Toward reliable characterization of nano-enabled indoor photovoltaics: accounting for device-dependent errors

Abstract Nano-enabled indoor photovoltaics (IPVs), based on molecular, nano-, and nanostructured absorbers, are emerging as promising energy harvesters for battery-free smart devices and internet-of-things sensor nodes. Despite rapid performance improvements, reliable characterization remains a critical challenge, with device-dependent errors arising from test light sources and measurement instrumentation representing a major contributor. Here, we establish the irradiance exponent σ in the photocurrent-irradiance relationship as a central, device-specific parameter governing these errors. We present analytical models linking temporal instability, spatial non-uniformity, and luxmeter inaccuracies to IPV efficiency characterization errors, demonstrating that σ strongly modulates error propagation. A literature survey of relevant absorbers shows that σ spans 0.53–2.39, highlighting the need to explicitly account for device-specific behavior to ensure accurate IPV characterization. Leveraging our models and considering a realistic σ range, we demonstrate that the current IPV standards, which adopt device-independent approaches, can result in misleading evaluations of IPV characterization accuracy. To address this issue, we introduce a new device-aware framework—with error metrics and an upgraded classification scheme for IPV measurements—that builds on existing IPV standards while overcoming their limitations, enabling more accurate and reliable measurement of nano-enabled IPVs. Our findings provide a reliable foundation for the future exploration and validation of novel IPV materials and device designs based on molecular, nano-, and nanostructured absorbers.