Graphitic carbon nitride (g-CN) has recently emerged as a promising visible-light-responsive polymeric photocatalyst; however, a molecular-level understanding of material properties and its application for water purification were underexplored. In this study, we rationally designed nonmetal doped, supramolecule-based g-CN with improved surface area and charge separation. Density functional theory (DFT) simulations indicated that carbon-doped g-CN showed a thermodynamically stable structure, promoted charge separation, and had suitable energy levels of conduction and valence bands for photocatalytic oxidation compared to phosphorus-doped g-CN. The optimized carbon-doped, supramolecule-based g-CN showed a reaction rate enhancement of 2.3-10.5-fold for the degradation of phenol and persistent organic micropollutants compared to that of conventional, melamine-based g-CN in a model buffer system under the irradiation of simulated visible sunlight. Carbon-doping but not phosphorus-doping improved reactivity for contaminant degradation in agreement with DFT simulation results. Selective contaminant degradation was observed on g-CN, likely due to differences in reactive oxygen species production and/or contaminant-photocatalyst interfacial interactions on different g-CN samples. Moreover, g-CN is a robust photocatalyst for contaminant degradation in raw natural water and (partially) treated water and wastewater. In summary, DFT simulations are a viable tool to predict photocatalyst properties and oxidation performance for contaminant removal, and they guide the rational design, fabrication, and implementation of visible-light-responsive g-CN for efficient, robust, and sustainable water treatment.

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Pd-based catalyst treatment represents an emerging technology that shows promise to remove nitrate and nitrite from drinking water. In this work we use vapor-grown carbon nanofiber (CNF) supports in order to explore the effects of Pd nanoparticle size and interior versus exterior loading on nitrite reduction activity and selectivity (i.e., dinitrogen over ammonia production). Results show that nitrite reduction activity increases by 3.1-fold and selectivity decreases by 8.0-fold, with decreasing Pd nanoparticle size from 1.4 to 9.6 nm. Both activity and selectivity are not significantly influenced by Pd interior versus exterior CNF loading. Consequently, turnover frequencies (TOFs) among all CNF catalysts are similar, suggesting nitrite reduction is not sensitive to Pd location on CNFs nor Pd structure. CNF-based catalysts compare favorably to conventional Pd catalysts (i.e., Pd on activated carbon or alumina) with respect to nitrite reduction activity and selectivity, and they maintain activity over multiple reduction cycles. Hence, our results suggest new insights that an optimum Pd nanoparticle size on CNFs balances faster kinetics with lower ammonia production, that catalysts can be tailored at the nanoscale to improve catalytic performance for nitrite, and that CNFs hold promise as highly effective catalyst supports in drinking water treatment.

Catalytic hydrogenation over Pd-based catalysts has emerged as an effective treatment approach for nitrate removal, but its full-scale application for direct treatment of drinking water or ion exchange regenerant brines requires improved selectivity for the end-product dinitrogen (N 2 ) over toxic ammonia species (