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The most well-known example of an ordered quantum state—superconductivity—is caused by the formation and condensation of pairs of electrons. Fundamentally, what distinguishes a superconducting state from a normal state is a spontaneously broken symmetry corresponding to the long-range coherence of pairs of electrons, leading to zero resistivity and diamagnetism. Here we report a set of experimental observations in hole-doped Ba1−xKxFe2As2. Our specific-heat measurements indicate the formation of fermionic bound states when the temperature is lowered from the normal state. However, when the doping level is x ≈ 0.8, instead of the characteristic onset of diamagnetic screening and zero resistance expected below the superconducting phase transition, we observe the opposite effect: the generation of self-induced magnetic fields in the resistive state, measured by spontaneous Nernst effect and muon spin rotation experiments. This combined evidence indicates the existence of a bosonic metal state in which Cooper pairs of electrons lack coherence, but the system spontaneously breaks time-reversal symmetry. The observations are consistent with the theory of a state with fermionic quadrupling, in which long-range order exists not between Cooper pairs but only between pairs of pairs.A state that breaks time-reversal symmetry is observed in the normal phase above the superconducting critical temperature in a multiband superconductor. This could be explained by correlations between the Cooper pairs formed in different bands.

Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conventional and stacking ensemble techniques. In the stacking framework, a linear regression model is employed as a meta-learner to improve base model performance. Additionally, two nature-inspired metaheuristic optimization algorithms—Particle Swarm Optimization and Grey Wolf Optimization—were used to fine-tune the hyperparameters of machine learning models. This research is based on a comprehensive dataset of 1,269 experimental nanofluid samples, with key inputs including nanofluid type (hybrid and direct), temperature, and volume concentration. To improve model generalization, data augmentation strategies inspired by polynomial/Fourier expansions and autoencoder-based methods were implemented. The results demonstrate that the stacked multi-layer perceptron model, integrated with linear regression, achieved the highest predictive accuracy, recording an R² score of 0.99927, a mean squared error of 466.06, and a root mean squared error of 21.58. Among standalone machine learning models, CatBoost was the best performer (R² score: 0.99923, MSE: 487.71, RMSE: 22.08), ranking second overall. The impact of metaheuristic optimization was significant; Grey Wolf Optimization, for instance, reduced the LightGBM model’s mean squared error from 29386.43 to 6549.006. These findings underscore the efficacy of hybrid ML/DL frameworks, advanced data augmentation, and metaheuristic optimization in predictive modeling of nanofluid thermophysical properties, providing a robust foundation for future research in heat transfer applications.

Molten salts are used as heat transfer fluids and for short-term heat energy storage in solar power plants. Experiments show that the specific heat capacity of the base salt may be significantly enhanced by adding small amounts of certain nanoparticles. This effect, which is technically interesting and economically important, is not yet understood. This paper presents a critical discussion of the existing attendant experimental literature and the phenomenological models put forward thus far. A common assumption, the existence of nanolayers surrounding the nanoparticles, which are thought to be the source of, in some cases, the large increase of a nanofluid’s specific heat capacity is criticized and a different model is proposed. The model assumes that the influence of the nanoparticles in the surrounding liquid is of long range. The attendant long-range interfacial layers may interact with each other upon increase of nanoparticle concentration. This can explain the specific heat maximum observed by different groups, for which no other theoretical explanation appears to exist.

The transient plane source (TPS) technique, also referred as the Hot Disk method, has been widely used due to its ability to measure the thermal properties of an extensive range of materials (solids, liquids, and powder). Recently, it has been recognized that typical Hot Disk sensors can influence TPS results of thermally insulating materials and lead to an overestimation of thermal conductivity. Although improvements have been proposed, they have not yet been implemented in the commercial TPS, leaving researchers with non-standardized modifications or options provided by a commercial Hot Disk apparatus. An empirical study of thermally insulating materials such as extruded polystyrene (XPS) and aerogel blanket is conducted in order to address the factors that affect the reliability of thermal conductivity k obtained using the commercial TPS apparatus. Sensor size, input power, duration of the measurements, applied pressure, and, in the case of anisotropic materials, heat capacity are investigated, and the results are compared with those using a Heat Flow Meter apparatus. The effect of sensor size on the k value is ascribed to heat loss through connecting leads and is more pronounced in smaller sensors and in materials with lower k values . In the case of XPS and aerogel, the effect becomes minimal for sensors with a radius r  ≥ 6.4 mm. The low input power yields a high scattering of the results and should be avoided. Applied contact pressure and the tested region of the specimen play an important role in experiments with low-density fibrous materials due to the large percentage of heat being transferred by radiation and the heterogeneous nature of the samples, respectively. Additionally, the sensitivity of anisotropic measurements to the value of the material’s volumetric heat capacity ( ρC p ) is shown, emphasizing the need for the precise determination.

Thermal energy storage (TES) is extremely important in concentrated solar power (CSP) plants since it represents the main difference and advantage of CSP plants with respect to other renewable energy sources such as wind, photovoltaic, etc. CSP represents a low-carbon emission renewable source of energy, and TES allows CSP plants to have energy availability and dispatchability using available industrial technologies. Molten salts are used in CSP plants as a TES material because of their high operational temperature and stability of up to 500°C. Their main drawbacks are their relative poor thermal properties and energy storage density. A simple cost-effective way to improve thermal properties of fluids is to dope them with nanoparticles, thus obtaining the so-called salt-based nanofluids. In this work, solar salt used in CSP plants (60% NaNO3 + 40% KNO3) was doped with silica nanoparticles at different solid mass concentrations (from 0.5% to 2%). Specific heat was measured by means of differential scanning calorimetry (DSC). A maximum increase of 25.03% was found at an optimal concentration of 1 wt.% of nanoparticles. The size distribution of nanoparticle clusters present in the salt at each concentration was evaluated by means of scanning electron microscopy (SEM) and image processing, as well as by means of dynamic light scattering (DLS). The cluster size and the specific surface available depended on the solid content, and a relationship between the specific heat increment and the available particle surface area was obtained. It was proved that the mechanism involved in the specific heat increment is based on a surface phenomenon. Stability of samples was tested for several thermal cycles and thermogravimetric analysis at high temperature was carried out, the samples being stable.PACS65.: Thermal properties of condensed matter; 65.20.-w: Thermal properties of liquids; 65.20.Jk: Studies of thermodynamic properties of specific liquids

PurposeAgricultural production is facing multiple challenges from global warming. Nitrogen (N) plays an essential role in high-temperature tolerance and crop yield, which may depend on leaf cooling capacity. However, the effects of nitrogen on the leaf temperature response and the regulatory mechanisms of leaf cooling capacity under increased environmental temperature are largely unknown.MethodsCurrently, we evaluated the effects of nitrogen on the dynamic change in leaf temperature and the balance between heat dissipation and absorption, which contributes to leaf cooling capacity.ResultsThe results showed that cucumber plant growth and transpiration rate significantly increased with N supply, while leaf temperature decreased. As the environmental temperature increased, the cucumber leaf temperature and leaf cooling capacity increased, and the leaf cooling capacity improved with the N supply. Leaf temperature was negatively correlated with transpiration rate, and the increased transpiration rate contributed to higher heat dissipation and leaf cooling capacity under a high nitrogen supply. Leaf water content and water potential increased with N supply, which resulted in higher leaf specific heat capacity and heat absorption. However, nitrogen-deficient plants increased the nonstructural carbon content (NSC), structural carbon content (SC), and NSC/SC ratio, thus increasing plant adaptation to environmental stresses.ConclusionsNitrogen supply improved leaf cooling capacity by increasing the leaf transpiration rate and specific heat capacity, thereby regulating the balance between heat dissipation and absorption. Improving leaf cooling capacity by N supply may be a new strategy to increase plant adaptation to increased environmental temperatures.

The current stage of the World is characterized by a very intense anthropogenic activity with the aim of providing energy for whole of humanity. The pollutants emitted as a result of anthropogenic activity accumulate in the atmosphere and some of them have a greenhouse effect. Greenhouse gases lead to an increase in atmospheric temperature and as a result we have negative consequences on flora and fauna with frequent natural cataclysms (floods, drought, torrential rains, strong winds, spontaneous natural wildfires, etc.). The main purpose of the work is the systematization of the very recent current literature with the elaboration of the qualitative and quantitative description of the atmospheric state influenced by pollutants. It is found the following moment from this generalization that the increase in the concentration of pollutants of the atmosphere leads to the increase of the Albedo values of the unique complex Earth-Atmosphere system. The proportionality coefficient between the Albedo and concentration values contains the time interval required for combustion as well as the power of the solar flux and the specific heat of combustion of the fuels. The following fact can be found by the quantitative expression that when the fuels with a high specific heat of combustion are used, then the level of pollutants in the atmosphere is as minimal as possible with minimal negative effects on the flora and fauna.

This study presents a new two-step method to synthesize molten salt-based nanofluids by replacing water with butanol and using an Emax high-energy mill to ensure good stability and homogeneity. Commercial HITEC molten salt was selected as the base fluid, and alumina nanoparticles (nominal size of 5,1 nm) were used as an additive in three different proportions: 0,5, 1,0, and 1,5 wt.%. The specific heat capacity was evaluated through two different methods: differential scanning calorimetry (DSC) and modulated differential scanning calorimetry (MDSC). According to the evaluation by MDSC, an increment of up to 4,27% in the specific heat capacity was achieved with 1,0 wt.% of alumina nanoparticles in comparison with the raw salt, without affecting the melting point and thermal stability of the salt. This behavior may be related to the good distribution of the nanoparticles in the salt. However, no significant improvement in the specific heat capacity of the nanofluid was observed when the standard DSC method was applied. This behavior may be due to the different sensitivities of the two methods to small changes in the sample, with MDSC being the more sensitive technique, as it establishes the contribution of the two phases that make up the nanofluid: the molten salt as the base fluid and the solid nanoparticles. Similarly, the heating rate used in each of the techniques can influence the sensitivity with regard to determining changes in nanofluids.

Spontaneous combustion of coal seams can produce a high temperature of about 800 ℃, which greatly changes the thermal conductivity of the overlying loess layer. The thermal conductivity of loess plays an important role in ecological restoration design and the calculation of roadbed and slope stability. In this study, loess in northern Shaanxi, China was taken as the research object to measure the mass-loss rate and heat conduction parameters of loess specimens after high temperature. The test results show that, between 23 and 900 °C, with temperature increasing, the mass-loss rate is reduced. And the heat conduction coefficient ( λ ), specific heat capacity ( c ), and thermal diffusion coefficient ( α ) decreased by 48.9%, 23.1%, and 35.6%. This is due to the air thermal resistance effect caused by the increase of pores and cracks in loess specimens after high temperature.

Injection moulding of liquid silicone rubber (LSR) requires reliable computer-aided engineering simulations to support process optimisation, which in turn depend on accurate material data. In this study, thermo-physical and kinetic properties of a highly filled injection moulding (IM) grade of LSR were systematically characterised using complementary experimental approaches, and their impact on simulation fidelity was critically assessed. Specific heat capacity was measured using both modulated DSC and the standard sapphire method, revealing temperature dependence but no intrinsic change during curing, with sapphire-based data incorporating enthalpic effects more realistically for process prediction. Thermal conductivity was found to be nearly constant across the processing temperature range. Curing kinetics were investigated by calorimetry and rheology, with the former supporting an autocatalytic mechanism and the latter suggesting an nth-order model, reflecting differences in detection sensitivity and onset characterisation. When implemented into injection moulding simulations, viscosity primarily affected injection pressures, while differences in specific heat capacity and curing kinetics strongly influenced predicted curing profiles and cycle times. These results emphasise that dataset choice, particularly for curing-related parameters, is critical to achieving predictive accuracy in LSR injection moulding simulations. Unlike previous studies on LSR injection moulding, which typically adapt thermoplastic-inspired characterisation methods without systematically addressing their limitations, this work introduces an organised and comparative methodology to evaluate how different material characterisation techniques influence simulation outcomes. The proposed approach establishes a methodological framework that can guide future research and improve the reliability of process simulations for LSR and other polymeric systems.