A comprehensive dataset of photonic features on spectral converters for energy harvesting

Multi-Surface Adhesion Luminescent Solar Concentrators for Supply-Less IoT

The growing prevalence of Internet of Things (IoT) devices hinges on resolving the challenge of powering sensors and transmitters. Addressing this, supply-less IoT devices are gaining traction by integrating energy harvesters. This study introduces a temperature sensor devoid of external power sources, achieved through a novel luminescent solar concentrator (LSC) device based on a stretchable, adhesive elastomer. Leveraging a lanthanide-doped styrene-ethylene-butylene-styrene matrix, the LSC yielded 0.09% device efficiency. The resultant temperature sensor exhibits a thermal sensitivity of 2.1%°C-1 and a 0.06 °C temperature uncertainty, autonomously transmitting real-time data to a server for user visualization via smartphones. Additionally, the integration of LED-based lighting enables functionality in low-light conditions, ensuring 24 h cycle operation and the possibility of having four distinct thermometric parameters without changing the device configuration, stating remarkable robustness and reliability of the system.

Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning

Building-integrated photovoltaics (BIPV) is an emerging technology in the solar energy field. It involves using luminescent solar concentrators to convert traditional windows into energy generators by utilizing light harvesting and conversion materials. This study investigates the application of machine learning (ML) to advance the fundamental understanding of optical material design. By leveraging accessible photoluminescent measurements, ML models estimate optical properties, streamlining the process of developing novel materials, offering a cost-effective and efficient alternative to traditional methods, and facilitating the selection of competitive materials. Regression and clustering methods were used to estimate the optical conversion efficiency and power conversion efficiency. The regression models achieved a Mean Absolute Error (MAE) of 10%, which demonstrates accuracy within a 10% range of possible values. Both regression and clustering models showed high agreement, with a minimal MAE of 7%, highlighting the efficacy of ML in predicting optical properties of luminescent materials for BIPV.