Photocatalytic CO₂ reduction is a most promising technique to capture CO₂ and reduce it to non-fossil fuel and other valuable compounds. Today, we are facing serious environmental issues due to the usage of excessive amounts of non-renewable energy resources. In this aspect, photocatalytic CO₂ reduction will provide us with energy-enriched compounds and help to keep our environment clean and healthy. For this purpose, various photocatalysts have been designed to obtain selective products and improve efficiency of the system. Semiconductor materials have received great attention and have showed good performances for CO₂ reduction. Titanium dioxide has been widely explored as a photocatalyst for CO₂ reduction among the semiconductors due to its suitable electronic/optical properties, availability at low cost, thermal stability, low toxicity, and high photoactivity. Inspired by natural photosynthesis, the artificial Z-scheme of photocatalyst is constructed to provide an easy method to enhance efficiency of CO₂ reduction. This review covers literature in this field, particularly the studies about the photocatalytic system, TiO₂ Z-scheme heterojunction composites, and use of transition metals for CO₂ photoreduction. Lastly, challenges and opportunities are described to open a new era in engineering and attain good performances with semiconductor materials for photocatalytic CO₂ reduction.

Perovskite solar cells achieve remarkable power conversion efficiencies, yet operational stability remains a major barrier to large‐scale deployment. Reliable and rapid assessment of device state of health is therefore essential. Conventional electrical diagnostics, such as illuminated current–voltage (J–V) sweeps, provide accurate performance metrics but are time‐consuming and do not resolve spatially localized degradation, motivating non‐invasive imaging‐based alternatives. Here, a deep‐learning framework is introduced to estimate PSC efficiency retention, R _PCE = PCE _t /PCE ₀ , directly from multimodal luminescence imaging acquired during device aging. Each sample combines electroluminescence (EL), open‐circuit photoluminescence (PL _oc ), and short‐circuit photoluminescence (PL _sc ) at an aged state with device‐specific reference images at t  = 0, enabling learning of degradation‐relevant spatial changes. LumPerNet, a compact convolutional neural network, is benchmarked against a spatially homogenized control in which each luminescence channel is replaced by its spatial average while retaining the same learning framework and leakage‐aware protocol. The comparison indicates that global luminescence evolution contains most of the predictive signal, while spatial information provides a secondary contribution to robustness. These results establish spatially resolved luminescence imaging as a practical route for accelerated stability testing and non‐invasive degradation monitoring in perovskite photovoltaics.