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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2020)

Bacterial surfaces can be used in engineered systems through the implementation of bioreactors as unconventional means of extracting critical metals. This dissertation discusses a genetically modified gram-negative bacterium, dLBTx8 induced Escherichia coli, which possesses lanthanide binding tag peptides that chelate and preferentially adsorb rare earth elements over other co-existing metals. A thermodynamic surface complexation model was first built to compare the cell surface terbium binding mechanisms of wild type and dLBTx8 engineered E. coli (Ch. 2). This surface complexation model was then expanded upon to include adsorption of yttrium and thirteen lanthanide elements (Ch. 3). The engineered dLBTx8 E. coli cells were immobilized in a non-sorbing polyethylene glycol diacrylate (PEGDA) polymer and packed into fixed-bed columns to demonstrate proof-of-principle bioreactor designs. A one-dimensional reactive transport model incorporating advective inter-bead and diffusive intra-bead transport of lanthanides was developed to account for the complex competitive adsorption processes taking place within the dLBTx8 E. coli packed fixed-bed column. After careful calibration and testing of the reactive transport model, predictions were made to optimize separation of valuable rare earths, exemplified by europium vs. lanthanum separation. This dissertation demonstrates the power of surface complexation and reactive transport modeling to allow for future engineering designs that can improve the efficacy of rare earth element bio-extraction and separation from geothermal fluid and mining leachate feedstocks.Bacterial surfaces can also play an important role in soils, surface water, and subsurface geologic systems, where the cell surface-based ligands can adsorb various metals. Due to the reactive nature of their cell surfaces, bacteria can influence fate, transport, and mobility of naturally occurring and anthropogenically introduced metals in soils. While many scientists have investigated the adsorption of divalent metals, such as lead and copper, onto bacterial surfaces found in soils and sediments, a knowledge gap currently exists in understanding how trivalent lanthanides impact these dynamic subsurface transport processes. In general, soil bacterial surfaces have been shown to have particularly strong electrostatic interactions with rare earths, implying that these trivalent metals can act as important controls over the transport of divalent metals. In this dissertation, a gram-positive soil bacterium, Arthrobacter nicotianae, has been shown to adsorb lanthanides through multiple different surface complexation mechanisms, including phosphodiester, phosphoryl, carboxyl, and carbonyl-based amide binding (Ch. 4). These results, supported through attenuated total reflectance Fourier transform infrared spectroscopy measurements (ATR-FTIR), are qualitatively corroborated by thermodynamic modeling of the bacterial cell wall. Notably, the modeling results suggest the relatively high affinities imposed by each of these site types, particularly through phosphate and amide monodentate adsorption. This work contributes to the currently existing knowledge gap whereby soil transport of divalent metals may significantly be affected by the presence of even low concentrations of trivalent rare earth elements.

    Cover page: Rare Earth Element Biosorption onto Bacterial Surfaces in Engineered and Soil Systems
    • Article
    • Peer Reviewed
    LBL Publications (2023)
    Historically, surface complexation model (SCM) constants and distribution coefficients (Kd) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases.
      Cover page: A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2019)
      The increasing demand for rare earth elements (REEs) in the modern economy motivates the development of novel strategies for cost-effective REE recovery from nontraditional feedstocks. We previously engineered E. coli to express lanthanide binding tags on the cell surface, which increased the REE biosorption capacity and selectivity. Here we examined how REE adsorption by the engineered E. coli is affected by various geochemical factors relevant to geothermal fluids, including total dissolved solids (TDS), temperature, pH, and the presence of specific competing metals. REE biosorption is robust to TDS, with high REE recovery efficiency and selectivity observed with TDS as high as 165,000 ppm. Among several metals tested, U, Al, and Pb were found to be the most competitive, causing >25% reduction in REE biosorption when present at concentrations ∼3- to 11-fold higher than the REEs. Optimal REE biosorption occurred between pH 5-6, and sorption capacity was reduced by ∼65% at pH 2. REE recovery efficiency and selectivity increased as a function of temperature up to ∼70 °C due to the thermodynamic properties of metal complexation on the bacterial surface. Together, these data define the optimal and boundary conditions for biosorption and demonstrate its potential utility for selective REE recovery from geofluids.
        Cover page: Recovery of Rare Earth Elements from Geothermal Fluids through Bacterial Cell Surface Adsorption
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2020)
        Lanthanide binding tags (LBTs) have been engineered onto the cell surface of E. coli to enhance biosorption and recovery of rare earth elements (REEs). The protonation behavior of the bacterial surfaces before and after LBT-display was compared by modeling acid-base titration data. A multiple discrete site, constant capacitance surface complexation model was constructed to examine rare earth (Tb) binding to cell surface functional groups, comparing wild type and LBT-engineered surfaces. Our acid-base titrations show similar pKa values between the two strains, suggesting induction of LBTs does not significantly alter cell surface protonation behavior. Tb sorption onto the wild type cell surface can be captured by a one-site carboxyl model. The LBT strain exhibited a higher metal loading that can be explained by the increase of sorption sites in the form of lanthanide binding tags. Furthermore, carboxyl site concentrations between wild type and LBT-induced cells were statistically indistinguishable. We thus attribute the engineered strains’ increase in Tb adsorption capacity and affinity to the addition of lanthanide binding tags to the cell surface. The Tb stability constant with the LBT site is two orders of magnitude higher than that with the carboxyl functional group. As a result, at low metal loading <10 μM, the Tb binding to the cell surface of the LBT-strain is controlled by the presence of high-affinity, but lower capacity, LBT sites. At higher metal loadings >10 μM, a more abundant but low affinity functional group becomes the main source of adsorption that results in an overall higher sorption capacity. This work demonstrates how surface complexation modeling can be implemented for bacterial surfaces engineered with a known protein tag to optimize REE recovery from fluids with variable pH and metal loadings.
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