Computational Method for Efficient Screening of Metal Precursors for Nanomaterial Syntheses

Abstract

A density functional theory (DFT) based method is proposed for efficient screening of metal precursors for nanomaterial syntheses. For this study, we examined the effectiveness of our DFT approach for predicting bulk properties of precursor metal complexes, which is a key of our method. The DFT calculations were applied for a series of copper(II) β-diketonate complexes to estimate values related to complex stabilities such as complex formation energies ΔE total complex, total energy changes for two-electron reduction ΔE total reduction, and so on. The value of ΔE total complex was compared to the stability constant β2 collected from the relevant literature; ΔE total reduction was compared with reduction potentials measured using cyclic voltammetry. Results obtained from these comparisons revealed that simple DFT calculations predicted the trend of the complex stabilities that were determined experimentally as a bulk property. Our method can predict precursor properties and can greatly contribute to efficient precursor selection for nanomaterial synthesis.

Modeling Mechanism and Growth Reactions for New Nanofabrication Processes by Atomic Layer Deposition

“…Following this rationale, Orimoto et al compute the stability of a series of copper(II)diketonate complexes. [ 82 ] Likewise, two novel N-heterocyclic carbene containing copper(I) amides are reported as precursors for copper ALD, and DFT is used to assess the strength of the carbene-copper bond, the charge distribution after dissociation and the adsorption on copper surfaces. [ 83 ] A copper(I) carbene hydride complex acting both as reducing agent and precursor for Cu ALD is proposed based on DFT calculations.…”

Competing reactions during metalorganic deposition: Ligand-exchange versus direct reaction with the substrate surface

“…The volatility was improved by using several substituents, including the use of fluorine. 21,22,39 In addition, it has been proposed that the strength of the ligand-metal bond ultimately causes the heavy carbon contamination. The deposition of metallic films often requires the use of hydrogen to hydrogenate the ligand and reduce copper to its metallic state.…”

“…3,11,[13][14][15][16][17] Moreover, in contrast to their inorganic analogs (e.g., metal halides), metalorganic precursors can be used efficiently at relatively low temperatures and without producing corrosive by-products. 7,[21][22][23][24] However, as the thickness of films decreased to satisfy the demands for smaller components, the role of surface-mediated reactions of the precursors became more important, especially considering that at the subnanometer scale sharp substrate/film interfaces are often required. Initially, this molecularlevel focus was mainly directed at fine-tuning physical properties (e.g., thermal stability, volatility) by varying the structure of precursor molecules.…”

Chemical Processes Involved in Atomic Layer Deposition of Gallium Sulfide: Insights from Theory

“…Atomic-scale modeling using density functional theory (DFT) can provide a complementary view. Computational atomistic studies describing ALD processes have been reported since the early days of so-called at that time “atomic layer epitaxy” (ALE), and the reader can refer to recent reviews. , A variety of surface models are used: periodic slab, − cluster, − a combination of both, or molecule models. ,− Also, studies specifically focused on the ALD precursors have been carried out and explored for their stability, , reactivity, and fast screening for the identification of best candidates. ,,, Reaction data obtained from atomistic DFT calculations lead to reaction rates that can be used to set up a kinetic simulation, where all the relevant simulated steps, reaction barriers, and energies are taken into account in order to model the complete growth (multiple ALD cycles). , …”

“…17,25−27 Also, studies specifically focused on the ALD precursors have been carried out and explored for their stability, 28,29 reactivity, 30 and fast screening for the identification of best candidates. 22,29,31,32 Reaction data obtained from atomistic DFT calculations lead to reaction rates that can be used to set up a kinetic simulation, where all the relevant simulated steps, reaction barriers, and energies are taken into account in order to model the complete growth (multiple ALD cycles). 33,34 While there are now plenty of materials deposited by ALD (mostly oxides and metals), relatively few sulfide materials have been studied, though this class of materials is attracting more and more attention.…”