Diffusion Process Research Articles

A highly polarizable concentrated dipole glass for ultrahigh energy storage

Relaxor ferroelectrics are highly desired for pulse-power dielectric capacitors, however it has become a bottleneck that substantial enhancements of energy density generally sacrifice energy efficiency under superhigh fields. Here, we demonstrate a novel concept of highly polarizable concentrated dipole glass in delicately-designed high-entropy (Bi1/3Ba1/3Na1/3)(Fe2/9Ti5/9Nb2/9)O3 ceramic achieved via substitution of multiple heterovalent ferroelectric-active principal cation species on equivalent lattice sites. The atomic-scaled polar heterogeneity of dipoles with different polar vectors between adjacent unit cells enables diffuse reorientation process but disables appreciable growth with electric fields. These unique features cause superior recoverable energy density of ~15.9 J cm−3 and efficiency of ~93.3% in bulk ceramics. We also extend the highly polarizable concentrated dipole glass to the prototype multilayer ceramic capacitor, which exhibits record-breaking recoverable energy density of ~26.3 J cm−3 and efficiency of ~92.4% with excellent temperature and cycle stability. This research presents a distinctive approach for designing high-performance energy-storage dielectric capacitors.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

Biopharmaceutical studies of a novel sedative sublingual lozenge based on glycine and tryptophan: A rationale for mucoadhesive agent selection

Effective sedative drugs are in great demand due to increasing incidence of nervous disorders. The present work was aimed to develop a novel sublingual sedative drug based on glycine and L-tryptophan amino acids. Carbopol and different hydroxypropyl methylcellulose species were alternatively tested as mucoadhesive agents intended to prolong tryptophan sublingual release time. A model lipid medium of fully hydrated L-α-dimyristoylphosphatidylcholine was used for optimal mucoadhesive agents selection. Simultaneous processes of drug release and diffusion in lipid medium were first investigated involving both experimental and theoretical approaches. Individual substances, their selected combinations as well as different drug formulations were consecutively examined. Application of kinetic differential scanning calorimetry method allowed us to reveal a number of specific drug-excipient effects. Lactose was found to essentially facilitate tryptophan release and provide its ability to get into the bloodstream simultaneously with glycine, which is necessary to achieve glycine-tryptophan synergism. Introduction of a mucoadhesive agent into the formulation was shown to change kinetics of drug-membrane interactions variously depending on viscosity grade. Among the mucoadhesive agents, hydroxypropyl methylcellulose species K4M and E4M were shown to further accelerate drug release, therefore they were selected as optimal. Thus, effectiveness of the novel sedative drug was provided by including some excipients, such as lactose and the selected mucoadhesive agent species. A dynamic mathematical model was developed properly describing release and diffusion in lipid medium of various drug substances. Our study clearly showed applicability of a lipid medium to meet challenges such as drug-excipient interactions and optimization of drug formulations.

Honeycomb-like macroporous crosslinked chitosan assisted EDTA-intercalated Ca-Mg-Al layered hydrotalcite composite foams for efficient U(VI) biosorption

The biosorption is considered to be highly efficient for the separation of radionuclide from radioactive wastewater. Herein, the crosslinked chitosan assisted EDTA intercalated Ca-Mg-Al layered double hydroxides composite foam (CS-EDTA-LDH) was synthesized by combining EDTA intercalation and freeze-drying methods. The macroporous and ultralight properties of CS-EDTA-LDH facilitates its rapid adsorption and facile recovery, and the inorganic/organic incorporation can avoid pore collapse and provide numerous adsorption sites, while the EDTA intercalation can enhance the complex capture of U(VI). The CS-EDTA-LDH presents various functional groups (carboxyl, hydroxyl and amino groups) for U(VI) adsorption, and the adsorption capacity for U(VI) reached 272.3 mg/g at pH 5.0 and 298 K. The adsorption kinetics of U(VI) conformed to PSO equation, whereas the isotherms conformed to the Freundlich model, indicating heterogeneous adsorption with diffusion process as a rate-controlling step. The thermodynamic parameters indicate that U(VI) adsorption by CS-EDTA-LDH is endothermic and spontaneous in nature. The adsorption mechanism is related to the synergic complexation by multi-functional groups, ion exchange, and possible isomeric substitution. Overall, CS-EDTA-LDH could be a promising biosorbent for the cleanup of radioactive pollution due to its high performance for U(VI) adsorption and facile recovery.

Classification of adsorbates in scanning tunneling microscopy images of Fe3O4(111) surfaces exposed to water and carbon monoxide

Understanding the structure of catalyst surfaces with adsorbed molecules is key to improving catalyst design. Scanning tunneling microscopy (STM) allows the observation of adsorption states and sites and provides insights into diffusion and desorption processes; however, the presence of multiple types of molecules on the surface presents challenges such as the identification of species and verification of reaction progress, particularly at room temperature or higher. In this study, we develop a protocol for the height classification analysis of STM images using the Watershed algorithm. This method is applied to a system involving the co-adsorption of H2O and CO on the Fe3O4(111) surface, which represents the beginning of the water-gas shift reaction. Water molecules and dissociated OH species were identified in STM images of the Fe3O4(111) surface following the adsorption of water. Furthermore, gradual changes in the types of surface species were observed upon exposure of the surface to CO, indicating reaction progression. Our observations suggest that CO may react with molecular water rather than with dissociated OH on Fe sites. Despite its simplicity, the height classification analysis effectively identifies changes in the adsorbates on the catalyst surface. This method can be extended to other catalyst surfaces with adsorbed gasses.

Epoxy composite microspheres as a versatile platform for enhancement of chlorophyll dispersion and photostability in coatings

Chlorophyll (Chl), as a rich natural pigment, is limited in applications due to poor photostability. The hydrophobic properties of Chl attributed to the tetrapyrrole ring and terminal long-chain hydrocarbons further constrains its dispersion in aqueous coatings. In this work, chlorophyll/epoxy composite microspheres (Chl/EMs) were prepared as candidate pigments via emulsion polymerization to provide a versatile platform for enhanced dispersion and photostability. The optimal reaction temperature of 75 °C for achieving the appropriate particle size distribution of epoxy microspheres (EMs) was first determined. Chl/EMs were then prepared by (1) the combination of Chl and DGEBA in the emulsification process, or by (2) mixing Chl with m-xylylenediamine (MXDA) during curing. The effects of Chl introduction at different steps on the emulsification, curing agent diffusion, and curing process were studied using off-site microscopy observation and aggregation-induced emission technique, to clarify the structure and morphology evolution during emulsion polymerization. The particle size of the microspheres was mainly determined by the emulsification process of the epoxy precursor. Chl participates in emulsification of Chl/EMs in case (1), resulting in a large average particle size and poor particle size distribution. In case (2), the diffusion of MXDA into epoxy emulsion particles is completed within 30 min and does not impede the quicker diffusion process of Chl which was mixed with MXDA. The prepared Chl/EMs with size ranging from 1 to 10 μm create a conducive oxygen blocking environment. Compared to the complete degradation period of untreated Chl coatings, the photostability of Chl/EMs coatings increased nearly 7-fold. Remarkably, Chl/EMs exhibit superior dispersion capability in waterborne polyurethane coatings compared to pure Chl coatings. The EMs with controllable morphology and size can be used as a versatile platform for enhancing dispersion and photostability of other organic or natural dyes and pigments in waterborne coatings.

Uncovering the stochastic dynamics of solitons of the Chaffee–Infante equation

In this paper, we apply stochastic differential equations with the Wiener process to investigate the soliton solutions of the Chaffee–Infante (CI) equation. The CI equation, a fundamental model in mathematical physics, explains concepts such as wave propagation and diffusion processes. Exact soliton solutions are obtained through the application of the modified extended tanh (MET) method. The obtained wave figures in 3D, 2D, and contour are highly localized and determine an individual frequency shift under the behavior of sharp peak, periodic wave, and singular soliton. The MET method shows to be a valuable analytical tool for obtaining soliton solutions, essential for understanding the dynamics of nonlinear wave phenomena. Numerical simulations enable us to explore soliton solutions in two and three dimensions, shedding light on their properties over time. Our results have wide applications in various domains, including stochastic processes and nonlinear dynamics, impacting advancements in physics, engineering, finance, biology, and beyond.

Overlooked drivers of the greenhouse effect: The nutrient-methane nexus mediated by submerged macrophytes

Submerged macrophytes remediation is a commonly used technique for improving water quality and restoring habitat in aquatic ecosystems. However, the drivers of success in the submerged macrophytes assembly process and their specific impacts on methane emissions are poorly understood. Thus, we conducted a mesocosm experiment to test the growth plasticity and carbon fixation of widespread submerged macrophytes (Vallisneria natans) under different nutrient conditions. A refined dynamic chamber method was utilized to concurrently collect and quantify methane emission fluxes arising from ebullition and diffusion processes. Significant correlations were found between methane flux and variations in the physiological activities of V. nantas by the fluorescence imaging system. Our results show that exceeding tolerance thresholds of ammonia in the water significantly interfered with the photosynthetic systems in submerged leaves and the radial oxygen loss in adventitious roots. The recovery process of V. natans accelerated the consumption of dissolved oxygen, leading to increase in the populations of methanogen (153.3 % increase of mcrA genes) and subsequently elevating CH4 emission fluxes (23.7 %) under high nutrient concentrations. Conversely, V. natans increased the available organic carbon under low nutrient conditions by radial oxygen loss, further increasing CH4 emission fluxes (94.7 %). Quantitative genetic and modeling analyses revealed that plant restoration processes drive ecological niche differentiation of methanogenic and methane oxidation microorganisms, affecting methane release fluxes within the restored area. The speciation process of V. natans is incapable of simultaneously meeting improved water purification and reduced methane emissions goals.

Diffusion Models-Based Purification for Common Corruptions on Robust 3D Object Detection.

LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering from gradient confusion during training. Moreover, they can only improve their robustness against specific types of data corruption. In this work, we propose LiDARPure, which leverages the powerful generation ability of diffusion models to purify corruption in the LiDAR scene data. By dividing the entire scene into voxels to facilitate the processes of diffusion and reverse diffusion, LiDARPure overcomes challenges induced from adversarial training, such as sparse point clouds in large-scale LiDAR data and gradient confusion. In addition, we utilize the latent geometric features of a scene as a condition to assist the generation of diffusion models. Detailed experiments show that LiDARPure can effectively purify 19 common types of LiDAR data corruption. Further evaluation results demonstrate that it can improve the average precision of 3D object detectors to an extent of 20% in the face of data corruption, much higher than existing defence strategies.

Fast and efficient chromium(VI) extraction by colloidal Mg/Al layered double hydroxide nanoparticles

Due to the constraints associated with diffusion and mixing time, traditional kinetic and thermodynamic approaches were inadequate for probing the true mechanism of interaction between chromate and Layered Double Hydroxide (LDH). To circumvent these limitations, colloidal suspensions of Mg/Al-NO3 LDH, characterized by a positively charged surface (approximately +50 mV) in ultrapure water and a mean average diameter of 140 nm, allowing the formation of stable suspensions for days, were swiftly mixed with Cr(VI) suspensions at both pH = 4 and 9 using a stopped flow technique. This rapid mixing, accomplished in <5 milliseconds, enabled the examination of the initial stages of interaction between the toxic anion and the host compound. Two distinct steps in the adsorption process were identified: a very fast step (completed in <5 ms), representing up to 80% of the measured variation, and a slower step lasting up to 100 s. The fast step assumed to be driven by electrostatic interaction (ζ ∼ +50 mV) with the surface, and sites close to the surface are easily accessible to the chromate anions. The slower step corresponded to a diffusion process close or inside the particles. Chromate extraction efficiency was investigated through ultrafiltration tests, varying the LDH and chromate amounts, indicating that 2 nitrate ions are exchanged for 1 chromate, regardless of the pH considered, and a total exchange can be fulfilled with 0.1 g L−1 of LDH within the explored concentration range.

ScDiffusion: conditional generation of high-quality single-cell data using diffusion model.

Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, diffusion models have shown their power in generating data with high fidelity, providing a new opportunity for scRNA-seq generation. In this study, we developed scDiffusion, a generative model combining the diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion could generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research. scDiffusion is openly available at the GitHub repository https://github.com/EperLuo/scDiffusion or Zenodo https://zenodo.org/doi/10.5281/zenodo.13268742.

Microstructure evolution and degradation mechanisms of amorphous FeCrAlMoSiY coated Zr in 1200 °C steam

Herein, structural evolution and degradation mechanisms of the amorphous FeCrAlMoSiY coating during oxidation up to 16 h in 1200 °C steam was studied, using scanning electron microscopy, transmission electron microscopy, x-ray diffractometer, and Raman spectroscopy. The results revealed that no significant oxidation of the Zr substrate occurred within 12 h, which was attributed to the retarded atomic diffusion processes. These processes were achieved through the Al outward diffusion and formation of α-Al2O3 scale, along with the formation of an in-situ Zr2Si-rich diffusion barrier at the coating-substrate interface and the Y2O3 particles segregated at grain boundaries within the coating. Degradation of the coating was primarily due to the destruction of the α-Al2O3 scale by cracks caused by internal oxidation and voids accumulation. These results have implications for understanding the degradation mechanisms and achieving long-term stable protection of the coatings for Zr-based nuclear fuel claddings.

MADMFuse: A multi-attribute diffusion model to fuse infrared and visible images

In the field of deep learning vision, infrared and visible image fusion (IVIF) has received significant attention due to its ability to enhance scene comprehension by combining the complementary features of both types of images. Existing methods based on generative models are either unstable in training, such as generative adversarial network, or only have a single denoising object, such as diffusion models, resulting in poor fused results that cannot fully contain multi-modal features. To solve these problems, we propose a multi-attribute diffusion model to fuse infrared and visible images, termed MADMFuse. Specifically, to preserve the salient information in infrared images and the texture details in visible images, we have designed a forward diffusion process with shared noise for multiple independent attributes, ensuring that the denoising network learns complementary features of different attributes simultaneously during training. Subsequently, our reverse process with multi-attribute as conditions employs the denoising network that iteratively reparameterizes noise, gradually adjusting and achieving fine image fusion. Furthermore, to address the issue of feature degradation resulting from solely focusing on a single denoising object, we derive a multiple diffusion object loss function based on pixel-level fusion target, the fidelity and luminance term in this loss function aim to guide the fused results towards visual similarity with the source image while preserving its brightness. Extensive experiments indicate that MADMFuse is more effective than other state-of-the-art image fusion methods.

Diffusion distribution model for damage mitigation in scanning transmission electron microscopy.

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modelled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage isminimised.

Nonlinear least squares estimator for generalized diffusion processes with reflecting barriers

In this paper, we investigate the parameter estimation problem for generalized diffusion processes with two-sided reflected barriers. The estimator is obtained using the nonlinear least squares method based on discretely observed processes. Under certain regularity conditions, we obtain consistency and establish the asymptotic normality of the proposed estimator. Our method can be readily applied to the one-sided reflected diffusion processes. Numerical results, including a two-factor financial model, show that the proposed estimator performs well with large sample sizes. The U.S. treasury rate data is used to illustrate the theoretical results.

Enhanced De/hydrogenation Kinetics and Cycle Stability of Mg/MgH2 by the MnOx-Coated Ti2CTx Catalyst with Optimized Ti-H Bond Stability.

MXene based catalysts can significantly enhance hydrogenation and dehydrogenation (de/hydrogenation) kinetics of Mg/MgH2, but they suffer from uncontrollable catalysts-hydrogen bond strength and structural instability. Here, we propose Tx density control of MXene-based catalysts and MnOx coating as a promising solution. The MnOx@Ti2CTx-catalyzed Mg/MgH2 can release 5.97 wt % H2 at 300 °C in 3 min and 5.60 wt % H2 at 240 °C in 15 min with an activation energy of 75.57 kJ·mol-1. In addition, the samples showed excellent de/hydrogenation-cycle stability, and the degradation of hydrogen storage capacity is negligible even after 100 cycles. DFT calculations combined with XPS analysis showed that the Tx defect on the surface of the MnOx@Ti2CTx catalyst could optimize the strength of the Ti-H bond, accelerating both hydrogen dissociation and diffusion processes. The catalyst's surface properties were protected by the MnOx coating, achieving high chemical and catalytic stability. These findings offer a strategy for surface structure optimization and protection of MXene-based catalysts, realizing controllable catalyst-hydrogen bond strength.

Novel approach to estimate the water isotope diffusion length in deep ice cores with an application to Marine Isotope Stage 19 in the Dome C ice core

Abstract. Accurate estimates of water isotope diffusion lengths are crucial when reconstructing and interpreting water isotope records from ice cores. This is especially true in the deepest, oldest sections of deep ice cores, where thermally enhanced diffusive processes have acted over millennia on extremely thinned ice. Previous statistical estimation methods, used with great success in shallower, younger ice cores, falter when applied to these deep sections, as they fail to account for the statistics of the climate on millennial timescales. Here, we present a new method to estimate the diffusion length from water isotope data and apply it to the Marine Isotope Stage 19 (MIS 19) interglacial at the bottom of the EPICA Dome C (EDC, Dome Concordia) ice core. In contrast to the conventional estimator, our method uses other interglacial periods taken from further up in the ice core to estimate the structure of the variability before diffusion. Through use of a Bayesian framework, we are able to constrain our fit while propagating the uncertainty in our assumptions. We estimate a diffusion length of 31±5 cm for the MIS 19 period, which is significantly smaller than previously estimated (40–60 cm). Similar results were obtained for each interglacial used to represent the undiffused climate signal, demonstrating the robustness of our estimate. Our result suggests better preservation of the climate signal at the bottom of EDC and likely other deep ice cores, offering greater potentially recoverable temporal resolution and improved reconstructions through deconvolution.

High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged < 15 and > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

Analysis of Structural Changes of pH-Thermo-Responsive Nanoparticles in Polymeric Hydrogels.

The pH- and thermo-responsive behavior of polymeric hydrogels MC-co-MA have been studied in detail using dynamic light scattering DLS, scanning electron microscopy SEM, nuclear magnetic resonance (1H NMR) and rheology to evaluate the conformational changes, swelling-shrinkage, stability, the ability to flow and the diffusion process of nanoparticles at several temperatures. Furthermore, polymeric systems functionalized with acrylic acid MC and acrylamide MA were subjected to a titration process with a calcium chloride CaCl2 solution to analyze its effect on the average particle diameter Dz, polymer structure and the intra- and intermolecular interactions in order to provide a responsive polymer network that can be used as a possible nanocarrier for drug delivery with several benefits. The results confirmed that the structural changes in the sensitive hydrogels are highly dependent on the corresponding critical solution temperature CST of the carboxylic (-COOH) and amide (-CONH2) functional groups and the influence of calcium ions Ca2+ on the formation or breaking of hydrogen bonds, as well as the decrease in electrostatic repulsions generated between the polymer chains contributing to a particle agglomeration phenomenon. The temperature leads to a re-arrangement of the polymer chains, affecting the viscoelastic properties of the hydrogels. In addition, the diffusion coefficients D of nanoparticles were evaluated, showing a closeness among with the morphology, shape, size and temperature, resulting in slower diffusions for larger particles size and, conversely, the diffusion in the medium increasing as the polymer size is reduced. Therefore, the hydrogels exhibited a remarkable response to pH and temperature variations in the environment. During this research, the functionality and behavior of the polymeric nanoparticles were observed under different analysis conditions, which revealed notable structural changes and further demonstrated the nanoparticles promising high potential for drug delivery applications. Hence, these results have sparked significant interest in various scientific, industrial and technological fields.

Dry spot propagation during boiling crisis on thin metal heaters

This work presents the results of an experimental investigation aimed at elucidating the role of heater-side parameters on the growth of an irreversible dry spot during the boiling crisis of FC-72 on thin metal foils. A two-sided funnel shape of the heater is adopted to induce the occurrence of the boiling crisis in the middle of the foil and capture the expansion of the critical dry patch through high-speed infrared thermometry. In agreement with pre-existing literature, critical heat flux values achieved during this experimental campaign are shown to increase with bulk subcooling, thermal effusivity, and thickness of the heater. The transient temperature field at the boiling surface is used to capture the spatiotemporal evolution of the dry spot and investigate the effect of heater’s parameter on dry spot propagation. Results collected in this work show that the growth of an irreversible dry patch is strongly affected by the heat diffusion process occurring within the thin heater. The dry spot propagation rate is found to be proportionally correlated with the thermal diffusivity of the heater and the volumetric heat dissipation rate achieved at the boiling crisis, the latter being inversely proportional to heater’s thickness. These results provide important insights into the dryout dynamics related to the boiling crisis in thin wall heat transfer devices. Nevertheless, they open questions about the role of fluid thermophysical properties, that still deserve to be studied in detail.