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Title:Quantitative analysis on the formation of metal nanocrystals
Author(s):Wang, Yiming
Director of Research:Lu, Yi
Doctoral Committee Chair(s):Lu, Yi
Doctoral Committee Member(s):Jain, Prashant K; Murphy, Catherine J; Yang, Hong
Department / Program:Chemistry
Degree Granting Institution:University of Illinois at Urbana-Champaign
quantitative analysis
predictive synthesis
noble metal
Abstract:It is desirable to rationally engineer nanomaterials with predictable structures and properties, thereby producing desirable functions for their applications on demand. However, most of the nanostructures are discovered by iterative screening of synthetic strategies, which is labor intensive and time consuming. The future of nanomaterials research lies in digitalizing the nanomaterials formation process, so scientists can design and optimize synthetic protocols by computer simulations. Therefore, in my thesis work, I focus on quantitative analysis on the formation of metal nanocrystals to extract appropriate parameters for predictive modeling and synthesis. Predictive synthesis of metal nanocrystals with desired structures relies on precise control of the reaction kinetics. Using a capping ligand is an effective method to affect the reduction kinetics of metal ions precursors. However, predictively synthesizing nanostructures has been difficult to achieve using conventional capping ligands. DNA, as a class of the promising biomolecular capping ligands, has been used to generate sequence-specific morphologies in various metal nanocrystals. However, mechanistic insight into the DNA-mediated nanocrystal formation remains elusive due to the lack of quantitative experimental evidence. In Chapter 2, I quantitatively analyzed the precise control of DNA over Ag+ reduction and the structures of resulting Au-Ag core shell nanocrystals. I derived the equilibrium binding constants between DNA and Ag+, the kinetic rate constants of sequence specific Ag+ reduction pathways, and the percentage of active surface sites remaining on the nanocrystals after DNA passivation. These three synergistic factors influence the nucleation and growth process both thermodynamically and kinetically, which contributed to the morphological evolution of Au-Ag nanocrystals synthesized with different DNA sequences. This study demonstrates the potential of using functional DNA sequences as a versatile and tunable capping ligand system for the predictable synthesis of metal nanostructures. Besides the challenges in predicting the reaction kinetics, the tangled relationships between each functional parameter and multiple structural parameters also make it more difficult to predicting the functions of the resulting nanostructures. Researchers need to deconvolute the structure-function relationships and understand the co-evolution of structural and functional parameters as the nanostructures grow. DNA is a programmable biomolecular capping ligand that has superior control over the kinetic evolution of metal nanostructures. In Chapter 3, I systematically analyzed the evolution of two structural parameters and several functional parameters in the growth of Au-Ag nanostructures controlled by two DNA sequences. I deconvoluted the contributions from the two structural parameters in affecting the plasmonic properties of the nanostructures in different kinetic and geometric domains. I further designed new nanostructures by exchanging DNA sequences in the growth environment, which also exchanged their kinetic evolution pathways. The resulting structural and functional parameters could be predictively tuned by the timing of the exchange. This study shows that by understanding the kinetic evolution of the structural parameters and their relationships with the function parameters, it is possible to design the precise combinations of structural and functional parameters in the nanostructured products of a synthesis reaction. To fully digitalize the reaction of the formation of metal nanocrystals, it is essential to establish the quantitative relationships between the reactants, the kinetics and the resulting nanostructures. The key parameters for the quantitative description are the energy terms of the reaction, such as free energy and activation energy. Typically, those energy terms are primarily obtained by simulation because they are very difficult to be measured experimentally. However, those energy terms can be easily measured in an electrochemical cell. The redox reaction in the synthesis of metal nanocrystals can be understood in the concept of a battery. The cathodic reaction is the reduction of metal ions, and the anodic reaction is the oxidation of reducing agents. The free energy, activation energy and reaction kinetics in the synthesis reaction can be converted to the potential, overpotential and current output of the battery. Using this concept, we can establish quantitative relationships between the reactants, the kinetics, and the resulting nanocrystals, similar to the voltage-current relationships in a battery. This conceptual model will enable scientists to make informed choices of chemical reactants in a synthetic protocol based on the desired nanostructured outcome, which will significantly reduce trial and error. In Chapter 4, I demonstrated this idea in a model system of synthesizing Palladium nanocrystals. By modulating redox potentials of the starting chemical reactants, the reaction kinetics and the size of the resulting Palladium nanostructures were tunable. The three works in this thesis constitute a comprehensive investigation of the quantitative relationships between starting reactants, reaction kinetics, structures, and functions of the resulting nanostructures in a synthesis reaction for metal nanocrystals. In a future predictive synthesis, once a specific function is designed by the end user, the structure-functional relationship in Chapter 3 will help the simulation of functional as well as the structural evolution pathways of the nanostructures; The quantitative kinetic analysis in Chapter 2 will enable the prediction of how much and how fast the atom should be added onto the growing crystal as the nanostructures evolve, and as a result the global reaction kinetics over the course of the reaction. Finally, based on the quantitative relationship between the kinetics and the driving force derived in Chapter 4, we could precisely choose the type of precursors and reducing agents as well as their concentrations based on the combination of their redox potentials that drive the desired reaction kinetics. The quantitative relationships between the starting reactants, reaction kinetics as well as the structures and functions of the nanostructures signify a major step towards predictive synthesis of nanostructures from functional design.
Issue Date:2021-09-03
Rights Information:Part of chapter 1 was published in “Wang, Y.; Reddy Satyavolu, N. S.; Lu, Y., Sequence-specific control of inorganic nanomaterials morphologies by biomolecules. Current Opinion in Colloid & Interface Science 2018, 38, 158-169”. Copyright 2018 Elsevier Part of chapter 2 was published in “Wang, Y.; Counihan, M. J.; Lin, J. W.; Rodríguez-López, J.; Yang, H.; Lu, Y., Quantitative Analysis of DNA-Mediated Formation of Metal Nanocrystals. Journal of the American Chemical Society 2020, 142 (48), 20368-20379.” Copyright 2020 American Chemical Society
Date Available in IDEALS:2022-04-29
Date Deposited:2021-12

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