Energy Entropy Research Articles

Deciphering the Entropy-Driven Host-Guest Interactions within Single Covalent Organic Frameworks for Trapping of 131I.

By combining dark-field optical microscopy with an in situ thermochemical aqueous solution system, we report a single-particle imaging strategy to investigate the host-guest interactions between covalent organic framework-300 (COF-300) as a representative COF host and a series of linear-chain fatty amines. The thermodynamic parameters, such as dissociation constant, Gibbs free energy changes, enthalpy changes, and entropy changes for the binding events within COF-300 are quantified. Correlation between the hydrophobicity of various amines and other data suggests that the mechanism of the host-guest bindings arises from the entropy-driven noncovalent interactions such as hydrogen bonds and van der Waals forces. These mechanistic insights allow for the rational design and preparation of COF-300-encapsulated n-octylamine with enhanced trapping performance of radioactive 131I-. This study not only provides thermodynamic insights into the host-guest interactions within the COF framework but also establishes a structure-property relationship between fatty amines and energetic magnitude information.

Structural Aspects, Binding Interactions, Biological Activity of Co(II)‐ and Ni(II)‐N′‐(2‐fluorobenzoyl)benzo[d]thiazole‐2‐carbohydrazide Chelatess

ABSTRACTN′‐(2‐Fluorobenzoyl)benzo[d]thiazole‐2‐carbohydrazide (OFBBTCH, HL) and its chelates with Co(II) and Ni(II) have been synthesized and characterized by elemental analysis, liquid chromatography‐mass spectrometry (LC–MS), Fourier transform infrared spectroscopy (FT‐IR), ultra‐violet visible (UV–vis), nuclear magnetic resonance (NMR) spectroscopy, thermogravimetric analysis (TGA), and powder X‐ray diffraction. The conductance measurements reveal that the chelates are non‐electrolytic, and the spectral analysis indicates octahedral geometry of the chelates. The kinetic and thermodynamic parameters were obtained by Coats–Redfern relations, which shows positive Gibbs's free energy and negative entropy for both chelates. The equilibrium studies carried out by pH‐metry revealed the dissociation constant (pKa) of OFBBTCH as 8.13 and the formation of 1:1 and 1:2 Co(II)‐L and Ni(II)‐L complexes in solution. The CT‐DNA binding studies carried out by electron absorption, fluorescence quenching, steady state emissions and viscosity studies revealed hyperchromism and groove binding for HL and chelates. The Kb values calculated from electron absorption studies are 2.4 × 106, 4 × 106, and 9 × 106 M−1, from the fluorescence quenching studies Ksv values are 0.1722, 0.278, and 0.748 M−1, and Kapp values are 5.9 × 104, 1.3 × 105, and 2.9 × 105 M−1 for HL, Co(II)‐OFBBTCH and Ni(II)‐OFBBTCH, respectively. Bovine serum albumin (BSA) and human serum albumin (HSA) binding interactions were carried out by electron absorption and fluorescence quenching studies. The light switching properties of the chelates, the hydrolytic and oxidative cleavage of CT‐DNA, and the antioxidant properties were evaluated. Anti‐microbial studies carried out by pour plate method showed high activity for Ni(II)‐OFBBTCH chelate. Cytotoxicity of the compounds was performed for HeLa and MCF cell lines by MTT assay. Molecular docking studies revealed the docking of HL and Co(II)‐HL at the minor groove, and Ni(II)‐HL was docked at the major groove with hydrogen bonding.

Molding blends of silicone polymer / polypropylene with durable resistant to extreme conditions based on migration and compatibility of molecular chains

Because of low surface energy, silicone polymers would easily peel off from polypropylene (PP) substrate as hydrophobic coatings and they are incompatible with PP in melt-blending process as well. Therefore, ethylene polymers modified with both silicone and alkane side-groups were prepared by nucleophilic substitution in this work. It was confirmed that these prepared polymers could migrate and aggregate to the surface of the blends at that melting temperature when blended with PP due to the low surface energy and high entropy of silicone side groups. Meanwhile, chain entanglement was achieved between alky side-groups of silicone polymers and polymer chain of PP by melt-blending process, so that the compatibility of the blends was improved and the bonding force of two polymers increased simultaneously. Accordingly, molding blends with stable and durable hydrophobility were successfully gained based on migration of silicone and entanglement of alkane and PP. It’s noticeable that the water contact angle of the blend remained to be about 106° from 113°, even soaking in acid (pH = 1) and alkali (pH = 14) solutions, deionized water and anhydrous ethanol, or under multiple strong frictions. This will provide a facile and effective strategy to endow general materials with durable antifouling properties directly by injection molding without additional coating.

Exergetic characterization of flat plate solar collector using genetic algorithm

With the rapid advancement of technology, the global demand for energy is increasing exponentially. It is crucial to focus on energy conservation and conversion across all sectors. This study conducted a second law or exergy analysis on a solar water heating system to uncover hidden losses during the process. To optimize exergetic efficiency, a genetic algorithm was employed to control the overall loss coefficient of a solar flat plate collector. Four decision variables were used in the genetic algorithm—heat flux rate, glass cover temperatures at the inlet and exit, and collector plate temperature to minimize the overall loss coefficient. Energy efficiency, entropy generation, and exergy destruction were estimated for various mass flow rates using thermodynamic equilibrium equations. Exergy correlations were developed concerning mass flow rate (0.001–0.009 kg/s), inlet temperature (300 K–360 K), collector plate area (1–10 m²), incident solar energy (400–900 W/m²), optical efficiency (0.4%–1%), and ambient temperature (300–315 K) under a range of initial conditions. It was found that exergy efficiency slightly increased (0.01%–0.08%) over a 12-h period. Although this increase is small, it can significantly contribute to energy conservation over the long term. The genetic algorithm predicted the minimum loss coefficient occurring during peak hours (12–14 p.m.) with respect to four decision variables. The results of this study were compared with existing literature and showed good agreement. In addition to the exergy analysis, an uncertainty error analysis was performed to assess the reliability and accuracy of the experimental measurements and computational predictions. By quantifying these uncertainties, the study ensured that the reported enhancements in exergy efficiency and the optimization results obtained through the genetic algorithm are robust and credible. This rigorous error analysis adds confidence to the findings, highlighting the potential for improved energy conservation in solar water heating systems.

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

Investigation of the impact of micro-nano pore geometry on CH4 adsorption

CH4 is a clean fossil fuel that is widely used in the gas supply of homes and industry, and its energy value is increasingly important. Coal pores provide certain conditions for the storage of CH4. In order to reveal the microscopic adsorption mechanism of CH4 in different pores under high pressure, based on the experimental results, a cross-shaped pore model was proposed, and then molecular simulation was used to construct three kinds of cross-shaped, round and slit pore models for simulation experiment verification. The changes of adsorption parameters, such as adsorption amount, adsorption heat, adsorption energy, adsorption potential and adsorption entropy, were analyzed during the adsorption process. Combined with adsorption and condensation theory, the influence of different geometric pores on the adsorption of CH4 was further verified. The results show that round pore have more adsorption sites, larger adsorption quantity and stronger adsorption capacity, followed by cross pore, while slit pore have the weakest adsorption capacity and least adsorption sites. Under high pressure susaturated adsorption, the interaction force between molecules is stronger, the affinity is higher, the molecules are arranged in order, and the adsorption system is more stable, and the adsorption capacity of slit pore > cross pore > round pore is displayed. This study further verifies the mechanism of the effect of pore geometry on CH4 adsorption, which provides a strong support and theoretical basis for CBM mining engineering.

Mood regulation in euthymic patients with a history of antidepressant-induced mania.

The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture of mood regulation in euthymic patients with and without a history of AIM. Eighty-four euthymic participants were included. Participants rated their mood, anxiety and energy levels daily using an electronic (e-) visual analog scale, for a mean (SD) of 280.8(151.4) days. We analyzed their multivariate time series by computing each variable's auto-correlation, inter-variable cross-correlation, and composite multiscale entropy of mood, anxiety, and energy. Then, we compared the data features of participants with a history of AIM and those without AIM, using analysis of covariance, controlling for age, sex, and current treatment. Based on 18,103 daily observations, participants with AIM showed significantly stronger day-to-day auto-correlation and cross-correlation for mood, anxiety, and energy than those without AIM. The highest cross-correlation in participants with AIM was between mood and energy within the same day (median (IQR), 0.58 (0.27)). The strongest negative cross-correlation in participants with AIM was between mood and anxiety series within the same day (median (IQR), -0.52 (0.34)). Patients with a history of AIM have a different underlying mood architecture compared to those without AIM. Their mood, anxiety and energy stay the same from day-to-day; and their anxiety is negatively correlated with their mood.

The feature extraction method based on quadratic wavelet packet energy entropy and t-SNE for bearing fault diagnosis

The feature extraction method based on quadratic wavelet packet energy entropy and t-SNE for bearing fault diagnosis

Translocation of Gaussian polymers across a nanometric cylindrical channel

We present an analytical model to study translocation of Gaussian polymers across a cylindrical channel of nanometric size with a chemical potential inside the channel. Results show that polymer conformational entropy generates an entropic M-like free energy barrier for translocation. The presence of a small negative chemical potential reduces the entropic free energy barrier rendering the translocation time to follow a power law τ = AN ν as function of polymer size N. Power law exponents ν found here in varying the channel radius R, run from 1.525 to 1.999 for unforced translocation, and from 1.594 to 2.006 for translocation with small chemical potentials when R = 1 nm. Presence of large negative chemical potentials generate a free energy well rendering the translocation time to follow an exponential growth τ = Ae α N . We show existence of a negative chemical potential μ c that minimizes the translocation time due to an interplay of conformational entropy and channel-polymer interactions. The translocation time as function of channel length L grows exponentially as τ = Ae cL , it runs from milliseconds up to decades in the range of lengths found in biological channels. Interestingly, small negative chemical potentials approaching μ c overcome the effect of large channel lengths reducing the translocation time below seconds. Translocation speeds <v(N) > show a maximum of micrometers per second then it decays with polymer size and channel length, the characteristic decay <v(N) > ∼ N −1 has been observed in previous experiments of voltage-driven DNA translocation.

Recent advances in applications of animal biowaste-based activated carbon as biosorbents of water pollutants: a mini-review.

Advances in green engineering and technology have revealed a number of environmentally acceptable alternatives for water purification. In line with this, recent advances in biosorption of pollutants from aqueous solutions using animal biowaste-based activated carbon (AC) are reported herein. Apart from the fish scale-derived AC which is extensively documented, animal bones, among the rest others, have been studied most widely, followed by hair and feathers. Out of the various target water pollutants, removal of heavy metals has been mostly studied. Majority of the reports showed the Freundlich isotherm and pseudo second order as the best fit. Few investigations on the thermodynamics of the adsorption studies and reports on the Gibbs free energy change (ΔG°), enthalpy change (ΔH°), and entropy change (ΔS°) have also been discussed in this report. It has been concluded that while plant-based AC has gained wide interest, the same is not true for the animal-based counterpart albeit the latter's potential for high sorption efficiency as seen in the present report.

Information-theoretic quantities as effective descriptors of electrophilicity and nucleophilicity in density functional theory.

Electrophilicity and nucleophilicity are two vastly important chemical concepts gauging the capability of atoms in molecules to accept and donate the maximal number of electrons. In our earlier studies, we proposed to simultaneously quantify them using the Kullback-Leibler divergence from the information-theoretic approach in density functional theory. However, several issues with this scheme remain to be clarified such as its general validity, predictability, and relationship with other information-theoretic quantities. In this work, we revisit the matter with bigger datasets and deeper theoretical insights. Five information-theoretic quantities including Kullback-Leibler divergence, Hirshfeld charge, Ghost-Berkowitz-Parr entropy, and second and third orders of relative Onicescu information energy are found to be reliable and robust descriptors of electrophilicity and nucleophilicity propensities. Employing these five descriptors, we design a list of new compounds and predict their electrophilicity and nucleophilicity scales. This work should markedly improve our confidence and capability in applying information-theoretic quantities to evaluate electrophilicity and nucleophilicity propensities and henceforth pave the route for more applications of these quantities from information-theoretic approach in density functional theory in the future. All structures were fully optimized at the M06-2X/6-311 + G(d) level of DFT functional using the Gaussian 16 package (version C01) with integration grids and tight self-consistent-field convergence. The solvent effect was taken into account by using the implicit solvent model (CPCM) in the CH2Cl2 solvent, and all 3D contour surfaces of Fukui function, local temperature, and ITA (information-theoretic approach) quantities were generated by GaussView. The Multiwfn 3.8 program was used to calculate the ITA indexes and atomic charges.

Synthesis and characterization of a nanocatalyst consisting of tungsten (VI) oxide and ruthenium for potential use in the hydrogen generation via hydrolysis of methylamine-borane

The demand for effective and safe chemical storage systems as an alternative energy carrier is unabated. This study reports the analysis of methylamine-borane (MeAB) as a new, efficient, and widely applicable technological storage material for mobile applications. Here, we report the preparation of tungsten (VI) oxide (WO3) supported ruthenium (Ru) nanoparticles (Ru/WO3) by impregnation-reduction method and their use as a nanocatalyst in the hydrolysis of MeAB for hydrogen (H2) production. Ru/WO3 nanocatalyst was characterized by a combination of advanced analytical tools such as XRD, XPS, TEM, TEM-EDX, SEM, and SEM-EDX. Ru/WO3 nanocatalyst, with an average particle size of 2.16 ± 0.23 nm obtained from TEM analysis, produces H2 at 298 K with an initial turnover frequency (TOFinitial) value of 43.57 min−1 in complete conversion in the hydrolysis of MeAB. In addition, the activation energy (Ea#), activation enthalpy (ΔH#) and activation entropy (ΔS#) values of the Ru/WO3 nanocatalyst were calculated as in MeAB hydrolysis are 51.54 kJ/mol, 3.7 kJ/mol and 184.57 J/mol × K, respectively, from the Arrhenius and Eyring-Polanyi equations. It was concluded that both the calculated TOFinitial value and activation parameters were quite valuable compared to previous catalysts used for MeAB hydrolysis.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Online Vibration Detection in High-Speed Robotic Milling Process Based on Wavelet Energy Entropy of Acoustic Emission

Online Vibration Detection in High-Speed Robotic Milling Process Based on Wavelet Energy Entropy of Acoustic Emission

Entropy and Negative Specific Heat of Doped Graphene: Topological Phase Transitions and Nernst's Theorem Revisited.

This study explores the thermodynamic properties of doped graphene using an adapted electronic spectrum. We employed the one-electron tight-binding model to describe the hexagonal lattice structure. The dispersion relation for graphene is expressed in terms of the hopping energies using a compositional parameter that characterizes the different dopant atoms in the lattice. The focus of the investigation is on the impact of the compositions, specifically the presence of dopant atoms, on the energy spectrum, entropy, temperature, and specific heat of graphene. The numerical and analytical results reveal distinct thermodynamic behaviors influenced by the dopant composition, including topological transitions, inflection points in entropy, and specific heat divergences. In addition, the use of Boltzmann entropy and the revision of Nernst's theorem for doped graphene are introduced as novel aspects.

Determination of Drying Characteristics and Physicochemical Properties of Mint (Mentha spicata L.) Leaves Dried in Refractance Window.

Drying is one of the most common and effective techniques for preserving the quantitative and qualitative characteristics of medicinal plants in the post-harvest phase. Therefore, in this research, the effect of the new refractance window (RW) technology on the kinetics, thermodynamics, greenhouse gasses, color indices, bioactive properties, and percentage of mint leaf essential oil was investigated in five different water temperatures in the form of a completely randomized design. This process was modeled by the methods of mathematical models and artificial neural networks (ANNs) with inputs (drying time and water temperature) and an output (moisture ratio). The results showed that with the increase in temperature, the rate of moisture removal from the samples increased and as a result, the drying time, specific energy consumption, CO2, NOx, enthalpy, and entropy decreased significantly (p < 0.05). In addition, the drying water temperature had a significant effect on the rehydration ratio, color indices, bioactive properties, and essential oil percentage of the samples (p < 0.05). The highest value of rehydration ratio was obtained at 80 °C. By increasing temperature, the main color indices such as b*, a*, L*, and Chroma decreased significantly compared to the control (p < 0.05). However, with the increase in temperature, the overall color changes (ΔE) and L* first had a decreasing trend and then an increasing trend, and this trend was the opposite for the rest of the indicators. The application of drying water temperature from 50 to 70 °C increased antioxidant, phenol content, and flavonoid content, and higher drying temperatures led to a significant decrease in these parameters (p < 0.05). On the other hand, the efficiency of the essential oil of the samples was in the range of 0.82 to 2.01%, and the highest value was obtained at the water temperature of 80 °C. Based on the analysis performed on the modeled data, a perceptron artificial neural network with 2-15-14-1 structure with explanation coefficient (0.9999) and mean square error (8.77 × 10-7) performs better than the mathematical methods for predicting the moisture ratio of mint leaves.

Numerical study on the behavior of a polymeric MHD nanofluid: entropy optimization and thermal analysis

PurposeAnalyzing and reducing entropy generation is useful for enhancing the thermodynamic performance of engineering systems. This study aims to explore how polymers and nanoparticles in the presence of Lorentz forces influence the fluid behavior and heat transfer characteristics to lessen energy loss and entropy generation.Design/methodology/approachThe dispersion model is initially used to examine the behavior of polymer additives over a magnetized surface. The governing system of partial differential equations (PDEs) is subsequently reduced through the utilization of similarity transformation techniques. Entropy analysis is primarily performed through the implementation of numerical computations on a non-Newtonian polymeric FENE-P model.FindingsThe numerical simulations conducted in the presence of Lorentz forces provide significant insights into the consequences of adding polymers to the base fluid. The findings suggest that such an approach minimizes entropy in the flow region. Through the utilization of polymer-MHD (magnetohydrodynamic) interactions, it is feasible to reduce energy loss and improve the efficiency of the system.Originality/valueThis study’s primary motivation and novelty lie in examining the significance of polymer additives as agents that reduce entropy generation on a magnetic surface. The author looks at how nanofluids affect the development of entropy and the loss of irreversibility. To do this, the author uses the Lorentz force, the Soret effect and the Dufour effect to minimize entropy. The findings contribute to fluid mechanics and thermodynamics by providing valuable insights for engineering systems to increase energy efficiency and conserve resources.

Mechanical State Fault Diagnosis Method of High Voltage Circuit Breaker Based on ICEEMDAN-Hilbert Marginal Spectral Energy Entropy

Mechanical State Fault Diagnosis Method of High Voltage Circuit Breaker Based on ICEEMDAN-Hilbert Marginal Spectral Energy Entropy

On interpretation of fluctuations of conserved charges at high T

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Novel combustion instability diagnosis method in a hydrogen/natural gas co-firing gas turbine combustor using a combination of four criteria: Temporal kurtosis, permutation entropy, energy of entropy, and zero-crossing rate

In this study, we developed a diagnosis method for combustion instability and validated its performance using experimental data obtained from a hydrogen/natural gas co-firing gas turbine combustor. Four criteria, namely temporal kurtosis, permutation entropy, energy entropy, and zero-crossing rate, were considered. First, the performance of each criterion was compared with that of the conventional method for determining combustion instability using the root mean square (RMS) of dynamic pressure. Temporal kurtosis the best performance in terms of detection speed (instability onset detected 0.71 s earlier than the RMS method). Permutation entropy yielded the best performance in terms of accuracy and precision with accuracy, recall, precision, and F1-score values of 96.6%, 100%, 92.6%, and 96.2%, respectively. The performances of combinations of these criteria were investigated using AND and OR gates. Twenty-two cases were considered and the combination of permutation entropy and zero-crossing rate (best case) achieved detection time, accuracy, precision, recall, and F1-score values of 8.49 s, 97.2%, 93.9%, 100%, and 96.8%, respectively. Other combinations exhibited competitive performance; however, the method combining two criteria was selected to simplify the computing process. The proposed method is expected to contribute to the safe and stable operation of hydrogen/natural gas co-firing gas turbines.