The study reports solid-liquid equilibrium data for binary mixtures of naphthalene or biphenyl with 1-tetradecanol or 1-hexadecanol, determined using differential scanning calorimetry. The systems exhibit simple eutectic behavior, and the data are correlated using thermodynamic models.
Solvents play a crucial role in the nucleation and growth of covalent organic frameworks, as revealed by in-situ optical microscopy observations of the liquid-liquid phase separation and structured solvent formation during the synthesis process.
Selective catalytic arylation of lignin-derived phenols enables efficient biomass fractionation and production of benign bisphenols as sustainable replacements for fossil-based counterparts.
Maschinelles Lernen zur Vorhersage von Reaktionsmechanismen mit einem großen mechanistischen Datensatz.
Surfactant CMC prediction using GNNs for temperature dependency.
MolNexTR is a novel deep learning model that accurately predicts molecular structures from images, achieving superior performance in recognizing diverse molecular styles.
Machine learning potentials can accurately predict free energy surfaces but require comprehensive training datasets.
GradNav algorithm accelerates exploration of energy surfaces by navigating potential barriers effectively.
A chain ether-based electrolyte enables long-term continuous ammonia synthesis.
MolNexTR is a novel deep learning model that accurately predicts molecular structures from diverse image styles, achieving superior performance in molecular structure recognition.