Bonding of Polyethylenimine in Covalent Organic Frameworks for CO Capture from Air.

We have developed a polyethylenimine-functionalized covalent organic framework (COF) for capturing CO from the air. It was synthesized by the crystallization of an imine-linked COF, termed imine-COF-709, followed by linkage oxidation and polyamine installation through aromatic nucleophilic substitution. The chemistry of linkage oxidation and amine installation was fully characterized through Fourier transform infrared spectroscopy, elemental analysis, and solid-state nuclear magnetic resonance (ssNMR) spectroscopy.

Gas Adsorption in Flexible COF-506 and COF-506-Cu.

Flexible covalent-organic frameworks (COFs) display a variety of guest-dependent dynamic behaviors, but because these are an emerging class of materials, very little experimental adsorption data exists. This work examines the adsorption properties of COF-506 and COF-506-Cu utilizing various adsorbates as probe molecules. These materials have small surface areas (<100 m/g) but still have significant capacity for methanol and isopropanol compared to activated carbon, even though the COF contains approximately 1/10th the surface area of many activated carbons.

Carbon dioxide capture from open air using covalent organic frameworks.

Capture of CO from the air offers a promising approach to addressing climate change and achieving carbon neutrality goals. However, the development of a durable material with high capacity, fast kinetics and low regeneration temperature for CO capture, especially from the intricate and dynamic atmosphere, is still lacking. Here a porous, crystalline covalent organic framework (COF) with olefin linkages has been synthesized, structurally characterized and post-synthetically modified by the covalent attachment of amine initiators for producing polyamines within the pores.

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo.

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type.