Perovskite solar cells with atomically coherent interlayers on SnO2 electrodes.

In perovskite solar cells, the interfaces between the perovskite and charge-transporting layers contain high concentrations of defects (about 100 times that within the perovskite layer), specifically, deep-level defects, which substantially reduce the power conversion efficiency of the devices1-3. Recent efforts to reduce these interfacial defects have focused mainly on surface passivation4-6. However, passivating the perovskite surface that interfaces with the electron-transporting layer is difficult, because the surface-treatment agents on the electron-transporting layer may dissolve while coating the perovskite thin film. Alternatively, interfacial defects may not be a concern if a coherent interface could be formed between the electron-transporting and perovskite layers. Here we report the formation of an interlayer between a SnO2 electron-transporting layer and a halide perovskite light-absorbing layer, achieved by coupling Cl-bonded SnO2 with a Cl-containing perovskite precursor. This interlayer has atomically coherent features, which enhance charge extraction and transport from the perovskite layer, and fewer interfacial defects. The existence of such a coherent interlayer allowed us to fabricate perovskite solar cells with a power conversion efficiency of 25.8 per cent (certified 25.5 per cent)under standard illumination. Furthermore, unencapsulated devices maintained about 90 per cent of their initial efficiency even after continuous light exposure for 500 hours. Our findings provide guidelines for designing defect-minimizing interfaces between metal halide perovskites and electron-transporting layers.

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons.

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules.

Risk of Primary Spontaneous Pneumothorax According to Chest Configuration.

BACKGROUND: We compared the chest configurations of patients with primary spontaneous pneumothorax (PSP) and age-sex-matched controls to determine the presence of chest wall deformities in patients with PSP. METHODS: We retrospectively enrolled 166 male patients with PSP (age, 18-19 years) and 85 age-sex-matched controls without PSP, who simultaneously underwent chest computed tomography (CT) and radiography at one of two institutes. After correcting for height, the following thoracic parameters were comparatively evaluated between the two groups: maximal internal transverse (T) and anteroposterior (W) diameters of the chest, maximal internal lung height (H), Haller index (T/W), and T/Height, T/H, W/Height, W/H, and H/Height ratios. RESULTS: Patients were taller than the control subjects (176.5 cm ± 5.9 cm versus 174.4 cm ± 5.6 cm; p = 0.007). After controlling for height, the patient group exhibited lower T and W and greater H and Haller index values than the control group (T: 95% confidence interval [CI], 24.8-25.2 cm versus 25.9-26.5; W: 95% CI, 8.9-9.2 cm versus 10.1-10.6 cm; H: 95% CI, 25.2-25.9 cm versus 23.4-24.4 cm; and Haller index, 95% CI, 2.7-2.9 versus 2.4-2.6; all, p < 0.001). The patient group also exhibited lower T/Height, T/H, W/Height, and W/H ratios and greater H/Height ratio than the control group. CONCLUSIONS: Patients with PSP have an anteroposteriorly flatter, laterally narrower, and craniocaudally taller thorax than subjects without PSP, suggesting that chest configuration is associated with the development of pneumothorax.

Relationship between recurrence and age in the diffuse sclerosing variant of papillary thyroid carcinoma: clinical significance in pediatric patients.

Background: The diffuse sclerosing variant (DSV) is among the aggressive variants of papillary thyroid carcinoma (PTC) and is more prevalent in pediatric patients than in adult patients. Few studies have assessed its characteristics owing to its low incidence. We aimed to evaluate the relationship between recurrence and age in the DSV of PTC. Methods: We retrospectively reviewed patients diagnosed with the DSV or conventional PTC (cPTC) after surgery at a medical center between May 1988 and January 2019. We compared the clinico-pathological characteristics and surgical outcomes of the DSV and cPTC groups and between adult and pediatric patients with DSV. Results: Among the 24,626 patients, 202 had the DSV, and 24,424 were diagnosed with cPTC. The recurrence rate was significantly higher in the DSV group than in the cPTC group. In the DSV group, the recurrence rate was significantly higher in the pediatric patient group than in the adult patient group. Moreover, the association between recurrence and age group showed different patterns between the DSV and cPTC groups with restricted cubic splines (RCS). While both RCS curves showed a U-shaped distribution, the RCS curve tended to be located within the younger age group. Conclusions: This study demonstrated that pediatric patients with DSV are at a greater risk for recurrence compared with adult patients; moreover, the pattern of recurrence risk according to age is different from that of cPTC.

GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning.

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6 pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.1.

Modulation of the optical bandgap and photoluminescence quantum yield in pnictogen (Sb3+/Bi3+)-doped organic-inorganic tin(IV) perovskite single crystals and nanocrystals.

Water-stable, lead-free zero-dimensional (0D) organic-inorganic hybrid colloidal tin(IV) perovskite, A2SnX6 (A is a monocationic organic ion and X is a halide) nanocrystals (NCs) with high photoluminescence (PL) quantum yield (QY) have rarely been explored. Herein, we report solution-processed colloidal NCs of blue light-emitting T2SnCl6 and orange light-emitting T2Sn1-xSbxCl6 [T+ = tetramethylammonium cation] from their corresponding single crystals (SCs). These colloidal NCs are well-dispersible in non-polar solvents, thereby maintaining their bright emission. This paves the way for fabricating homogeneous thin films of these NCs. Due to organic cation (T+)-controlled large spin-orbit coupling (SOC), the T2Sn1-xSbxCl6 NCs exhibit bright orange emission with an enhancement in PL QY of 41% compared to their bulk counterpart. Furthermore, we explore T2Sn1-xBixCl6 and T2Sn1-x-yBixSbyCl6 SCs, which show blue and green emission, respectively; the latter is attributed to the newly formed Sb 5p and Sb 5 s orbital-driven band structures confirmed by applying density functional theory (DFT) calculations. The SCs and NCs exhibit excellent stability in water under ambient conditions because of the in-situ generation of a hydrophobic and oxygen-resistant passivating layer of oxychloride in the presence of water. Our findings open a pathway for designing lead-free perovskites materials for thin-film-based optoelectronic devices.

Determination of critical decision points for COVID-19 measures in Japan.

Coronavirus disease 2019 (COVID-19) has spread throughout the world. The prediction of the number of cases has become essential to governments' ability to define policies and take countermeasures in advance. The numbers of cases have been estimated using compartment models of infectious diseases such as the susceptible-infected-removed (SIR) model and its derived models. However, the required use of hypothetical future values for parameters, such as the effective reproduction number or infection rate, increases the uncertainty of the prediction results. Here, we describe our model for forecasting future COVID-19 cases based on observed data by considering the time delay (tdelay). We used machine learning to estimate the future infection rate based on real-time mobility, temperature, and relative humidity. We then used this calculation with the susceptible-exposed-infectious-removed (SEIR) model to forecast future cases with less uncertainty. The results suggest that changes in mobility affect observed infection rates with 5-10 days of time delay. This window should be accounted for in the decision-making phase especially during periods with predicted infection surges. Our prediction model helps governments and medical institutions to take targeted early countermeasures at critical decision points regarding mobility to avoid significant levels of infection rise.