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Lead (Pb) halide perovskite solar cells (PSCs) exhibit impressive power conversion efficiencies close to those of their silicon counterparts. However, they suffer from moisture instability and Pb safety concerns. Previous studies have endeavoured to address these issues independently, yielding minimal advancements. Here, a general nanoencapsulation platform using natural polyphenols is reported for Pb‐halide PSCs that simultaneously addresses both challenges. The polyphenol‐based encapsulant is solution‐processable, inexpensive (≈1.6 USD m−2), and requires only 5 min for the entire process, highlighting its potential scalability. The encapsulated devices with a power conversion efficiency of 20.7% retained up to 80% of their peak performance for 2000 h and up to 70% for 7000 h. Under simulated rainfall conditions, the encapsulant rich in catechol groups captures the Pb ions released from the degraded perovskites via coordination, keeping the Pb levels within the safe drinking water threshold of 15 ppb. [ABSTRACT FROM AUTHOR]

The phenomenon of the self‐formation of a passivation layer at the interface of the perovskite/electron‐transport layer (ETL) is observed. FA0.6MA0.4PbI3−xClx perovskite thin film is deposited on a SnO2 nanoparticle thin‐film ETL. It is observed from the depth‐resolved spectroscopy that the Sn2+ ion migrates toward the perovskite layer within the ETL. At the same time, Cl− ion also migrates toward ETL within the perovskite layer. This unique ion migration phenomenon leads us to conclude that a passivating SnCl2 layer is formed at the perovskite/ETL interface. It is found that this SnCl2 layer at the interface works as a passivation layer like Al2O3. There is a significant effect of this self‐formed passivating layer behind the improvement of the device's efficiency and stability. It is believed that this SnCl2 passivation layer helps to reduce the recombination loss at the interface and boosts the performance of the perovskite solar cell (PSC). The perovskite/hole‐transport layer is also passivated with octylammonium bromide. Finally, the PSC offers a photoconversion efficiency (PCE) of 20.81% under 1 sun and AM1.5 G condition. Again, it maintains more than 80% of PCE under open‐air room conditions, white light emitting diode, and 85 °C continuous heating for more than 12 h without encapsulation. [ABSTRACT FROM AUTHOR]

This study aimed to develop a geochemical database by thoroughly analyzing groundwater and sediments from coastal aquifers of southwest Bangladesh. Moreover, we investigated the source of sediment deposition and the mechanisms behind the presence of arsenic and salinity in groundwater. The seasonal distribution patterns of arsenic among the shallow and deep coastal aquifers were found to be 45.12 µg/l and 20.65 µg/l during dry and wet seasons, respectively. Moreover, the groundwater salinity distribution ranged from 3262.88 mg/l to 1930.88 mg/l during the dry and wet seasons. Cored sediment samples showed fine to medium sands of 92%, with silt and clay particles. The petrographic study of authigenic and heavy minerals revealed that the mineral grains were subangular to angular, indicating their textural immaturity of coastal sediments. The reactivity of goethite (FeOOH) and siderite (FeCO3) minerals suggests that the aquifers were subjected to slightly oxidized to moderately reducing conditions, with ORP values ranging from +50.40 mv to −149.5 mv. Such redox conditions could potentially result in the enrichment and mobility of arsenic in the groundwater. Although arsenic concentrations in deep aquifers are relatively low, higher salinity values are found in both shallow and intermediate coastal aquifers. [ABSTRACT FROM AUTHOR]

Banks rely heavily on loans as a primary source of revenue; however, distinguishing deserving applicants who will reliably repay loans presents an ongoing challenge. Conventional selection processes often struggle to identify the most suitable candidates from a pool of loan applicants. In response to this challenge, we present an innovative machine learning (ML) based loan prediction system designed to identify qualified loan applicants autonomously. This comprehensive study encompasses data preprocessing, effective data balancing using SMOTE, and the implementation of diverse ML models, including Logistic Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, AdaBoost, Gradient Boosting, and advanced deep learning models such as deep neural networks, recurrent neural networks, and long short-term memory models. The model’s performance is rigorously assessed in terms of accuracy, recall, and F1_score. Our experimental analysis reveals that the Extra Trees outperforms its counterparts. Furthermore, we successfully predict bank loan defaulters through an ensemble voting model, which includes the top three ML models, achieving a remarkable 0.62% increase in accuracy compared to the Extra Trees. To facilitate user interaction, we have developed a user-friendly desktop-based application. Notably, our findings demonstrate that the voting-based ensemble model surpasses both individual ML models, including Extra Trees, and existing stateof-the-art approaches, achieving an impressive accuracy of 87.26%. This innovative system has the potential to significantly streamline and enhance the efficiency of bank loan approval processes, ultimately benefiting both financial institutions and loan applicants alike. [ABSTRACT FROM AUTHOR]

Perovskite solar cell (PSCs) is considered as the game changer in emerging photovoltaics technology. The highest certified efficiency is 22% with high temperature processed (∼500 °C) TiO2 based electron transport layer (ETL). High temperature process is a rudimentary hindrance towards roll-to-roll processing of PSCs on flexible substrates. Low temperature solution process (

Universities, industry and government organizations all play an important role in growth and development of knowledge-based economies in the modern era. These institutions also play a significant role in knowledge creation and its deployment to the benefit of society at large. In this article, we measure and characterize the university-industry-government (UIG) relationship in the research and innovation landscape of India. Research output data for 10 years (2005-14) obtained from Web of Science have been analysed to measure collaboration among different actors of the UIG collabor ation network. We have also measured the collaboration variations across different disciplines and identified significant UIG institutional networks. The article presents useful output and analysis, and an informative account of the UIG collaboration network at present. [ABSTRACT FROM AUTHOR]

Zinc oxide (ZnO) tetrapods have received much interest due to their unique morphology, that is, four arms connected to one centre. Tetrapod networks possess the excellent electronic properties of the ZnO semiconductor, which is attractive for photoelectrode materials in energyconversion devices because of their advantages in electron extraction and transportation. In this review, we have discussed recent advancements in the field of ZnO tetrapod synthesis, including vapour transport synthesis and the wet chemical method, together with their advantages and disadvantages in terms of morphology control and yield regulation. The developments and improvements in the applications of ZnO nanotetrapods in photovoltaics, including dye-sensitized solar cells and polymer solar cells, are also described. Our aim is to give readers a comprehensive and critical overview of this unique morphology of ZnO, including synthesis control and growth mechanism, and to understand the role of this particular morphology in the development of solar cells. The future research directions in ZnO tetrapods-based solar cell are also discussed. [ABSTRACT FROM AUTHOR]