Extending the Range of Distances Accessible by 19F Electron-Nuclear Double Resonance in Proteins Using High-Spin Gd(III) Labels.

Fluorine electron-nuclear double resonance (19F ENDOR) has recently emerged as a valuable tool in structural biology for distance determination between F atoms and a paramagnetic center, either intrinsic or conjugated to a biomolecule via spin labeling. Such measurements allow access to distances too short to be measured by double electron-electron resonance (DEER). To further extend the accessible distance range, we exploit the high-spin properties of Gd(III) and focus on transitions other than the central transition (|-1/2⟩ ↔ |+1/2⟩), that become more populated at high magnetic fields and low temperatures. This increases the spectral resolution up to ca. 7 times, thus raising the long-distance limit of 19F ENDOR almost 2-fold. We first demonstrate this on a model fluorine-containing Gd(III) complex with a well-resolved 19F spectrum in conventional central transition measurements and show quantitative agreement between the experimental spectra and theoretical predictions. We then validate our approach on two proteins labeled with 19F and Gd(III), in which the Gd-F distance is too long to produce a well-resolved 19F ENDOR doublet when measured at the central transition. By focusing on the |-5/2⟩ ↔ |-3/2⟩ and |-7/2⟩ ↔ |-5/2⟩ EPR transitions, a resolution enhancement of 4.5- and 7-fold was obtained, respectively. We also present data analysis strategies to handle contributions of different electron spin manifolds to the ENDOR spectrum. Our new extended 19F ENDOR approach may be applicable to Gd-F distances as large as 20 Å, widening the current ENDOR distance window.

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Surfactant-Free, Size-Controllable, and Scalable Green Synthesis of ZIF-8 Particles with Narrow Size Distribution by Tuning Key Reaction Parameters in Water Solvent.

The chemical/physical properties and reliable performance of nanoporous materials are strongly influenced by the particle size and corresponding distribution. Among many types of MOFs, ZIF-8, is still widely used and many studies have been conducted to control the particle size and uniformity of ZIF-8 using surfactants and organic solvents. However, the use of surfactants and organic solvents process is expensive and may cause environmental pollution. For the first time, in this paper, a surfactant-free, size-controllable, and scalable green synthesis method of ZIF-8 particles is reported using four reaction parameters (temperature, concentration, pouring time, and reactant ratio) that affect the formation of nuclei and growth of ZIF-8 crystals. The as-synthesized ZIF-8 nanoparticles show great uniformity and controllable particle sizes in the wide range of 147-915 nm. In addition, a 2 L large-scale synthesis of ZIF-8 with narrow size distribution is developed by finely tuned particle size in water without any additives. To demonstrate the efficient utilization of nanopores according to the particle size and size distribution, an adsorption test is conducted on the ZIF-8 nanoparticles. This study will support the synthesis of size-controlled ZIF-8 with narrow size distribution and their composites for achieving high performance in the emerging applications.