Surface Modification of a Lignin-Derived Carbon-Supported Co-Based Metal/Oxide Nanostructure for Alkaline Water Splitting.

Exploring low-cost and eco-friendly bifunctional electrocatalysts of the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in alkaline electrolytes is still highly desired, and is crucial for water electrolysis and sustainable hydrogen generation. In this work, we report a facile pyrolysis-oxidation strategy to convert by-product lignin into bifunctional OER/HER electrocatalysts (Co/Co3O4-NPC-400) composed of Co/Co3O4 anchored on N-doped carbon with a surface of rich oxygen vacancies and oxygen-containing groups. The co-pyrolysis of lignin and NH4Cl can achieve a N-doped carbon matrix with a hierarchical pore structure, while the air-annealing process can induce the formation of oxygen-containing groups and oxygen vacancies. Owing to its surface properties, hierarchical pore structure and multiple active components, the constructed Co/Co3O4-NPC-400 possesses bifunctional catalytic activity and superior stability for OER/HER, especially for unexpected OER activity with a high current density of about 320 mA∙cm-2 at a potential of 1.8 V (vs. RHE). Water electrolysis using Co/Co3O4-NPC-400 as both the anode and the cathode needs a cell voltage of 1.95 and 2.5 V to attain about 10 and 400 mA∙cm-2 in 1 M KOH. This work not only provides a general strategy for the preparation of carbon-supported electrocatalysts for water splitting, but also opens up a new avenue for the utilization of lignin.

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Then, we divided the data into two independent radiomic cohorts for training (70 COVID-19 patients and 70 other pneumonias patients), and validation (20 COVID-19 patients and 20 other pneumonias patients) by using support vector machine (SVM). This model used 20 rounds of tenfold cross-validation for training. Finally, single-shot testing of the final model was performed on the independent validation cohort. In the COVID-19 patients, correlation analysis (multiple comparison correction-Bonferroni correction, P < 0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The final model showed good discrimination on the independent validation cohort, with an accuracy of 89.83%, sensitivity of 94.22%, specificity of 85.44%, and AUC of 0.940. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some textural features were positively correlated with WBC, and NE, and also negatively related to SPO2H and NE. Our results showed that radiomic features can classify COVID-19 patients and other pneumonias patients. The SVM model can achieve an excellent diagnosis of COVID-19.