Bioengineering of 2D Nanomaterials via Green and Sustainable Routes

Applications nanomaterials in biology resulted in many exciting fields of research such as nanomedicine, nanotheraputics and bionanosensors. The development of these fields highly depends on the nature and stability of nanomaterials in biological fluids. The poor stability of nanomaterials in biological media due to the aggregation and/or agglomeration with the biomolecules, particularly with serum proteins, is a challenging problem. Conversely, the defined formation of protein corona around the nanomaterials is very useful to improve properties such as cellular uptake and compatibility of the materials. The primary goal of this thesis will be to design and develop methods for biologically stable, nontoxic nanomaterials. In specific, the project is designed to biofunctionalize 2D nanolayers such as graphene and use the resulting materials in biology. The initial aims of this study was focused on the utility of graphene as a platform for the design of ‘stable–on-the-table’ biomaterials using the well-established techniques in our lab. The developments in this study will lead to the engineering of enzymatic biofuel cells with improved power density and sustainability. The second part of the project is to produce graphene in large quantities, in aqueous media using proteins as exfoliators. The produced graphene, called biographene, was characterized and used to evaluate the protein binding capabilities. Finally, graphene and other 2D analogues, such as Boron nitride (BN), Molybdenum sulfide (MoS2) and Zirconium phosphate (ZrP), will be exfoliated in animal serum. Production and characterization of the layered materials in serum from bovine, porcine, chicken, rat, human and so on will be executed and the in vivo and in vitro toxicity will be tested for specific samples

Manifold Projection Image Segmentation for Nano-XANES Imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nano-XANES imaging, where X-ray Absorption Near Edge Structure (XANES) spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used Principal Component Analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as X-ray Fluorescence (XRF) data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including Scanning Transmission X-ray Microscopy (STXM), where the length scale of domains is larger than the resolution of the experiment

Surface characterization and chemical speciation of adsorbed iron(iii) on oxidized carbon nanoparticles.

Carbonaceous nanomaterials represent a significant portion of ultra-fine airborne particulate matter, and iron is the most abundant transition metal in air particles. Owing to their high surface area and atmospheric oxidation, carbon nanoparticles (CNP) are enriched with surface carbonyl functional groups and act as a host for metals and small molecules. Using a synthetic model, concentration-dependent changes in the chemical speciation of iron adsorbed on oxidized carbon surfaces were investigated by a combination of X-ray and electron microscopic and spectroscopic methods. Carbon K-edge absorption spectra demonstrated that the CNP surface was enriched with carboxylic acid groups after chemical oxidation but that microporosity was unchanged. Oxidized CNP showed a high affinity for sorption of Fe(iii) from solution (75-95% uptake) and spectroscopic measurements confirmed a 3+ oxidation state of Fe on CNP irrespective of surface loading. The bonding of adsorbed Fe(iii) at variable loadings was determined by iron K-edge X-ray absorption spectroscopy. At low loadings (3 and 10 μmol Fe m-2 CNP), mononuclear Fe was octahedrally coordinated to oxygen atoms of carboxylate groups. As Fe surface coverage increased (21 and 31 μmol Fe m-2 CNP), Fe-Fe backscatters were observed at interatomic distances indicating iron (oxy)hydroxide particle formation on CNP. Electron-donating surface carboxylate groups on CNP coordinated and stabilized mononuclear Fe(iii). Saturation of high-affinity sites may have promoted hydroxide particle nucleation at higher loading, demonstrating that the chemical form of reactive metal ions may change with surface concentration and degree of CNP surface oxidation. Model systems such as those discussed here, with controlled surface properties and known chemical speciation of adsorbed metals, are needed to establish structure-activity models for toxicity assessments of environmentally relevant nanoparticles

Rationally Designed, ``Stable-on-the-Table'' NanoBiocatalysts Bound to Zr(IV) Phosphate Nanosheets

Rational approaches for the control of nano-bio interfaces for enzyme stabilization are vital for engineering advanced, functional nanobiocatalysts, biosensors, implants, or ``smart'' drug delivery systems. This chapter presents an overview of our recent efforts on structural, functional, and mechanistic details of enzyme nanomaterials design, and describes how progress is being made by hypothesis-driven rational approaches. Interactions of a number of enzymes having wide ranges of surface charges, sizes, and functional groups with alpha-Zr(IV) phosphate (alpha-ZrP) nanosheets are carefully controlled to achieve high enzyme binding affinities, excellent loadings, significant retention of the bound enzyme structure, and high enzymatic activities. In specific cases, catalytic activities and selectivities of the nanobiocatalysts are improved over those of the corresponding pristine enzymes. Maximal enzyme structure retention has been obtained by coating the nanosheets with appropriate proteinaceous materials to soften the enzyme-nanosheet interface. These systematic manipulations are of significant importance to understand the complex behavior of enzymes at inorganic surfaces

Accelerating Nano-XANES Imaging via Feature Selection

We investigate feature selection algorithms to reduce experimental time of nanoscale imaging via X-ray Absorption Fine Structure spectroscopy (nano-XANES imaging). Our approach is to decrease the required number of measurements in energy while retaining enough information to, for example, identify spatial domains and the corresponding crystallographic or chemical phase of each domain. We find sufficient accuracy in inferences when comparing predictions using the full energy point spectra to the reduced energy point subspectra recommended by feature selection. As a representative test case in the hard X-ray regime, we find that the total experimental time of nano-XANES imaging can be reduced by ~80% for a study of Fe-bearing mineral phases. These improvements capitalize on using the most common analysis procedure – linear combination fitting onto a reference library – to train the feature selection algorithm and thus learn the optimal measurements within this analysis context. We compare various feature selection algorithms such as Recursive Feature Elimination (RFE), random forest, and decision tree, and find that RFE produces moderately better recommendations. We further explore practices to maintain reliable feature selection results, especially when there is large uncertainty in the system, thus requiring a more expansive reference library, resulting in high linear mutual dependence within the reference set. More generally, the class of spectroscopic imaging experiments that scan energy by energy (rather than collecting an entire spectrum at once) is well-addressed by feature selection, and our approach is equally applicable to the soft X-ray regime via Scanning Transmission X-ray Microscopy (STXM) experiments

Surface characterization and chemical speciation of adsorbed iron(iii) on oxidized carbon nanoparticles.

Carbonaceous nanomaterials represent a significant portion of ultra-fine airborne particulate matter, and iron is the most abundant transition metal in air particles. Owing to their high surface area and atmospheric oxidation, carbon nanoparticles (CNP) are enriched with surface carbonyl functional groups and act as a host for metals and small molecules. Using a synthetic model, concentration-dependent changes in the chemical speciation of iron adsorbed on oxidized carbon surfaces were investigated by a combination of X-ray and electron microscopic and spectroscopic methods. Carbon K-edge absorption spectra demonstrated that the CNP surface was enriched with carboxylic acid groups after chemical oxidation but that microporosity was unchanged. Oxidized CNP showed a high affinity for sorption of Fe(iii) from solution (75-95% uptake) and spectroscopic measurements confirmed a 3+ oxidation state of Fe on CNP irrespective of surface loading. The bonding of adsorbed Fe(iii) at variable loadings was determined by iron K-edge X-ray absorption spectroscopy. At low loadings (3 and 10 μmol Fe m-2 CNP), mononuclear Fe was octahedrally coordinated to oxygen atoms of carboxylate groups. As Fe surface coverage increased (21 and 31 μmol Fe m-2 CNP), Fe-Fe backscatters were observed at interatomic distances indicating iron (oxy)hydroxide particle formation on CNP. Electron-donating surface carboxylate groups on CNP coordinated and stabilized mononuclear Fe(iii). Saturation of high-affinity sites may have promoted hydroxide particle nucleation at higher loading, demonstrating that the chemical form of reactive metal ions may change with surface concentration and degree of CNP surface oxidation. Model systems such as those discussed here, with controlled surface properties and known chemical speciation of adsorbed metals, are needed to establish structure-activity models for toxicity assessments of environmentally relevant nanoparticles

Iron Speciation in Respirable Particulate Matter and Implications for Human Health

Particulate matter (PM) air pollution poses a major global health risk, but the role of iron (Fe) is not clearly defined because chemistry at the particle-cell interface is often not considered. Detailed spectromicroscopy characterizations of PM2.5 samples from the San Joaquin Valley, CA identified major Fe-bearing components and estimated their relative proportions. Iron in ambient PM2.5 was present in spatially and temporally variable mixtures, mostly as Fe(III) oxides and phyllosilicates, but with significant fractions of metallic iron (Fe(0)), Fe(II,III) oxide, and Fe(III) bonded to organic carbon. Fe(0) was present as aggregated, nm-sized particles that comprised up to ∼30% of the Fe spectral fraction. Mixtures reflect anthropogenic and geogenic particles subjected to environmental weathering, but reduced Fe in PM originates from anthropogenic sources, likely as abrasion products. Possible mechanistic pathways involving Fe(0) particles and mixtures of Fe(II) and Fe(III) surface species may generate hydrogen peroxide and oxygen-centered radical species (hydroxyl, hydroperoxyl, or superoxide) in Fenton-type reactions. From a health perspective, PM mixtures with reduced and oxidized Fe will have a disproportionate effect in cellular response after inhalation because of their tendency to shuttle electrons and produce oxidants and electrophiles that induce inflammation and oxidative stress

Manifold projection image segmentation for nano-XANES imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment