Enhancing Electrochemical CO2 Reduction on Perovskite Oxide for Solid Oxide Electrolysis Cells through In Situ A-Site Deficiencies and Surface Carbonate Deposition Induced by Lithium Cation Doping and Exsolution.

Solid oxide electrolysis cells (SOECs) hold enormous potential for efficient conversion of CO2 to CO at low cost and high reaction kinetics. The identification of active cathodes is highly desirable to promote the SOEC's performance. This study explores a lithium-doped perovskite La0.6- x Lix Sr0.4 Co0.7 Mn0.3 O3-δ (x = 0, 0.025 0.05, and 0.10) material with in situ generated A-site deficiency and surface carbonate as SOEC cathodes  for CO2 reduction. The experimental results indicate that the SOEC with the La0.55 Li0.05 Sr0.4 Co0.7 Mn0.3 O3-δ cathode exhibits a current density of 0.991 A cm-2 at 1.5 V/800 °C, which is an improvement of ≈30% over the pristine sample. Furthermore, SOECs based on the proposed cathode demonstrate excellent stability over 300 h for pure CO2 electrolysis. The addition of lithium with high basicity, low valance, and small radius, coupled with A-site deficiency, promotes the formation of oxygen vacancy and modifies the electronic structure of active sites, thus enhancing CO2 adsorption, dissociation process, and CO desorption steps as corroborated by the experimental analysis and the density functional theory calculation. It is further confirmed that Li-ion migration to the cathode surface forms carbonate and consequently provides the perovskite cathode with an impressive anti-carbon deposition capability, as well as electrolysis activity.

A comparative study on environmental performance of 3D printing and conventional casting of concrete products with industrial wastes.

3D printing construction techniques are believed to have potential sustainability benefits, including improved resource efficiency, increased construction productivity, and construction of complex geometries without supporting structures. 3D printable concrete materials, when introducing industrial wastes such as fly ash, silica fume, and slag, may also bring additional sustainability benefits. These advantages need to be verified quantitatively. This study investigated the environmental impact of 3D printable concrete materials using industrial wastes compared with the conventional ones via life cycle assessment (LCA). Two types of concrete materials applied in concrete casting or 3D printing were compared, that is, cement-based concrete and geopolymer concrete. The results indicate that using waste materials as cement replacement could bring environmental benefits; however, such environmental benefits might be diminished with increasing activator content in geopolymer concrete for 3D concrete printing. Based on the material-level LCA results, this study further conducted an LCA study at the component level, which investigated the life-cycle environmental impact of concrete components of different shapes constructed by Contour Crafting method. Results show that the potential environmental benefit of 3D concrete printing increases with the level of building complexity while decreases with the reuse times of formwork, which leads to the conclusion that 3D concrete printing method is more desirable for constructing non-repetitive freeform concrete structures.

A comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks.

While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations-the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.