Water is the most abundant liquid on earth and also the substance with the largest number of anomalies in its properties. It is a prerequisite for life and as such a most important subject of current research in chemical physics and physical chemistry. In spite of its simplicity as a liquid, it has an enormously rich phase diagram where different types of ices, amorphous phases, and anomalies disclose a path that points to unique thermodynamics of its supercooled liquid state that still hides many unraveled secrets. In this review we describe the behavior of water in the regime from ambient conditions to the deeply supercooled region. The review describes simulations and experiments on this anomalous liquid. Several scenarios have been proposed to explain the anomalous properties that become strongly enhanced in the supercooled region. Among those, the second critical-point scenario has been investigated extensively, and at present most experimental evidence point to this scenario. Starting from very low temperatures, a coexistence line between a high-density amorphous phase and a low-density amorphous phase would continue in a coexistence line between a high-density and a low-density liquid phase terminating in a liquid–liquid critical point, LLCP. On approaching this LLCP from the one-phase region, a crossover in thermodynamics and dynamics can be found. This is discussed based on a picture of a temperature-dependent balance between a high-density liquid and a low-density liquid favored by, respectively, entropy and enthalpy, leading to a consistent picture of the thermodynamics of bulk water. Ice nucleation is also discussed, since this is what severely impedes experimental investigation of the vicinity of the proposed LLCP. Experimental investigation of stretched water, i.e., water at negative pressure, gives access to a different regime of the complex water diagram. Different ways to inhibit crystallization through confinement and aqueous solutions are discussed through results from experiments and simulations using the most sophisticated and advanced techniques. These findings represent tiles of a global picture that still needs to be completed. Some of the possible experimental lines of research that are essential to complete this picture are explored.
Summary: Joule heating technology, as an ultrafast, efficient, and scalable synthesis strategy, provides a novel approach for the preparation of high-performance electrocatalysts toward various energy conversion systems, such as hydrogen production, metal-air battery, fuel cell, and so on. This review summarizes recent progress in ultrafast synthesis strategy (especially Joule heating technology) for the precise construction of highly active electrocatalysts. First, the principle of Joule heating technology has been discussed. The fundamental electrocatalytic mechanisms, such as hydrogen evolution reaction (HER), oxygen evolution reaction (OER), overall water splitting, nitrate reduction reaction (NO3RR), oxygen reduction reaction (ORR), and carbon dioxide reduction reaction (CO2RR), are also comprehensively highlighted. The recent advances of electrocatalysts prepared by ultrafast Joule heating technology have been generalized. Furthermore, this review also addresses the inherent limitations of the Joule heating approach and outlines prospects and challenges, aiming to lay a foundation for breakthroughs and applications of Joule heating in electrocatalysis.
Abstract During July–August 2022, Pakistan (PKT) experienced catastrophic flooding while the Yangtze River Basin (YRB) endured unprecedented heatwaves. While previous studies have examined the physical teleconnections, there remains a critical gap in quantifying the role of anthropogenic forcing in shaping such trans‐regional concurrent extremes. Here, we bridge this gap by combining probabilistic and storyline attribution frameworks to assess both historical and future risks of 2022‐like events. We find that the 2022 event represents a warming‐amplified analogue of the 2010 event, driven by a westward extension of the Western Pacific Subtropical High (WPSH) and an eastward shift of the South Asian High (SAH). Moisture and heat budget diagnosis reveal that dynamically horizontal moisture transport dominated the 2022 PKT precipitation, while surface cloud‐radiative forcing drove the YRB heatwave. Using complex network analysis, we uncover intensified cross‐regional linkages under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios. Crucially, our bivariate probabilistic attribution indicates that anthropogenic forcing accounts for nearly 100% of the likelihood of the 2022 event. Projections show that, by 2071–2100, the probability of such events could rise by 57–326 times, relative to a baseline probability of 0.0015 in historical simulations. Further, storyline attribution demonstrates that anthropogenic thermodynamics and circulation dynamics contributed approximately 60% and 40% to the 2022 event, with nearly half of the dynamic effect attributable to anthropogenic forcing. These results offer a quantitative perspective on the rising risk of concurrent Pluvial Pakistan–Hot Yangtze events under climate change, offering valuable insights for regional climate resilience and adaptation planning.
Building on initial work on the Thermodynamic Split Conjecture (TSC), which posits that black hole and cosmological horizon thermodynamics are generically inequivalent, we examine the consequences of that split for the Gibbons Hawking temperature and its role across cosmology. We consider many key results in both early and late universe cosmology and show that many important results such as those governing eternal inflation, vacuum tunneling, quantum breaking and primordial black holes can change. The analysis further reveals that small, TSC motivated corrections to horizon thermodynamics can subtly modify Friedmann dynamics, potentially helping to address the $H_0$ and $S_8$ tensions. The work thus provides a unified route from quantum gravity motivated thermodynamics to observational cosmology and motivates dedicated tests of the thermal laws governing the Universe itself.
We re-derive an exact analytic three-parameter expressions for the non-rotating metric, describing a Taub-NUT-like black hole (BH), and its associated bumblebee field that are solutions to the Einstein-bumblebee gravity. We construct a consistence thermodynamics for the Taub-NUT-like BH and determine its thermodynamic topological class. The Lorentz symmetry breaking affects the mass and temperature of the BH but does not affect its thermodynamic topological classification.
We study the thermodynamics of black holes in the framework of non-commutative geometry, where spacetime fuzziness is modelled by smeared Lorentzian distributions. Corrected black hole solutions with this quantum fuzziness are obtained, and their thermodynamic analysis is performed. We show that the conventional first law of black hole thermodynamics is violated since the entropy deviates from the Bekenstein-Hawking form. Introducing a correction to the mass restores consistency, yielding a modified first law compatible with Bekenstein-Hawking entropy. Next, we investigate the effects of spacetime non-commutativity on the thermodynamic universality of these black holes. We demonstrate that non-commutativity modifies the standard universality relations of black holes and can induce thermodynamic stability by altering the underlying microscopic interactions. Our results suggest that quantum features of spacetime can have significant macroscopic consequences for black hole thermodynamics.
Alex Ayedun Avwunuketa, Michael Quaicoe, Francis Ayomide Adedeji
Much research has been undertaken on the sustainable aviation fuels as the alternative to jet A-1 fuels due to magnitude demands of having carbon-free alternative fuel in the aviation industry. This study gives information on the combustion dynamics and emission performance of synthetic fuel blends of a small PTD 500 turbofan engine using a kinetically shaped reactivity model that is dependent on chemical kinetics and thermodynamics. Several blends of fuels containing different volumes of SAF content were created and tested on their conformance with ASTM D7566 and ASTM D4054 requirements. The experimental test of the engine measured thrust, fuel consumption and emissions over a range of operating conditions, reactivity model included activation energy and combustion kinetics to measure the dependence of fuel mass flow on exhaust gas forming Combustion behaviours was correlated to thrust output and CO and emissions by use of α 1 reactivity coefficient. In terms of the Take-off operation, the thrust contribution of engines ranged between 80%-90% but in idle operation, recorded 10%-15.As evident in the results, addition of SAF has insignificant impact on engine thrust gain, with significant impacts on combustion paths and routes related to the incomplete combustion formation. This was proved empirically as there was no difference in the consistency of the fuel blends and these differences in combustion chemistry were related to the change in the composition and calorific value of the hydrocarbons. The present research has established the compatibility of SAF with the existing engine systems and also the need to have a detailed kinetic model to optimize the use of fuel and reduce its environmental effects. The reactivity model is expected to be extended in future to include long-term engine operating conditions under typical SAF applications, which can be used to support the propulsion aviation sector transition to green propulsion.
Luis Manuel Lopez-Jimenez, Esteban Tlelo-Cuautle, Luis Fortino Cisneros-Sinencio
et al.
This paper presents two classic analog oscillators: a relaxation oscillator and a Wien bridge one, where a memristor replaces a resistor. The circuits are simulated in TopSPICE 7.12 using a memristor emulation circuit and commercially available components to evaluate the memristor’s impact. In the case of the relaxation oscillator, which includes the memristor, a notable increase in oscillation frequency was observed compared to the classical circuit, with a nearly 10-fold increase from 790 Hz to 7.78 kHz while maintaining a constant amplitude. This confirms the influence of the memristor’s dynamic resistance on the circuit time constant. On the other hand, the Wien-bridge oscillator exhibits variations in specific parameters, such as peak voltage, amplitude, and frequency. In this case, the oscillation frequency decreased from 405 Hz to 146 Hz with the addition of the memristor, a characteristic introduced by the proposed memristive element’s nonlinear interactions. Experimental results confirm the feasibility of incorporating memristors into classical oscillator circuits, enabling frequency changes while maintaining stable oscillations, allowing reconfigurable and adaptable analog designs that leverage the properties of memristive devices.
Sanghati Saha, Ertan Güdekli, Surajit Chattopadhyay
et al.
A four-parameter generalized entropy has recently been developed based on the generalized cut-off of holographic dark energy (HDE) formalism. It reduces to various known entropies for appropriate parameter limits, as shown in recent studies by Odintsov and others. In the current work, we investigate the evolution of the universe in its early and late phases within the framework of entropic cosmology, where the entropic energy density functions are reconstructed within the framework of the equivalence of holographic dark energy and four-parameter generalized entropy (Sg). Also, we examined different entropic dark energy models in this regard, including the generalized holographic dark energy with Nojiri-Odintsov(NO) cut-off, the Barrow entropic HDE (BHDE), and the Tsallis entropic HDE (THDE) as IR cut-off, all of three particular cases of the most generalized four parameter entropic holographic dark energy. Inspired by the works of Nojiri and others, our current work reports a study on cosmological parameters and thermodynamics with entropy corrections to cosmological horizon entropy as well as black hole entropy with a highly generalized viscous coupled holographic dark fluid along with its particular cases.
Nuclear and particle physics. Atomic energy. Radioactivity
Recent advances in quantum information science have shed light on the intricate dynamics of quantum many-body systems, for which quantum information scrambling is a perfect example. Motivated by considerations of the thermodynamics of quantum information, this perspective aims at synthesizing key findings from several pivotal studies and exploring various aspects of quantum scrambling. We consider quantifiers such as the Out-of-Time-Ordered Correlator (OTOC), the quantum Mutual Information, and the Tripartite Mutual Information (TMI), their connections to thermodynamics, and their role in understanding chaotic versus integrable quantum systems. With a focus on representative examples, we cover a range of topics, including the thermodynamics of quantum information scrambling, and the scrambling dynamics in quantum gravity models such as the Sachdev-Ye-Kitaev (SYK) model. Examining these diverse approaches enables us to highlight the multifaceted nature of quantum information scrambling and its significance in understanding the fundamental aspects of quantum many-body dynamics at the intersection of quantum mechanics and thermodynamics.
Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.
Meteorological factors, specifically wind direction and magnitude, influence the dispersion of atmospheric pollutants due to road traffic by affecting their spatial and temporal distribution. In this study, we are interested in the effect of the evolution of horizontal wind components, i.e., in the plane perpendicular to the altitude axis. A two-dimensional numerical model for solving the coupled traffic flow/pollution problem, whose pollutants are generated by vehicles, is developed. The numerical solution of this model is computed via an algorithm combining the characteristics method for temporal discretization with the finite-element method for spatial discretization. The numerical model is validated through a sensitivity study on the diffusion coefficient of road traffic and its impact on traffic density. The distribution of pollutant concentration, computed based on a source generated by traffic density, is presented for a single direction and different magnitudes of the wind velocity (stationary, Gaussian, linearly increasing and decreasing, sudden change over time), taking into account the stretching and tilting of plumes and patterns. The temporal evolution of pollutant concentration at various relevant locations in the domain is studied for two wind velocities (stationary and sudden change). Three regimes were observed for transport pollution depending on time and velocity: nonlinear growth, saturation, and decrease.
Thermodynamics, Descriptive and experimental mechanics
As an alloying element in steel, manganese can considerably enhance the mechanical properties of structural steel. However, the Mn volatilisation loss in vacuum melting is severe because of the high saturated vapour pressure, resulting in an unstable Mn yield and Mn content fluctuation. Therefore, a systematic study of the volatilisation behaviour of Mn in vacuum melting is required to obtain a suitable Mn control process to achieve precise control of Mn composition, thereby providing a theoretical basis for industrial melting of high-Mn steel. In order to explore the Mn volatilization behavior, the volatilization thermodynamics and volatilisation rate of Mn, as well as the influence factors are discussed in this study. The results shows that Mn is extremely volatilised into the vapour phase under vacuum, the equilibrium partial pressure is closely related to Mn content and temperature. With an increase in the Mn content, a higher C content has a more obvious inhibitory effect on the equilibrium partial pressure of Mn. The maximum theoretical volatilisation rate of Mn shows a linear upward trend with an increase in Mn content. However, a higher C content has a more obvious effect on the reduction of the maximum theoretical volatilisation rate with the increase of Mn content. This study provides an improved understanding of Mn volatilisation behaviour as well as a theoretical foundation for consistent Mn yield control during the vacuum melting process of high-Mn steel.
Kumar Nishant Ranjan Sinha, Vijay Kumar, Nirbhay Kumar
et al.
Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis. A carefully acquired and well-labeled dataset is a primary requirement for utilizing any data-driven learning framework to extract valuable descriptors. Here, we present a comprehensive dataset of boiling acoustics presented in our recent work [1]. We collect the audio files through meticulously controlled near-saturated pool boiling experiments under steady-state conditions. To this end, we connect a high-sensitivity hydrophone to a pre-amplifier and a data acquisition unit for accurate and reliable acquisition of acoustic signals. We organize the audio files into four categories as per the respective boiling regimes: background or natural convection (BKG, 2−5W/cm2), nucleate boiling (NB, 8−140W/cm2), excluding those at higher heat flux values preceding the onset of boiling crisis or the critical heat flux (Pre-CHF, ≈145W/cm2), and transition boiling (TB, uncontrolled). Each audio file label provides explicit information about the heat flux value and the experimental conditions. This dataset, consisting of 2056 files for BKG, 13367 files for NB, 399 files for Pre-CHF, and 460 files for TB, serves as the foundation for training and evaluating a deep learning strategy to predict boiling regimes. The dataset also includes acoustic emission data from transient pool boiling experiments conducted with varying heating strategies, heater surface, and boiling fluid modifications, creating a valuable dataset for developing robust data-driven models to predict boiling regimes. We also provide the associated MATLAB® codes used to process and classify these audio files.
Computer applications to medicine. Medical informatics, Science (General)
This paper first proposes a fractional prospect theory-based method for modeling the bidding strategy of a power retail company in the uniform pricing electricity market under price uncertainty. Different from the traditional methods which assume that the retail company always bids completely rationally to maximize its individual payoffs, this paper introduces the prospect theory (PT) into the bidding model to reflect the impacts of psychological factors and subjective perceptions. To address the partial uncertainties brought by the continuous probability distribution in the value function, this paper modifies the classical PT into the fractional prospect theory (FPT) and builds up the FPT-based bidding strategy model under 1-segment and 3-segment bidding rules. The simulation results show that the proposed method can effectively model the psychological factors in the bidding strategy of a power retail company in the uniform pricing electricity market under price uncertainty, which can help to further study the competition and equilibrium of the uniform pricing market considering the psychological factors of the participants.
Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.