback, calibrated to produce lower gas fractions and hence lower gas densities, results in higher temperatures, entropies, and metallicities, but lower pressures. The scaling relations and thermodynamic profiles show almost no evolution with respect to self-similar expectations, except for the metallicity decreasing with redshift. We find that the temperature, density, pressure, and entropy profiles of clusters in the fiducial FLAMINGO simulation are in excellent agreement with observations, while the metallicities in the core are too high.

Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: a self-supervised visual identification aid

ABSTRACT We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D Sérsic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster’s centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large data sets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys.

Proton polytropic behavior of periodic density structures in the solar wind

Context. In recent years, mesoscales have gained scientific interest because they have been determined to be important in a broad range of phenomena throughout heliophysics. The solar wind mesoscale structures include periodic density structures (PDSs), which are quasi-periodic increases in the density of the solar wind that range from a few minutes to a few hours. These structures have been extensively observed in remote-sensing observations of the solar corona and in in situ observations out to 1 AU, where they manifest as radial length scales greater than or equal to the size of the Earth’s dayside magnetosphere, that is, from tens to hundreds of Earth radii (RE). While the precise mechanisms that form PDSs are still debated, recent studies confirmed that most PDSs are of solar origin and do not form through dynamics during their propagation in the interplanetary space. Aims. We further investigate the origin of PDSs by exploring the thermodynamic signature of these structures. To do this, we estimate the values of the effective polytropic index (Y) and the entropy of protons, which in turn are compared with the corresponding values found for the solar wind. Methods. We used an extensive list of PDS events spanning more than two solar cycles of Wind measurements (the entire Wind dataset from 1995 to 2022) to investigate the thermodynamic signatures of PDSs. With the use of wavelet methods, we classified these PDSs as coherent or incoherent, based on the shared periodic behavior between proton density and alpha-to-proton ratio, and we derive the proton polytropic index. Results. Our results indicate that the coherent PDSs exhibit lower Y values (Ῡ≈1.54) on average and a higher entropy than the values in the entire Wind dataset (Ῡ≈1.79), but also exhibit similarities with the magnetic cloud of an interplanetary coronal mass ejection. In contrast, incoherent PDSs exhibit the same Y values as those of the entire Wind dataset.

TESTING AN ENTROPY ESTIMATOR RELATED TO THE DYNAMICAL STATE OF GALAXY CLUSTERS

We propose the entropy estimator HZ, calculated from global dynamical parameters, in an attempt to capture the degree of evolution of galaxy systems. We assume that the observed (spatial and velocity) distributions of member galaxies in these systems evolve over time towards states of higher dynamical relaxation (higher entropy), becoming more random and homogeneous in virial equilibrium. Thus, the HZ-entropy should correspond to the gravitacional assembly state of the systems. This was probed in a sample of 70 well sampled clusters in the Local Universe whose gravitational assembly state, classified from optical and X-ray analysis of substructures, shows clear statistical correlation with HZ. This estimator was also tested on a sample of clusters (halos) from the IllustrisTNG simulations, obtaining results in agreement with the observational ones.

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain gaps, especially those based on visual domain prompts. To address these issues, this paper proposes a self-prompt strategy based on uncertainty, guiding the model to continuously focus on regions with high uncertainty (i.e., regions with a larger domain gap). Specifically, we still use the Mean-Teacher architecture with the predicted entropy from the teacher network serving as the input to the prompt module. The prompt module processes uncertain maps and guides the student network to focus on regions with higher entropy, enabling continuous adaptation to new scenes. This is a self-prompting strategy that requires no prior knowledge and is tested on widely used benchmarks. In terms of the average performance, our method outperformed the baseline algorithm in TTA and continual TTA settings of Cityscapes-to-ACDC by 3.3% and 3.9%, respectively. Our method also outperformed the baseline algorithm by 4.1% and 3.1% on the more difficult Cityscapes-to-(Foggy and Rainy) Cityscapes setting, which also surpasses six other current TTA methods.

Thermal analysis of a viscoelastic Maxwell hybrid nanofluid with graphene and polythiophene nanoparticles: Insights from an artificial neural network model

The utilization of solar radiation by converting them into thermal energy is discussed in this paper. Nanoparticles improve the ability of heat transfer therefore, it is beneficial in the use of solar thermal systems and energy storage devices. The novel mixture of nanoparticles Graphene and Polythiophene in base fluid, which has high thermodynamic properties for the improvement of thermal effect with electromagnetic effect by using Maxwell fluid model is discussed. Polyvinyl alcohol water is taken as base fluid flowing through a moveable flat plat. The governing partial differential equations are transformed into ordinary differential equations. The semi-analytical technique, homotopy analysis method is used to obtain the solution of the ordinary differential equations. The velocity is enhanced with magnetic and electric field strength. The increase of the Prandtl number, Eckert number and chemical reaction parameter, exceeds the thermal effect which produces more entropy generation and heat enhancement. The results show that the hybrid nanofluid with this Novel mixture is highly thermodynamic with higher entropy and rapid thermal augmentation which can be used in energy production and energy storage devices. A novel intelligent numerical computing technique multi-layer perceptron with feed-forward back-propagation, an artificial neural networking method with the Levenberg-Marquard algorithm is used in this model. The data is gathered for the neural networking method training, validation, and testing. The efficiency of the model is obtained and mean square error is obtained by artificial neural networking.

Image Edge Detection by Global Thresholding Using Riemann–Liouville Fractional Integral Operator

It is difficult to give a fractional global threshold (FGT) that works well on all images as the image contents are totally different. This paper describes an interesting use of fractional calculus in the field of digital image processing. In the proposed method, the fractional global threshold-based edge detector (FGTED) is established using the Riemann–Liouville fractional integral operator. FGTED is used to find the microedges in minimum time for any input digital images. The results demonstrate that the FGTED outperforms conventional techniques for detecting microtype edges. The image with a higher entropy was produced by the FGT value-based approach. Tables and images are used to summarize the output performance analysis of various images using structural similarity index measure, F-score (F-measure), precision and recall, signal-to-noise ratio, peak signal-to-noise ratio, and computational time. The FGTED can be used to detect very thin or microtype edges more accurately in minimum time without training or prior knowledge.

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Monitoring and predicting dissolved oxygen are an important tool for water quality management, which helps to effectively maintain water ecological balance and prevent environmental problems. A single model cannot describe the dynamic characteristics of dissolved oxygen sequence, which affects the prediction accuracy. In order to obtain more accurate dissolved oxygen prediction results, decomposition techniques are commonly used to extract the main fluctuations and trends of water quality sequences. However, the high-frequency modes obtained from decomposition are still unstable. To solve this problem, this paper proposed a hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Firstly, dissolved oxygen sequence is preliminarily decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and obtain several intrinsic mode functions (IMF). The fuzzy entropy (FE) is calculated to quantify the complexity of the IMF. Then, variational mode decomposition improved by northern goshawk optimization is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened. Finally, the bidirectional gate recurrent unit (BiGRU) neural network is used to predict each IMF component, and the final prediction result is obtained by reconstructing the prediction results of each component. In order to verify the effectiveness of the proposed model, this paper selects the dissolved oxygen data of Xin'anjiang Reservoir as the research object. The experimental results show that the RMSE, MAE, MAPE, and R2 of the proposed model are 0.1164, 0.0894, 1.0403%, and 0.9939, respectively, which is best among other comparative prediction models (BP, LSTM, GRU, BiGRU, EMD-BiGRU, CEEMDAN-BiGRU, VMD-BiGRU, and GNO-VMD-BiGRU). Therefore, this model effectively deals with high volatility and nonlinear dissolved oxygen data and provides reference for water environment management and ecological protection.

Wasserstein HOG: Local Directionality Extraction via Optimal Transport.

Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.

Temperature‐humidity‐density dependent evaporation behaviour of clay and sandy clay

AbstractTo compare the disparities between free water evaporation and soil water evaporation, and unveil the temporal impact of soil water evaporation in diverse environments, a series of experiments were conducted to observe the soil water content in different temperature and humidity environments. Meanwhile, the soil water characteristic curve (SWCC) and the evaporation process of free water in air were determined, and the sensitivities of temperature, humidity and density on the soil water evaporation process were analysed. The results demonstrated that both external temperature and humidity can serve as variables for controlling the evaporation rate of soil water. These factors exerted a dynamic influence on the evaporation process, with the impact of each factor tending to stabilize as soil water content decreased. The evaporation rate of soil water was higher than that of free water at the initial stage and was lower than that of free water after the soil reached the residual water content; soil water tended to exist in a state with higher entropy than that of free water. This study provides valuable insights for the establishment of a soil water evaporation model.