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Abstract The capture, utilization, and storage of CO₂are the primary options to minimize the adverse effects of global warming and related climate change resulting from increased anthropogenic CO₂emissions. In recent years, amino acids and amino acid‐based ionic liquids (AAILs) are proposed as promising alternatives to the traditional aqueous amine solvent‐based CO₂capture technology due to the presence of the ─NH₂group and a CO₂adsorption mechanism like amines, but with many additional advantages. Besides CO₂absorption in solvent form, amino acids/AAILs‐functionalized porous sorbents demonstrate potential in CO₂adsorption technology, a promising alternative to solvent‐based CO₂absorption technology, as they can avoid the huge energy penalty associated with aqueous solution regeneration by heating. Additionally, amino acids/AAILs, with their CO₂capture abilities, have demonstrated their potential in other promising CO₂sequestration technologies: direct air capture, CO₂mineralization using alkaline industrial waste, and conversion of CO₂into value‐added products. This article reviews the mechanism, comparative performance, and prospects of amino acid‐based state‐of‐the‐art technologies for CO₂absorption and adsorption, direct air capture, bio‐mineralization, and conversion of CO₂into value‐added products, which is helpful for the further development of amino acid‐based CO₂sequestration technologies. 

Abstract Under-oil open microfluidic system, utilizing liquid-liquid boundaries for confinements, offers inherent advantages including clogging-free flow channels, flexible access to samples, and adjustable gas permeation, making it well-suited for studying multi-phase chemical reactions that are challenging for closed microfluidics. However, reports on the novel system have primarily focused on device fabrication and functionality demonstrations within biology, leaving their application in broader chemical analysis underexplored. Here, we present a visualization-enhanced under-oil open microfluidic system for in situ characterization of multi-phase chemical reactions with Raman spectroscopy. The enhanced system utilizes a semi-transparent silicon (Si) nanolayer over the substrate to enhance visualization in both inverted and upright microscope setups while reducing Raman noise from the substrate. We validated the system’s chemical stability and capability to monitor gas evolution and gas-liquid reactions in situ. The enhanced under-oil open microfluidic system, integrating Raman spectroscopy, offers a robust open-microfluidic platform for label-free molecular sensing and real-time chemical/biochemical process monitoring in multi-phase systems. 

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si–Si, Si–O, and O–O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields.