The development of redox-targeting co-catalysts is one of the important tasks in realizing hybrid photocatalytic systems for CO2 reduction reaction (CO2 RR), which has been sought after as a promising way to mitigate the energy and environmental crisis. In this study, hollow nickel hydroxide nanocages are successfully fabricated via an ion-assisted etching protocol using ZIF-8 as the structural template, and they are used as cocatalysts along with a molecular photosensitizer and sacrificial electron donor for reducing visible-light CO2. A remarkable CO evolution rate of 1.44 × 105 μmol·g ‑1 co‑cat·h–1, a CO selectivity of 96.1%, and a quantum efficiency of 2.50% are achieved using the optimal cavernous structure with thin walls, attributing to the significantly improved light harvest owing to multiple light reflection and scattering, static electron transfer, abundant surface oxygen vacancies, as well as coherent energy flow among well-aligned band levels. This study highlights the design and development of hollow entities toward CO2 RR and provides insights into the structure-mediated photocatalytic response.

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Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R 2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R 2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

The functionalized A/E/F ring system of C20-diterpenoid alkaloid racemulsonine has been efficiently synthesized. The Key steps involved a diastereoselective Au(I)-catalyzed annulation to form cis-fused cyclopentene and a PIDA promoted transannular aziridination of primary amine followed by regio- and stereoselective ring cleavage of bridged aziridine.

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