Entropy Values Research Articles

Classification of Term and Preterm Birth Data from Elektrohisterogram (EHG) Data by Empirical Wavelet Transform Based Machine Learning Methods

Accurate prediction of preterm birth can significantly reduce birth complications for both mother and baby. This situation increases the need for an effective technique in early diagnosis. Therefore, machine learning methods and techniques used on Electrohysterogram (EHG) data are increasing day by day. The aim of this study is to evaluate the effectiveness of the Empirical Wavelet Transform (EWT) approach on EHG data and to propose an algorithm for estimating preterm birth using single EHG signal. The data used in the study were taken from Physionet's Term-Preterm Electrohysterogram Database (TPEHGDB) and scored in one-minute windows. The feature matrix was obtained by calculating the sample entropy value from each of the discretized EHG modes obtained as a result of this method, which was used for the first time on EHG data, and the average energy value from the signal obtained by recombining the modes. The obtained features were applied to Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) algorithms to predict preterm birth. Among the classifier algorithms, the RF algorithm achieved the best result with a success rate of 98,20%.

Thermodynamic properties, interfacial adhesion energy and tribological properties between MCN (M = Hf, Ta, Cr, Nb) coating and Ti alloy substrates investigated by experiment and first-principles calculations

A series of MCN (M = Hf, Ta, Cr, Nb) coatings were prepared by double glow plasma surface alloying (DGPSA) as protective hard coatings for Ti60 (Ti-5.5Al-4.0Zr-4.0Sn), aiming to solve the problems of low bonding strength between the ceramic coating and the substrate as well as the poor wear resistant of ceramic alloy coatings. First principles calculation was used to study the elastic constant of MC0.5N0.5 solids, thermomechanical parameters and the interfacial adhesive strength of Ti(101)/MCN(220). The calculated results indicated the HfCN solids show excellent mechanical properties, lower thermal expansion coefficient α, lower Gibbbs values and higher values of entropy (S), superior adhesion strength Wad ∼ 5.7 J/m2. The high-temperature tribological performance tests show that the HfCN and TaCN coatings exhibited better wear resistance compared with the CrCN and NbCN coatings at 500 °C -700 °C. The wear rate of HfCN coatings at 500 °C and 700 °C is about 2 times and 5 times lower than that of CrCN, which is in accordance with the HfCN coating’s excellent thermal stability and high interfacial bonding strength with the Ti substrate. The adhesion wear mechanism of all coatings gradually intensified with increasing temperature.

Adsorption of Uranium (VI) From Aqueous Solution Using Melamine Formaldehyde Organo Clay Nanocomposite Foam as a Novel Adsorbent

Uranium is a toxic radioactive element and is usually found in the environment in hexavalent form. It has become necessary to remove uranium, which can be released into the environment in excessive amounts through nuclear industrial activities, from access waters. Melamine formaldehyde/organo clay nano composite foam (MFCNCF) was prepared as a novel adsorbent by hardening with thermal treatment. Structural, crystallographic textural and surface morphological characterization of the prepared adsorbent was made by Fourier transform infrared spectrophotometry (FTIR), X-Ray diffraction (XRD), Scanning electron microscopy (SEM) and High-resolution transmission electron microscopy (HRTEM) analyses. This study aimed to investigate the adsorption of U(VI) from aqueous solutions by MFCNCF and its dependence on various variables, such as initial uranium concentration, adsorption time, adsorbent dosage, initial pH, and temperature. Additionally, its adsorption isotherm analysis and adsorption kinetics and thermodynamics were also examined. For this purpose, batch adsorption experiments were carried out and the equilibrium concentration of U(VI) in the aqueous solution was measured with a UV-Vis spectrophotometer at a wavelength of 433 nm, which corresponds to its maximum absorptivity. The results regarding adsorption kinetics showed that 30 min was sufficient to reach adsorption equilibrium and the data showed a high fit to the pseudo-second-order kinetic model. Isotherm analysis also revealed a high fit of the data to the Type III isotherm and the Halsey model. Based on these fits, an adsorption mechanism involving two regions (electrostatic and complex formation) is proposed for the current adsorption system. The adsorption isosteric enthalpy and entropy values for the first and second regions were found to be −5.35 and 2.02 kJ/mol and −16.98 and 6.40 J/mol, respectively. The experimental results showed that the prepared nanocomposite foam adsorbent is an effective and potential alternative for the effective removal of U(VI) from aqueous solutions.

Spatial restructuring and development characteristics of villages and the revitalization path: a case study of the X County of Zhejiang Province in China

Rural development and restructuring are critical steps toward achieving optimal spatial adjustment for regional and rural revitalization. Using the rural regional system and the TOPSIS model supported by the entropy value method, this study analyzed the model of rural development and spatial restructuring in the study area from 2000 to 2020. The study examined the exchange and change and discussed the internal logical correlation and role relation among them. The results indicated that: (1) During the study period, the level of rural development in the study area increased considerably. The rural regional function shifted from a lower level of homogenous development to a higher level of diversified development, which are important features of the evolution of economic and social development levels. (2) Spatial restructuring was characterized by temporal continuity and non-linearity of stages. The overall shape transformed from “dispersion-homogeneity-differentiation.” (3) The coupling coordination degree of the two subsystems showed a constant rise to a low level, with apparent temporal heterogeneity. The type of coupling coordination became a benign optimizing direction with a significant level of heterogeneity. Rural revitalization depends on the coupling and coordination among economic, social, ecological, and spatial systems. The research results provide baseline information to decipher the path of rural revitalization at various degrees of coupling and promoting sustainable rural development.

A lossless probabilistic image encryption algorithm based on QSD decomposition and matrix transformations

With the development of Internet technology, images have become widely used in people's daily life. However, some images carry significant importance and sensitivity, making the security and protection of digital information more crucial. Standard encryption schemes such as RSA, DES, and AES, which have proven effective for textual data, face limitations when applied to images due to their unique features, such as large data capacity and strong pixel correlation. In recent years, numerous effective image encryption schemes utilizing diverse techniques have been proposed. However, the use of integer decomposition in encryption techniques is still underexplored. This paper introduces a novel, lossless, and probabilistic image encryption scheme designed to enhance the security of sensitive images during transmission and storage. This scheme is based on a new integer decomposition technique and utilizes a key with a very large key space. In the proposed approach, an image is encrypted into a pair of images using quasi-square decomposition and new matrix transformations. Security is further reinforced through the use of orthogonal matrices, which enhance diffusion while ensuring data integrity and preservation. Experimental results demonstrate that the proposed scheme is highly secure against most known attacks and cryptanalysis methods. Additionally, a comparison with various existing image encryption techniques shows the effectiveness of the proposed approach. This research provides a significant contribution by introducing new techniques that leverage integer decomposition, which are highly effective against CPA (Chosen Plaintext Attack) and KPA (Known-Plaintext Attack). Moreover, these techniques enhance the entropy value of encrypted images and improve the values of NPCR and UACI, which help prevent differential attacks. The proposed scheme has the potential to advance applications in secure image transmission, storage, and privacy protection.

A Four Laws Structure for Looking at Economics through the Eyes of Thermodynamics

Thermodynamics is based on its Four Laws structure. Based on Thermodynamics and its Four Laws structure, this work presents and explores its economic counterpart, proposing an economic Four Laws structure to look at Economics through the eyes of Thermodynamics. The Economic Zeroth Law is based on the merchandise economic thermal equilibrium, thus defining the economic temperature. The Economic First Law is based on balance equations for the units of merchandise and monetary species. The economic Second Law is based on the observation that the traded merchandise units are transferred in the increasing unit price direction (in the decreasing economic temperature direction). This allows economic entropy to be defined through a differential equation, as well as the definition of economic irreversibility and economic entropy generation. The economic Third Law states that the merchandise economic entropy of an economic system at the null economic temperature (at an infinite merchandise unit price) is null. Conjugation of the economic Second and Third Laws sets how to evaluate the absolute value of the economic entropy of an economic system. The presented developments setting the proposed economic Four Laws structure introduce and explore a large set of concepts, variables, and relations that are of major relevance for looking at Economics through the eyes of Thermodynamics.

Computation of rate coefficients in solutions based on transition state theory combined with a heuristically corrected polarizable continuum model: intermolecular Diels-Alder reactions as case studies.

Transition state theory (TST) based on activation parameters computed using quantum mechanics calculations combined with the polarizable continuum model (QM/PCM) is a fundamental tool for investigating reaction rates in the liquid phase. In conventional QM/PCM methods, thermodynamic data and partition functions for a solute are often derived from a quasi-ideal gas treatment (IGT) widely implemented in commercially available computation packages. This approach tends to overestimate entropy because calculations of thermodynamic parameters in the liquid phase ignore hindered translational and rotational modes in real solutions. The present work formulated partition functions for more realistic solutes hindered by surrounding solvent molecules in conjunction with the basic QM/PCM concept. In addition, a configuration partition function for solute molecules at a standard concentration of 1 mol dm-3 was incorporated using a simple lattice model. The canonical partition function and thermodynamic functions were derived based on statistical thermodynamics for localized systems. Expressions for rate coefficients within TST were also derived with a consistent formalism based on the standard state selected in partition function calculations. The performance of the proposed method was assessed by predicting rate coefficients for three different Diels-Alder reactions and comparing these with experimental results. QM/PCM calculations at the G4//ωB97X-D/6-311++G(d,p)/IEF-PCM level of theory with corrections for the dispersion and repulsion energies were performed to obtain the electronic structures of stationary points on potential energy surfaces as a means of finding activation enthalpy, entropy and Gibbs energy values based on revised partition functions as well as predicting rate coefficients. The activation Gibbs energies obtained from our proposed method were lower than those obtained from the IGT method due to reasonable entropy computations. The proposed method overestimated the rate coefficients by one to two orders of magnitude compared to the experimental values, whereas the IGT method underestimated them by the same amount. This discrepancy arises because the proposed method calculates the partition function from the viewpoint of a localized system, whereas the IGT method calculates it from the viewpoint of a non-localized system. Given that actual liquids exist in a state between non-localized and localized systems, it is essential to formulate the partition function in a way that more accurately represents the liquid state.

Enhanced multimodal medical image fusion via modified DWT with arithmetic optimization algorithm

Medical image fusion (MIF) techniques are proficient in combining medical images in distinct morphologies to obtain a reliable medical analysis. A single modality image could not offer adequate data for an accurate analysis. Therefore, a novel multimodal MIF-based artificial intelligence (AI) method has been presented. MIF approaches fuse multimodal medical images for exact and reliable medical recognition. Multimodal MIF improves diagnostic accuracy and clinical decision-making by combining complementary data in different imaging modalities. This article presents a new multimodal medical image fusion model utilizing Modified DWT with an Arithmetic Optimization Algorithm (MMIF-MDWTAOA) approach. The MMIF-MDWTAOA approach aims to generate a fused image with the significant details and features from each modality, leading to an elaborated depiction for precise interpretation by medical experts. The bilateral filtering (BF) approach is primarily employed for noise elimination. Next, the image decomposition process uses a modified discrete wavelet transform (MDWT) approach. However, the approximation coefficient of modality_1 and the detailed coefficient of modality_2 can be fused interchangeably. Furthermore, a fusion rule is derived from combining the multimodality data, and the AOA model is enforced to ensure the optimum selection of the fusion rule parameters. A sequence of simulations is accomplished to validate the enhanced output of the MMIF-MDWTAOA technique. The investigational validation of the MMIF-MDWTAOA technique showed the highest entropy values of 7.568 and 7.741 bits/pixel over other approaches.

Impairment of inhibitory control due to repetitive subconcussions from indirect brain impacts: Evidence from event-related potentials and resting-state EEG complexity in parachuters

The present study aims to investigate the unknown relationship between inhibitory control and repetitive subconcussion induced by the indirect brain impacts. We enrolled 28 parachuters exposed to repetitive subconcussion (SC) and 27 matched health controls (HC). Parachuters who have completed at least 70 actual parachuting (71–112 times) and at least 1500 simulated platform jumps (1500–4500 times) were included in the SC group. The SC group had a reduced accuracy rate in both the Stroop congruent and incongruent conditions. Larger N2 and N450 amplitudes were elicited in the frontal regions of the SC group, which indicate compensatory adaptations to the deficit in conflict monitoring. The reduced frontal resting-state EEG complexity in full-band (1–40 Hz) may demonstrate the frontal structural damage following the indirect brain impacts of repetitive subconcussion. Pearson correlation analysis showed that in the SC group, the frontal beta-band sample entropy values are positively correlated with the accuracy rate of the Stroop incongruent condition, suggesting the frontal beta-band sample entropy values may serve as potential electrophysiological markers of impaired inhibitory control after indirectly repetitive brain impacts. This study provides the robust evidence that repetitive subconcussion resulting from indirect brain impacts may lead to impairment of inhibitory control.

Sample Entropy Computation on Signals with Missing Values.

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values. While deletion ignores missing values, interpolation replaces them using approximations based on neighboring points. This paper proposes a novel approach for the computation of sample entropy when values are considered as missing or invalid. The proposed algorithm accommodates points in the m-dimensional space and handles them there. A theoretical and experimental comparison of the proposed algorithm with deletion and interpolation demonstrates several advantages over these other two approaches. Notably, the deviation of the expected sample entropy value for the proposed methodology consistently proves to be lowest one.

Thermodynamic water adsorption analysis of biodegradable films based on corn starch-chitosan added with triblock copolymers

Adding triblock copolymers to hydrophilic films is an option to improve the functional properties and water resistance under high equilibrium relative humidity (RHeq) environments. Sorption isotherms of corn starch-chitosan based films added with triblock copolymers (L64 and P123) measured at 4, 20, and 36 °C were fitted using the Guggenheim-Anderson-De Boer equation. Thermodynamic analysis allowed allocating the minimum integral entropy value between 0.09 and 0.50 of water activity. The compensation enthalpy-entropy showed that the entropy driven the process at low water activities (0.10–∼0.33) and the enthalpy driven the water sorption at high water activities (∼0.33–∼0.90).

A Study of Micellar Catalyses on Oxidation of Glycine by QDC in the Presence of Sodium Dodecyl Sulphate (SDS) in Aqueous Perchloric Acid Medium

Aims: To study the micellar effect of SDS on the oxidation of glycine by Quinolinium Dichromate (QDC) in perchloric acid medium. Background: Among the amino acids, glycine plays a major role in multiple metabolic reactions, such as glutathione synthesis and one-carbon metabolism. The oxidation of glycine has received importance because it is the major neurotransmitter inhibitor in the spinal cord and brainstem. In the oxidation process of amino acids, highly toxic chromium (VI) compounds are converted into non-toxic chromium (III) by quinolinium dichromate (QDC) oxidant in an appropriate pH value medium. Objective: 1. To study the catalytic role of anionic surfactant (SDS) on the oxidation of glycine by QDC through micellization, evaluation of critical micelles concentration (CMC) in the presence and absence of glycine and other surface properties along with thermodynamic quantities. 2. Determination of rate constant and order of reaction with respect to QDC, glycine, acid and surfactant which will help to study the kinetics of the reaction 3. Analysis of oxidation product by FT-IR and calculation of activation parameters. 4. Synthesis of oxidant (QDC) and its characterization by UV-Visible spectrophotometer and NMR spectroscopy. Results: First-order kinetics was observed with respect to oxidant, glycine and hydrogen ions. The rate of reaction increased remarkably with an increase in the concentration of surfactant (SDS). The kinetic results show that the ionic strength variation does not have any significant effect on the rate whereas the increase in the dielectric constant of the medium shows a remarkable effect on the rate constant. From stoichiometry study, it was found that 2 moles of oxidant (QDC) consumed 3 moles of glycine to produce aldehyde (Formaldehyde). Conclusion: The observed negative value of (ΔS) entropy of activation and positive (ΔH) enthalpy of activation suggests a more ordered activated complex formation and highly solvated transition state. The kinetics of the reaction in a perchloric acid medium is found to be accelerated in the presence of surfactant (SDS). The kinetics of the reaction follow pseudo first-order decay of Cr(VI) species (QDC), a unity dependence of rate on glycine and perchloric acid. The oxidation product formaldehyde was identified by FTIR. NMR spectrum analysis of synthesized QDC shows a resemblance with pure QDC.

Entropy-Driven Hydrated Eutectic Electrolytes with Diverse Solvation Configurations for All-Temperature Zn-ion Batteries.

Batteries always encounter uncontrollable failure or performance decay under extreme temperature environments, which is largely limited by the properties of electrolytes. Herein, an entropy-driven hydrated eutectic electrolyte (HEE) with diverse solvation configurations is proposed to expand the operating temperature range of Zn-ion batteries. The HEE possesses over 40 types of Zn2+ solvation structure with uniform distribution, contributing to its much higher solvation configurational entropy compared to the conventional aqueous counterpart (only 6 types). These effectively promote its anti-freezing ability under ultralow temperatures, with a high ionic conductivity of 0.42 mS cm-1 even at a low temperature of -40 °C. Moreover, the entropy-driven property can simultaneously enhance the thermal stability under a high temperature over +140 °C. Therefore, the HEE can enable full cells stably working over a wide temperature range of -40~+80 °C, performing over 1500 cycles with 100 % capacity retention at -40 °C and 1000 cycles with ~72 % capacity retention at +80 °C. This inspiring concept of entropy-driven electrolyte with quantized solvation configurational entropy value has charming potential for designing future special batteries with excellent adaptability towards extreme temperature environments.

Entropy‐Driven Hydrated Eutectic Electrolytes with Diverse Solvation Configurations for All‐Temperature Zn‐ion Batteries

AbstractBatteries always encounter uncontrollable failure or performance decay under extreme temperature environments, which is largely limited by the properties of electrolytes. Herein, an entropy‐driven hydrated eutectic electrolyte (HEE) with diverse solvation configurations is proposed to expand the operating temperature range of Zn‐ion batteries. The HEE possesses over 40 types of Zn2+ solvation structure with uniform distribution, contributing to its much higher solvation configurational entropy compared to the conventional aqueous counterpart (only 6 types). These effectively promote its anti‐freezing ability under ultralow temperatures, with a high ionic conductivity of 0.42 mS cm−1 even at a low temperature of −40 °C. Moreover, the entropy‐driven property can simultaneously enhance the thermal stability under a high temperature over +140 °C. Therefore, the HEE can enable full cells stably working over a wide temperature range of −40~+80 °C, performing over 1500 cycles with 100 % capacity retention at −40 °C and 1000 cycles with ~72 % capacity retention at +80 °C. This inspiring concept of entropy‐driven electrolyte with quantized solvation configurational entropy value has charming potential for designing future special batteries with excellent adaptability towards extreme temperature environments.

Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator

High accuracy prediction of urban flood risk is conducive to avoid potential losses, however, it's negatively affected by unbalanced data. Furthermore, ensemble model has been demonstrated to have the ability to improve to prediction accuracy. Nevertheless, the performance of ensemble model is influenced by basic model and ensemble rules, and determining the best ensemble model remains an open issue. To improve the accuracy of flood risk prediction, an approach covering data optimization and ensemble modeling was presented to optimize unbalanced flood data and the selection of various ensemble models based on efficiency and performance. A practical application in Zhengzhou City shows that Borderline-SMOTE2 is the most applicable for optimizing the flood risk data among the state-of-the-art oversampling algorithm utilized, because of the excellent entropy value. The effect of unbalanced data on the performance of the basic models was pervasive according to changes of the common indicators. The optimal ensemble model for flood risk prediction is composed of K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes and Extreme Gradient Boosting under Stacking rule in the current study. The results of this study supply the valuable reference for the flood prediction and mitigation.

Study on the mixed fracture characteristics of concrete-rock Brazilian disks with different fracture angles

The fracture characteristics of cracked straight-through Brazilian discs for concrete-rock composites (CSTBD-CRC) with different fracture inclination angles (Mode I-II) were investigated to evaluate the stability of concrete reinforcement engineering. Acoustic emission (AE) and digital image correlation (DIC) techniques were employed to study crack propagation and fracture modes in CSTBD-CRC Brazil splitting tests, as well as the entropy evolution of AE and DIC. Additionally, 3D scanning technology and fractal theory were utilized to investigate the effect of the fracture mode on the fracture surface morphology. The results indicate that both the fracture energy and peak load of the CSTBD-CRC increase with increasing fracture inclination angle. Moreover, the section fractal dimensions of 0°, 10°, 15° and 25° samples are 2.01879, 2.01528, 2.01186, and 2.01072. Shear fracture mode increases as the dip angle increases and the fracture surface morphology decreases in fractal dimension. Notably, sharp increases in entropy values for both AE and DIC occurred at peak load, suggesting their use as failure criteria for CSTBD-CRC specimens. Furthermore, cracks were found to initiate at the tips of preexisting cracks within the concrete. The displacement in the x direction undergoes the most significant changes on both sides of the crack during crack growth, while the strain in the x direction experiences its greatest variations at the crack location.

IMPLEMENTATION OF THE DECISION TREE METHOD PREDICTING STUDENT GRADUATION ON TIME WITH C5.0 ALGORITHM

The percentage of students who graduate on time is one of the benchmarks for assessing a university's accreditation. To get good accreditation results, it is necessary to predict the graduation rate using a method so that students can anticipate delays in graduating. The C5.0 algorithm is a Decision Tree based algorithm which is a refinement of the ID3 and C4.5 algorithms created by Ross Quinlan in 1987. In this research, researchers analyzed several variables used in the input variables, there are several variables used, namely the SKS variable, Academic Leave, Student Status, Relationship/Communication with Family, Interests and Talents, Family Economics, Relationship/Communication with Lecturers, Number of Revisions, Final Assignment Material or Method. Meanwhile, in the target variable there is the Study Period. The aim of this research is to obtain the results of the implementation of graduation predictions for Strata 1 (S1) Mathematics Study Program students, Faculty of Science and Technology, UIN North Sumatra Medan using the C5.0 Decision Tree Algorithm. There are several steps taken, namely data preprocessing, data division, calculating information gain for the entire case, calculating information gain and entropy for each attribute. Calculate the total entropy and Gain value of each attribute, create a branch from each root node category, if the result of the branch becomes an internal node, the process is carried out until the attribute categories cannot be split. After carrying out the analysis, the accuracy rate was 82.81%. With a high level of accuracy, it can be developed into a rule that can provide predictions or input in making decisions about whether students will graduate on time

Removal of anionic dye from textile effluent using zirconium phosphate loaded polyaniline-graphene oxide composite: Lab to pilot scale evaluation

Efficient filtering of dyes is essential for the protection of ecosystem and human health due to the considerable water pollution caused by the effluents released from the sector. We present a simple, scalable UV radiation-assisted method for treating methyl orange dye-polluted water from the textile industry using zirconium phosphate-loaded polyaniline-graphene oxide (PGZrP) composite. The new material was synthesized by sonochemically incorporating a polyaniline-graphene oxide composite with hydrothermally synthesized zirconium phosphate. The efficacy of PGZrP in eliminating methyl orange was evaluated using experimental conditions, and the adsorption capacity was investigated as a function of pH, temperature, adsorbent dosage, and adsorption period. The system follows Langmuir adsorption isotherm with pseudo-second-order kinetics. Thermodynamics studies showed that enthalpy (H°) and entropy (S°) values are positive, indicating that the dye adsorption increases with increasing temperature and is an endothermic reaction. The maximum adsorption capacity was found to be 36.45379 mg/g for methyl orange. Using the COMSOL Multiphysics CFD Platform, an attempt was made to check the temperature and concentration profile of a PGZrP composite in a real industrial system. The predicted result shows that there is no significant temperature change in the material during the adsorption process and the concentration of dye is mainly located on the top region of the bed. The developed zirconium phosphate decorated polyaniline-graphene oxide composite can be successfully utilized for the effective removal of methyl orange from industrial wastewater in bulk quantity which is coming from the textile industry, and the composite can be reused for several cycles with good efficiency. In this work, we have designed a miniaturized proof of concept to remove methyl orange from water which showed good dye removal efficiency.

Exploring Entropy Measures with Topological Indices on Subdivided Cage Networks via Linear Regression Analysis

ABSTRACT In this study, we investigate entropy measurements for subdivided cage networks based on topological indices. We specifically calculate different entropy, redefining Zagreb entropy, HM ( G ) , M 1 ( G ) , M 2 ( G ) entropy, atom bond connection entropy, and Randic entropy. We examine the graphical behavior of various entropy measures using the line fit approach. The results highlight patterns in the distribution of entropy values and interactions between them, which shed light on the intricate connectivity and structural properties of segmented cage networks. This work improves our understanding of cage network dynamics and provides a visual framework for interpreting their behavior.

Investigation of micellization and thermodynamic properties of cationic gemini surfactant (12-2-O-2-12) in the presence of organic solvents

The micellization behaviour and thermodynamic properties of synthesized cationic gemini surfactant (12-2-O-12-12), N,N′-didodecyl-N,N,N′,N′-tetramethyl-N,N′-(oxybis(ethane-2,1-diyl))-diammonium dibromide were studied in the presence of organic solvents for four different temperatures (25, 30, 35 and 40 °C). Aqueous solutions of 10 % and 20 % methanol (MeOH), ethanol (EtOH), isopropanol (IPOH) and dimethyl sulfoxide (DMSO) were used as solvents. The critical micelle concentration (CMC) of cationic gemini surfactant and the degree of counterion dissociation (α) were determined by the conductometric method. Using the CMC and α values for each of the four temperatures, the values of the standard Gibbs free energy (ΔGmo), Gibbs free energy of transfer (ΔGm,transo), enthalpy (ΔHmo), and entropy (ΔSmo) of micellization were calculated. It was observed that the CMC and α values increased with increasing solvent percentage and temperature. It was found that in all cases, the standard Gibbs free energy and enthalpy values were negative while both the micellization entropy and the standard Gibbs free energy of transfer are positive. Negative Gibbs free energy values indicated that micellization was a spontaneous situation, while positive entropy values indicated the existence of a disordered situation. The positive ΔGm,transo values show that the presence of solvents makes the environment unfavorable for micelle formation.