Krafft Point Prediction of Anionic Surfactants Using Group Contribution Method: First‐Order and Higher‐Order Groups

Modular Toolbox as Snap Jewelry for Biomimetic Synthesis of Multifunctional Amino Acid Surfactants Inspired by Melanin

Amino acid surfactants have gained significant importance in overcoming the limitations of conventional surfactants, notably, their low biocompatibility and biodegradability. However, the current amino acid surfactants lack multifunctional properties due to the nonreactivity of their aliphatic chains, necessitating the development of a new type of amino acid surfactant. A novel melanin-like amino acid surfactant and a biomimetic synthesis route were devised by mimicking the biosynthesis of melanin. Renewable natural polyphenol compounds with catechol moieties were utilized as building blocks for the hydrophobic group. In a proof-of-concept experiment, ethyl protocatechuate was oxidized to o-quinone and subsequently covalently linked to the amino group of lysine via Michael addition. The chemical structure was verified using liquid chromatography-tandem mass spectroscopy. The melanin-like amino acid surfactant exhibited excellent surface-active properties, with a critical micelle concentration of 1.59 mN m-1. Furthermore, it demonstrated remarkable emulsifying, foaming, solubilizing, dispersing, and wetting capabilities. Notably, it also possessed multifunctionality, including antibacterial activity, antioxidant activity, robustness, and mildness. These outstanding properties indicate significant potential for various applications. This strategy offers innovative insights and a versatile, modular toolbox for synthesizing multifunctional amino acid surfactants that mimic melanin. The approach allows for the easy interchange of o-quinone building blocks, which is akin to snap jewelry.

Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks

In this study, a framework for the prediction of thermophysical properties based on transfer learning from existing estimation models is explored. The predictive capabilities of conventional group-contribution methods and traditional machine-learning approaches rely heavily on the availability of experimental datasets and their uncertainty. Through the use of a pretraining scheme, which leverages the knowledge established by other estimation methods, improved prediction models for thermophysical properties can be obtained after fine-tuning networks with more accurate experimental data. As our experiments show, for the case of critical properties of compounds, this pipeline not only improves the performance of the models on commonly found organic structures but can also help these models generalize to less explored areas of chemical space, where experimental data is scarce, such as inorganics and heavier organic compounds. Transfer learning from estimation models data also allows for graph-based deep learning models to create more flexible molecular features over a bigger chemical space, which leads to improved predictive capabilities and can give insights into the relationship between molecular structures and thermophysical properties. The generated molecular features can discriminate behavior discrepancy between isomers without the need of additional parameters. Also, this approach shows better robustness to outliers in experimental datasets.

Effects of the Hydrophilic-Lipophilic Balance of Alternating Peptides on Self-Assembly and Thermo-Responsive Behaviors

A series of N-substituted poly(Gly–alter–Val) peptides were successfully synthesized for the systematic evaluation of the micellization behavior of alternating peptides. Three-component polymerization employing an aldehyde, a primary ammonium chloride, and potassium isocyanoacetate afforded four alternating peptides in excellent yields. We investigated the dependence of the hydrophilic–lipophilic balance of alternating peptides on the micellization behavior. All the aqueous solutions of alternating peptides exhibited upper critical solution temperature (UCST) behaviors, strongly indicating that the alternating binary pattern would mainly contribute to the UCST behaviors. The cloud points of alternating peptides shifted to higher temperatures as the side chains became more hydrophilic, which is opposite to the trend of typical surfactants. Such unusual micellization behaviors appeared to be dependent on the quasi-stable structure of single polymer chains formed in water.