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The natural sunlight‐driven conversion of CO2 into valuable C2+ products is urgently being pursued at ambient pressure, yet it poses a substantial challenge. Herein, a bifunctional Cu‐promoted CoFe alloy catalyst is designed for the natural light‐driven CO2 hydrogenation into light olefins. Under a weak solar‐irradiation intensity of 0.45 kW m−2 (0.45 sun), the optimal catalyst exhibits excellent activity and selectivity, with an impressive 73.7% selectivity for C2+ hydrocarbons and an outstanding 56.5% selectivity for C2‐4 olefins, which is the best catalyst for C2+ hydrocarbons photosynthesis from natural sunlight to date. The bifunctional design of the catalyst combines the advantages of both metallic Cu and CoFe alloy components, providing a synergistic effect that enhances the CO2 hydrogenation performance. The Cu promoter plays a crucial role in enhancing the adsorption of CO2 and hydrogen spillover, while the CoFe alloy provides a stable coupling site of the C1 intermediate for promoting the C2‐4 olefins. This study provides new insights into the design of bifunctional catalysts for CO2 hydrogenation and opens up new possibilities for sustainable production of light olefins from renewable resources. [ABSTRACT FROM AUTHOR]

Background: Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC)., Objective: This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC., Methods: This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features., Results: Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90., Conclusion: The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.