
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.


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The operando imaging of the early formation stages of covalent organic frameworks with the optical technique interferometric scattering microscopy leads to mechanistic insights, enabling the rational development of a synthesis protocol at room temperature instead of elevated temperatures.

Early stages of covalent organic framework formation imaged in operando - Nature

in-situ-observation-of-covalent-organic-framework-formation-reveals-role-of-solvents-in-nucleation-and-growth


In-Situ Observation of Covalent Organic Framework Formation Reveals Role of Solvents in Nucleation and Growth


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Selective catalytic arylation of lignin-derived phenols enables efficient biomass fractionation and production of benign bisphenols as sustainable replacements for fossil-based counterparts.


By controlling C–C bond formation in catalytic arylation, lignin can be efficiently extracted from biomass and converted into benign bisphenols that can be used as replacements for their fossil-based counterparts.

Selective lignin arylation for biomass fractionation and benign bisphenols - Nature

selective-lignin-arylation-for-efficient-biomass-fractionation-and-production-of-eco-friendly-bisphenols


Selective Lignin Arylation for Efficient Biomass Fractionation and Production of Eco-Friendly Bisphenols



Maschinelles Lernen zur Vorhersage von Reaktionsmechanismen mit einem großen mechanistischen Datensatz.


reproducing-reaction-mechanisms-with-machine-learning-models-trained-on-a-large-scale-mechanistic-dataset


Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset



Surfactant CMC prediction using GNNs for temperature dependency.


surfactant-cmc-temperature-prediction-using-graph-neural-networks


Surfactant CMC Temperature Prediction Using Graph Neural Networks



MolNexTR is a novel deep learning model that accurately predicts molecular structures from images, achieving superior performance in recognizing diverse molecular styles.


molnextr-a-deep-learning-model-for-molecular-image-recognition


MolNexTR: A Deep Learning Model for Molecular Image Recognition



Machine learning potentials can accurately predict free energy surfaces but require comprehensive training datasets.


considerations-in-the-use-of-machine-learning-interaction-potentials-for-free-energy-calculations


Considerations in the Use of Machine Learning Interaction Potentials for Free Energy Calculations



GradNav algorithm accelerates exploration of energy surfaces by navigating potential barriers effectively.


gradnav-accelerated-exploration-of-potential-energy-surfaces-with-gradient-based-navigation


GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation



A chain ether-based electrolyte enables long-term continuous ammonia synthesis.


Long-term continuous ammonia electrosynthesis - Nature

long-term-continuous-ammonia-electrosynthesis-solvent-s-crucial-role


Long-term Continuous Ammonia Electrosynthesis: Solvent's Crucial Role



MolNexTR is a novel deep learning model that accurately predicts molecular structures from diverse image styles, achieving superior performance in molecular structure recognition.


molnextr-a-novel-deep-learning-model-for-molecular-image-recognition


MolNexTR: A Novel Deep Learning Model for Molecular Image Recognition



Enhancing exploration of potential energy surfaces with GradNav algorithm.


exploring-potential-energy-surfaces-with-gradnav-algorithm


Exploring Potential Energy Surfaces with GradNav Algorithm
