What does this research mean for the field?

Selective spectral filtering of crystalline silicon photovoltaic modules reduces surface temperature by approximately 10°C, functioning primarily as a thermal mitigation strategy rather than a direct power enhancement technique. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To experimentally assess the impact of selective spectral filtering on the thermal management and electrical performance of crystalline silicon photovoltaic modules in tropical outdoor environments.

September 3, 2026International Journal of ThermodynamicsOpen Access

Spectral Selective Filtering for Thermal Mitigation and Efficiency Enhancement of Crystalline Silicon Photovoltaic Modules under Tropical Climatic Conditions: Experimental Investigation

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Authors

GDGajanan Datar CLChandrakishor L. Ladekar

Discussion

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Overview

Experimental study demonstrates that spectral filtering reduces operating temperature by 10°C in silicon photovoltaic modules, indicating its utility for thermal mitigation rather than power boost.

Key Points

To experimentally assess the impact of selective spectral filtering on the thermal management and electrical performance of crystalline silicon photovoltaic modules in tropical outdoor environments.
Evaluated 20 W monocrystalline and polycrystalline photovoltaic modules under natural pre-monsoon outdoor conditions in Pune, India.
Tested four distinct optical bands (400–450 nm, 450–570 nm, 570–750 nm, and 750–1100 nm) against full-spectrum exposure at an irradiance of 1000 W/m².
The 570–750 nm band produced 15.49 W of output power in the monocrystalline module, achieving a 25% band-limited photon utilization efficiency, but an overall system efficiency of 11.06% compared to 14.06% under full-spectrum irradiance.
Selective spectral filtering decreased module surface temperature by approximately 10°C, which corresponds to an estimated power retention benefit of about 4.5%.
Monocrystalline modules exhibited superior spectral response and lower recombination sensitivity compared to polycrystalline modules.

Cite This Study

Datar et al. (2026) studied this question.

synapsesocial.com/papers/6a993592636c6408cfa7de96 https://doi.org/10.5541/ijot.1903135

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September 3, 2026International Journal of ThermodynamicsOpen Access

Spectral Selective Filtering for Thermal Mitigation and Efficiency Enhancement of Crystalline Silicon Photovoltaic Modules under Tropical Climatic Conditions: Experimental Investigation

View Full Paper

Ask AI

Bookmark

Authors

GDGajanan Datar CLChandrakishor L. Ladekar

Discussion

Member takes

Overview

Experimental study demonstrates that spectral filtering reduces operating temperature by 10°C in silicon photovoltaic modules, indicating its utility for thermal mitigation rather than power boost.

Key Points

To experimentally assess the impact of selective spectral filtering on the thermal management and electrical performance of crystalline silicon photovoltaic modules in tropical outdoor environments.
Evaluated 20 W monocrystalline and polycrystalline photovoltaic modules under natural pre-monsoon outdoor conditions in Pune, India.
Tested four distinct optical bands (400–450 nm, 450–570 nm, 570–750 nm, and 750–1100 nm) against full-spectrum exposure at an irradiance of 1000 W/m².
The 570–750 nm band produced 15.49 W of output power in the monocrystalline module, achieving a 25% band-limited photon utilization efficiency, but an overall system efficiency of 11.06% compared to 14.06% under full-spectrum irradiance.
Selective spectral filtering decreased module surface temperature by approximately 10°C, which corresponds to an estimated power retention benefit of about 4.5%.
Monocrystalline modules exhibited superior spectral response and lower recombination sensitivity compared to polycrystalline modules.

Cite This Study

Datar et al. (2026) studied this question.

synapsesocial.com/papers/6a993592636c6408cfa7de96 https://doi.org/10.5541/ijot.1903135

View Full Paper

Ask AI

Bookmark