## 5 Conclusions  
This systematic review has explored the potential of microstrip antennas for RF energy harvesting applications, offering a detailed synthesis of antenna geometries, substrate materials, and rectifier system configurations. By analyzing 80 studies under the PRISMA methodology, the review identified dominant trends and design strategies across various RF harvesting contexts. Notably, fractal antennas demonstrated the highest efficiency among the geometries studied, while rectangular patch antennas remain the most prevalent due to their ease of fabrication and wide applicability. Substrates such as RT Duroid and Rogers consistently outperformed lower-grade materials like FR4 in high-frequency scenarios, confirming their critical role in achieving high-efficiency performance. Furthermore, Schottky-based rectifiers emerged as the most effective rectification topology, offering superior RF-to-DC conversion rates across multiple implementations. Despite these contributions, this review acknowledges certain limitations. The reliance on a focused set of keywords such as rectenna and energy harvesting may have excluded relevant interdisciplinary innovations, particularly those emerging under alternative terminologies or frameworks. Additionally, due to the heterogeneous reporting standards and lack of unified testing conditions across studies, the meta-analysis was largely qualitative in nature. Looking forward, several areas for further research have emerged. There is a clear need to deepen investigations into impedance matching techniques, particularly adaptive and broadband models that can improve energy capture under varying input conditions. Likewise, nonlinear rectifier modeling and circuit-level co-optimization strategies should be more explicitly explored to enhance overall system efficiency in low-power regimes. Integration of emerging modeling approaches, such as joint channel estimation and power transfer optimization, represents a promising direction to unify communication and energy transfer design frameworks, especially within advanced wireless systems like 6G and IoT. Moreover, future research should expand the analysis toward underrepresented domains such as near-field energy harvesting, polar-domain modeling, and mmWave/THz band operation, which are gaining traction in applications involving wearable sensors, implantable devices, and ultra-dense wireless environments. These areas remain largely unexplored in the current literature corpus and hold the potential to reshape the design principles of next-generation RF energy harvesting systems. Future integration of RF energy harvesting systems with data-intensive industrial IoT platforms could benefit from joint optimization strategies, including sparse signal recovery and low-complexity channel estimation methods, such as MIMO-FBMC frameworks. These approaches would allow simultaneous energy transfer and reliable data decoding, particularly in high-density environments. See, for example, \"Low Complexity MIMO-FBMC Sparse Channel Parameter Estimation for Industrial Big Data Communications\". By addressing these challenges and exploring advanced domains and modeling strategies, future developments can lead to more robust, compact, and scalable energy harvesting architectures, aligned with the growing demands of autonomous low-power systems, including wearable technology, smart environments, and distributed sensing networks. To ensure methodological transparency and adherence to established reporting standards, we include the PRISMA 2020 Checklist in this Supplementary Materials. This checklist outlines the key components of the systematic review and meta-analysis conducted in this study, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.