High ionic conduction in a robust anionic metal-organic framework containing Mg2+ under guest vapors.

We report on the synthesis and high ionic conductivity of a highly crystalline Mg2+-containing metal-organic framework (MOF) with Type A feature (i.e., anionic framework having Mg2+ as a counter cation). We synthesized Mg[Zr(C14H3O8)2] (SU-102-Mg) through ion exchange reaction. SU-102-Mg showed a high ionic conductivity of 3.6 × 10-5 S cm-1 (25 °C, under MeCN vapor).

In Situ Spectroscopic Study of CO2 Capture and Methanation over Ni-Ca Based Dual Functional Materials.

Carbon dioxide capture and reduction (CCR) to CH4 using dual-functional materials (DFMs) have recently attracted significant attention as a promising strategy for carbon capture and utilization. In this study, we investigate the mechanism of CCR to CH4 over Al2 O3 -supported Ni-Ca DFMs (Ni-Ca/Al2 O3 ) under cyclic feeds of model combustion exhaust (2.5 % CO2 +0 or 10 % O2 /N2 ) and H2 at 500 °C. Various spectroscopic analyses, including time-resolved in situ X-ray diffraction and X-ray absorption spectroscopy, were conducted during CO2 capture and the subsequent H2 -reduction steps. Based on these analyses, we propose a mechanism of CCR to CH4 over Ni-Ca based DFMs. During the CO2 capture step, the Ni0 species underwent complete oxidation in the presence of O2 to yield NiO. Subsequently, CO2 was captured through the interaction between the CaO surface and CO2 , resulting in the formation of CaCO3 layers on the CaO particles. When the gas flow was switched to H2 , NiO was partially to provide Ni0 sites, which acted as active sites for H2 -reduction of the adjacent CaCO3 layers to yield CaO and gas-phase products, CH4 and H2 O.

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Photoelectrochemical detection of ultra-trace fluorine ion using TiO2 nanorod arrays as a probe.

A photoelectrochemical (PEC) method based on the etching reaction of F ions on the surface of TiO2 nanorod arrays (TNRs) was proposed for the high sensitivity and selectivity detection of F ions. With the increase of F ion concentration, the surface etching reaction on TNR becomes more intense, resulting in the increased number of surface active sites, the reduction of electron transfer resistance, and the increase of photocurrent density. The prepared TNRs as a PEC probe exhibits a good linear relationship between photocurrent increment and the logarithm of F ion concentration in the range from 0.05 to 1000 nM with an ultra-trace detection limit of 0.03 nM for F ion detection.