Multiparticle nanofluid optimization for spectral-splitting energy harvesting

Power production from renewable sources has advanced significantly in the United States in the past decade. Hybrid photovoltaic-thermal systems based on nanofluid spectral-splitting are one example of solar energy harvesting systems that have received considerable attention from the scientific community due to their dual energy conversion capacity. One of the most important aspects of these systems is their capability of transmitting and absorbing solar radiation only in certain ranges of the spectrum. Therefore, the task of finding the best optical fluid filter to be used is essential. In this work, a multiparticle opti-mization routine was developed for selection of the best particle parameters, such as volume fraction and size, for three different base fluids (water, ethylene glycol, and Therminol VP-1) and solar cells (including Si, GaAs, and GaInP/GaAs). A demonstration of this optimization routine based on the filter efficiency metric was performed where efficiency values near 40% were obtained for Si and GaAs solar cells, including using a lower-cost nanofluid solution. Additionally, novel insights on the parameters affecting the plasmon resonance and damping of indium tin oxide nanocrystals are detailed. The findings in this work provide a pathway for the development of nanofluid solutions as optical filters for photovoltaic-thermal systems. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Renewable energy power production has made significant advancements in the United States over the past decade, with hybrid photovoltaic-thermal systems based on nanofluid spectral-splitting garnering attention from the scientific community. This study developed an optimization routine to select the best particle parameters and provided insights on factors affecting the use of nanofluid solutions as optical filters for photovoltaic-thermal systems.