Advancing renewable energy thermal management with cellulose-derived materials through meta-analysis, machine learning, and comparative material evaluation

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Chinese explanation / 中文解读

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Original abstract

Cellulose-based materials have emerged as sustainable thermal-management solutions for photovoltaic (PV), photovoltaic/thermal (PV/T), and solar thermal systems because of their renewable origin, tunable properties, and potential to enhance thermal regulation and energy-conversion performance. However, quantitative evidence regarding their overall effectiveness remains fragmented across different applications and material types. This study employed an integrated framework combining Meta-Analysis, multivariate statistical analysis, machine learning (ML), and comparative material assessment. A total of 32 eligible studies were selected through a PRISMA-based systematic review process and analyzed to evaluate thermal, electrical, and durability-related performance indicators. The synthesized results demonstrated that cellulose-based materials achieved pooled reductions in temperature of 13.65% and in the heat transfer coefficient (HTC) of 106.53%, as well as improvements in PV efficiency of 14.40%. Temperature reduction was identified as the strongest predictor of PV performance enhancement. ML models achieved high predictive accuracy (R² = 0.894), while principal component and clustering analyses revealed distinct thermal-performance pathways among cellulose-derived materials. CNF exhibited the highest application potential, whereas CNC produced the greatest PV efficiency improvement. The findings indicate that enhanced thermal transport improves PV performance primarily through effective temperature suppression rather than direct conductivity enhancement. Cellulose morphology significantly influences thermal-management effectiveness and operational stability. Cellulose-based materials provide substantial thermal, electrical, and durability benefits across renewable-energy systems. The integration of Meta-Analysis and ML establishes a robust framework for material selection, performance prediction, and future optimization of sustainable thermal-management technologies.

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