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From Loss Diagnosis to Rational Design: A Unified Analytical Model for Photoelectrochemical Cells
One-line summary
A solar energy research paper on From Loss Diagnosis to Rational Design: A Unified Analytical Model for Photoelectrochemical Cells.
Engineering notes
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Photoelectrochemical (PEC) cells are a compelling route to solar-driven chemical energy storage and feedstock synthesis, yet their deployment is hindered by coupled losses spanning light absorption, carrier transport, interfacial charge transfer, and semiconductor-electrolyte matching. Existing models address these losses in an architecture-specific manner and fall short of quantitative experimental diagnosis or actionable design guidance. Here, we introduce a unified loss-analysis framework applicable to both built-in junction (BIJ) and semiconductor-electrolyte junction (SEJ) photoelectrodes within a consistent set of physically meaningful parameters. The framework delivers current-voltage curves and efficiency metrics under ideal and real conditions, constructing efficiency maps to delineate theoretical limits and material-selection windows. Critically, by fitting experimental current-voltage data, it enables quantitative energy-loss decomposition into thermodynamic, optical, recombination, and interfacial contributions, pinpointing performance bottlenecks in real devices and mapping them directly onto optimization strategies such as co-catalyst integration or nanostructuring. Energy flows are visualized through Sankey diagrams, providing an intuitive picture of how incident solar energy is absorbed, dissipated, or converted into chemical output. Validated against state-of-the-art literature results spanning solar water splitting, CO2 reduction, NH3 synthesis, and solar redox flow batteries, the framework further enables systematic comparison of photovoltaic-grade absorbers (e.g., Si, perovskites) with intermediate-bandgap semiconductors (e.g., hematite, BiVO4), identifying key factors limiting each material class. Together, these capabilities support a paradigm shift from empirical optimization to mechanism-informed rational design of high-efficiency PEC energy-conversion systems.
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