Hansen solubility parameters obtained via molecular dynamics simulations as a route to predict siloxane surfactant adsorption

How bulk liquid viscosity shapes capillary suspensions

HYPOTHESIS

Capillary suspensions offer a new approach to generate novel materials. They are ternary liquid-liquid-solid systems characterized by particles connected by liquid bridges of one fluid suspended in a second immiscible bulk fluid. The viscosity of the bulk liquid can be modulated to customize the structure and rheological properties of capillary suspensions. Experiments and simulations: Using experiments and numerical simulations, we investigated capillary suspensions in the pendular state, using silica particles and water as a bridging liquid. To modulate the viscosity of the bulk fluid, we use different ratios of either dodecane and diisononyl phthalate, or silicone oils with varying chain lengths as bulk liquids. The rheological behavior was characterized using the maximum storage and loss moduli and the yielding behavior. This was related to structural changes of the systems, which was visualized using confocal laser scanning microscopy. In addition, we used Molecular Dynamics (MD) simulations to gain more insights into the behavior of two particles connected by a liquid bridge for various bulk liquids.

FINDINGS

Experiments show that higher bulk liquid viscosity reduces strength, yield stress, and yield strain in capillary suspensions, which is partly attributed to a reduced inter-connectivity of the percolating network. This is caused by the breakup of liquid bridges occurring at shorter distances in the presence of highly viscous bulk liquids, as indicated by numerical simulations.

Multiparameter Method for Developer Screening of Molecular Glass Resists via Molecular Simulations

Efficient and accurate developer screening is critical for high-resolution lithography. In this study, a multiparameter (MP) method simultaneously based on the Hansen solubility parameters (HSP), two-component solubility parameters (TSP), and order parameter (OP) is proposed for the developer screening of molecular glass resists via molecular dynamics simulation. A customized solvent database, including 80 organic solvents, is created for the subsequent developer screening. Two diagram forms of HSP (δD-δP-δH diagram and δV-δH diagram) were investigated, and the δV-δH diagram is utilized due to its higher solubility prediction consistency with OP. Then, two solubility prediction diagrams, i.e., HSP&OP and TSP&OP diagrams, were formed using the MP method. The developer screening scheme using the MP method is illustrated for a decomposable resist, AD10BOC, and a cross-linkable resist, AD4C. Meanwhile, the feasibility of the developer screening scheme is verified by practical lithography experiments. In addition, relative solvation free energy (RSFE) calculations and dissolution experiments of six resists are implemented to further confirm the accuracy of OP in reflecting the relative solubility of the resist in various solvents. This study presents a viable approach for developer screening, which will facilitate the development of resist materials.

Stability Challenges of Amorphous Solid Dispersions of Drugs: A Critical Review on Mechanistic Aspects

The most common drawback of the existing and novel drug molecules is their low bioavailability because of their low solubility. One of the most important approaches to enhance the bioavailability in the enteral route for poorly hydrophilic molecules is amorphous solid dispersion (ASD). The solubility of compounds in amorphous form is comparatively high because of the availability of free energy produced during formulation. This free energy results in the change of crystalline nature of the prepared ASD to the stable crystalline form leading to the reduced solubility of the product. Due to the intrinsic chemical and physical uncertainty and the restricted knowledge about the interactions of active molecules with the carriers making, this ASD is a challenging task. This review focused on strategies to stabilize ASD by considering the various theories explaining the free-energy concept, physical interactions, and thermal properties. This review also highlighted molecular modeling and machine learning computational advancement to stabilize ASD.

Predicting the Performance of Functional Materials Composed of Polymeric Multicomponent Systems Using Artificial Intelligence-Formulations of Cleansing Foams as an Example

Cleansing foam is a common multicomponent polymeric functional material. It contains ingredients in innumerable combinations, which makes formulation optimization challenging. In this study, we used artificial intelligence (AI) with machine learning to develop a cleansing capability prediction system that considers the effects of self-assembled structures and chemical properties of ingredients. Over 500 cleansing foam samples were prepared and tested. Molecular descriptors and Hansen solubility index were used to estimate the cleansing capabilities of each formulation set. We used five machine-learning models to predict the cleansing capability. In addition, we employed an in silico formulation by generating virtual formulations and predicting their cleansing capabilities using an established AI model. The achieved accuracy was R2 = 0.770. Our observations revealed that mixtures of cosmetic ingredients exhibit complex interactions, resulting in nonlinear behavior, which adds to the complexity of predicting cleansing performance. Nevertheless, accurate chemical property descriptors, along with the aid of in silico formulations, enabled the identification of potential ingredients. We anticipate that our system will efficiently predict the chemical properties of polymer-containing blends.

Linking intermolecular interactions and rheological behaviour in capillary suspensions

HYPOTHESIS

Capillary suspensions feature networks of particles connected by liquid bridges, which are obtained by adding a small amount of a second immiscible liquid to a suspension. It is possible to link the network formation as well as the rheological behaviour of capillary suspensions to the intermolecular interactions of their constituents.

EXPERIMENTS AND SIMULATIONS

Through a combination of experimental and numerical methods, we present a novel approach, based on Hansen solubility parameters computed from Molecular Dynamics (MD) simulations, to rationalize and predict the rheological behaviour of capillary suspensions. We investigated the formation of capillary suspensions for various combinations of bulk and secondary liquids mixed with hydrophilic silica particles. The predictions were confirmed experimentally by rheological analysis, interfacial tension measurements and microscopy (CLSM) imaging.

FINDINGS

Numerical and experimental results show that the Hansen solubility parameters theory allows to predict the formation of capillary suspensions, whose strength exponentially decays with decreasing intermolecular interactions between the secondary liquids and the dispersed particles. High immiscibility between the bulk and secondary liquid strengthens the gel up to a critical immiscibility point, above which the strength of the gel remains mostly affected by the affinity between the secondary liquids and the dispersed particles. Furthermore, we find that hydrogen-bonding and polar interactions control the formation of capillary suspensions. This simple approach can guide the selection of adequate solvents and immiscible secondary liquids, allowing an easy formulation of new particulate-based gels.

Domination of H-Bond Interactions in the Solvent-Triggering Gelation Process

Gels prepared with the solvent-triggering method are attractive for their easy and fast preparation; however, the role of solvents in this process remains unclear, which hinders the efficient and accurate control of desired gel properties. In this study, the role of solvents in the solvent-triggering gelation process is studied using 9-fluorenylmethoxycarbonyl (Fmoc)-protected diphenylalanine (Fmoc-FF) as the gelator. Density functional theory (DFT)-based calculations and corresponding wavefunction analyses are conducted to identify the H-bonding interaction sites between the molecules. The calculation results clearly annotate the activating role of DMF and the triggering role of H₂O in the gelation process. The solvation of Fmoc-FF by DMF can activate the H-bonding sites on the peptide chain, showing a conformation reversal and higher electrostatic potentials. Then, the H-bonding between Fmoc-FF and H₂O is facilitated to trigger gelation. The physical Fmoc-FF/DMF/H₂O gels show easily tuned mechanical strengths (G' of 10²-10⁵ Pa), injectable potentials (general yield strain < 100%), and stable recoverability (80-98% within 100 s). The regulation of these properties depends on not only the gelator concentration but also the H-bonding interactions with solvent molecules, which have seldom been studied in detail before. By understanding the effect of solvents, low-molecular-weight gelator-based gels can be designed, prepared, and tuned efficiently for potential applications.

Surface adhesion of viruses and bacteria: Defend only and/or vibrationally extinguish also?! A perspective

Coronaviruses COVID-19, SARS-CoV and NL63 use spikes in their corona to bind to angiotensin converting enzyme 2 (ACE2) sites on cytoskeletal membranes of host cells to deliver their viral payload. While groups such as disulfides in ACE2's zinc metallopeptidase, and also in COVID-19's spikes, facilitate such binding, it is worth exploring how similar complementary sites on materials such as polymers, metals, ceramics, fabrics, and biomaterials promote binding of viruses and bacteria and how they could be further engineered to prevent bioactivity, or to act as agents to collect viral payloads in filters or similar devices. In that vein, this article offers a perspective on novel tools and approaches for chemically and topologically modifying most utilitarian surfaces via defensive topological vibrational engineering to either prevent such adhesion or to enhance adhesion and elicit vibrational characteristics/'musical signatures' from the surfaces so that the structure of the binding sites of viruses and bacteria is permanently altered and/or their cellular machinery is permanently disabled by targeted chemical transformations.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1557/s43580-021-00079-0.

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".

New Insights into the Quantitative Relationship between Surface Chemistry of Fullerene (C60) and Solubility Parameters and Compatibility with Polymers

The quantitative relationship between the surface chemistry of carbon materials and the compatibility with polymers is a fundamental and vital physical chemistry problem in the field of polymer nanocomposites. Traditional experimental methods are difficult to solve this problem, so no theory has been formed to guide the functionalization of carbon materials. In this work, the quantitative relationship between functional groups and Hildebrand (δ_T) and transformed Hansen (δ_vdW and δ_ele) solubility parameters of fullerene (C60) was determined by molecular dynamics simulation. Besides, which solubility parameter can more accurately predict the compatibility between C60 and three typical polymers with different polarity as a function of grafting ratio is investigated. Very interestingly, no matter which group is grafted, δ_T and δ_vdW of C60 show a slight increase first and then a decrease with the grafting ratio, whereas δ_ele first increases abruptly and then decreases slightly. The introduction of polar groups (-OH, -COOH, and -NH₂) is conducive to improving the compatibility between C60 and polymers, whereas the introduction of the nonpolar group (-CH₃) is not. In terms of predicting compatibility, the Hildebrand solubility parameter is better than the Hansen solubility parameter due to the nonpolar nature of the polymers, even for nitrile butadiene rubber. Finally, the optimum grafting ratios corresponding to the maximum binding energies of C60/polymers mixtures were obtained. This study provides a new understanding of the functionalization of C60 at the molecular level and promotes the development of the theory of the thermodynamics of mixing.

Absorption of surfactant-laden droplets into porous media: A numerical study

HYPOTHESIS

Droplets can absorb into permeable substrates due to capillarity. It is hypothesized that the contact line dynamics influence this process and that an unpinned contact line results in slower absorption than a pinned contact line, since the contact area between the droplet and the substrate will decrease over time for the former. Furthermore, it is expected that surfactants can be used to accelerate the absorption.

SIMULATIONS

Lubrication theory is employed to model the droplet and Darcy's law is combined with the conservation law of mass to describe the absorption dynamics. For the surfactant transport, several convection-diffusion-adsorption equations are solved.

FINDINGS

It is found that moving contact lines result in a parabola-shaped wetted area and a slower absorption and a deeper penetration depth than pinned contact lines. The evolution of the penetration depth was quantitatively validated by comparison with two experimental studies from literature. Surfactants were shown to accelerate the absorption process, but only if their adsorption kinetics are slow compared to the absorption. Otherwise, all surfactant adsorbs onto the pore walls before reaching the wetting front, resulting in the same absorption rate as without surfactants. This behavior agrees with both experimental and analytical literature.

Oil sludge washing with surfactants and co-solvents: oil recovery from different types of oil sludges

Different physicochemical and biological treatments have been used to treat oil sludges, and oil recovery techniques are preferred such as oil sludge washing (OSW) with surfactants and co-solvents. Toluene is commonly used as co-solvent, but it is non-benign to the environment. This study tested alternative co-solvents (n-pentane, n-hexane, cyclohexane, and isooctane) at 1:1 and 2:1 C/OS (co-solvent to oil sludge ratio). Also, this study evaluated the effect on the oil recovery rate (ORR) of three main parameters in the washing: type, concentration, and application ratio (S/OS) of surfactants to oil sludges. To date, no study has assessed these parameters in the washing of oil sludges from different sources. Four types of oil sludges and five surfactants (Triton X-100 and X-114, Tween 80, sodium dodecyl sulphate (SDS), and rhamnolipid) were used. The results showed that cyclohexane had high ORR and could be used instead of toluene because it is more benign to the environment. The S/OS ratio had a high effect on the ORR and depended on the type of oil sludge. Rhamnolipid, Triton X-100, and Triton X-114 had the highest oil recovery rates (40 - 70%). In addition, it was found that the surfactant concentration had no effect on the ORR. Consequently, the addition of surfactant was not significantly different compared to the washing with no surfactants, except for one sludge. The use of the surfactant in the washing solution can help in the selective extraction of specific oil hydrocarbon fractions in the recovered oil to assess its potential reuse as fuel. Further recommendations were given to improve the OSW process.