The effective removal of metal contaminants from drinking water at Superfund sites is critical to protect human health. However, this process is challenged by the presence of naturally co-occurring, otherwise health-benign ions. Such ions compete for surface adsorption sites in common treatment processes, such as adsorbents, intended to remove the target metal pollutants. Further, these competitors frequently occur at comparable or higher concentrations, exhibit analogous chemical structures, and demonstrate similar or superior affinities for sorption sites. Conventional adsorbent technologies are top-down, wherein surface adsorption sites are created using or mimicking natural materials. Yet, recent advances in polymer- and nano-science allow for unprecedented bottom-up capabilities to thermodynamically model, characterize, and controllably synthesize adsorbents. Here, we will exploit chemical behavioral differences such as polarity, charge distribution, size, and hydrophobicity between target oxoanion metal pollutants and naturally occurring competing ions to generate highly selective and tunable polymeric and nano-surfaces. In conjunction with developing oxoanion mass transport models within treatment processes, these new bottom-up design strategies will be applied to develop macro-scale sorbents with improved efficiency and effectiveness over current commercial top-down designed sorbents. We will realize our innovative bottom-up approach by iteratively synthesizing, modeling, and scaling highly selective sorbents from two platforms offering solutions at multiple scales and under varying drinking water system conditions (e.g., point-of-use (POU) at individuals household tap vs. point-of-entry (POE) community- scale applications): 1) utilize biopolymers with various transition metals crosslinkers (TMC) for point of entry (POE) applications and 2) controlling size, surface area, morphology, and crystallinity of nano-metal oxides (NMOs) that are integrated into porous electrospun polymer fibers for single-use POU applications. In (1), resultant crosslinking complexes can exclude competitive ions electrostatically and/or sterically. In (2), the presence of certain high-energy crystal facets and the coordination of terminal surface groups create surface chemistry that is favorable toward the sorption of specific target contaminants such that a blend of different NMOs within a fiber could be used to target specific mixtures of metals. Our preliminary results demonstrate the potential of both systems to realize selectivity of Superfund-relevant metals that cannot be achieved by current sorbents. Thus, we propose to revolutionize the approach to removing mixed metal pollutants from Superfund site drinking water through processes that can simultaneously reduce operational costs, hazardous waste generation, and drinking water compliance violations while improving the protection of public health.

Public Health Relevance

Removing pollutants from contaminated drinking water is critical for public health, but traditional approaches to sorption of pollutants are insufficiently selective towards remove arsenic, chromium or selenium. We propose to revolutionize sorbent design by combining novel polymer and nano-science technologies with quantum mechanical simulations, machine learning, and molecular mass transport models. The proposed approach will lead to new selective sorbent materials for a variety of water treatment systems (e.g., individual households on wellwater, small-scale community systems or schools) that are more effective, efficient, and sustainable to reduce exposure to Superfund metals from drinking water compared against existing technologies.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Hazardous Substances Basic Research Grants Program (NIEHS) (P42)
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Special Emphasis Panel (ZES1)
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Harvard University
United States
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