Hybrid PV/T enhancement using selectively absorbing Ag–SiO2/carbon nanofluids

By Natasha E. Hjerrild, Sara Mesgari, F. Crisostomo, Jason A. Scott, R. Amal, R. Taylor. 2016

239 citations recorded by the source corpus.

Abstract Optical filters can be used to increase solar conversion efficiency in hybrid photovoltaic/thermal (PV/T) collectors by partitioning incident light into two spectra: one desirable for direct electrical conversion and one for thermal collection. This article is the first to present both modeled and experimental results for a spectrally-tailorable, multi-particle nanofluid filter positioned between a concentrated light source and a silicon cell. The nanofluid is composed of suspended core–shell silver-silica (Ag–SiO2) nanodiscs and carbon nanotubes (CNTs) in water. The core–shell particles were specifically synthesized and designed to absorb the majority of the visible spectrum, while transmitting the light which corresponds to the PV cell. The silver nanodiscs strongly absorb visible light with minimal scattering, whereas the silica shell maintains the shape and absorption spectrum of the silver cores. Alternatively, low-concentration carbon nanotube (CNT) solutions were used to enhance absorption (particularly of ultra-violet light) and to provide a comparison for selective filters versus broadband absorbers. Varying dilutions of the Ag–SiO2 nanofluid are compared to solutions diluted with dispersed CNTs. The CNTs enhance the heating rate of the nanofluid with the caveat of non-selective light absorption, which reduces the electrical output. Ag–SiO2 nanofluids (0.026 wt%) increased combined efficiencies by 30% compared to the base fluid filter alone. For a small additional cost of

Subfields: Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/1b3e14b15301ad57e800f4eb5b8adcbd03eeadd8

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In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network (2018, background): “…or so, describing the nanofluid's effect on the PV/T systems in terms of the overall performance and thermal output (Sardarabadi and Fard, 2016; Hjerrild et al., 2016; Sardarabadi et al., 2014; Radwan et al., 2016; An et al., 2016; Khanjari et al., 2016; Hussien et al., 2015; Manikandan and…”