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The MXene‐supported single transition metal systems have been reported as promising electrocatalysts for hydrogen evolution reaction (HER) and carbon dioxide reduction reaction. Herein, the potential performance of MXene‐based catalysts was explored on nitrogen reduction reaction (NRR). Density functional theory computations are carried out to screen a series of transition metal atoms confined in a vacancy of MXene nanosheet (Mo2TiC2O2). The results reveal that the Zr, Mo, Hf, Ta, W, Re, and Os supported on defective Mo2TiC2O2 layer can significantly promote the NRR process. Among them, Zr‐doped single atom catalyst (Mo2TiC2O2‐ZrSA) possesses the lowest barrier (0.15 eV) of the potential‐determining step, as well as high selectivity over HER competition. To the best of knowledge, 0.15 eV is the lowest barrier of potential‐determining step that has been reported for NRR so far. Besides, the formation energy of Mo2TiC2O2‐ZrSA is much more negative than that of the synthesized Mo2TiC2O2‐PtSA catalyst, suggesting that the experimental preparation of Mo2TiC2O2‐ZrSA is feasible. This work thus predicts an efficient electrocatalyst for the reduction of N2 to NH3 at ambient conditions.

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Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo. These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [R2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily (R2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy (R2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily (R2 < 0.3 and RPD < 1.2). This study suggested the potential usefulness of hyperspectral imaging as a high-throughput phenotyping technology for plant chemical traits. Future research is needed to test the method more thoroughly by designing experiments to vary plant nutrients individually and cover more plant species, genotypes, and growth stages.