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  1. “Concentrate-and-degrade” is an effective strategy to promote mass transfer and degradation of pollutants in photocatalytic systems, yet suitable and cost-effective photocatalysts are required to practice the new concept. In this study, we doped a post-transition metal of Indium (In) on a novel composite adsorptive photocatalyst, activated carbon-supported titanate nanotubes (TNTs@AC), to effectively degrade perfluorooctanoic acid (PFOA). In/TNTs@AC exhibited both excellent PFOA adsorption (>99% in 30 min) and photodegradation (>99% in 4 h) under optimal conditions (25 °C, pH 7, 1 atm, 1 g/L catalyst, 0.1 mg/L PFOA, 254 nm). The heterojunction structure of the composite facilitated a cooperative adsorption mode of PFOA, i.e., binding of the carboxylic head group of PFOA to the metal oxide and attachment of the hydrophobic tail to AC. The resulting side-on adsorption mode facilitates the electron (e‒) transfer from the carboxylic head to the photogenerated hole (h+), which was the major oxidant verified by scavenger tests. Furthermore, the presence of In enables direct electron transfer and facilitates the subsequent stepwise defluorination. Finally, In/TNTs@AC was amenable to repeated uses in four consecutive adsorption-photodegradation runs. The findings showed that adsorptive photocatalysts can be prepared by hybridization of carbon and photoactive semiconductors and the enabled “concentrate-and-degrade” strategy ismore »promising for the removal and degradation of trace levels of PFOA from polluted waters.« less
    Free, publicly-accessible full text available August 1, 2023
  2. Light incident on a periodic plasmonic nanostructure is shown to exhibit a pushing or pulling pressure, depending on regulation of the surface wave on the top or bottom, respectively, thereby allowing wavelength control.
  3. Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting. We provide three examples to illustrate the theoretical results with replication files in Ding and Li (2019).