## 9 Conclusions
Therefore, understanding the nature of intermediates formed and tailoring treatment sequences accordingly is essential to ensure synergistic performance. Energy efficiency is a critical factor for real-world deployment of photocatalytic systems, particularly in large-scale or decentralized applications. Metrics such as quantum yield (QY), which measures the number of pollutant molecules degraded per photon absorbed, and electrical energy per order (EEO), which quantifies the energy required to achieve a 90% pollutant reduction, are increasingly used to assess and compare system performance. These indicators provide practical insights into the energy cost of treatment, allowing researchers and engineers to optimize operational parameters and select materials that balance efficiency with economic feasibility. Solar-driven photocatalytic systems, including flat plate, parabolic trough, and compound parabolic concentrators, demonstrate the strong potential for decentralized treatment in low-resource and remote settings. These configurations support low-carbon, off-grid applications and align with global efforts to promote energy-efficient water purification technologies. However, their performance remains sensitive to fluctuations in solar irradiance and weather conditions, necessitating robust design and adaptive operational strategies. Smart materials, such as magnetically recoverable photocatalysts and stimuli-responsive systems, offer operational advantages by enabling catalyst separation and potential reuse. However, practical implementation is still challenged by factors such as magnetic particle aggregation, reduced recovery efficiency over time, and loss of photocatalytic activity after multiple cycles. Addressing these limitations requires advances in material design to enhance stability, dispersibility, and long-term performance under realistic water treatment conditions. The integration of artificial intelligence (AI) and machine learning (ML) into photocatalyst development and process optimization holds strong transformative potential. These tools can accelerate material screening, predict performance parameters, and support real-time process control. However, their effectiveness is currently limited by the scarcity of high-quality, domain-specific datasets, which can constrain model training and reduce prediction reliability. Advancing this integration will require standardized data collection protocols and open-access repositories to support reproducible and data-driven photocatalysis research. Key contributions of this work include:  
A structured analysis of photocatalytic materials, with a focus on visible-light-active semiconductors, their functional modifications (e.g., doping, heterojunctions), and challenges related to recombination and stability. * A critical synthesis of operational conditions (e.g., pH, catalyst dose, light intensity), showing how they influence the photocatalytic performance and identifying bottlenecks in optimization and reproducibility. * A comparative evaluation of reactor configurations (slurry, immobilized, annular, membrane), including their advantages and limitations for field-scale deployment. * Discussion of integrated and hybrid systems, such as adsorption-photocatalysis and membrane-photocatalysis, emphasizing how such combinations extend treatment capability and enhance process synergy. * Inclusion of real-world applications and pilot-scale demonstrations, emphasizing the role of reactor scalability, solar integration, and process automation in bringing laboratory innovations closer to practical implementation. In terms of practical applications, photocatalysis offers significant advantages: operation without added chemicals, use of solar energy, and potential for integration into circular water systems. However, its widespread adoption depends on overcoming barriers such as energy costs, catalyst durability, scalability, and safety concerns regarding nanomaterials.