Publications

Many simulation methods concerning solvated molecules are based on the assumption that the solvated species and the solvent can be characterized by some representative structures of the solute and some embedding potential corresponding to this structure. While the averaging of the solvent configurations to obtain an embedding potential has been studied in great detail, this hinges on a single solute structure representation. This assumption is re-examined and generalized for conformationally flexible solutes and tested on 4 nonrigid systems. In this generalized approach, the solute is characterized by a set of representative structures and the corresponding embedding potentials. The representative structures are identified by means of subdividing the statistical ensemble, which in this work is generated by a constant-temperature molecular dynamics simulation. The embedding potential defined in the Frozen-Density Embedding Theory is used to characterize the average effect of the solvent in each subensemble. The numerical examples concern the vertical excitation energies of protonated retinal Schiff bases in protein environments. It is comprehensively shown that subensemble averaging leads to huge computational savings compared with explicit averaging of the excitation energies in the whole ensemble while introducing only minor errors in the case of the systems examined.Many simulation methods concerning solvated molecules are based on the assumption that the solvated species and the solvent can be characterized by some representative structures of the solute and some embedding potential corresponding to this structure. While the averaging of the solvent configurations to obtain an embedding potential has been studied in great detail, this hinges on a single solute structure representation. This assumption is re-examined and generalized for conformationally flexible solutes and tested on 4 nonrigid systems. In this generalized approach, the solute is characterized by a set of representative structures and the corresponding embedding potentials. The representative structures are identified by means of subdividing the statistical ensemble, which in this work is generated by a constant-temperature molecular dynamics simulation. The embedding potential defined in the Frozen-Density Embedding Theory is used to characterize the average effect of the solvent in each subensemble. The numerical examples concern the vertical excitation energies of protonated retinal Schiff bases in protein environments. It is comprehensively shown that subensemble averaging leads to huge computational savings compared with explicit averaging of the excitation energies in the whole ensemble while introducing only minor errors in the case of the systems examined.

Designed to enhance the solubility of hardly soluble species in water, many excipient formulations for active drugs or other solutes contain two or more cosolutes. And yet, little is known about the mechanism through which excipients act in combination, and how the efficacy of each component toward drug solubility changes compared to when they are acting alone. Here we study aqueous mixtures of urea and inorganic salts and determine their efficacy to solubilize β-cyclodextrin, a cyclic carbohydrate and a key ingredient in many drug formulations. We show that the order in which the salts increased β-cyclodextrin solubility follows the Hofmeister series both in the presence and absence of urea. However, the solubility in many of the urea-salt mixtures is notably higher than the sum of the solubilities in each cosolute on its own, suggesting a synergistic effect between solutes. By determining the activity of urea and NaClO₄, the combination showing the strongest synergy, we show that their remarkable solubility enhancement at high concentration is due to a strong urea-assisted accumulation of NaClO₄ at the vicinity of β-cyclodextrin. We discuss the molecular interactions that lead to this induced accumulation of NaClO₄. Our findings provide new insight into the mechanism of solvation by multiple cosolutes and should aid in the rational design of tailored excipient formulations.

Wearable electronics is an emerging field in academics and industry, in which electronic devices, such as smartwatches and sensors, are printed or embedded within textiles. The electrical circuits in electronics textile (e-textile) should withstand many cycles of bending and stretching. Direct printing of conductive inks enables the patterning of electrical circuits; however, while using conventional nanoparticle-based inks, printing onto the fabric results in a thin layer of a conductor, which is not sufficiently robust and impairs the reliability required for practical applications. Here, we present a new process for fabricating robust stretchable e-textile using a thermodynamically stable, solution-based copper complex ink, which is capable of full penetrating the fabric. After printing on knitted stretchable fabrics, they were heated, and the complex underwent an intermolecular self-reduction reaction. The continuously formed metallic copper was used as a seed layer for electroless plating (EP) to form highly conductive circuits. It was found that the stretching direction has a significant role in resistivity. This new approach enables fabricating e-textiles with high stretchability and durability, as demonstrated for wearable gloves, toward printing functional e-textile.

As long as we have attempted to sanction untoward speech, others have devised strategies for expressing themselves while dodging such sanctions. In this intervention, I review the arms race between technological filters designed to curb hate speech, and evasive language practices designed to avoid detection by these filters. I argue that, following important advances in the detection of relatively overt uses of hate speech, further advances will need to address hate speech that relies on culturally or situationally available context knowledge and linguistic ambiguities to convey its intended offenses. Resolving such forms of hate speech not only poses increasingly unreasonable demands on available data and technologies, but does so for limited, uncertain gains, as many evasive uses of language effectively defy unique valid classification.