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Hypothesis: Non-ionic surfactants containing polyethylene oxide (PEO) chains are widely used in drug formulations, cosmetics, paints, textiles and detergents. High quality molecular dynamics models for PEO surfactants can give us detailed, atomic-scale information about the behavior of surfactant/water mixtures.
Simulations: We used two molecular dynamics force fields (FFs), 2016H66 and 53A6, to model the simple non-ionic PEO surfactant, hexaoxyethylene dodecyl ether (CE). We investigated surfactant/water mixtures that span the phase diagram of starting from randomly distributed arrangements. In some cases, we also started with prebuilt, approximate models. The simulations results were compared with the experimentally observed phase behavior.
Findings: Overall, this study shows that the spontaneous self-assembly of PEO non-ionic surfactants into different colloidal structures can be accurately modeled with MD simulations using the 2016H66 FF although transitions to well-formed hexagonal phase are slow. Of the two FFs investigated, the 2016H66 FF better reproduces the experimental phase behavior across all regions of the CEwater phase diagram.
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http://dx.doi.org/10.1016/j.jcis.2020.12.032 | DOI Listing |
Nano Lett
September 2025
School of Chemistry and Materials Science, Hunan Agricultural University, Changsha 410128, China.
Passivating detrimental defects is essential for improving perovskite solar cells (PSCs) performance. While hydrogen interstitials are often considered harmful, their role in defect passivation remains unclear. Using nonadiabatic molecular dynamics, we uncover a self-passivation mechanism between hydrogen (H) and bromine (Br) interstitials in all-inorganic CsPbBr perovskites.
View Article and Find Full Text PDFAutophagy
September 2025
Department of Biochemistry and Molecular Biology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Macroautophagy/autophagy is an evolutionarily conserved process through which cells degrade cytoplasmic substances via autophagosomes. During the initiation of autophagosome formation, the ULK/Atg1 complex serves as a scaffold that recruits and regulates downstream ATG/Atg proteins and ATG9/Atg9-containing vesicles. Despite the essential role of the ULK/Atg1 complex, its components have changed during evolution; the ULK complex in mammals consists of ULK1 (or ULK2), RB1CC1, ATG13, and ATG101, whereas the Atg1 complex in the yeast lacks Atg101 but instead has Atg29 and Atg31 along with Atg17.
View Article and Find Full Text PDFNano Lett
September 2025
Department of Physics, Columbia University, New York, New York 10027, United States.
Graphene-based photonic structures have emerged as fertile ground for the controlled manipulation of surface plasmon polaritons (SPPs), providing a two-dimensional platform with low optoelectronic losses. In principle, nanostructuring graphene can enable further confinement of nanolight─enhancing light-matter interactions in the form of SPP cavity modes. In this study, we engineer nanoscale plasmonic cavities composed of self-assembled C arrays on graphene.
View Article and Find Full Text PDFAnal Chem
September 2025
Institute of Biological Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria.
The discovery of solute precursors of crystalline materials, such as biominerals, recently challenged the classical nucleation theory (CNT). One emerging method for investigating these early-stage intermediates in solution is dissolution dynamic nuclear polarization (dDNP)-enhanced nuclear magnetic resonance (NMR) spectroscopy. Recent applications of dDNP to calcium carbonate (CaC) and calcium phosphate (CaP) mineralization have demonstrated the feasibility of identifying and tracing very early-stage prenucleation clusters (PNCs).
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, Pavia 27100, Italy.
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric.
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