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To address the critical issues in solar energy, the current research has focused on developing advanced solar harvesting materials that are low cost, lightweight, and environmentally friendly. Among many organic photovoltaics (PVs), the porphyrin compounds exhibit unique structural features that are responsible for strong ultraviolet (UV) and near infrared absorptions and high average visible transmittance, making them ideal candidates for solar-based energy applications. The porphyrin compounds have also been found to exhibit strong photothermal (PT) effects and recently applied for optical thermal insulation of building skins. These structural and optical properties of the porphyrin compounds enable them to function as a PT or a PV device upon sufficient solar harvesting. It is possible to develop a transparent porphyrin thin film with PT- and PV-dual-modality for converting sunlight to either electricity or thermal energy, which can be altered depending on energy consumption needs. A building skin can be engineered into an active device with the PT- and PV-dual modality for large-scale energy harvesting, saving, and generation. This review provides the current experimental results on the PT and PV properties of the porphyrin compounds such as chlorophyll and chlorophyllin. Their PT and PV mechanisms are discussed in correlations to their electronic structures. Also discussed are the synthesis routes, thin film deposition, and potential energy applications of the porphyrin compounds. 

This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the Standardized Root Mean Squared Error (SRMR) with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). We use Unweighted Least Squares (ULS) estimation with a mean and variance corrected test statistic. The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almost invariably rejects the model in large samples if data consist of five categories. The unbiased RMSEA produces better rejection rates, but it is only accurate enough when the number of variables is small (e.g., p = 10) and the degree of misfit is small. In contrast, across all simulated conditions, the tests of close fit based on the SRMR yield acceptable type I error rates. SRMR tests of close fit are also more powerful than those using the unbiased RMSEA.