Characterizing Molecular Adsorption on Biodegradable MnO2

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C: Physical Processes in Nanomaterials and Nanostructures 2

Characterizing Molecular Adsorption on Biodegradable MnO Nanoscaffolds Gangotri Dey, Letao Yang, Ki Bum Lee, and Lu Wang J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b09562 • Publication Date (Web): 27 Nov 2018 Downloaded from http://pubs.acs.org on December 1, 2018

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The Journal of Physical Chemistry

Characterizing Molecular Adsorption on Biodegradable MnO2 Nanoscaffolds Gangotri Dey†, Letao Yang†, Ki-Bum Lee†‡ and Lu Wang†* †

Department of Chemistry and Chemical Biology, Rutgers University, 123 Bevier Road, Piscataway, NJ 08854, USA

‡College of Pharmacy, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea E-mail: [email protected]

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Abstract Biodegradable MnO2 nanoscaffolds have recently been designed for advanced stem cell therapy. These nanomaterials strongly bind extracellular matrix proteins and effectively deliver therapeutic molecules, which significantly enhance stem cell survival and neuronal differentiation both in vitro and in vivo. In this work, we combine molecular dynamics simulations, density functional theory calculations and UV-Vis spectroscopy experiments to examine the selectivity and efficiency of a MnO2 nanosheet in adsorbing neurogenic drugs. To uncover the fundamental principles governing the drug loading process, we have systematically examined a series of model aromatic and alkyl compounds with characteristic functional groups and demonstrated that molecular adsorption on the MnO2 nanosheet results from an interplay of dispersion, electrostatic and charge transfer interactions. We have then proposed a metric that efficiently predicts the qualitative adsorption affinity of a guest molecule on the MnO2 nanosheet based on its structural and chemical features, which will facilitate the experimental screening of proper adsorbates for efficient molecular delivery and aid the development of MnO2-based nanoscaffolds for biomedical applications.


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Introduction One of the critical issues in current stem cell therapy is to improve survival, integration, and differentiation of stem cells (or stem cell-derived cell lineages) after transplantation. To this end, bioscaffolds using 0D, 1D, 2D, and 3D nanomaterials have become promising materials in stem cell therapy to treat many devastating injuries and diseases of the central nervous system.1-7 These multi-dimensional nanoscaffolds can mimic the topography of the natural extracellular matrix and further provide a favorable microenvironment for the adhesion, migration, and proliferation of stem cells to repair damaged tissues.4, 8-9 By tailoring the morphology, architecture and surface chemistry of the nanoscaffolds, scientists can control the differentiation of stem cells into the desired cell types and enhance neural regeneration.7, 10-11 As such, a wide variety of natural and synthetic materials based nanoscaffolds have been developed as potential regenerative medicine in cell therapy and tissue engineering.1, 3, 8, 12-14 In particular, the 2D/3D nanomaterial-based bioscaffolds are excellent delivery systems for cellular regulator molecules such as drugs, growth factors, genes and proteins to control stem cell differentiation.1, 4, 9, 14-17 For example, since 2008,18 graphene and graphene-based nanomaterials have been extensively explored as agents for drug and gene delivery both experimentally and computationally.3, 19-31 Graphene and its derivatives possess superior surface areas as well as rich and delocalized π-electrons that readily interact with guest molecules. As a result, they exhibit large drug loading capacities and enable spatiotemporally controlled delivery of small molecule drugs in stem cell therapy.3, 22, 32-33 Furthermore, these nanomaterials have strong mechanical strength to facilitate stem cell transplantation and high electrical conductivity to better control stem cell differentiation, both of which act to enhance cell viability and proliferation.12, 20, 24, 34 However, a major challenge in using graphene-based nanoscaffolds for biomedical applications is their non-



or slow- biodegradability, which not only restricts their long-term biocompatibility due to chronic inflammation but also leads to undesired low efficiency in releasing drugs in the transplanted sites.21, 32, 35 Recently, Lee and coworkers have designed a novel MnO2-based hybrid nanoscaffolds that, both in vitro and in vivo, exhibits high biocompatibility and biodegradability.36 These atomic-thin MnO2 nanosheets have strong binding affinities towards an extracellular matrix protein, laminin, which promotes stem cell adhesion and neuronal growth. These hybrid nanoscaffolds also show highly efficient loading of neurogenic drugs that regulate stem cell differentiation, while minimal burstrelease is observed due to the strong adsorption of the drug molecules on the material. Upon addition of vitamin C, a bioreductant, the MnO2 nanoscaffolds undergo a controlled degradation process via a redox mechanism, which results in the tunable and sustained release of drug molecules over a few weeks. In addtion, rates of the scaffold degradation, and thus the drug release, can be quantitatively followed by monitoring the Magnetic Resonance Imaging (MRI) signals of the degradation product Mn2+. Because of their outstanding physicochemical properties, the biocompatible MnO2 nanoscaffolds have been shown to enhance stem cell differentiation and neurite outgrowth, and significantly improve stem cell transplantation as a therapeutical approach to treat spinal cord injuries in vivo.36 The ability of the MnO2 nanoscaffolds to bind extracellular matrix proteins, to deliver neurogenic drug molecules in both the controlled and sustained manner, and to monitor the drug release roots from the strong adsorption of the guest molecules on the surface of the 2D/3D nanomaterials. To elucidate the origin of such strong interactions, we combine molecular dynamics (MD) simulations, density functional theory (DFT) calculations and UV-Vis spectroscopy to investigate the adsorption of five neurogenic drugs: N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-


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phenylglycine t-butyl ester (DAPT),37 retinoic acids, JQ1,38 1-azakenpaullone,39 and rhodamine B, on a MnO2 nanosheet. Among the drug molecules, DAPT is a γ-secretase inhibitor that improves neuron regeneration40 and can be efficiently incorporated in the hybrid MnO2 nanoscaffolds.36 Retinoic acid is a metabolite of vitamin A that is able to induce neuronal cell differentiation. 41-42 JQ1 is a potent inhibitor of the bromodomain and extra-terminal domain (BET)38 and has been shown to strongly affect stem cell proliferation and neuronal function.43-44 1-azakenpaullone selectively inhibits glycogen synthase kinase-3β,39 which plays an important role in regulating cell cycles and is closely associated with neurodegenerative diseases.45-46 In addition, we have examined a fluorescent model drug molecule, rhodamine B, as it has been successfully used to monitor and optimize the drug delivery processes of the MnO2 nanoscaffolds.36 The chemical structures of the five neurogenic drugs are shown in Figure 1.

Figure 1. Chemical structures of the neurogenic drugs.

To unravel the physical principles that determine the drug adsorption, we have carried out DFT calculations to systematically examine a series of aromatic and alkyl model compounds, and computed their adsorption properties on the MnO2 nanosheet. Based on this study, we have



developed a metric that qualitatively predicts the affinities for a guest molecule adsorbing on the MnO2 nanosheet. While DFT calculations have been extensively used to analyze the binding of small molecules on metal oxides, such as NiO, RuO2, CuO, TiO2 and V2O5,47-51 their applications to MnO2 are limited to the adsorption of mercury species.52 Our calculations provide novel insights into the structure, energy and key forces for the adsorption of guest molecules on the MnO2 surface, and can be used as benchmark values for future studies in this field. Materials and Methods UV-Vis absorption spectroscopy experiments We utilized UV-Vis absorption spectroscopy (Varian Cary 50 spectrophotometer, quartz cuvette) to quantify the drug loading efficiency of DAPT, retinoic acid, JQ1 and rhodamine B on MnO2 nanosheets. A concentration of 20 μM dissolved in 10% DMF aqueous solution was used for each drug. The drug concentration was chosen to minimize molecular aggregations and to better represent real loading conditions commonly used in drug delivery. After dissolution, UVVis absorption was measured and peaks of individual drugs were identified and recorded (all absorption spectroscopies were baselined using 10% DMF aqueous solution condition): Retinoic acid at 300 nm; JQ1 at 260 nm; DAPT at 260 nm; rhodamine B at 505 nm. To initiate drug loading process, 1.0 ml MnO2 nanosheets aqueous solution at a concentration of 1.2 mg/ml were slowly added into 2.0 ml drug solution (20 μM) and then gently shaken for 12 hours at room temperature. Afterwards, MnO2 nanosheets were precipitate down using 1.0 ml phosphate buffered saline (PBS). By further extensive centrifugation under 11,000 rpm for 1 hour, supernatants were collected for measurements. As controls, 20 uM drug solutions were also diluted using PBS and characterized by UV-Vis absorption. The loading efficiency of the drug was calculated based on the following equation, 6 ACS Paragon Plus Environment

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Loading efficiency =

𝐼𝑏 − 𝐼𝑎 × 100% 𝐼𝑏

In this equation, Ib and Ia represent the absorption peak intensity before and after MnO2 adsorption, respectively. More details of the experimental methods are provided in the Supporting Information. MD simulations of drug adsorption on the MnO2 nanosheet We carried out MD simulations to model the adsorption of the neurogenic drugs on the MnO 2 nanosheet using GROMACS 2016.1.53 These simulations allowed us to study the impact of solvent molecules in the adsorption process and sample favorable adsorption geometries. For each system, the initial configuration was generated by putting a drug molecule on top of a MnO2 surface and solvating the resulting structure with water in a cubic box of a length of 10 nm. Among the drug molecules studied, DAPT, retinoic acid, JQ1 and 1-azakenpaullone are neutral in solution. Rhodamine B exists in a cationic form, and a Cl- counterion was added to neutralize the system. The MnO2 nanosheet, the drug molecules and the Cl- ion were modeled using the universal force field (UFF),54 with the parameters generated using the OBGMX web service.55 Water molecules were described using the SPC/E model.56 Periodic boundary conditions were applied to all the simulations, and long-range electrostatic interactions were treated with the particle-mesh Ewald method.57 All simulations were performed with rigid bonds using the LINCS algorithm,58 and a time step of 2 fs. For each drug molecule, after equilibration, the production run was performed for 5 ns at a temperature at 300 K and a pressure of 1 bar using the Berendsen thermostat59 and Parrinello-Rahman barostats.60 From the MD trajectories, we extracted five represented configurations for each drug molecule based on their adsorption free energy profiles (Figure S3). More details of the simulation methods are reported in the Supporting Information.



DFT calculations of the adsorption of drugs and model compounds on the MnO2 nanosheet After extracting the conformations from MD simulations, we carried out DFT calculations to compute the adsorption energies of the drug molecules on the MnO2 surface using the Quantum ESPRESSO package.61 All of the calculations were performed in vacuum without inclusion of solvent molecules. To model the MnO2 nanosheet, we used a surface that contained 64 Mn and 128 O atoms with lattice parameters a = b = 23.2 Å and γ = 120o. To model the two-dimensional MnO2 nanosheet, we set the lattice constant c to be 40.0 Å to avoid self-interactions between the periodic images. Here geometry optimizations were performed for the MnO2 surface, the drug molecules and each of the MnO2-drug complexes. The DFT calculations were performed within the generalized gradient approximation (GGA) using the PBE density functional.62 The Hubbard parameter U was used to account for the strong correlation effect among the partially filled 3d orbitals of the Mn atoms,63-65 and we used the value of U = 4 eV and J = 0.65 Spin-polarized calculations were performed on all the structures containing MnO2. The calculations were carried out using ultrasoft pseudopotentials,66 and the valence electron states were expanded in a planewave basis set with an energy cutoff of 240 Ry. For geometry optimizations, a quasi-Newton algorithm was used with a convergence threshold of 10−3 Ry/Bohr, and the Brillouin-zone integration was sampled using the Γ-centered k-points with a Gaussian broadening of 0.005 Ry. Considering that the interactions of the molecules and the MnO2 surface are relatively weak, it is essential to include van der Waals interactions and we adopted the D2 dispersion correction67 in the calculations. To evaluate the performance of the D2 dispersion correction, we also computed the adsorption energies using the nonlocal correlation functional rVV1068 and the van der Waals density functional revPBE-vdW.69-70 As shown in Table S1, the three methods of dispersion


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corrections gave similar values of adsorption energies for the model compounds benzoic acid, acetic acid and chloromethane. For each drug molecule, the reported adsorption energy (Table S3) was obtained by averaging over the values from the five different configurations. To disentangle the contribution of the binding energy due to different functional groups, we used the same computational parameters and performed DFT calculations on the adsorption of model aromatic and alkyl molecules on the MnO2 surface. The adsorption energies and the dispersion contributions are listed in Table S4. Considering that the model alkyl compounds can have different adsorption sites, we tested five model compounds and found that the energetically most favorable conformations occurred when the most electronegative atom in each molecule was on top of the O atoms on the surface (Table S2). The intermolecular interactions between the adsorbate molecules (drug molecules or the model compounds) were examined from the electron density of the optimized structures using the noncovalent interactions analysis.71-72 To determine the partial charges of the atoms in the model aromatic and alkyl molecules and the MnO2 surface, we obtained the electron density grids for the optimized structures and computed the Bader charges using the Bader Charge Analysis program.73 For an atom, the amount of charge transfer was computed as the difference in its partial charge before and after the guest molecule is adsorbed on the MnO2 surface. The partial charges of polar atoms and the amount of charge transfer are reported in Table S5. Results and discussion Adsorption of neurogenic drugs on the MnO2 nanosheet The biodegradable MnO2 nanoscaffolds have proven to be excellent drug delivery systems for stem cell therapy both in vitro and in vivo.36 To quantitatively characterize their binding affinities 9 ACS Paragon Plus Environment


towards neurogenic drug molecules, we have first focused on the small molecule drugs DAPT, retinoic acid, JQ1 and rhodamine B (Figure 1), and measured their loading efficiencies on the MnO2 nanosheet using UV-Vis absorption spectroscopy. Among the four drug molecules, rhodamine B has the largest loading efficiency of 67.0%, suggesting that it strongly adsorbs to the nanoscaffold. This is followed by DAPT and JQ1, with loading efficiencies of 64.1% and 60.7%, respectively. However, as DAPT self-aggregates in aqueous solution, its loading efficiency is expected to be slightly smaller than the value reported above. Retinoic acid has the smallest loading efficiency of 51.0%, indicating its weak interactions with the MnO2 surface. To uncover the key factors that govern the selectivity and efficiency of the binding events, we have performed MD simulations of the neurogenic drugs on MnO2 nanosheets. In addition to the four drug molecules studied experimentally, we have also computationally investigated 1azakenpaullone (Figure 1), given its important roles in regulating stem cell behaviors.39, 45-46 From the MD simulations, we have found that retinoic acid gradually moves away from the MnO2 nanosheet and has an average distance of 8.1 Å from the surface. The fact that retionic acid does not adsorb on the MnO2 nanosheet is consistent with the experimental observation that it has the smallest loading efficiency amongst the drug molecules studied. From the structure of retinoic acid (Figure 1), this lack of adsorption is likely due to the weak interactions between its conjugated chain and the MnO2 surface. In addition, the terminal carboxyl group of the drug molecule preferably interacts with the solvent, forming 3.7 hydrogen bonds with the surrounding water molecules on average as observed during the MD simulations. Therefore, solvent plays a crucial role in mediating the adsorption of retinoic acid on the MnO2 nanosheet, and explicit inclusion of solute-solvent interactions is required to provide the correct description.


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In contrast to retinoic acid, the other four neurogenic drugs stay adsorbed on the MnO2 surface throughout the MD simulations. From the MD trajectories, we have extracted 5 representative conformations for each drug molecule and further investigated their adsorption properties through DFT calculations. Representative optimized structures of the MnO2-drug complexes are shown in Figure 2. To visualize the intermolecular interactions between the drug molecules and the MnO2 surface, we have carried out non-covalent interaction (NCI) analysis71-72 and plotted the isosurfaces of the reduced electron density gradient in Figure 2. The light blue color of the isosurfaces demonstrates that the intermolecular interactions are strong and attractive.71-72

Figure 2. Optimal configurations of the neurogenic drugs adsorbed on the MnO2 nanosheet. The light blue isosurfaces of the reduced electron density gradient (s = 0.5 au) are generated using the NCIPLOT program.71-72 Color codes are red: O, orange: Mn, grey: C, white: H, blue: N, yellow: S, cyan: F, lime: Cl, Brown: Br.

To quantify the adsorption affinities of each drug molecule, we have computed the adsorption energy, ΔEad, as Δ𝐸𝑎𝑑 = 𝐸𝑐𝑜𝑚𝑝𝑙𝑒𝑥 − 𝐸𝑀𝑛𝑂2 − 𝐸𝑚𝑜𝑙 .

(1)

Here 𝐸𝑀𝑛𝑂2 , 𝐸𝑚𝑜𝑙 and 𝐸𝑐𝑜𝑚𝑝𝑙𝑒𝑥 are the energies of the MnO2 surface, the adsorbate molecule in the gas phase, and the MnO2-adsorbate complex in their optimized conformations, respectively. 11 ACS Paragon Plus Environment


As shown in Figure 3 and Table S3, all the adsorption energies are negative, demonstrating that the four drug molecules adsorb favorably onto the MnO2 surface. Among the four compounds, 1azakenpaullone binds most strongly on the MnO2 nanosheet with a ΔEad of -74.5 kcal/mol. This is closely followed by rhodamine B, which has a ΔEad of -70.9 kcal/mol, consistent with the experimental observations that it has a large loading efficiency. In qualitative agreement with experiment, JQ1 and DAPT adsorb much more weakly on the MnO2 surface and their adsorption affinities are comparable, with ΔEad of -30.3 kcal/mol for JQ1 and -28.6 kcal/mol for DAPT.

Figure 3. Adsorption energies of neurogenic drugs on the MnO2 nanosheet. AZP and RhB stand for 1azakenpaullone and rhodamine B, respectively.

The distinct ΔEad for the neurogenic drugs, and hence the selectivity in drug adsorption, arises from the different intermolecular interactions at the interface between the guest molecules and the MnO2 nanosheet. As shown in Figure 2, in the optimized conformations, the drug molecules are oriented such that their aromatic rings are nearly parallel to the MnO2 nanosheet. The density gradient isosurfaces show that there are favorable intermolecular interactions between the π electrons of the aromatic rings and the surface. Accordingly, dispersion interactions play a key role in the adsorption of the drug molecules on the MnO2 surface, as their contributions range from


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40.6% for 1-azakenpaullone to 88.6% for JQ1 (Figure 3). In addition, the interfacial interactions also concentrate on polar functional groups on the drug molecules, including ester, amide, amine, carbonyl and aryl halide groups (Figure 2). Given the importance of these chemical moieties in determining the adsorption properties of the drug molecules, in the following, we will analyze a series of model aromatic and alkyl molecules with characteristic functional groups and investigate their binding properties to the MnO2 nanosheet. Adsorption properties of model aromatic molecules on the MnO2 nanosheet The chemical structures of the neurogenic drugs DAPT, JQ1, 1-azakenpaullone and rhodamine B all contain aromatic components, including the phenyl group and its derivatives as well as heterocyclic rings (Figure 1). To examine how these aromatic moieties impact the drug adsorption, we have carried out DFT calculations on the binding of model compounds with six-membered and five-membered rings on the MnO2 nanosheet. First, we have computationally investigated benzene and its alkyl and fluoro substituents (Figure 4 structures a-e). In the optimized structures, these six-membered ring compounds all adsorb on the MnO2 surface with distances between 2.7 and 3.1 Å. Accordingly, they all have large and negative adsorption energies with magnitude greater than 10 kcal/mol (Figure 4 and Table S4). Benzene and toluene, which differ by a single methyl group, adsorb on the MnO2 nanosheet mainly through the aromatic rings (Figure S5). As a result, they have very similar binding affinities, with ΔEad values of -10.6 kcal/mol for benzene and -11.7 kcal/mol for toluene. This suggests that interactions between the π electrons in the phenyl ring and the p orbitals of the surface O atoms dominate their adsorption, which manifests as a large (> 72%) contribution from dispersion interactions to the total adsorption energy (Figure 4). This finding is consistent with the well-



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known phenomenon that dispersion interactions play a significant role in the adsorption of aromatic compounds on metal and non-metal surfaces.74-78

Figure 4. Chemical structures of the model six-membered aromatic molecules (top panel) and their energies of adsorption on the MnO2 nanosheet (bottom panel). The total adsorption energies are divided into contributions from dispersion interactions (Dispersion) and other forces (Other).

The aromatic 3-fluorotoluene, 3,5-difluorotoluene and 4-methylbenzotrifluoride are model systems that mimic the aryl halide groups in DAPT, JQ1 and 1-azakenpaullone. As shown in Figure 4, compared to toluene, fluorine substitutions significantly increase the adsorption affinities of the compounds. The ΔEad values of 3-fluorotoluene and 3,5-difluorotoluene are -14.1 and -16.5 14 ACS Paragon Plus Environment

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kcal/mol, respectively. While the dispersion contributions of these two compounds are comparable to that of toluene, the fluorine substitution on the phenyl ring considerably enhances the nondispersion contribution and results in an increase in the overall ΔEad by -2.4 kcal/mol per F addition. For 4-methylbenzotrifluoride, addition of the –CF3 group to the toluene ring increases the dispersion contribution by 72%, leading to a large ΔEad of -17.6 kcal/mol. To elucidate why the fluorine-substituted compounds have more substantial adsorption affinities than that of toluene, we have carried out Bader charge analysis and found that each of the electronegative fluorine atoms has a partial charge of -0.8 electrons (e), as shown in Table S5. Meanwhile, the MnO2 surface is highly polar with the Mn and O atoms bearing an average charge of 1.8e and -0.9e, respectively, indicating the importance of electrostatic attractions in the adsorption process. Furthermore, upon binding, each of the fluorine-substituted compounds undergoes charge redistribution and donates ~0.2e to the MnO2 nanosheet where the transferred charges are delocalized over the whole surface (Table S5). As such, the binding of 3-fluorotoluene, 3,5difluorotoluene or 4-methylbenzotrifluoride is partly quantum mechanical in nature, as it results in the sharing and transferring of electrons between the guest molecule and the MnO2 nanosheet and alters the electronic structures of both the adsorbate and the adsorbent. We thus attribute the stronger adsorption of the fluoro-substituted toluene to the enhanced electrostatic and charge transfer interactions between the guest molecules and the MnO2 surface. Next, we have studied a set of heterocyclic aromatic molecules considering that the neurogenic drugs often comprise heterocyclic moieties. For example, the pyridine ring in 1-azakenpaullone is one of the key groups that directly interact with the MnO2 surface (Figure 2). As shown in Figure 4, ΔEad of pyridine is -11.1 kcal/mol. Similar to benzene and toluene, the majority (88.9%) of the adsorption energy comes from dispersion interactions between the π electrons in the pyridine



molecule and the MnO2 nanosheet. Electrostatic attractions also contribute to the physisorption as the heterocycle is polar and the N atom bears a charge of -2.7e. We have also observed partial chemisorption of pyridine with a transfer of 0.23e from the molecule to the surface. As a final example of six-membered heterocyclic compounds, we have considered pyran. Although it is not an aromatic molecule, pyran comprises the middle ring of the chromophore in rhodamine B (Figure 1). As shown in Figure 4, pyran has strong adsorption on the MnO2 nanosheet with a ΔEad of -15.6 kcal/mol. While only 56.7% of the total adsorption energy comes from dispersion interactions, an important contributor is electrostatic attractions as the electronegative O atom bears a charge of -1.7e. In addition, we have observed a large amount of 0.73e to be transferred from the molecule to the surface, and hence chemisorption plays an essential role. To demonstrate how the significant amount of charge transfer impacts the electronic structure of the system, we compare the density of states of the MnO2 nanosheet and the pyran molecule before and after the adsorption occurs, and plot them relative to the Fermi level of the MnO2 surface. As shown in Figure 5a, the highest-occupied molecular orbital (HOMO) of the pyran molecule at 0.4 eV (red dashed line) is very close in energy to the lowest unoccupied molecular orbital (LUMO) of the MnO2 nanosheet at 0.8 eV (blue dashed line). As a result, upon pyran adsorption, an electron transfer occurs between the two states, leading to an increase of 0.7 eV in the Fermi level of the MnO2 nanosheet and an increase of 0.05 eV in the HOMO peak of pyran. We then take a closer look at the HOMO peak of pyran around 0.45 eV, and project the wave function onto different atoms. As shown in Figure 5b, while the 2p orbitals of the C and O atoms in pyran dominate the density of states, there are considerable contributions from the 3d and 2p orbitals of the surface Mn and O atom, leading to orbital hybridization.


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Figure 5. (a) Density of states of the MnO2 nanosheet, pyran and the MnO2-pyran complex. The red and blue dashed lines represent the HOMO and LUMO of pyran and MnO2 nanosheet, respectively. (b) Components of the density of states around the HOMO peak of pyran in the MnO2-pyran complex. The Fermi level of the MnO2 nanosheet, EF, is -5.4 eV.

In addition to the six-membered heterocycles, we have examined pyrrole, triazole and thiophene as representative structures of five-membered heterocyclic aromatic moieties in the neurogenic drugs. As shown in Figure 6 and Table S5, their adsorption on the MnO2 nanosheet results from an interplay of dispersion, electrostatic and charge transfer interactions. The nitrogen-containing ring structures adsorb strongly to the MnO2 surface with a distance around 2.9 Å (Table S4). The ΔEad values are -15.8 kcal/mol for pyrrole and -10.8 kcal/mol for triazole, to which dispersion interactions contribute to 43.1% and 52.7%, respectively. Both heterocycles have permanent dipoles and the N atoms bear a partial charge of -1.9e, resulting in large electrostatic interactions between the molecules and the surface. As the molecule-to-surface charge transfer is larger for pyrrole (0.44e) than that for triazole (0.22e), pyrrole has a greater overall adsorption affinity between the two molecules. In contrast to the N-containing heterocycles, thiophene adsorbs much weaker on the MnO2 nanosheet with a ΔEad of -5.8 kcal/mol. This is partly because thiophene stays further away from the MnO2 surface with a distance of 3.7 Å. While dispersion accounts for 64.7% of its total adsorption energy, thiophene has smaller electrostatic and charge transfer interactions 17 ACS Paragon Plus Environment


with the MnO2 nanosheet than pyrrole and triazole: the molecule is less polar with the S atom having a charge of 0.4e, and a smaller amount of 0.20e is observed to transfer from thiophene to the surface. Hence the S-containing thiophene has an overall smaller adsorption affinity than that of the N-containing heterocycles.

Figure 6. Chemical structures and adsorption energies of the five-membered aromatic molecules (a) pyrrole, (b) triazole and (c) thiophene.

From the above analyses, dispersion interactions play a major role in the adsorption of aromatic molecules, especially benzene and its derivatives, on the MnO2 nanosheet. When heteroatoms are present in a molecule, its adsorption affinity is significantly enhanced by the electrostatic and charge transfer interactions with the MnO2 surface. In particular, partial chemisorption has been observed for aromatic compounds containing halogen, oxygen, nitrogen or sulfur atoms, as the intermolecular electron transfer alters the electronic states of both the adsorbate molecules and the MnO2 surface. Adsorption properties of model alkyl molecules on the MnO2 nanosheet Besides the aromatic moieties, the neurogenic drugs also contain a variety of polar functional groups, including amide, amine, carboxyl and halide groups (Figure 1). To elucidate their impact


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on the affinity of drug adsorption, we have computationally studied model alkyl molecules with characteristic functional groups. The chemical structures of these model compounds are shown in Figure 7. In the optimized geometry of each alkyl molecule, the polar functional group adsorbs on the MnO2 nanosheet and the most electronegative atom of the compound sits on top of the surface O atoms.

Figure 7. Chemical structures of the model alkyl molecules (top panels) and their energies of adsorption on the MnO2 nanosheet (bottom panel).

First, we focus on three N-containing alkyl compounds (Figure 7 structures a-c). We have chosen N-methylacetamide as a model system for the amide groups that are present in DAPT and 1azakenpaullone, and trimethylamine and methylamine to mimic the tertiary amine group in rhodamine B. As shown in Figure 7 and Table S5, all three molecules have strong adsorption 19 ACS Paragon Plus Environment


affinities towards the MnO2 nanosheet, with ΔEad of -16.8 kcal/mol for N-methylacetamide, -15.5 kcal/mol for trimethylamine and -10.2 kcal/mol for methylamine. Similar to their aromatic counterparts, adsorption of the N-containing alkyl compounds on the MnO2 surface arises from a combination of dispersion, electrostatic and charge transfer interactions. For N-methylacetamide, dispersion accounts for 53.1% of the total adsorption energy. The molecule is highly polar and partial charges of the electronegative N and O atoms are -2.8e and -1.9e, respectively. Upon binding on the MnO2 nanosheet, electrons in the system are redistributed and a charge transfer of 0.15e is observed from the molecule to the surface. For trimethylamine, dispersion interactions only contribute -4.7 kcal/mol to its total adsorption energy. While the N atom bears a charge of 1.0e, it is 4.9 Å away from the surface due to the steric hindrance from the bulky methyl groups, leading to a small stabilization energy from electrostatic interactions. However, upon adsorption, a prominent amount of 0.53e is transferred from trimethylamine to the MnO2 nanosheet, resulting in partial chemisorption and an increase in adsorption energy. The mechanism of charge transfer is demonstrated in Figure S6: the HOMO of trimethylamine has an energy of 0.5 eV, which donates electrons to the LUMO of the nanosheet at an energy of 0.8 eV. This redistribution of electrons significantly alters the electronic structures of the system, leading to an increase of 0.8 eV in the Fermi level of the MnO2 nanosheet, and a decrease of 0.7 eV of the HOMO peak of the trimethylamine molecule. To evaluate how steric hindrance impacts the adsorption of trimethylamine, we have examined methylamine and found that removing the two methyl groups brings the molecule close to the MnO2 nanosheet with a distance of 3.0 Å. However, for methylamine, the dispersion contribution is only -3.9 kcal/mol and the intermolecular charge transfer is reduced to 0.35e, leading to a decrease of its overall adsorption affinity compared to trimethylamine.


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Next, we have studied the adsorption of methanethiol (Figure 7 structure d) on the MnO2 nanosheet and found its ΔEad to be -9.8 kcal/mol. Because the S atom is highly polarizable, dispersion interactions contribute to -5.5 kcal/mol to the total adsorption energy of CH3SH, which is 40% larger than that of CH3NH2. Methanethiol, together with thiophene (Figure 6 structure c), represents the aliphatic and aromatic S-containing functional groups in drug molecules. Compared to thiophene, methanethiol transfers a more substantial amount of 0.31e to the MnO2 nanosheet, leading to a stronger chemisorption and a larger adsorption affinity. We have then examined the O-containing acetaldehyde, acetic acid and methanol, where they serve as model systems for the ester, carboxyl and hydroxyl groups commonly observed in neurogenic drugs. As shown in Figure 7, these compounds adsorb relatively strongly on the MnO2 nanosheet with ΔEad between -6.4 and -8.5 kcal/mol. Their adsorption is dominated by dispersion interactions, which contribute more than 69% to ΔEad in all compounds. In addition, in all three compounds, the O atom has a partial charge of about -1.8e, providing an electron-rich moiety to interact with the MnO2 surface electrostatically (Table S5). However, compared to the N-based compounds, the O-containing molecules have much smaller charge transfer to the MnO2 surface. For example, while a transfer of 0.18e is observed for acetaldehyde, no significant charge transfer is observed for acetic acid and methanol. Hence the binding of the three O-containing molecules on the MnO2 nanosheet are mainly through physical adsorption, and the dominating forces are dispersion and electrostatic interactions at the interface between the molecules and the surface. Finally, given that the drug molecules often contain halogen atoms, we have investigated the adsorption of CH3F, CH3Br and CH3Cl, as model compounds on the MnO2 surface (Figure 7 structures h-j). As shown in Figure 7, these compounds interact weakly with the MnO2 surface, leading to physisorption. Fluoromethane has an adsorption energy of -5.1 kcal/mol, for which



41.8% comes from dispersion interactions. In this polar molecule, the F atom has a partial charge of -0.7e, and hence the interfacial electrostatic interactions also contribute to its overall ΔEad. For bromomethane and chloromethane, the ΔEad values are -4.7 and -3.6 kcal/mol, respectively. Their adsorption energies are dominated by dispersion interactions, as Br and Cl are large and polarizable atoms, with a slight contribution from electrostatic interactions. Since each of the three compounds contains a single methyl group, ΔEad of the halogen groups follows the trend of –F > –Br > –Cl, which is consistent with a recent study on the adsorption of halogen molecules on graphene sheets.79 After examining a set of model alkyl molecules, we have demonstrated that dispersion forces are a key type of interaction that dominates the adsorption of a variety of functional groups on the MnO2 nanosheet, especially for the chemical moieties with large and polarizable atoms. As these alkyl molecules all contain polar functional groups, electrostatic interactions play an essential role in the stabilization of the physically adsorbed molecules. In addition, N and S atoms, and sometimes O, in these small alkyl molecules are often involved in sharing and transferring of electron densities with the surface atoms, forming chemisorption on the MnO2 nanosheet. The qualitative metric for the adsorption of guest molecules on the MnO2 nanosheet To effectively use the MnO2 nanosheet as carriers for cellular regulator molecules such as neurogenic drugs and peptides, it is desirable to develop a scheme to estimate the adsorption affinities of guest molecules based on their structures and chemical compositions, particularly the aromatic and polar functional groups. From the analyses of the model compounds, we have shown that the selectivity of molecular adsorption on the MnO2 nanosheet is dominated by the interfacial dispersion, electrostatic and charge transfer interactions. In particular, the dispersion interactions between the π electrons and the surface play a major role in the adsorption of aromatic groups. 22 ACS Paragon Plus Environment

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Assuming the contributions from these forces are additive, we propose a metric that predicts the qualitative adsorption affinity, Xad, of a guest molecule on the MnO2 surface, 𝑋𝑎𝑑 (𝑘𝑐𝑎𝑙/𝑚𝑜𝑙) = 𝐸6 × 𝑁6 + 𝐸5 × 𝑁5 + 𝐸𝑓𝑔 .

(2)

In this equation, E6 and E5 are the adsorption energies of aromatic moieties with six-membered and five-membered rings, respectively. The value of E6 is calculated to be -12.2 kcal/mol, which is the average adsorption energies of toluene, benzene, pyran, and pyridine. Likewise, E5 takes the value of -10.8 kcal/mol, calculated from the average adsorption energy of pyrrole, triazole, and thiophene. For a guest molecule, N6 and N5 are the number of six-membered and five-membered aromatic rings, respectively, that are accessible to the MnO2 surface. For example, when a naphthalene molecule adsorbs parallelly on the MnO2 surface, 𝑋𝑎𝑑 = −12.2 × 2 = −24.4 kcal/mol. This value agrees well with its ΔEad, -21.6 kcal/mol, as obtained from our DFT calculations. However, if a molecule contains multiple aromatic groups, but only one sixmembered ring is parallel to the surface due to steric hindrance, one counts N6 to be 1. In Eq. 2, Efg is the adsorption energy of polar functional groups on the MnO2 surface. Efg includes contributions from dispersion, electrostatics and charge transfer interactions. As each alkyl model compound contains a methyl group (Figure 7), we have carried out DFT calculations and found that the adsorption energy of a methane molecule on the MnO2 surface, ΔEm, is -1.66 kcal/mol. We have then obtained Efg of each functional group by subtracting ΔEm from the ΔEad value of the corresponding compound. The resulting Efg for different functional groups is listed in Table 1. For other functional groups, one takes the Efg value of a group that most closely resembles its chemical structure in Table 1.



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Table 1. Efg for different functional groups

Functional Group

Efg (kcal/mol)

Functional group

Efg (kcal/mol)

–CONHCH3

-15.1

–COOH

-5.6

–N(CH3)2

-13.9

–OH

-4.8

–NH2

-8.6

–F

-3.4

–SH

-8.1

–Br

-3.0

–CHO

-6.8

–Cl

-1.9

From the discussions above, Xad can be calculated as 𝑋𝑎𝑑 (𝑘𝑐𝑎𝑙 ⁄𝑚𝑜𝑙 ) = −12.2 × 𝑁6 − 10.8 × 𝑁5 + 𝐸𝑓𝑔 .

(3)

For Eq. 3, the qualitative adsorption affinity of a molecule depends on its chemical composition and structure. In other words, the type of aromatic rings and polar functional groups and their accessibility to the MnO2 surface play a key role in determining the Xad value. This metric thus provides an efficient way to qualitatively predict the adsorption affinity of an arbitrary molecule on the MnO2 nanosheet without performing detailed quantum chemistry calculations. To evaluate the performance of the qualitative metric, we first apply it to model compounds with substituted phenyl rings: 3-fluorotoluene and 3,5-difluorotoluene and (Figure 4 structures c and d). 3-fluorotoluene contains one fluorine substitution on the aromatic ring, which is fully accessible to the MnO2 surface, and thus its qualitative adsorption affinity is 𝑋𝑎𝑑 = −12.2 × 1 − 3.4 = −15.6 kcal/mol. Similarly, for 3,5-difluorotoluene 𝑋𝑎𝑑 = −12.2 × 1 − 3.4 × 2 = −19.0 kcal/mol. As we have assumed that the contributions of different functional groups are additive in the calculation of Xad and thus double counted the energies of the bonding electrons between the phenyl ring and the fluorine atoms, we have slightly overestimated the adsorption energy compared to their ΔEad values. However, the metric correctly predicts the trend that 3-


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fluorotoluene adsorbs less strongly than 3,5-difluorotoluene on the MnO2 nanosheet (Figure 4 and Table S4). To further validate the metric, we use it to identify the most stable conformation for the adsorption of rhodamine B on the MnO2 surface. As shown in Figure 1, rhodamine B contains two aromatic components: a benzoic acid group and a chromophore that is composed of three conjugated rings. However, the two aromatic moieties are oriented almost perpendicularly in the three-dimensional structure of the molecule (Figure 2), and as a result, only one of them is available to adsorb on the MnO2 surface. Given the importance of dispersion interactions between the aromatic π-electrons and the surface, we have considered two possible conformations of rhodamine B, with one or the other aromatic component adsorbing on the MnO2 surface. In the first conformation, we position the chromophore of the rhodamine B molecule close to the MnO2 nanosheet (Figure S7a). As the chromophore is composed of three six-membered aromatic rings, and the molecule does not contain any five- membered rings, N6 = 3 and N5 = 0. Due to steric hindrance, only one of the tertiary N atoms at the ends of the chromophore is close to the surface, and we will represent its contribution using the Efg value of the –N(CH3)2 functional group. Thus for conformation 1, 𝑋𝑎𝑑,1 = −12.2 × 3 − 13.9 = −50.5 kcal/mol. In the second conformation, we place the benzoic acid group parallel to the MnO2 nanosheet while the chromophore is almost perpendicular to the surface (Figure S7b). In this case N6 = 1 and we will include the contributions from the –COOH functional group for calculating the qualitative adsorption affinity, 𝑋𝑎𝑑,2 = −12.2 − 5.6 = −17.8 kcal/mol. As 𝑋𝑎𝑑,1 has a much larger magnitude than that of 𝑋𝑎𝑑,2 , the metric suggests that the first adsorption conformation is energetically more favorable than the second one. This prediction is confirmed by our DFT calculations, where ΔEad is -70.9 kcal/mol for conformer 1 and -48.1 kcal/mol for conformer 2.



Note that one only uses the metric for qualitative predictions of the trend in adsorption affinities, as it neglects the contributions from the chemical moieties that are not in direct contact with the MnO2 surface. As a result, the absolute value of Xad is much smaller than that of ΔEad for rhodamine B. Finally, we validate the metric by analyzing the trend in Xad for all the drug molecules: DAPT, JQ1, 1-azakenpaullone and rhodamine B. Based on the chemical structure of the drug molecules, we have determined Xad to be -34.3 kcal/mol for DAPT, -35.8 kcal/mol for JQ1 and -53.3 kcal/mol for 1-azakenpaullone (more details of the calculations are provided in the Supporting Information). In agreement with the DFT calculations (Figure 2), the metric correctly predicts that the adsorption affinity of the drug molecules follows the trend of 1-azakenpaullone > rhodamine B > JQ1 ≈ DAPT. From these analyses, we have demonstrated that the metric in Eq. 3 can be used to predict the trend in adsorption affinities of different molecules on the MnO2 nanosheet and can potentially predict the efficiency of loading drug and protein molecules by the nanoscaffold in stem cell therapy.

Conclusions MnO2-based nanoscaffolds exhibit excellent biocompatibility and biodegradability, enhanced binding affinity towards extracellular matrix proteins and efficient loading and sustained release of drug molecules, making them potent materials to regulate cell adhesion and differentiation for advanced stem cell therapy.36 In this work, we have combined MD simulations, DFT calculations and UV-Vis spectroscopy experiments to examine the binding of neurogenic drugs on the MnO2 nanosheet and demonstrated that they have a wide range of adsorption affinities. From MD simulations, retinoic acid does not adsorb on the nanosheet, highlighting the importance of solutesolvent interactions in the adsorption process. For the other four drug molecules, 1-azakenpaullone 26 ACS Paragon Plus Environment

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and rhodamine B adsorb very strongly on the MnO2 surface whereas DAPT and JQ1 have considerably lower adsorption affinities, in good agreement with experimental measurements. To uncover the origin of the observed trend in adsorption affinity, we have carried out DFT calculations to systematically examine a series of aromatic and alkyl model compounds with characteristic functional groups. We have demonstrated that dispersion, electrostatic and charge transfer interactions play significant roles in the molecular adsorption on the MnO2 nanosheet. To describe the physical and chemical adsorption of guest molecules on the MnO2 nanosheet, we have developed a metric to efficiently predict their qualitative adsorption affinities. Using this metric, we demonstrated that the distinct loading efficiencies of the drug molecules on the MnO2 nanosheet arise from the differences in their chemical compositions and structures, which in turn lead to different interactions with the nanosheet. Given their superior surface areas, the two-dimensional MnO2 nanosheets are desirable materials for the transport and delivery of cellular regulator molecules, including extracellular matrix proteins and neurogenic drugs, which are composed of aromatic and polar functional groups. The metric developed in this work is readily applicable to these molecules, and hence it will facilitate the experimental screening of proper adsorbates based on their composition and chemical structures, and aid the experimental modulations of pH and solvent for optimal molecular delivery efficiency. The metric can be further extended by incorporating interactions between commonly observed functional groups and modified MnO2 surfaces, which will guide the design of novel nanoscaffolds that have enhanced selectivity and efficiency for the adsorption and delivery of cellular regulator molecules in stem cell therapy. Supporting Information



The Supporting Information includes detailed descriptions of the materials and methods, and data from the experimental and simulation analyses. Acknowledgements G.D. and L.W. acknowledge the Office of Advanced Research Computing at Rutgers, The State University of New Jersey, for providing access to the Amarel cluster. This work used resources from the Rutgers Discovery Informatics Institute, which are supported by Rutgers and the State of New Jersey. It also used the Extreme Science and Engineering Discovery Environment (XSEDE) through the allocation TG-CHE170034. G.D. acknowledges Dr. Giovanni Garberoglio and Professor Graeme Henkelman for their help with generating the universal force field parameters and carrying out Bader charge analysis. K-B.L. acknowledges financial support from the New Jersey Commission on Spinal Cord Research (CSCR17IRG010). References 1. Shah, S.; Solanki, A.; Lee, K.-B., Nanotechnology-Based Approaches for Guiding Neural Regeneration. Acc. Chem. Res. 2016, 49 (1), 17-26. 2. Chueng, S.-T. D.; Yang, L.; Zhang, Y.; Lee, K.-B., Multidimensional Nanomaterials for the Control of Stem Cell Fate. Nano Converg. 2016, 3 (1), 23. 3. Yin, P. T.; Shah, S.; Chhowalla, M.; Lee, K.-B., Design, Synthesis, and Characterization of Graphene–Nanoparticle Hybrid Materials for Bioapplications. Chem. Rev. 2015, 115 (7), 2483-2531. 4. Cao, H.; Liu, T.; Chew, S. Y., The Application of Nanofibrous Scaffolds in Neural Tissue Engineering. Adv. Drug Deliv. Rev. 2009, 61 (12), 1055-1064. 5. Zhang, L.; Webster, T. J., Nanotechnology and Nanomaterials: Promises for Improved Tissue Regeneration. Nano Today 2009, 4 (1), 66-80. 6. Lu, P.; Wang, Y.; Graham, L.; McHale, K.; Gao, M.; Wu, D.; Brock, J.; Blesch, A.; Rosenzweig, Ephron S.; Havton, Leif A., et al., Long-Distance Growth and Connectivity of Neural Stem Cells after Severe Spinal Cord Injury. Cell 2012, 150 (6), 1264-1273. 7. Shah, S.; Yin Perry, T.; Uehara Thiers, M.; Chueng Sy‐Tsong, D.; Yang, L.; Lee, K. B., Guiding Stem Cell Differentiation into Oligodendrocytes Using Graphene‐Nanofiber Hybrid Scaffolds. Adv. Mater. 2014, 26 (22), 3673-3680. 8. Jafari, S.; Ahmadian, E.; Fard, J. K.; Yari Khosroushahi, A., Biomacromolecule Based Nanoscaffolds for Cell Therapy. J. Drug Deliv. Sci. Techno. 2017, 37, 61-66. 28 ACS Paragon Plus Environment

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Characterizing Molecular Adsorption on Biodegradable MnO2

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