Conformational Impact on Amino Acid-Surface π−π Interactions on a (7,7) Single-Walled Carbon Nanotube: A Molecular Mechanics Approach Linda Grabill †,† and Andreas Riemann* ,‡ † Department of Chemistry and ‡ Department of Physics & Astronomy, Western Washington University, 516 High Street, Bellingham, Washington 98225, United States *S Supporting Information ABSTRACT: A study of π−π interactions between a (7,7) single-walled carbon nanotube (SWNT) and three different aromatic amino acids (AAA), namely L‑tyrosine (Tyr), L- tryptophan (Trp), and L-phenylalanine (Phe) was conducted with a molecular mechanics (MM) approach. For each of the amino acids we investigated the behavior of six different conformers. We examined the impact of the so-called edge effects by testing the parameters of the built-in switching function in MM. We found the optimal SWNT length to be approximately 80 Å for the size of the molecules in our conformational studies. The positional effect of electron withdrawing groups with respect to the aromatic tail was studied to understand the influence of this interaction specific to adsorption strength and geometry. We decomposed the aromatic amino acid−surface interactions into three components: overall energy, aromatic ring, and amino acid head adsorption energies. We found that the ability of the amino acid’s head to interact with the surface π-densities had a greater impact on the overall energy than the amino acid head interaction with its substituent’s aromatic ring’s π-electrons. ■ INTRODUCTION The interactions of certain biomolecules with carbon-based substrates, single-walled carbon nanotubes (SWNT), graphene, and graphite, hold the attention of researchers for their potential uses in electronics, chemical sensors, medicine delivery systems, and many more applications. 1−9 Hydrophobic biomolecules, such as aromatic amino acids, have the potential of allowing researchers to modify substrates to obtain desirable properties. These interactions have been explored with physical experiments and an array of theoretical modeling. 10−13 While understanding the nature of these interactions is a critical component for engineering them for their desired character- istics, the sheer size of the systems is computationally very expensive, and therefore, it is practically prohibitive to sample multiple molecular conformers in order to theoretically study these systems. 14 Previous work, in this regard, has been accomplished with either the use of analogues when modeling amino acids (AA) or with using only the conformer with the lowest energy. 15,16 Some of the more common methods to model molecule− surface interactions includes the use of Hartree−Fock (HF) methods, density functional theory (DFT), density functional tight binding (DFTB), and molecular mechanics (MM)/ molecular dynamics (MD). Each of these approaches can be evaluated according to accuracy, applicability to specific systems of molecules and substrates, computational requirements, costs, and time. The optimal configuration of molecules and substrates can be found by determining the minimum total energy of the system. DFT and MP2 require the generation of energy maps for determination of molecular interaction distances and energies. Molecules are brought within a given distance of the substrate and moved by a predesignated step size, or rotated by given angles, generating energy maps by either a single point (energy) calculation or after geometry optimization of the molecule followed by a single point calculation. This time- consuming approach leads to the determination of the system’s energy minima and, therefore, an optimal molecule−surface adsorption geometry. Ab initio HF methods, such as second-order Møller−Plesset perturbation theory (MP2) can be used for these calculations. However, these are computationally expensive and prohibitive for larger systems. MP2 without a counterpoise correction and sufficient basis set can overestimate the energy of π−π aromatic interactions with the carbon substrates by as much as 100%. 17 An additional and more computationally expensive calculation, Received: November 28, 2017 Revised: January 11, 2018 Published: January 12, 2018 Article pubs.acs.org/JPCA Cite This: J. Phys. Chem. A 2018, 122, 1713-1726 © 2018 American Chemical Society 1713 DOI: 10.1021/acs.jpca.7b11716 J. Phys. Chem. A 2018, 122, 1713−1726