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Porous carbon is considered an effective adsorbent for CO2 uptake thanks to its high textural feature, tunable surface decoration, and stable chemical/physical characteristics. Herein, a one-pot self-activating synthesis approach has been introduced to fabricate disodium 2,6-naphthalene disulfonate (NDS)-derived self-S-doped porous carbon. With this method, there is no external chemical activating agents for the activation process, and the self-activating process occurs by releasing CO, H2O, and CO2 gases during pyrolysis treatment. It was found that activating temperatures can carefully control the porous textural and elemental compositions of the as-prepared carbons. Upon the activating process, the optimal S-doped porous carbon was prepared at 700 °C, providing CO2 uptake capacities of 2.36 and 3.56 mmol/g at 25 and 0 °C and 1 bar, respectively. An in-depth investigation indicates that the joint effect of narrow microporosity and S content determines the CO2 uptake for this series of carbons. In addition, these NDS-derived self-S-doped porous carbons exhibit moderate CO2 heats of adsorption, fast adsorption kinetics, reasonable CO2/N2 selectivities, good dynamic CO2 capture capacities, and stable recyclabilities. The presented synthesis method is promising for fabricating facile carbon-based adsorbents from various organic precursors.

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Objective. To explore the application value of the radiomics method based on enhanced T1WI in glioma grading. Materials and Methods. A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification. Results. The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905–0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871–1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%. Conclusions. The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.