Unlocking the Secrets of Liquid-Liquid Interfaces and Phase Equilibria: Exploring the Interplay of Critical Composition, Interfacial Tension, and Mutual Solubility.

This study reveals pivotal insight into liquid-liquid interfaces by demonstrating that the interface composition mirrors that at the critical point. This revelation leads to the formulation of a novel liquid-liquid distribution law and thermodynamic inequality, establishing a direct connection between mutual solubility values and critical compositions. While particularly accurate for regular solutions, the findings exhibit substantial reliability in nonregular systems, supported by experimental data on binary and ternary mixtures. Importantly, the study illustrates that, with known critical compositions, interfacial tension data alone are sufficient for calculating mutual solubilities, providing a practical alternative for assessing molecular solubility. The paper further showcases the versatility of the simple bottle-testing (cloud-testing) method, effectively serving as both a tensiometer and a mutual-solubility meter. This method utilizes established critical composition data to predict the compositions and interfacial tension of coexisting phases concurrently, offering a cost-effective alternative to complex analytical techniques. As a notable outcome, given critical compositions, basic laboratory equipment such as a beaker and a syringe can equivalently function as both a tensiometer and a mutual solubility detector (e.g., GC). The paper also discusses the application of this method in understanding the liquid-liquid phase behavior in biological systems, exemplified by biomolecular condensates or Lewy bodies.

Evaluation of Interfacial Structure of Self-Assembled Nanoparticle Layers: Use of Standard Deviation between Calculated and Experimental Drop Profiles as a Novel Method.

The self-assembly of nanoparticles (NPs) at interfaces is currently a topic of increasing interest due to numerous applications in food technology, pharmaceuticals, cosmetology, and oil recovery. It is possible to create tunable interfacial structures with desired characteristics using tailored nanoparticles that can be precisely controlled with respect to shape, size, and surface chemistry. To address these functionalities, it is essential to develop techniques to study the properties of the underlying structure. In this work, we propose an experimental approach utilizing the standard deviation of drop profiles calculated by the Laplace equation from experimental drop profiles (STD), as an alternative to the Langmuir trough or precise microscopic methods, to detect the initiation of closely packed conditions and the collapse of the adsorbed layers of CTAB-nanosilica complexes. The experiments consist of dynamic surface/interfacial tension measurements using drop profile analysis tensiometry (PAT) and large-amplitude drop surface area compression/expansion cycles. The results demonstrate significant changes in STD values at the onset of the closely packed state of nanoparticle-surfactant complexes and the monolayer collapse. The STD trend was explained in detail and shown to be a powerful tool for analyzing the adsorption and interfacial structuring of nanoparticles. Different collapse mechanisms were reported for NP monolayers at the liquid/liquid and air/liquid interfaces. We show that the interfacial tension (IFT) is solely dependent on the extent of interfacial coverage by nanoparticles, while the surfactants regulate only the hydrophobicity of the self-assembled complexes. Also, the irreversible adsorption of nanoparticles and the increasing number of adsorbed complexes after the collapse were observed by performing consecutive drop surface compression/expansion cycles. In addition to a qualitative characterization of adsorption layers, the potential of a quantitative calculation of the parameter STD such as the number of adsorbed nanoparticles at the interface and the distance between them at different states of the interfacial layer was discussed.

Reaction of human walking to transient block of vision: analysis in the context of indirect, referent control of motor actions.

Human locomotion may result from monotonic shifts in the referent position, R, of the body in the environment. R is also the spatial threshold at which muscles can be quiescent but are activated depending on the deflection of the current body configuration Q from R. Shifts in R are presumably accomplished with the participation of proprioceptive and visual feedback and responsible for transferring stable body balance (equilibrium) from one place in the environment to another, resulting in rhythmic activity of multiple muscles by a central pattern generator (CPG). We tested predictions of this two-level control scheme. In particular, in response to a transient block of vision during locomotion, the system can temporarily slow shifts in R. As a result, the phase of rhythmical movements of all four limbs will be changed for some time, even though the rhythm and other characteristics of locomotion will be fully restored after perturbation, a phenomenon called long-lasting phase resetting. Another prediction of the control scheme is that the activity of multiple muscles of each leg can be minimized reciprocally at specific phases of the gait cycle both in the presence and absence of vision. Speed of locomotion is related to the rate of shifts in the referent body position in the environment. Results confirmed that human locomotion is likely guided by feedforward shifts in the referent body location, with subsequent changes in the activity of multiple muscles by the CPG. Neural structures responsible for shifts in the referent body configuration causing locomotion are suggested.

Influence of indoor environmental conditions on airborne transmission and lifetime of sneeze droplets in a confined space: a way to reduce COVID-19 spread.

Effects of indoor temperature (T∞) and relative humidity (RH∞) on the airborne transmission of sneeze droplets in a confined space were studied over the T∞ range of 15-30 °C and RH∞ of 22-62%. In addition, a theoretical evaporation model was used to estimate the droplet lifetime based on experimental data. The results showed that the body mass index (BMI) of the participants played an important role in the sneezing jet velocity, while the impact of the BMI and gender of participants was insignificant on the size distribution of droplets. At a critical relative humidity RH∞,crit of 46%, the sneezing jet velocity and droplet lifetime were roughly independent of T∞. At RH∞ < RH∞,crit, the sneezing jet velocity decreased by increasing T∞ from 15 to 30 °C, while its trend was reversed at RH∞ > RH∞,crit. The maximum spreading distance of aerosols increased by decreasing the RH∞ and increasing T∞, while the droplet lifetime increased by decreasing T∞ at RH∞ > RH∞,crit. The mean diameter of aerosolized droplets was less affected by T∞ than the large droplets at RH∞ < RH∞,crit, while the mean diameter and number fraction of aerosols were more influenced by RH∞ than the T∞ in the range of 46% ≤ RH∞ ≤ 62%. In summary, this study suggests suitable indoor environmental conditions by considering the transmission rate and lifetime of respiratory droplets to reduce the spread of COVID-19.

Effect of indoor temperature on the velocity fields and airborne transmission of sneeze droplets: An experimental study and transient CFD modeling.

The spread of the COVID-19 pandemic through the airborne transmission of coronavirus-containing droplets emitted during coughing, sneezing, and speaking has now been well recognized. This study presented the effect of indoor temperature (T∞) on the airflow dynamics, velocity fields, size distribution, and airborne transmission of sneeze droplets in a confined space through experimental investigation and computational fluid dynamic (CFD) modeling. The CFD simulations were performed using the renormalization group k-ε turbulence model. The experimental shadowgraph imaging and CFD simulations showed the time evolution of sneeze droplet concentrations into the turbulent expanded puff, droplet cloud, and fully-dispersed droplets. Also, the predicted mean velocity of droplets was compared with the obtained experimental data to assess the accuracy of the results. In addition, the validated computational model was used to study the sneeze complex airflow behavior and airborne transmission of small, medium, and large respiratory droplets in confined spaces at different temperatures. The warm room showed more than ∼14 % increase in airborne aerosols than the room with a mild temperature. The study provides information on the effect of room temperature on the evaporation of respiratory droplets during sneezing. The findings of this fundamental study may be used in developing exposure guidelines by controlling the temperature level in indoor environments to reduce the exposure risk of COVID-19.

A simple one-dimensional map-based model of spiking neurons with wide ranges of firing rates and complexities.

This paper introduces a simple 1-dimensional map-based model of spiking neurons. During the past decades, dynamical models of neurons have been used to investigate the biology of human nervous systems. The models simulate experimental records of neurons' voltages using difference or differential equations. Difference neuronal models have some advantages besides the differential ones. They are usually simpler, and considering the cost of needed computations, they are more efficient. In this paper, a simple 1-dimensional map-based model of spiking neurons is introduced. Sample entropy is applied to analyze the complexity of the model's dynamics. The model can generate a wide range of time series with different firing rates and different levels of complexities. Besides, using some tools like bifurcation diagrams and cobwebs, the introduced model is analyzed.

Development of a Computational Fluid Dynamics Compositional Wellbore Simulator for Modeling of Asphaltene Deposition.

The asphaltene problem is a two-step process: (1) asphaltene precipitation, as predicted by the thermodynamic model, and (2) asphaltene deposition, the amount of which is estimated by the kinetic model. Asphaltene precipitation is a prerequisite but not a sufficient condition for deposition. Deposition is dependent on other factors such as surface properties, phase behavior, rheology, and flow patterns. As a result, in addition to understanding thermodynamic and kinetic models, it is critical to also understand flow models. In fact, multiphase flow modeling is at the core of simulation, and it must be coupled with thermodynamic and kinetic models. Numerous studies on modeling asphaltene deposition on pipe walls have been performed theoretically and experimentally, but a comprehensive theory to properly understand this phenomenon has not yet been presented. In thermodynamic modeling, the perturbed chain statistical associating fluid theory (PC-SAFT) equation of state is used to predict the asphaltene phase behavior. In this study, we show that the proposed PC-SAFT model is more accurate than the solid model used in commercial software. Unlike prior research that neglected flow patterns or used empirical relations to model multiphase flow, this study simulates multiphase flow using separate momentum equations for each phase. Among the existing kinetic models, the Kurup model has been used to predict the asphaltene deposition profile in the wellbore due to its greater compatibility for computational fluid dynamics application. The results of the proposed model show good agreement with field case data of asphaltene deposition thicknesses along the wellbore tubing.

Collective behavior in a two-layer neuronal network with time-varying chemical connections that are controlled by a Petri net.

In this paper, we propose and study a two-layer network composed of a Petri net in the first layer and a ring of coupled Hindmarsh-Rose neurons in the second layer. Petri nets are appropriate platforms not only for describing sequential processes but also for modeling information circulation in complex systems. Networks of neurons, on the other hand, are commonly used to study synchronization and other forms of collective behavior. Thus, merging both frameworks into a single model promises fascinating new insights into neuronal collective behavior that is subject to changes in network connectivity. In our case, the Petri net in the first layer manages the existence of excitatory and inhibitory links among the neurons in the second layer, thereby making the chemical connections time-varying. We focus on the emergence of different types of collective behavior in the model, such as synchronization, chimeras, and solitary states, by considering different inhibitory and excitatory tokens in the Petri net. We find that the existence of only inhibitory or excitatory tokens disturbs the synchronization of electrically coupled neurons and leads toward chimera and solitary states.

Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy.

In the current research, an analytical method was proposed for rapid quantitative determination of saturates, aromatics, resins and asphaltenes (SARA) fractions of crude oil samples. Rapid assessments of SARA analysis of crude oil samples are of substantial value in the oil industry. The conventional SARA analysis procedures were determined with the standards established by the American Society for Testing and Materials (ASTM). However, the standard test methods are time consuming, environmental nonfriendly, expensive, and require large amounts of the crude oil samples to be analyzed. Thus, it be would useful to approve some supportive approaches for rapid evaluation of the crude oils. The attenuated total reflection Fourier-transform infrared spectroscopy ATR-FTIR coupled with chemometric methods could be used as analytical method for crude oil analysis. A hybrid of genetic algorithm (GA) and support vector machine regression (SVM-R) model was applied to predict SARA analysis of crude oil samples from different Iranian oil field using ATR-FTIR spectroscopy. The result of GA-SVM-R model were compared with genetic algorithm-partial least square regression (GA-PLS-R) model. Correlation coefficient (R2) and root mean square error (RMSE) for calibration and prediction of samples were also calculated, in order to evaluate the calibration models for each component of SARA analysis in crude oil samples. The performance of GA-SVM-R is found to be reliably superior, so that it can be successfully applied as an alternative approach for the quantitative determination of the SARA analysis of crude oil samples.

Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods.

Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.

Visual deprivation is met with active changes in ground reaction forces to minimize worsening balance and stability during walking.

Previous studies suggest that visual information is essential for balance and stability of locomotion. We investigated whether visual deprivation is met with active reactions tending to minimize worsening balance and stability during walking in humans. We evaluated effects of vision on kinetic characteristics of walking on a treadmill-ground reaction forces (GRFs) and shifts in the center of mass (COM). Young adults (n = 10) walked on a treadmill at a comfortable speed. We measured three orthogonal components of GRFs and COM shifts during no-vision (NV) and full-vision (FV) conditions. We also computed the dynamic balance index (DN)-the perpendicular distance from the projection of center of mass (pCOM) to the inter-foot line (IFL) normalized to half of the foot length. Locally weighted regression smoothing with alpha-adjusted serial T tests was used to compare GRFs and DN between two conditions during the entire stance phase. Results showed significant differences in GRFs between FV and NV conditions in vertical and ML directions. Variability of peak forces of all three components of GRF increased in NV condition. We also observed significant increase in DN for NV condition in eight out of ten subjects. The pCOM was kept within BOS during walking, in both conditions, suggesting that body stability was actively controlled by adjusting three components of GRFs during NV walking to minimize stability loss and preserve balance.

Synthesis and physicochemical properties of dual-responsive acrylic acid/butyl acrylate cross-linked nanogel systems.

HYPOTHESIS: A cross-linked amphiphilic nanogel containing a high mole% of hydrophilic pH-responsive moiety can provide enhanced functionality regarding stimuli-responsiveness, water-dispersibility, hydrophobic substance loading, and structural stability under harsh environmental conditions. These nanogels could be synthesized using a one-pot procedure for large-scale applications. Moreover, the interplay of various interaction forces in these colloidal systems is being investigated. EXPERIMENTS: Model nanogels consisting of acrylic acid-butyl acrylate-ethylene glycoldimethacrylate were synthesized using an emulsion copolymerization via a seeded semi-batch process under an acidic condition. The structures were assessed by Fourier transform infrared spectroscopy and potentiometric-conductometric titrations. Zeta potential, field-emission scanning electron microscopy, and transmission electron microscopy were used to evaluate the dispersion stability, size distribution, and structural distribution, respectively. Their stimuli-responsive behavior was studied by combining static and dynamic light scattering and titration analyses. FINDINGS: Monodisperse nanospheres of approximately 150 nm were successfully prepared by implementing a one-pot practical pathway. These nanogels displayed a dual thermo- and pH-responsive behavior, reflecting the high efficiency of physical cross-linking make it ideal for drug delivery and oil industry applications. Moreover, a novel symmetric pH-activated morphology transformation behavior was revealed. Accordingly, a compositional distribution was proposed and assessed by exploring the polymerization process.

Influence of hydrophilic silica nanoparticles on the adsorption layer properties of non-ionic surfactants at water/heptane interface.

There is a notable paucity of studies investigating the impact of charged nanoparticles on the interfacial behavior of nonionic surfactants, assuming that the interactions are negligible in the absence of electrostatic forces. Here, we argue about our observations and the existence of a complex interfacial behavior in such systems depending on the type and chemical structure of surfactant. This study set out to investigate the effects of interactions between hydrophilic silica nanoparticles (NP) and non-ionic surfactants on water/heptane dynamic interfacial properties using drop profile analysis tensiometry (PAT). Three surfactants were studied, namely Triton X-100 (significantly soluble in water phase), C12DMPO (well soluble in both phases) and SPAN 80 (oil-soluble). The different chemical structures and partition coefficients of the surfactants enabled us to cover possible interactions and differentiate between bulk and interfacial interactions. We observed that hydrophilic silica NPs had a negligible effect on the interfacial behavior of Triton X-100, that they increased the surface activity of C12DMPO when both compounds are initially in the aqueous phase. Most interestingly is that the added NPs generated unstable interfacial NP-surfactant complexes and reduced the pseudo-equilibrium interfacial tension of oil-soluble surfactant, Span 80, even though NPs and surfactants were in different bulk phases.

The Role of Electrostatic Repulsion on Increasing Surface Activity of Anionic Surfactants in the Presence of Hydrophilic Silica Nanoparticles.

Hydrophilic silica nanoparticles alone are not surface active. They, however, develop a strong electrostatic interaction with ionic surfactants and consequently affect their surface behavior. We report the interfacial behavior of n-heptane/anionic-surfactant-solutions in the presence of hydrophilic silica nanoparticles. The surfactants are sodium dodecyl sulfate (SDS) and dodecyl benzene sulfonic acid (DBSA), and the diameters of the used particles are 9 and 30 nm. Using experimental tensiometry, we show that nanoparticles retain their non-surface-active nature in the presence of surfactants and the surface activity of surfactant directly increases with the concentration of nanoparticles. This fact was attributed to the electrostatic repulsive interaction between the negatively charged nanoparticles and the anionic surfactant molecules. The role of electrostatic repulsion on increasing surface activity of the surfactant has been discussed. Further investigations have been performed for screening the double layer charge of the nanoparticles in the presence of salt. Moreover, the hydrolysis of SDS molecules in the presence of silica nanoparticles and the interaction of nanoparticles with SDS inherent impurities have been studied. According to our experimental observations, silica nanoparticles alleviate the effects of dodecanol, formed by SDS hydrolysis, on the interfacial properties of SDS solution.

Superamphiphobic Surfaces Prepared by Coating Multifunctional Nanofluids.

Construction of surfaces with the capability of repelling both water and oil is a challenging issue. We report the superamphiphobic properties of mineral surfaces coated with nanofluids based on synthesized Co-doped and Ce-doped Barium Strontium Titanate (CoBST and CeBST) nanoparticles and fluorochemicals of trichloro(1H,1H,2H,2H-perfluorooctyl)silane (PFOS) and polytetrafluoroethylene (PTFE). Coating surfaces with these nanofluids provides both oil (with surface tensions as low as 23 mN/m) and water repellency. Liquids with high surface tension (such as water and ethylene glycol) roll off the coated surface without tilting. A water drop released from 8 mm above the coated surface undergoes first a lateral displacement from its trajectory and shape deformation, striking the surface after 23 ms, bouncing and rolling off freely. These multifunctional coating nanofluids impart properties of self-cleaning. Applications include coating surfaces where cleanliness is paramount such as in hospitals and domestic environments as well as the maintenance of building facades and protection of public monuments from weathering. These superamphiphobic-doped nanofluids have thermal stability up to 180 °C; novel industrial applications include within fracking and the elimination of condensate blockage in gas reservoirs.

Interfacial equation of state for ionized surfactants at oil/water interfaces.

A new mathematical approach has been developed for describing the interfacial behaviour of oil/water interfaces in the presence of ionic surfactants. The approach relies on the ideal behaviour of ionized surfactants at oil/water interfaces, which is previously demonstrated by Lucassen-Reynders (J. Phys. Chem., 1966, 70, 1777-1785). The new derived equation simply relates the interfacial tension to the surfactant molecular size and the cmc value of the surfactant in the aqueous phase. The predicted values are in a reasonable agreement with the measured experimental data. Formation of complex multi-layers is considered and the related development is performed. It is shown that, assuming a multi-layer interface, the proposed model gives an area per surfactant molecule similar to the values obtained by techniques such as neutron reflectivity (NR), while a monolayer assumption yields about half the value. The discussion describes the impact of dissolved oil and ionic components on the interfacial tension of the ionized surfactants at oil/water interfaces.

The adsorption behavior of ionic surfactants and their mixtures with nonionic polymers and with polyelectrolytes of opposite charge at the air-water interface.

The surface phase approach of Butler has been used to derive a model of the surface tension (ST) of surfactant solutions in terms of the ST of the surfactant in the absence of water, an area parameter corresponding approximately to the limiting area per molecule, and the critical micelle concentration (CMC). This isotherm is then used to account for the ST behavior of aqueous solutions of weakly interacting polymer-surfactant (P-S) and strongly interacting polyelectrolyte-surfactant (PE-S) mixtures. For P-S systems, no additional parameters are required other than the critical aggregation concentration (CAC) and the onset of the ST plateau at micellization (T3). The model accounts for experimental isotherms for sodium dodecyl sulfate (SDS) with poly(ethylene oxide) or poly(vinylpyrrolidone). For PE-S systems, the initial CAC has no effect on the ST and is well below the decrease in ST that leads to the first ST plateau at T1. This decrease is modeled approximately using a Langmuir isotherm. The remaining ST behavior is analyzed with the model surfactant isotherm and includes modeling the ST when there is separation into two phases. The behavior in the phase separation region depends on the dissociability of the PE-S complex. Loss of surface activity accompanied by a peak in the ST may occur when there is part formation of a nondissociable complex (neutral with segment/surfactant = 1). The model successfully explains the ST of several experimental systems with and without ST peaks, including poly(dimethyldiallylammonium chloride)-SDS and poly(sodium styrenesulfonate)-alkyltrimethylammonium bromide (C(n)TAB) with n = 12, 14, and 16.