Managing Interfacial Defects and Charge-Carriers Dynamics by a Cesium-Doped SnO2 for Air Stable Perovskite Solar Cells.

A high-quality nanostructured tin oxide (SnO2) has garnered massive attention as an electron transport layer (ETL) for efficient perovskite solar cells (PSCs). SnO2 is considered the most effective alternative to titanium oxide (TiO2) as ETL because of its low-temperature processing and promising optical and electrical characteristics. However, some essential modifications are still required to further improve the intrinsic characteristics of SnO2, such as mismatch band alignments, charge extraction, transportation, conductivity, and interfacial recombination losses. Herein, an inorganic-based cesium (Cs) dopant is used to modify the SnO2 ETL and to investigate the impact of Cs-dopant in curing interfacial defects, charge-carrier dynamics, and improving the optoelectronic characteristics of PSCs. The incorporation of Cs contents efficiently improves the perovskite film quality by enhancing the transparency, crystallinity, grain size, and light absorption and reduces the defect states and trap densities, resulting in an improved power conversion efficiency (PCE) of ≈22.1% with Cs:SnO2 ETL, in-contrast to pristine SnO2-based PSCs (20.23%). Moreover, the Cs-modified SnO2-based PSCs exhibit remarkable environmental stability in a relatively higher relative humidity environment (>65%) and without encapsulation. Therefore, this work suggests that Cs-doped SnO2 is a highly favorable electron extraction material for preparing highly efficient and air-stable planar PSCs.

Individualized estimation of arterial carbon dioxide partial pressure using machine learning in children receiving mechanical ventilation.

BACKGROUND: Measuring arterial partial pressure of carbon dioxide (PaCO2) is crucial for proper mechanical ventilation, but the current sampling method is invasive. End-tidal carbon dioxide (EtCO2) has been used as a surrogate, which can be measured non-invasively, but its limited accuracy is due to ventilation-perfusion mismatch. This study aimed to develop a non-invasive PaCO2 estimation model using machine learning. METHODS: This retrospective observational study included pediatric patients (< 18 years) admitted to the pediatric intensive care unit of a tertiary children's hospital and received mechanical ventilation between January 2021 and June 2022. Clinical information, including mechanical ventilation parameters and laboratory test results, was used for machine learning. Linear regression, multilayer perceptron, and extreme gradient boosting were implemented. The dataset was divided into 7:3 ratios for training and testing. Model performance was assessed using the R2 value. RESULTS: We analyzed total 2,427 measurements from 32 patients. The median (interquartile range) age was 16 (12-19.5) months, and 74.1% were female. The PaCO2 and EtCO2 were 63 (50-83) mmHg and 43 (35-54) mmHg, respectively. A significant discrepancy of 19 (12-31) mmHg existed between EtCO2 and the measured PaCO2. The R2 coefficient of determination for the developed models was 0.799 for the linear regression model, 0.851 for the multilayer perceptron model, and 0.877 for the extreme gradient boosting model. The correlations with PaCO2 were higher in all three models compared to EtCO2. CONCLUSIONS: We developed machine learning models to non-invasively estimate PaCO2 in pediatric patients receiving mechanical ventilation, demonstrating acceptable performance. Further research is needed to improve reliability and external validation.