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Being a non-renewable resource and a source of potential water pollution, phosphorus could be recovered from animal manure in the form of struvite (MgNH 4 PO 4 ?6H 2 O) to be used as a slow-release fertilizer. It was found recently that the majority of phosphorus in anaerobically digested dairy effluent is tied up in a fine suspended calcium-phosphate solid, thus becoming unavailable for struvite formation. Acidification and use of a chelating agent were investigated for converting the calciumassociated phosphorus in the digested effluent to dissolved phosphate ions, so that struvite can be produced. The results demonstrated that the phosphorus in the effluent was released into the solution by lowering the pH. In addition, the phosphorus concentration in the solution increased significantly with increased ethylenediaminetetraacetic acid (EDTA) concentration, as EDTA has a high stability constant with calcium. Most of the phosphorus (91%) was released into the solution after adding EDTA. Further, the freed phosphorus ion precipitated out as struvite provided that sufficient magnesium ions (Mg 2+ ) were present in the solution. Furthermore, the phase structure of the solid precipitate obtained from the EDTA treatment matched well with standard struvite, based on the data from X-ray diffraction analysis. These results provide methods for altering the forms of phosphorus for the design and application of phosphorus-removal technologies for dairy wastewater management. Water Environ. Res., 82, 34 (2010).

been described as a key metric for understanding the effects of global warming due to its direct impact on climate change. [2] Extensive modeling with energy-economyenvironment scenarios or projections to keep the global temperature from rising above 1.5 °C by the year 2100 shows that such a positive outlook is only possible if the global energy system is completely decarbonized (i.e., net-zero global CO 2 emissions) by mid-century, followed by active CO 2 removal (i.e., carbon-negative) in the second half of the century. [3] The catalytic conversion of CO 2 to valuable energy-related products (e.g., syngas, CH 4 , CH 3 OH, and longer-chain hydrocarbons) [4] by thermal reforming and hydrogenation, [5][6][7][8][9][10][11] electrocatalysis, [12][13][14] or photocatalysis [15] could occupy an important position in a future carbon-negative economy by directly replacing nonrenewable sources of these molecules, provided that the energy inputs to these processes are themselves derived from renewable sources. [16,17] In this regard, photocatalytic strategies stand out by not requiring a secondary medium of energy storage, and being able to realize the CO 2 conversion into high value-added fuels directly via renewable solar energy without external energy input. [18] Indeed, photocatalytic CO 2 reduction has been considered to be a "kill two birds with one stone" approach for sustainable energy production and greenhouse gas reduction. [19] In contrast, thermocatalytic approachesThe solar-energy-driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform CO 2 into valuable energy sources under mild conditions. As a clean-burning fuel and drop-in replacement for natural gas, CH 4 is an ideal product of CO 2 photoreduction, but the development of highly active and selective semiconductor-based photocatalysts for this important transformation remains challenging. Hence, significant efforts have been made in the search for active, selective, stable, and sustainable photocatalysts. In this review, recent applications of cutting-edge experimental and computational materials design strategies toward the discovery of novel catalysts for CO 2 photocatalytic conversion to CH 4 are systematically summarized. First, insights into effective experimental catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet engineering, and single atoms, are presented. Then, datadriven photocatalyst design spanning density functional theory (DFT) simulations, high-throughput computational screening, and machine learning (ML) is presented through a step-by-step introduction. The combination of DFT, ML, and experiments is emphasized as a powerful solution for accelerating the discovery of novel catalysts for photocatalytic reduction of CO 2 . Last, challenges and perspectives concerning the interplay between experiments and data-driven rational design strategies for the industrialization of large-scale CO 2 photoreduction technologies are described.