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An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients. • Developed Bayesian optimization decision tree models to estimate CHL-a in Bay of Bengal • Effectively utilized satellite remote sensing data for training and testing models • CHL-a demonstrated a larger positive relationship with POC and CDOM. • XGBoost outperforms other models in predicting spatial distribution of CHL-a. [ABSTRACT FROM AUTHOR]

Hybrid organic–inorganic hetero-structures, especially inorganic two-dimensional (2D) materials and organic semiconductors are attracting great attention for optoelectronic applications. The quantum confinement effects of 2D materials and their low-cost processing and high performance in devices enable a high degree of tunable optoelectronic properties for these heterostructure. Herein, we attempted to doped poly(3-hexylthiophene) (P3HT) with 2D nanosheets of Molybdenum sulfide (MoS 2 ) to improve its mobility. We synthesized 2D MoS 2 nanosheets using ultrasonication method for exfoliation of MoS 2 . The successful synthesis of 2D MoS 2 monolayers is evidenced through substantial characterization viz Atomic Force Microscopy and UV-visible Spectroscopy. The thickness of these nanosheets varies between 8 and 10 nm. The doping of MoS 2 significantly improved the mobility of the P3HT as evident from the enhanced drain current of organic field effect transistors (OFET) as compared to the undoped P3HT. [ABSTRACT FROM AUTHOR]