Pnictogen-based semiconductors for Harvesting EneRgy from Ambient Light to power autonomous Devices (HERALD)

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HERALD aims to instigate a step-change in how smart devices are powered by developing new classes of pnictogen-based semiconductors to more efficiently collect the widely-available energy from lighting inside buildings. Such energy can be renewably harvested with indoor photovoltaics (IPV), which is highly appealing for powering the billions of autonomous smart devices driving the fourth industrial revolution. However, industry-standard IPV (hydrogenated amorphous silicon; a-Si:H) have efficiencies up to only ~20%, with most commercial devices <10% efficient.HERALD will develop IPV from novel classes of rudorffites and chalcohalides, which have potential to reach >48% efficiency under indoor lighting. These are low-toxicity, high-stability materials based on the pnictogens bismuth and antimony, and their considerable potential for indoor light harvesting is just starting to emerge. HERALD will transform these novel compounds into leading IPV using a hierarchical characterisation approach, from the macro- to near-atomic-scale. Along the way, fundamental understanding will be gained to learn what the performance-limiting factors are and how they can be systematically mitigated. The endpoint will be high-performing, durable test devices with low environmental impact. The materials will be rapidly grown at scale using a novel plasma-spray technique, and the IPV prototyped in commercial smart devices.The pnictogen-based IPV developed can have a transformative impact on smart devices by decreasing their reliance on being powered only by batteries, which need to be regularly replaced, creating significant waste. The new IPV can be deployed without harming the environment and will harvest more power than a-Si:H IPV to sustain smart devices with more advanced capabilities. The pioneering development of pnictogen-based semiconductors will also push them forward for numerous PV, from clean solar fuel production to radiation detection for medical imaging.