Ideal Entropy Research Articles

Engineered nanomaterials reduce metal(loid) accumulation and enhance staple food production for sustainable agriculture.

Metal(loid) contaminants in food pose a global health concern. This study offers a global analysis of the impact of nanomaterials (NMs) on crop responses to metal(loid) stresses. Our findings reveal that NMs have a positive effect on the biomass production of staple crops (22.8%), while showing inhibitory effects on metal(loid) accumulation in plants (-38.3%) and oxidative damage (-21.6%) under metal(loid) stress conditions. These effects are influenced by various factors such as NM dose, exposure duration, size and composition. Here we introduce a method using interval-valued intuitionistic fuzzy values by integrating the technique for order preference by similarity to an ideal solution and entropy weights to compare the effectiveness of different NM application patterns. These results offer practical insights for the application of NMs in similar multi-criteria decision-making scenarios, contributing to sustainable agriculture and global food safety.

Unveiling the thermodynamic landscape of liquid Ti–Al–Ni alloys through first-principles simulations

We conducted ab initio molecular dynamics simulations to systematically examine the composition-dependent thermodynamic properties and atomic-scale interactions in liquid Ti–Al–Ni alloys throughout the entire ternary phase space at 2033 K. The calculated enthalpies of mixing demonstrated exothermic tendencies, with a distinct minimum in the composition of Ti0.0Al0.50Ni0.50, indicating significant Al–Ni attractive interactions. Incorporating ternary interaction parameters into the Redlich-Kister-Muggianu equation enabled accurate modeling of the complex variations in mixing enthalpy. Analysis of partial pair correlation functions and structure factors revealed chemical and topological short-range ordering (SRO), as well as medium-range ordering, within the liquid alloy. Quantifying deviations from ideal configurational entropy clarifies the coupling between chemical SRO and topological SRO, significantly impacting the overall Gibbs energy of mixing. This comprehensive atomistic study provides insights into the thermodynamics of Ti–Al–Ni alloys, paving the way for tailoring their properties for high-performance applications.

A Spin–Orbit Torque Knob to Tailor Stochasticity for Physically Secured Applications

AbstractThe deterministic switching of spin–orbit torque (SOT) memory has long been the primary challenge for implementation. Conversely, stochastic switching has recently sparked significant interest for physically secured applications. This study investigates the effects of an external field (Hx) applied to break the SOT symmetry for deterministic switching in a Pt/Co bilayer with perpendicular anisotropy. This external field serves as a tuning mechanism to adjust the stochasticity at the intermediate state during SOT switching. It is observed that moderate Hx could lead to a substantial variation in electrical output during the repeating write/read operations. In contrast, a large Hx enables precise control over the intermediate states of the Pt/Co bilayer, effectively functioning as a memristor. The stochasticity derives from small Hx resulting from the dynamics of dendritic domain wall propagation during SOT switching. Herein, the oblique domain spiking from the initially reversed domain is notably influenced by the transverse component of SOT. Increasing Hx suppresses this oblique dendritic domain growth, allowing for controllable domain propagation and reducing stochasticity. By utilizing Hx as an external knob to tailor the stochasticity at the intermediate state, the high stochastic state can be employed as an ideal entropy source for an SOT‐based random number generator.

Entropy-based guidance of deep neural networks for accelerated convergence and improved performance

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.

Entropy approximations for simple fluids.

Liquid state entropy formulas based on configurational probability distributions are examined for Lennard-Jones fluids across a range temperatures and densities. These formulas are based on expansions of the entropy in a series of n-body distribution functions. We focus on two special cases. One, which we term the "perfect gas" series, starts with the entropy of an ideal gas; the other, which we term the "dense liquid" series, removes a many-body contribution from the ideal gas entropy and reallocates it among the subsequent n-body terms. We show that the perfect gas series is most accurate at low density, while the dense liquid series is most accurate at high density. We propose empirical interpolation methods that are capable of connecting the two series and giving consistent predictions in most situations.

The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function

In a digital fringe projection structured light system, the nonlinear phase error is generated by the gamma effect of both the projector, camera, and other electronic devices. One of the existing nonlinear correction methods is active correction by projecting ideal fringes as far as possible, and the other is passive compensation after capturing aberrant fringes. The former has higher accuracy but needs to capture a large number of fringe patterns, while the latter does not need many fringe patterns, but is not only greatly affected by random noise and out-of-focus effects, but also has poor accuracy. In this paper, an optimal algorithm for eliminating nonlinear error based on global statistical phase feature function (GSPF) is proposed. The phase distribution can be estimated from the difference between the global cumulative distribution function (CDF) and the normalized (CDF). For an ideal fringe pattern without nonlinear error and a fringe pattern with nonlinear error, the region wrapped by the x-axis normalized CDF is much smaller than the region wrapped by the x-axis global CDF, and the larger the nonlinear error is, the larger the difference between the two is. Therefore, the GSPF can be used for nonlinear error correction. Then the optimal nonlinear error correction is performed based on the minimum difference between the compensated phase entropy and the ideal phase entropy. The method does not require too many steps of phase-shifting, and only three fringe patterns are needed to realize accurate and robust correction. Experimental results show that the method is fast, highly accurate and robust. Using this technique, high accuracy measurements can be achieved with the traditional three-step phase-shifting algorithm.

An entropic theory of homogeneous ice nucleation in non-ionic aqueous solutions.

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ∼TSliquid-Ssolid and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors.

Optimizing the Entropy of the Unsteady Flow of Ternary Nanofluids in an Inclined Conduit: Smart Pumping Using Electro‐Osmotic Flow

The purpose of this study is to investigate the flow of liquid (specifically water) supported by three different types of nanoparticles (copper, silver, and aluminum oxide) in a slanted microcorrugated pipe with varying diameters and wave displacements. This model fulfills several key uses in the fields of environmental and water treatment, such as improving fluid mixing within inclined microchannels to boost the effectiveness of filtration and separation processes and designing inclined channels with ripples to better mix waste materials and more effectively separate different components. In this perception, we analyzed a model for the fluids flow inside the microchannel with the electromagnetic field (EMF) effects and pressure variation in the conduit under the external influence of thermal radiation and heat source which did not appear in the last published literature. The analytical techniques with assistance of mathematical software were used to solve the main governing equations such as Poisson, momentum, and energy equations and then deduce the heat transfer rate at the peristaltic conduit surfaces and the system’s ideal entropy. The results of the model simulation suggested that a number of factors could have a big influence on how thermal systems are built. It was claimed that the irreversibility resulting from friction and Joule heating, as opposed to thermal irreversibility, controls the system’s entropy buildup more tightly. Furthermore, the alteration of the conduit’s form and geometry resulted in a substantial enhancement in the heat transfer rate at its bottom wall, ranging from 20% to 600%. Despite numerous studies, an accurate model regarding the mechanism of the liquid’s flow supported by tri‐nanoparticles in an inclined microcorrugated conduit enclosing variable diameters and wave shifts is far from being understood.

Composition-based phase stability model for multicomponent metal alloys

The vastness of the space of possible multicomponent metal alloys is hoped to provide improved structural materials but also challenges traditional, low-throughput materials design efforts. Computational screening could narrow this search space if models for materials stability and desired properties exist that are sufficiently inexpensive and accurate to efficiently guide experiments. Toward this effort, here we develop a method to rapidly assess the thermodynamic stability of a metal alloy composition of an arbitrary number of elements, stoichiometry, and temperature based on density functional theory (DFT) data. In our model, the Gibbs free energy of the solid solution contains binary enthalpy contributions and ideal configurational entropy, whereas only enthalpy is considered for intermetallic competing phases. Compared to a past model for predicting the formation of single-phase high-entropy alloys [M. C. Troparevsky et al., Phys. Rev. X 5, 011041 (2015)], our method is similarly inexpensive, since it assesses enthalpies based on existing DFT data, but less heuristic, more broadly applicable, and more accurate (70%–75%) compared to experiment.

Multi-objective optimization of micro-gas turbine coupled with LCPV/T combined cooling, heating and power (CCHP) system based on following electric load strategy

A distributed energy system integrated with solar and natural gas can achieve cascade energy utilization, high efficiency and energy-saving, and significantly reduce carbon emissions, which is receiving increasing attention. Therefore, this paper establishes an optimization model for primary energy consumption saving rate (PECSR), annual total cost saving rate (ATCSR), carbon dioxide emission reduction rate (CDERR) and exergy efficiency of micro-gas turbine coupled with low-concentrating photovoltaic/thermal (LCPV/T) CCHP system, and analyzes the influence of the parameters, such as solar irradiation, LCPV/T inlet temperature, LCPV/T inlet flow, LCPV/T area, low-temperature water source heat pump capacity, high-temperature water source heat pump capacity, the heat transfer efficiency of waste heat recovery boiler (WHRB) and gas boiler capacity, on the objective functions. The analysis results indicate that when the heat exchange efficiency of WHRB reaches 0.71, the CCHP system is most economical. Based on this, the Strength Pareto Evolutionary Algorithm-II (SPEA-II) optimization algorithm and decision-making method combining Technique for order preference by similarity to an ideal solution (TOPSIS) and weight entropy method are employed to solve the model comprehensively, and acquire the optimum solution. After multi-objective optimization and selection, the system exergy efficiency remains around 25 %. Compared with the separation production system, the PECSR and CDERR of the CCHP system are improved by more than 24 % and 41 %, respectively.

Why Thermodynamic Entropy and Statistical Entropy are Two Different Physical Quantities

Background: Historically, the coincidence of classical thermodynamic entropy and statistical entropy is based on an assumption (the so-called Boltzmann-Planck relation). The study shows that the Boltzmann-Planck relation is not valid in general. Aims: The study demonstrates that thermodynamic entropy is a physical entity distinct from statistical entropy. Methods: Initially, the entropy of ideal gases is examined and demonstrated to be independent of the gas volume. The ideal gas's true entropy is then expressed correctly. Results: The Boltzmann-Planck entropy equation is demonstrated to be incapable of describing the thermodynamic entropy of ideal gases. Conclusion: In general, the Boltzmann-Planck entropy cannot be considered a statistical interpretation of the thermodynamic entropy. The two entropies represent two different physical quantities. The physical quantity that enters the second law of thermodynamics is the thermodynamic entropy, not the statistical entropy. 6: The study advocates for a fundamental rethinking of today's statistical understanding of thermodynamics.

Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure

Urbanization has aggravated the conflict between continuously increasing urban travel demands and limited supply. Moreover, the inability to expand urban roads due to previous land planning and utilization has resulted in significant traffic congestion, traffic safety issues, and environmental problems. To address these problems, this work attempted to develop a multiobjective model to optimize the passenger traffic system while considering carbon emissions, transport costs, and resource utilization. In addition, the ideal point method and entropy weight method were combined to obtain the optimal solution. Based on the operational data on traffic modes and travel data on passengers in Harbin, the northern capital of China, the proposed method was used to solve the case in Harbin. The results show that the proportion of buses increased by 1.05%, that of subways increased by 36.60%, that of taxis decreased by 11.86%, and that of private cars decreased by 25.78% after optimization. Furthermore, the analyses of the results show that the optimized passenger traffic system structure can promote the sustainable development of urban transport and demonstrate the practicality of the proposed method for solving multiobjective optimization problems. Relative to the ideal point method and genetic algorithm, the proposed method is more applicable for optimizing the passenger traffic structure in Harbin. In addition, this study explored the sensitivity of the optimization goals to the four motorized modes. The results show that subways and private cars are the key areas to prioritize in adjusting the urban passenger traffic system structure. Based on the analysis results, recommendations for the development of transportation in Harbin are given. This study provides a reference for decision-makers to formulate policies for the urban sustainable development of Harbin as well as for transportation development in other cities.

Ultrafast Random Number Generation Based on Random Laser

True random numbers with high security originate from non-deterministic physical processes. Random laser with random feedback arising from multiple elastic scattering in a highly disordered structure is an ideal physical entropy source theoretically. In this work, a secure optical ultrafast random number generator based on random laser spectra was demonstrated. Unlike the usual method of recording laser intensity over time, the random laser spectrum under each pump pulse was recorded and converted into random numbers through post-processing. Approaches in three different dimensions—space, spectrum and time were used to increase the rate of random number generation. The randomness of resulting random bit streams was verified by the NIST statistics tests. Experimental results showed that an instantaneous bit rate of 810 terabits per second with off-line post-processing could be theoretically reached with an ideal experimental condition. This novel technique has big advantages in secure communications and promises to further increase the speed of random number generators to the lever of petabits per second or even higher.

Prediction of phases in high entropy alloys using machine learning

Predicting the phases of high entropy alloys (HEAs) is a complex task as it depends on a large number of thermal, physical and geometrical parameters. The phases consist of intermetallic (IM) and amorphous (AM) forms. As, the trial-and-error approach is exhaustive and time-consuming process for both experiments and numerical simulations. Standard statistical models such as XGboost, random forest, adaboost, decision tree, logistic regression, support vector machine (SVM) and k-nearest neighbors (KNN) were adopted. The XGboost algorithm yields the better accuracy (90 %). Machine learning methods well predicted the phases and also identified the main significant variables such as mean atomic radius, atomic size difference, mixing enthalpy, ideal mixing entropy and valence electron concentration, which are in consistence with experimental results. As a whole, it is recommended that the widespread use of machine learning approaches could accelerate HEA process with tailored configurations. The use of multiple models in ensemble learning reduces the risk of overfitting, increases the accuracy of predictions, and enhances the generalizability of models.

Entropy Scaling of Molecular Dynamics in a Prototypical Anisotropic Model near the Glass Transition.

Dynamics and thermodynamics of molecular systems in the vicinity of the boundary between thermodynamically nonequilibrium glassy and metastable supercooled liquid states are still incompletely explored and their theoretical and simulation models are imperfect despite many previous efforts. Among them, the role of total system entropy, configurational entropy, and excess entropy in the temperature-pressure or temperature-density dependence of global molecular dynamics (MD) timescale relevant to the glass transition is an essential topic intensively studied for over half of a century. By exploiting a well-known simple ellipsoidal model recently successfully applied to simulate the supercooled liquid state and the glass transition, we gain a new insight into the different views on the relationship between entropy and relaxation dynamics of glass formers, showing the molecular grounds for the entropy scaling of global MD timescale. Our simulations in the anisotropic model of supercooled liquid, which involves only translational and rotational degrees of freedom, give evidence that the total system entropy is sufficient to scale global MD timescale. It complies with the scaling effect on relaxation dynamics exerted by the configurational entropy defined as the total entropy diminished by vibrational contributions, which was earlier discovered for measurement data collected near the glass transition. Moreover, we argue that such a scaling behavior is not possible to achieve by using the excess entropy that is in excess of the ideal gas entropy, which is contrary to the results earlier suggested within the framework of simple isotropic models of supercooled liquids. Thus, our findings also warn against an excessive reliance on isotropic models in theoretical interpretations of molecular phenomena, despite their simplicity and popularity, because they may reflect improperly various physicochemical properties of glass formers.

Vulnerability assessment of the eco-geo-environment of mining cities in arid and semi-arid areas: A case study from Zhungeer, China

Assessing the ecological and geological environment vulnerability (EGEV) of cities arid and semi-arid regions is important for urban planning, development, and ecological protection. This study investigated the ecological and geological environmental problems and possible potential risk factors in Zhungeer City and constructed an EGEV assessment index system covering the geological environment, human activities, coal mining, and hydrological conditions. We proposed a linear weighted model to calculate the comprehensive weight of each indicator using the Analytical Hierarchy Process (AHP) and Entropy method based on distance function optimization (AELW), and frequency ratio accuracy is then introduced to compare the remarkable classification performance between AELW and other models. The frequency ratio accuracy results show that AELW model has the highest accuracy for EGEV assessment (62.77 %), followed by Multiplicative Integrated Normalization Model (MINM, 61.46 %), AHP (60.98 %), Ideal Point Model (IPM, 60.30 %) and Entropy (53.55 %). The EGEV of Zhungeer was classified as I-V from lowest into highest, among which the EGEV of I-II-III accounted for 78.15 %, EGEV of IV accounted for 16.42 %, and the EGEV of V risk area accounted for 5.43 %, with a good overall ecological and geological environment quality. These results can provide tangible management suggestions for reducing EGEV in Zhungeer city, and the AELW model provides direction for EGEV assessment in other similar areas.

The performance of major airports in the Europe, North America and Asia

Purpose This study aims to provide a meaningful comparison of airports’ performance and better understand the differences observed in the analysed airport performance by presenting a model to analyse the relationship between operational and financial performance and airport characteristics.Design/methodology/approach This study uses a quantitative analysis approach. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy weight were utilised to analyse 17 airports in three Airports Council International regions: Asia, Europe and North America. Through operational and financial factors, these sample airports identified the most efficiently operated airports from 2016 to 2019.Findings Overall, Asian airports were superior in operational and financial efficiency. Unlike operating performance, the sample airport’s financial and total performance results show a similar trend. There were no noticeable changes in operational factors. Therefore, differences in financial variables for each airport may affect the total performance.Practical implications This study provides insightful implications for airport policymakers to establish a standardised information disclosure foundation for consistent analysis and encourage airports to provide this information.Originality/value The adoption of Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) to debt ratio and EBITDA per passenger, which had previously been underutilised in the previous study as financial factors, demonstrated differences between airports for airport stakeholders. In addition, the study presented a model that facilitates producing more intuitive results using TOPSIS, which was relatively underutilised compared to other methodologies such as date envelopment analysis.

In situ reaction synthesis, microstructure and thermomechanical properties of novel medium-entropy (Ti,V,Nb,Ta)2AlC ceramics

High- and medium-entropy (HE and ME) materials have attracted considerable interest in recent years due to the advantages of designable components and tunable structures with modifiable physicochemical performances. Herein, a dense equiatomic quaternary (Ti,V,Nb,Ta)2AlC solid solution, or denoted as a medium-entropy MAX phase, was fabricated by in-situ reaction in hot-pressing sintering process using easily available elemental powders as raw materials. X-ray diffraction with its Rietveld refinement analysis, scanning electron microscope and high-resolution scanning transmission electron microscope equipped with energy-dispersed spectrometer analysis identify the phase composition and crystal structure of the novel ME-M2AlC (Ti,V,Nb,Ta)2AlC phase. Especially, the atomic-resolution HAADF images directly prove the mutual solution of four metallic elements of the ME (Ti,V,Nb,Ta)2AlC phase. Both the flexural strength and Vickers hardness are enhanced compared with the reported individual components, and the strengthening and hardening results largely benefit from solid-solution effect with lattice distortion in structure. The fracture toughness has not been enhanced due to the lack of effective toughening mechanism. Furthermore, the electrical conductivity of ME (Ti,V,Nb,Ta)2AlC ceramic is distinctly decreased as a result of the increasing carrier lattice scattering and point defect scattering, which is caused by severe lattice distortion. Similarly, the scattering effect of solid solutions is also responsible for the lower thermal conductivity. It is worth noting that size disorder parameter (δsize) is a more effective criterion to evaluate the reduced thermal conductivity than the ideal mixing configurational entropy itself. As a result, applying the medium- and high-entropy design to the MAX phases with chemical and structural diversity can provide a large compositional space with corresponding large range of unexpected performances, which is highly anticipated.

Hardware-Intrinsic Physical Unclonable Functions by Harnessing Nonlinear Conductance Variation in Oxide Semiconductor-Based Diode

With the advancement of the Internet of Things (IoT), numerous electronic devices are connected to each other and exchange a vast amount of data via the Internet. As the number of connected devices increases, security concerns have become more significant. As one of the potential solutions for security issues, hardware intrinsic physical unclonable functions (PUFs) are emerging semiconductor devices that exploit inherent randomness generated during the manufacturing process. The unclonable security key generated from PUF can address the inherent limitations of conventional electronic systems which depend solely on software. Although numerous PUFs based on the emerging memory devices requiring switching operations have been proposed, achieving hardware intrinsic PUF with low power consumption remains a key challenge. Here, we demonstrate that the process-induced nonlinear conductance variations of oxide semiconductor-based Schottky diodes provide a suitable source of entropy for the implementation of PUF without switching operation. Using a mild oxygen plasma treatment, the surface electron accumulation layer that forms naturally in oxide semiconductor film can be partially eliminated, resulting in a large variation of nonlinearity as an exotic entropy source. The mild plasma-treated Schottky diodes showed near ideal 50% average uniformity and uniqueness, as well as an ideal entropy value without the need for additional hardware area and power costs. These findings will pave the way for the development of hardware intrinsic PUFs to realize energy-efficient cryptographic hardware.

Enhancing Firm Value through the Lens of ESG Materiality: Evidence from the Banking Sector in OECD Countries

Momentous developments in the regulatory environment, increasing investor demand, and growing awareness of climate change and societal issues are leading banks to adopt a comprehensive approach beyond the traditional financial lens. Assessing performance towards sustainability issues, including environmental, social, and corporate governance (ESG), and its’ relevance in firm value in the banking sector offers a field of continuous interest for researchers. This paper investigates the role of ESG materiality in firm value, based on a sample of banks operating in OECD countries, for the period 2016–2020. Adopting the materiality classification for the banking sector provided by the Sustainability Accounting Standards Board (SASB), the study consists of a multi-layer methodology. In the first stage, a dynamic technique for order preference by similarity to ideal solution (TOPSIS) and entropy methods are utilized to calculate ESG score based on ESG materiality for 1115 bank-year observations while in the second stage, value relevance analyses are applied in order to reveal whether ESG materiality affects firm value. The results depict that ESG performance based on ESG materiality has a positive influence on the firm value for both models, price-to-book value ratio (PBV), and Tobin’s Q (TQ). Moreover, collected from the Refinitiv database, ESG combined has a low impact on PBR whereas there is no significant effect on TQ. The implication is that the firm value is influenced by the materiality-adjusted ESG performance than by the extended ESG spectrum.