# Supplement to Model-aided Design and Integration of Functionalized Hybrid Nanomaterials for EnhancedBioremediation of PFASs Using Supercritical Fluid Chromatography/Mass Spectrometry

> **NIH NIH R01** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2022 · $27,509

## Abstract

ABSTRACT
 Global public health concern is growing over per- and polyfluoroalkyl substances (PFASs) toxicity,
environmental persistence, and potential to bioaccumulate in humans and wildlife. Nearly every person who has
been tested for PFASs shows measurable levels in their blood resulting from contamination of the environment
and continued use in consumer products and industrial applications. In particular, drinking water appears to be
the major source of PFAS exposure for people living near contaminated sites. Importantly, some PFASs have been
linked to liver damage, developmental impacts, and several cancers (e.g., kidney, testicular). Environmental
remediation is urgently needed, but efforts are hampered by the extreme persistence of the carbon-fluorine bond.
Biodegradation typically involves only the non-fluorinated components of polyfluorinated PFASs, resulting in the
creation of shorter-chain perfluorinated acids that are more persistent and mobile. Complete mineralization has
not been demonstrated. Abiotic treatment technologies can be more effective but require extremely high energy
inputs, and the degradation mechanisms are poorly understood. There is a critical need for a treatment
technology with lower energy requirements, and for enhanced degradation pathways that efficiently mineralize
PFASs without formation of perfluorinated acids that persist after treatment.
 The overarching goal of this proposal is to develop an innovative nanomaterial-biological strategy to tackle
the challenge of PFAS biodegradation. Our central hypothesis is that pretreatment by tailored nanomaterials can
facilitate transformation of structurally diverse PFASs to achieve more efficient and complete biodegradation.
Our previous work has shown that functionalized nanohybrid catalysts incorporating reduced graphene oxide
(rGO) and nano zerovalent iron (nZVI) can successfully initiate degradation of long-chain PFASs. Here, we will
employ this abiotic transformation as an innovative pretreatment to unlock the biodegradation of PFASs.
Leveraging our expertise in molecular modeling and ‘omics’ techniques, we will test and tailor the ability of
microbial communities to more efficiently degrade pretreated PFASs and their initial degradation products. All
degradation products will be characterized by high-resolution mass spectrometry and 19F-nuclear magnetic
resonance spectroscopy to reveal the mechanisms that enable this nano-bioremediation strategy. This research
will tackle a pressing environmental contamination problem with three complementary specific aims:
Aim 1: Synthesize multifunctional redox-active nanohybrid materials and evaluate their catalytic
properties for PFAS degradation (dehalogenation, degradation of long-chains to short-chains). We will
synthesize and characterize two multifunctional and hierarchical carbon-metal nanohybrids: (i) redox-active
reduced graphene oxide nano zerovalent iron (rGO–nZVI) and (ii) photocatalytic rGO-nZVI- titanium dioxide
(...

## Key facts

- **NIH application ID:** 10601888
- **Project number:** 3R01ES032717-02S2
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Diana S Aga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $27,509
- **Award type:** 3
- **Project period:** 2022-08-04 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10601888

## Citation

> US National Institutes of Health, RePORTER application 10601888, Supplement to Model-aided Design and Integration of Functionalized Hybrid Nanomaterials for EnhancedBioremediation of PFASs Using Supercritical Fluid Chromatography/Mass Spectrometry (3R01ES032717-02S2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10601888. Licensed CC0.

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