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Thermoelectric materials play a crucial role in converting heat into electricity, offering significant potential for applications in waste heat recovery and cooling. Herein, an innovative approach that combines an organic–inorganic hybrid superlattice structure with nanocrystal‐amorphous composite nanolayers is introduced. The nanocrystal‐amorphous composite enhances the Seebeck coefficient resulting in a notable twofold improvement in the power factor. The superlattice, alternating self‐assembled organic monolayers and inorganic nanolayers, effectively reduces lattice thermal conductivity by creating multiple interfaces that scatter phonons effectively. The integration of the nanocrystal‐amorphous composite nanolayers into the superlattice provides a dual advantage, simultaneously boosting the power factor and suppressing thermal conductivity. This synergistic effect leads to exceptional thermoelectric performance in the 4‐mercaptophenol/Sb2Te3 superlattice, with achieved figure of merit (ZT) values of 3.48 at 300 K and reaching a peak ZT value exceeding 4.0 at 400 K while surpassing 2.5 over the temperature range from 300 to 500 K. These results suggest that this innovative approach paves the way for the development of highly efficient thermoelectric materials, propelling efforts toward more energy‐efficient and environmentally friendly solutions. [ABSTRACT FROM AUTHOR]

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility. [ABSTRACT FROM AUTHOR]