Indiana University-Purdue University Indianapolis (IUPUI) / The field of two-dimensional (2D) nanomaterials first began in earnest with the discovery of graphene in 2004 due to their unique shape-dependent optical, electronic, and mechanical properties. These properties arise due to their one-dimensional confinement and are further influenced by the elemental composition of the inorganic crystal lattice. There has been an intense focus on developing new compositions of 2D nanomaterials to take advantage of their intrinsic beneficial properties in a variety of applications including catalysis, energy storage and harvesting, sensing, and polymer nanocomposites. However, compared to the field of bulk materials, the influence of surface chemistry on 2D nanomaterials is still underdeveloped. 2D nanomaterials are considered an “all-surface” atomic structure with heights of a single to few layers of atoms. The synthetic methods used to produce 2D materials include bottom-up colloidal methods and top-down exfoliation related techniques. Both cases result in poorly controlled surface chemistry with many undercoordinated surface atoms and/or undesirable molecules bound to the surface. Considering the importance surfaces play in most applications (i.e., catalysis and polymer processing) it is imperative to better understand how to manipulate the surface of 2D nanomaterials to unlock their full technological potential. Through a focus of the ligand-surface atom bonding in addition to the overall ligand structure we highlight the ability to direct morphological outcomes in lead free halide perovskites, maximize optoelectronic responses in substoichiometric tungsten oxide, and alter physicochemical properties titanium carbide MXenes. The careful control of precursor materials including poly(ethylene glycol) (PEG) surface ligands during the synthesis of bismuth halide perovskites resulted in the formation of 2D quasi-Ruddlesden-Popper phase nanomaterials. Through small angle X-ray scattering (SAXS) and in conjunction with X-ray photoelectron spectroscopy (XPS) we were able to conclude that an in-situ formation of an amino functional group on our PEG-amine ligand was inserted into the perovskite crystal lattice enabling 2D morphology formation. Additionally, through UV-vis absorption and ultraviolet photoelectron spectroscopies we were able to develop a complete electronic band structure of materials containing varying halides (i.e., Cl, Br, and I). Furthermore, through the increased solubility profile of the PEG ligands we observed solvent controlled assemblies of varying mesostructures. We developed an ex-situ ligand treatment to manipulate the localized surface plasmon resonance (LSPR) response of anion vacancy doped tungsten oxide (WO3-x) nanoplatelets (NPLs). Upon ligand treatment to alter the surface passivating ligand from carboxylic acid containing myristic acid (MA) to tetradecylphosphonic acid (TDPA) we observed a >100 nm blue shift in the LSPR response. Using Fourier transform infrared (FTIR) and Raman spectroscopies in conjunction with DFT calculated Raman spectra we were able to conclude this shift was due to the formation of tridentate phosphonate bonds on the NPLs surface. Phosphonate bonding allows for an increase in surface passivation per ligand decreasing surface trapped electrons. These previously trapped electrons were then able to participate as free electrons in the LSPR response. Electron paramagnetic spectroscopy (EPR) further supported this decrease in surface traps through a decrease and shift of the EPR signal related to metal oxide surface trapped electrons. Lastly, using our knowledge of PEG ligands we were able to modify esterification chemistry to covalently attach PEG ligands to a MXene surface. The successful formation of an ester bond between a carboxylic acid containing PEG ligand and hydroxyl terminating group on the MXene surface was supported by FTIR spectroscopy and thermogravimetric analysis. The attachment of PEG resulted in a drastic change in the hydrophilicity of the MXene surface. Where MXenes were previously only processed in extremely polar solvents the PEG attachment allowed for high dispersibility in a wide range of polar and non-polar organic solvents, effectively increasing their processability. Further, this chemistry was modified to include an additional functional group on the PEG ligand to increase the valency of the post-modification MXene nanoflakes. Overall, work presented in this dissertation represents the development and application of surface chemistry to relatively new 2D nanomaterials. We believe our work significantly increases the knowledge of 2D halide perovskite formation, manipulation of LSPR active metal oxide materials, and the future processing of MXene materials.

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<p>A wide range of optoelectronic applications demand semiconductors optimized for purpose.</p> <p>My research focused on data-driven identification of ABX3 Halide perovskite compositions for optimum photovoltaic absorption in solar cells.</p> <p>I trained machine learning models on previously reported datasets of halide perovskite band gaps based on first principles computations performed at different fidelities.</p> <p>Using these, I identified mixtures of candidate constituents at the A, B or X sites of the perovskite supercell which leveraged how mixed perovskite band gaps deviate from the linear interpolations predicted by Vegard's law of mixing to obtain a selection of stable perovskites with band gaps in the ideal range of 1 to 2 eV for visible light spectrum absorption.</p> <p>These models predict the perovskite band gap using the composition and inherent elemental properties as descriptors.</p> <p>This enables accurate, high fidelity prediction and screening of the much larger chemical space from which the data samples were drawn.</p> <p><br></p> <p>I utilized a recently published density functional theory (DFT) dataset of more than 1300 perovskite band gaps from four different levels of theory, added to an experimental perovskite band gap dataset of \textasciitilde{}100 points, to train random forest regression (RFR), Gaussian process regression (GPR), and Sure Independence Screening and Sparsifying Operator (SISSO) regression models, with data fidelity added as one-hot encoded features.</p> <p>I found that RFR yields the best model with a band gap root mean square error of 0.12 eV on the total dataset and 0.15 eV on the experimental points.</p> <p>SISSO provided compound features and functions for direct prediction of band gap, but errors were larger than from RFR and GPR.</p> <p>Additional insights gained from Pearson correlation and Shapley additive explanation (SHAP) analysis of learned descriptors suggest the RFR models performed best because of (a) their focus on identifying and capturing relevant feature interactions and (b) their flexibility to represent nonlinear relationships between such interactions and the band gap.</p> <p>The best model was deployed for predicting experimental band gap of 37785 hypothetical compounds.</p> <p>Based on this, we identified 1251 stable compounds with band gap predicted to be between 1 and 2 eV at experimental accuracy, successfully narrowing the candidates to about 3% of the screened compositions.</p>

<p>This dissertation describes how the surface chemistries of 2D nanomaterials can be modified to alter overall material properties. Specifically, through a focus of the ligand-surface atom bonding in addition to the overall ligand structure we highlight the ability to direct morphological outcomes in lead free halide perovskites, maximize optoelectronic responses in substoichiometric tungsten oxide, and alter physicochemical properties titanium carbide MXenes.   </p>