Prediction of Molecular Affinity on Solid Surfaces via Three

The HSP were derived from the interfacial free energy, which can easily be .... (24) However, such influence of the surfaces of the solid materials us...
0 downloads 0 Views 2MB Size
Download PDF
Article Cite This: J. Phys. Chem. C 2019, 123, 13246−13252

pubs.acs.org/JPCC

Prediction of Molecular Affinity on Solid Surfaces via Three-Dimensional Solubility Parameters Using Interfacial Free Energy as Interaction Threshold Masakazu Murase* and Riichiro Ohta* Toyota Central R&D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan

Downloaded via LUND UNIV on August 20, 2019 at 13:24:27 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

S Supporting Information *

ABSTRACT: Various adhesive and wetting phenomena in nature and practical applications originate from the interaction between the surfaces of materials and other substances. In this study, we developed a method for calculating the Hansen solubility parameters (HSP) of the surface of the solid materials to elucidate the surface interaction by quantitatively and visually representing adsorptivity with a three-dimensional vector. The HSP were derived from the interfacial free energy, which can easily be calculated from the contact angles of three organic solvents on the solid materials. The HSP for a glassy carbon (GC) surface calculated using our method were correlated with the adsorptivity on the GC surface of several organic molecules. The adsorptivity was evaluated using electrochemical impedance spectroscopy and molecular mechanics simulations; the latter of which also revealed that the HSP calculated for the Pt surface were highly correlated with its interactivity. Moreover, the HSP of the polytetrafluoroethylene (PTFE) surface obtained herein appropriately reflect the molecular structure of PTFE. The results underpin that our method enables the elucidation of various surface phenomena involving noncovalent interaction and allows the affinity between solid surfaces and tens of thousands of substances recorded in the HSP database to be predicted.

■

INTRODUCTION Interaction between the surfaces of materials and other substances is the origin of various interesting natural phenomena, such as the adhesion of geckos to a wall, arising from the combination of affinitive interaction and morphological effects,1 and the superhydrophobicity and self-cleaning effect of a lotus leaf, arising from the repulsive interactions between its surface and water and contaminants from surrounding environments.2 Moreover, adsorption and desorption are in equilibrium and depend on the strength of the interactions on the microscopic scale, such as that of the solid materials with volatile substances in air3 and controlled environments,3−5 and cell surface receptors interacting with acceptors in biological systems.6 Therefore, fundamental insights and understanding of adsorption phenomena are basic issues of surface science in chemistry,7,8 biology,9 and various other academic areas and are of high importance in the field of manufacturing, including coating, antifouling, demolding, and sensing of chemical agents. Additionally, the ability to predict the adsorptivity on the surface of materials can facilitate and accelerate materials design for the above industrial applications. Adsorptivity on the surface of materials has been intensively studied by employing quantitative calculations using Monte Carlo simulations,10,11 molecular dynamics (MD) simulations,12,13 etc. These calculated results have been found to be correlated with the experimental results for the adsorptivity of © 2019 American Chemical Society

specific materials and have made the correlation-based prediction of adsorptivity possible. However, these methods generally provide the adsorptivity for an adsorbate via an individual calculation process, in which the adsorbate is intentionally selected according to the range of knowledge and interest of researchers. Thus, adsorbates not considered by the researchers could unintentionally be omitted from the selection using these methods, which would be an obstacle for the thorough prediction of the adsorptivity of material surfaces. In this study, we propose a method for characterizing and predicting the adsorptivity on the surfaces of the solid materials with a wide variety of adsorbates, including those that are unintentional, more simultaneously compared to conventional methods. The method expands the concept of Hansen solubility parameters (HSP) to the surfaces of the solid materials and represents the surface adsorptivity quantitatively with a three-dimensional (3D) vector. The HSP express the inherent solubility of substances by 3D vectors composed of the dispersion (δD), polarization (δP), and hydrogen bonding (δH) solubility parameters.14,15 The HSP of various common chemical substances were clarified by Hansen and other HSP developers and have been reported.16 Using the substances on Received: January 7, 2019 Revised: April 5, 2019 Published: May 1, 2019 13246

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C

Chemical Industries Ltd.) as adsorbates on the GC surface. Tetrahydrofuran (THF) and acetonitrile (ACN) were used as purchased (Dojindo Laboratories Co., Ltd.) as adsorbates. Method for Measuring Contact Angles. The static contact angles (θC) of the probe liquids (DIM, EG, and BN) on the GC, Pt, and PTFE surfaces were measured by an optical contact angle measuring device (DM-501; Kyowa Interface Science Co., Ltd.) at room temperature after removing contamination from the surface by rinsing sequentially with ethanol, acetone, and hexane. θC was obtained from the photographic images captured at 1.0 s after contacting a droplet of each probe liquid (0.5 μL) on the surface. The mean values of θC at five different points for each probe liquid were used for the following calculation and are shown in Table S1. Method for Calculating Interfacial Free Energy. γInt was calculated by the following procedures: i. The θC values of the three probe liquids (DIM, EG, and BN) were measured on the surfaces of the solid materials. ii. The surface free energy of the solid materials (γS), composed of dispersion (γSD), polarization (γSP), and hydrogen bonding (γSH) terms, was calculated using the following equation proposed by Kitazaki and Hata22

the list as probe solvents, the unknown HSP of a substance of interest can be predicted using the Hansen sphere method. This method entails drawing a sphere that includes good solvents and excludes poor solvents with the so-called interaction radius in 3D vector space, in which the center of the sphere is defined as its HSP.17 The above calculation can be conducted using commercial software (HSPiP 4th Edition 4.1.07). Application of the abovementioned analysis using the HSP has conventionally been limited to dissoluble chemical substances17 and fine particles composed of polymers,17 carbonbased nanomaterials,18−20 and metallic oxides,18 which are dispersible in organic or aqueous solvents. Attempts by Hansen to expand the HSP to solid surfaces involved observing whether the probe solvents spontaneously spread on solid surfaces to identify solvents with HSP similar to those of the solid surfaces.21 However, they noted that the abovedescribed method needed improvement by presenting a contradictory example in which hexane, which cannot dissolve epoxy polymers, spontaneously spreads on these polymers because of its low surface tension.21 We employed the interfacial free energy (γInt), which represents the stability of the interfacial states between two materials in contact, as the degree of similarity of the HSP between the surface of the solid materials and substances with known HSP (hereinafter referred to as the “reference substance”). Here, the magnitude of γInt was used as the parameter to draw an interaction sphere, the center of which was defined as the HSP of the surface of the solid materials. Note that this interaction sphere differs from the conventional Hansen sphere, which was drawn by using the solubility as its parameters.17 In this study, γInt was calculated from the surface free energy of the solid materials of interest (γS) and that of the reference substances (γR) as described in the Experimental Section, where γS was calculated from the contact angles of the probe liquids on the surface of the solid materials using the extended Fowkes theory proposed by Kitazaki and Hata.22 Then, similar to the classification using a threshold for good/ poor solubility in the conventional Hansen sphere method, we sorted the reference substances by setting a threshold (γTh) for γInt, where reference substances with γInt ≤ γTh have higher affinity for the surface of a solid material compared to those with γInt > γTh. After the classification, we draw the interaction sphere by including the HSP of the reference substances with γInt ≤ γTh and excluding the HSP of those with γInt > γTh in 3D vector space. To verify our method, we investigated whether the HSP calculated using this method could be appropriately correlated with the surface adsorptivity of glassy carbon (GC) and platinum (Pt), which are often used as an electrode for electrochemical studies. In addition, we confirmed whether the HSP calculated for the polytetrafluoroethylene (PTFE) surface appropriately reflect the molecular structure of this polymer, as further verification.

cos θC = 2 γL DγS D /γL + 2 γL PγSP /γL + 2 γL HγS H /γL − 1 (1)

where γL is the surface free energy of the probe liquid, γL is the dispersion term, γLP is the polarization term, γLH is the hydrogen bonding term, and γL is the sum of γLD, γLP, and γLH. The γLD, γLP, and γLH values of the probe liquids were obtained from the literature22 and are shown in Table S2. The γSD, γSP, and γSH values were obtained by solving the three-way simultaneous equation derived by substituting γL, γLD, γLP, and γLH and the measured θC value of the three probe liquids into eq 1. iii. γInt was calculated as22 D

γInt = γS + γR − 2 γS DγR D − 2 γSPγR P − 2 γS HγR H

γSD,

(2)

where γS is the sum of and and γR is the surface free energy of the reference substance and the sum of the dispersion (γRD), polarization (γRP), and hydrogen bonding (γRH) terms. Reported values of γRD, γRP, and γRH for the reference substances22,23 were used for the calculation and are shown in Table S3. Setting the Value of Threshold to Interfacial Free Energy. Integer values were used for the threshold (γTh). Setting γTh to an excessively large value causes a shortage of reference substances with γInt > γTh and leads to a decrease in the accuracy of determination of the boundary of the interaction sphere. In addition, a higher value of γTh distorts the shape of the interaction sphere, resulting in a reduction in the accuracy of calculating the HSP. As a notable case, the interaction region of materials comprising multiple molecular structures with different properties, such as amphiphilic surfactants, copolymers, and a mixture of substances, is difficult to draw as a single sphere in 3D vector space, because the HSP of such materials are composed of multiple HSP according to their individual structures. For cases in which materials have two structures with different properties, Hansen et al. proposed the use of the double sphere method.24 However, such influence of the surfaces of the solid materials used herein, that is, GC, Pt, and PTFE, on the shape of the sphere should be small, because

■

EXPERIMENTAL SECTION Materials. Glassy carbon (GC; GC-20SS; Tokai Fine Carbon Ltd.), polytetrafluoroethylene (PTFE; Flon Industry Co., Ltd.), and platinum (Pt; The Nilaco Corporation) were polished to a mirror finish using alumina paste (particle diameter: 0.05 μm). Probe liquids (diiodomethane (DIM), ethylene glycol (EG), and 1-bromonaphthalene (BN); Wako Pure Chemical Industries Ltd.) were used as purchased. Benzyl alcohol (BZA), acetone (ACT), dimethyl sulfoxide (DMSO), and 1-butanol (1BA) were used as purchased (Wako Pure 13247

γSP,

γSH,

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C these materials have uniform chemical structures. In addition, we adopted the smallest γTh using which the sphere can be drawn encompassing the HSP plots of at least two reference substances with γInt ≤ γTh, which are the smallest number of plots necessary to draw the sphere, to further minimize the above influence. Method for Calculating the Distance between the HSP of Organic Compounds and the Surfaces of GC and Pt. The distance (Ra) between the HSP of the surface of the solid materials and organic molecules (OMs) was calculated as15 Ra =

4(δ D1 − δ D2)2 + (δ P1 − δ P2)2 + (δ H1 − δ H2)2 (3)

where δD1, δP1, and δH1 are the HSP on the surfaces of GC and Pt and were calculated using our method, and δD2, δP2, and δH2 are the reported HSP of the OMs shown in Tables S4 and S5.16 Method for Measuring and Results of the Rate of Charge Transfer Resistance Using Electrochemical Impedance Spectroscopy. The rate of charge transfer resistance (ΔRCT) on the GC surface in an aqueous electrolyte was measured using electrochemical impedance spectroscopy (EIS). ΔRCT was calculated as25 ΔR CT = (R CT2 − R CT1)/R CT1 × 100

Figure 2. System for measuring impedance on the GC surface. An aqueous solution of 0.1 M KCl (20 mL) was used as the electrolyte. Ag/AgCl and Pt were used as the reference and counter electrodes, respectively. The electrode area in contact with the electrolyte was 1.13 cm2 (φ12.0 mm).

(4)

where RCT1 and RCT2 are the charge transfer resistance of the electric double layer before and after adsorption of the OMs, respectively. RCT1 and RCT2 were obtained by fitting the measured Nyquist plots to the equivalent circuit representing the electric double layer on the GC surface as shown in Figure 1.

Figure 1. Equivalent circuit of the electric double layer on the GC surface in an aqueous electrolyte. Rsol is the solution resistance, while RCT and the constant phase element (CPE) are the charge transfer resistance and capacitance of the electric double layer, respectively. The CPE is composed of the CPE constant (T) and the CPE index (p). The impedance of CPE (ZCPE) is obtained as ZCPE = 1/(jω)pT, where j is an imaginary unit and ω is the angular frequency.

The Nyquist plots were obtained using a potentiostat (ModuLab XM ECS; Solartron analytical Ltd.) in the frequency range from 10 kHz to 0.1 Hz with 10 frequencies per decade. The AC amplitude was set at ±5 mV from the open circuit potential. The measurement system is shown in Figure 2; details of the measuring conditions are described in the caption of Figure 2. The Nyquist plots of the GC surface before and after the addition of the OMs (1.0 mM) to the electrolyte are shown in Figure S1. The calculated values of RCT1 and RCT2 are shown in Table S6. All chi-squared values between the Nyquist plots and fitting results were lower than 0.001, indicating that all the calculated values shown in Table S6 are of high reliability. Method for Calculating EInt Using Molecular Mechanics Simulations. The lattice structures of graphite and Pt were obtained from the structure database of the Materials

Figure 3. Interaction sphere of the GC surface when γTh was set to 2 mJ·m−2. The center of the sphere (green plot), that is, the HSP of the GC surface, is located at δD = 17.3 MPa1/2, δP = 8.3 MPa1/2, and δH = 11.4 MPa1/2. The blue plots indicate the HSP of reference substances with γInt ≤ γTh. The red plots indicate the HSP of reference substances with γInt > γTh. The radius of the interaction sphere is 3.6 MPa1/2, and Fit,15 which denotes the goodness of fit between the interaction sphere and the classification of the HSP of the reference substances by γTh, is 0.971.

Studio version 8.0 software package (Accelrys Software Inc.). The graphite surfaces, prepared by cleaving the repeat unit of the lattice along the (001), (100), and (110) planes, were used as representative models of the GC whose surface consists of randomly oriented crystal planes of graphite.26 The dimensions of the models were set at 2.46 nm × 2.46 nm × 1.70 nm for the 13248

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C

ACN for the graphite surfaces and BN, toluene (TOL), methyl ethyl ketone (MEK), γ-butyrolactone (GBL), DMSO, and ACN for the Pt surface. The atom positions were optimized using molecular mechanics simulations while retaining the cell sizes and shapes. The total energy is calculated as the sum of the energies of bonding and nonbonding interactions. The nonbonding interaction is composed of electrostatic and van der Waals terms. Ewald summation method with an accuracy of 0.00001 kcal mol−1 and a buffer width of 0.5 Å was used for the electrostatic terms. An atom-based summation method and a cubic spline truncation method, with a cutoff distance of 18.5 Å, a spline width of 1 Å, and a buffer width of 0.5 Å, adopting a long-range correction, were used for the van der Waals terms. The convergence criteria of the optimization were set to 0.00002 kcal mol−1 for the total energy and 0.001 kcal mol−1 Å−1 for the forces on the nuclei. The interaction energy (EInt) between the surface and the OMs was calculated according to the following equation

Figure 4. Correlation plots of ΔRCT versus Ra. The dashed curve is given as ΔRCT = 10βRaα, where α is the slope of log Ra, and β is the log ΔRCT- intercept in log ΔRCT = α log Ra + β (α and β are −1.39 and 0.24, respectively). R2 is calculated to be 0.81. The HSP of the OMs are provided in Table S4.

E Int = ESurf + OM − ESurf − EOM

(001) surface, 2.71 nm × 2.72 nm × 1.70 nm for the (100) surface, and 2.56 nm × 2.72 nm × 1.72 nm for the (110) surface. Dangling bonds on the (100) and (110) surfaces were terminated by hydrogen atoms. The Pt surface was prepared by cleaving the repeat unit of the lattice along the (100) plane, and the dimension of the unit cell was set at 2.77 nm × 2.77 nm × 1.57 nm. The thickness of a vacuum slab was set at 3.00 nm. The following calculations were performed using the Forcite module in Materials Studio. Condensed-phase optimized molecular potentials for atomistic simulation studies II (COMPASSII) was adopted as the force field.27,28 The OMs were placed on the graphite and Pt surfaces (Figures S2 and S3, respectively). The examined OMs were BZA, THF, 1BA, DMSO, ACT, and

(5)

where EInt + OM is the total energy of the surface of the solid materials adsorbed with the OMs, and ESurf and EOM are the total energy of the surface of the solid materials and the OMs, respectively. The calculation results are listed in Tables S7 and S8.

■

RESULTS AND DISCUSSION The HSP for the GC surface were calculated as follows. The surface free energy of GC was calculated as γS = 49.1 mJ·m−2, the components of which were γSD = 42.6 mJ·m−2, γSP = 3.4 mJ·m−2, and γSH = 3.1 mJ·m−2 from the measured θC (Table S1). Then, the values of γInt between the GC surface

Figure 5. Correlation plots of −EInt between the model graphite surface with each crystal plane and the OMs versus Ra. The dashed curve is given as −Eint = 10βRaα, where α is the slope of log Ra, and β is the log | − EInt|-intercept in log| − EInt| = α log Ra + β. (a) On the (001) plane, α = −0.61, β = 1.49, and R2 = 0.8305. (b) On the (100) plane, α = −0.42, β = 1.27, and R2 = 0.7441. (c) On the (110) plane, α = −0.54, β = 1.33, and R2 = 0.7388. 13249

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C and various reference substances were calculated from γS and the pre-known γR (Table S3). Figure 3 shows the interaction sphere of the GC surface drawn in 3D vector space by setting γTh at 2 mJ·m−2 for γInt (details of our approach to setting the value of γTh are provided in the Experimental Section). The center of the interaction sphere, which is the HSP of the GC surface based on our definition, is located at δD = 17.3 MPa1/2, δP = 8.3 MPa1/2, and δH = 11.4 MPa1/2. We investigated whether the HSP of the GC surface calculated by using our method can be correlated with the increased ΔRCT measured using EIS. The Ra of the HSP between an OM and the GC surface in 3D vector space should represent the degree of affinity of the OMs for the GC surface,16 whereas the RCT has been used to evaluate the adsorptivity of OMs on electrode surfaces.25,29 Figure 4 shows the correlation plot of ΔRCT versus Ra between the GC surface and various OMs (BZA, THF, BA, DMSO, ACT, and ACN). ΔRCT tends to increase with decreasing Ra, with the coefficient of determination (R2) of the regression equation being ∼0.81, indicating that ΔRCT and Ra are strongly correlated. However, the correlation between ΔRCT and Ra is not perfect, as indicated by the deviation of ACT and DMSO from the regression line. We presumed that the correlation can be improved by increasing the number of reference substances to enhance the accuracy of the interaction sphere in 3D vector space. Moreover, Ra was correlated with EInt between the OMs and the crystal planes of graphite, (001), (100), and (110), as shown in Figure 5. These crystal planes were employed as models of the GC surface on which the graphite-like layers are orientated randomly. The R2 values of the regression equations were ∼0.83, 0.74, and 0.74 for the (001), (100), and (110) planes, respectively. The strong correlation of Ra with EInt on all the crystal planes indicates that the strength of the interaction between the GC surface and OMs should be the origin of Ra as well as the adsorptivity evaluated above from ΔRCT. We also revealed a strong correlation of Ra with EInt for the Pt surface by showing that the large R2 (∼0.90) was obtained in the regression equation as shown in Figure 6. The HSP and interaction sphere of the Pt surface are shown in Figure S4. The model structure for the EInt calculation, experimental data, and calculation results for the Pt surface are shown in Figure S3 and Tables S1−S3, S5, and S8.

Our method was also proven to provide the appropriate HSP for the PTFE surface. The surface free energy of PTFE was calculated as γS = 22.3 mJ·m−2, of which the components were γSD = 22.3 mJ·m−2, γSP = 0 mJ·m−2, and γSH = 0 mJ·m−2, from the measured θC (Table S1). Then, the γInt value between the PTFE surface and various reference substances was calculated from γS and pre-known γR, as provided in Table S3. Figure 7 shows the interaction sphere of the PTFE surface

Figure 7. Interaction sphere of the PTFE surface when γTh was set to 1 mJ·m−2. The center of the sphere (green plot), that is, the HSP of the PTFE surface, is located at δD = 16.2 MPa1/2, δP = 0 MPa1/2, and δH = 0 MPa1/2. The blue plots indicate the HSP of the reference substances with γInt ≤ γTh. The red plots indicate the HSP of the reference substances with γInt > γTh. The radius of the interaction sphere is 3.7 MPa1/2, and Fit is 1.000.

drawn in 3D vector space by setting γTh = 1 mJ·m−2 for γInt. Here, the region where the interaction sphere entered into the negative polar and hydrogen domains is an artifact, caused by using a sphere as the interaction region for convenience; the HSP values should be physically located in the positive domains as reported.30 The center of the interaction sphere, the HSP of the PTFE surface, is located at δD = 16.2 MPa1/2, δP = 0 MPa1/2, and δH = 0 MPa1/2. Hansen reported the HSP of PTFE to be δD = 16.2 MPa1/2, δP = 1.8 MPa1/2, and δH = 3.4 MPa1/2, calculated based on the breakthrough time against some solvents.31,32 Our method and that of Hansen provided the same δD value, whereas δP and δH varied according to the method. PTFE should exhibit very low δP and δH values because it has neither polar nor hydrogen-bonding functional groups, similar to other perfluorocarbon molecules like perfluorobutane, perfluoropentane, and perfluorohexane as shown in Figure 8. PTFE has higher δD compared with these molecules, which have low molecular weights, because δD

Figure 6. Correlation plots of −EInt between the model Pt surface with (100) plane and the OMs versus Ra. The dashed curve is given as −Eint = 10βRaα, where α is the slope of log Ra, and β is the log | − EInt|intercept in log| − EInt| = α log Ra + β (α = −0.86, β = 2.44, and R2 = 0.9045). 13250

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C

Figure 8. HSP of the perfluorocarbon molecules in 3D vector space; green plot: HSP of the PTFE surface, calculated using our method; light blue plot: HSP of PTFE reported by Hansen;31 and blue plots: HSP of small-molecular-weight perfluorocarbon molecules, as per the commercial HSP calculation software (HSPiP 4th edition).

ORCID

denotes that the HSP component originated from the dispersion force, which generally increases with increasing molecular surface area.

Masakazu Murase: 0000-0002-1512-9080 Riichiro Ohta: 0000-0003-2386-9721

■

Notes

CONCLUSIONS Herein, we have proposed a method to calculate the HSP for the surface of the solid materials using the interfacial free energy as a threshold parameter. The validity of this method was demonstrated for GC and Pt surfaces by correlating their HSP with the adsorptivity and surface interactivity of various substances and on the PTFE surface by showing that the calculated HSP reflect its molecular structure. Further application of our method, such as its extension to composite surfaces and interaction analysis with mixed substances, could be anticipated by combining it with a method to calculate the HSP of mixed substances by determining the internal division between them33 and the double sphere method,24 both of which have been proposed for use with the conventional Hansen method. Although it is difficult to apply the HSP to covalent bonding and ionic interactions, it would be possible to explain or predict versatile adsorption phenomena not involving these interactions on the surfaces of the solid materials, such as those in this study, based on the distance between the HSP of various adsorbates in the database15 and the HSP of surfaces of the solid materials calculated by our method.

■

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors thank Dr. T. Kinjo, Dr. S. Shirai, Dr. S. Dong, and Dr. T. Iseki for the insightful discussions.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.9b00154.

■

REFERENCES

(1) Autumn, K; Liang, Y. A.; Hsieh, S. T.; Zesch, W.; Chan, W. P.; Kenny, T. W.; Fearing, R.; Full, R. J. Adhesive Force of a Single Gecko Foot-hair. Nature 2000, 405, 681−685. (2) Barthlott, W.; Neinhuis, C. Purity of the Sacred Lotus, or Escape from Contamination in Biological Surfaces. Planta 1997, 202, 1−8. (3) McDonald, T. M.; Lee, W. R.; Mason, J. A.; Wiers, B. M.; Hong, C. S.; Long, J. R. Capture of Carbon Dioxide from Air and Flue Gas in the Alkylamine-appended Metal-organic Framework mmenMg2(dobpdc). J. Am. Chem. Soc. 2012, 134, 7056−7065. (4) Krause, S.; Bon, V.; Senkovska, I.; Stoeck, U.; Wallacher, D.; Többens, D. M.; Zander, S.; Pillai, R. S.; Maurin, G.; Coundert, F.-X.; Kaskel, S. A Pressure-amplifying Framework Material with Negative Gas Adsorption Transitions. Nature 2016, 532, 348−352. (5) Zhao, X.; Xiao, B.; Fletcher, A. J.; Thomas, K. M.; Bradshaw, D.; Rosseinsky, M. J. Hysteretic Adsorption and Desorption of Hydrogen by Nanoporous Metal-organic Frameworks. Science 2004, 306, 1012− 1015. (6) LaPorte, S. L.; Juo, Z. S.; Vaclavikova, J.; Colf, L. A.; Qi, X.; Heller, N. M.; Keegan, A. D.; Garcia, K. C. Molecular and Structural Basis of Cytokine Receptor Pleiotropy in the Interleukin-4/13 System. Cell 2008, 132, 259−272. (7) Langmuir, I. Surface Chemistry. Chem. Rev. 1933, 13, 147−191. (8) Somarjai, G. A.; Li, Y. Impact of Surface Chemistry. Proc. Natl. Acad. Sci. 2011, 108, 917−924. (9) Castner, D. G.; Ratner, B. D. Biomedical Surface Science: Foundations to Frontiers. Surf. Sci. 2002, 500, 28−60. (10) Garberoglio, G.; Skoulidas, A. I.; Johnson, J. K. Adsorption of Gases in Metal Organic Materials: Comparison of Simulations and Experiments. J. Phys. Chem. B 2005, 109, 13094−13103. (11) Chen, L.; Grajciar, L.; Nachtigall, P.; Düren, T. Accurate Prediction of Methane Adsorption in a Metal-Organic Framework with Unsaturated Metal Sites by Direct Implementation of an ab Initio Derived Potential Energy Surface in GCMC Simulation. J. Phys. Chem. C 2011, 115, 23074−23080. (12) Comer, J.; Chen, R.; Poblete, H.; Vergara-Jaque, A.; Riviere, J. E. Predicting Adsorption Affinities of Small Molecules on Carbon

Nyquist plots of the GC surface measured using EIS, model structures of the graphite and Pt surfaces for molecular mechanics simulations, interaction sphere and HSP of the Pt surface, contact angle data on the GC and Pt surfaces, surface free energy data, HSP data of OMs, EIS parameters calculated from Nyquist plots, and energy values obtained using molecular mechanics simulations (PDF)

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (M.M.). *E-mail: [email protected] (R.O.). 13251

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252

Article

The Journal of Physical Chemistry C Nanotubes Using Molecular Dynamics Simulation. ACS Nano 2015, 9, 11761−11774. (13) Emami, F. S.; Puddu, V.; Berry, R. J.; Varshney, V.; Patwardhan, S. V.; Perry, C. C.; Heinz, H. Prediction of Specific Biomolecule Adsorption on Silica Surfaces as a Function of pH and Particle Size. Chem. Mater. 2014, 26, 5725−5734. (14) Hansen, C. M. Chapter 2: The Three-dimensional Solubility Parameter. The Three-dimensional Solubility Parameter and Solvent Diffusion Coefficient; Danish Technical Press: Copenhagen, 1967; pp 13−29. (15) Hansen, C. M. Solubility Parameters - An Introduction. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 1−26. (16) Hansen, C. M. Appendix A: Table A.1. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 345−483. (17) Hansen, C. M. Chapter 5: Methods of Characterization − Polymers. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 95−111. (18) Hansen, C.M. Chapter 7: Methods of Characterization for Pigments, Fillers, and Fibers. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 125−135. (19) Bergin, S. D.; Sun, Z.; Rickard, D.; Streich, P. V.; Hamilton, J. P.; Coleman, J. N. Multicomponent Solubility Parameters for Singlewalled Carbon Nanotube-solvent Mixtures. ACS Nano 2009, 3, 2340−2350. (20) Hernandez, Y.; Lotya, M.; Rickard, D.; Bergin, S. D.; Coleman, J. N. Measurement of Multicomponent Solubility Parameters for Graphene Facilitates Solvent Discovery. Langmuir 2010, 26, 3208− 3213. (21) Hansen, C. M. Chapter 6: Methods of Characterization − Surface. Hansen Solubility Parameters A User’s Handbook, 2nd ed.; CRC Press: Boca Raton, 2007; pp 113−123. (22) Kitazaki, Y.; Hata, T. Extension of Fowkes’ equation and Estimation of Surface Tension of Polymer Solids. J. Adhesion Soc. Jpn. 1972, 8, 131−142. (23) Izumi, R.; Yamazaki, K.; Hayashi, T. (Mitsubishi Materials Corp.). Jpn. Kokai Tokkyo Koho JP 2012078190A, 2012. (24) Abbott, S.; Hansen, C. M.; Yamamoto, H. Chapter 3: Your First HSP Sphere (Determining the HSP sphere). Hansen solubility parameters in practice - complete with eBook, software and data; 5th ed. [Online]; 2015; pp 18−23. http://www.hansen-solubility.com. (25) Solmaz, R.; Kardaş, G.; Ç ulha, M.; Yazıcı, B.; Erbil, M. Investigation of Adsorption and Inhibitive Effect of 2-Mercaptothiazoline on Corrosion of Mild Steel in Hydrochloric Acid Media. Electrochim. Acta 2008, 53, 5941−5952. (26) Noda, T.; Inagaki, M. The Structure of Glassy Carbon. Bull. Chem. Soc. Jpn. 1964, 37, 1534−1538. (27) Sun, H. COMPASS: An ab Initio Force-Field Optimized for Condensed-Phase ApplicationsOverview with Details on Alkane and Benzene Compounds. J. Phys. Chem. B 1998, 102, 7338−7364. (28) Sun, H.; Jin, Z.; Yang, C.; Akkermans, R. L. C.; Robertson, S. H.; Spenley, N. A.; Miller, S.; Todd, S. M. COMPASS II: Extended Coverage for Polymer and Drug-Like Molecule Databases. J. Mol. Model. 2016, 22, 47. (29) Oliveira-Brett, A. M.; de Silva, L. A.; Brett, C. M. A. Adsorption of Guanine, Guanosine, and Adenine at Electrodes Studied by Differential Pulse Voltammetry and Electrochemical Impedance. Langmuir 2002, 18, 2326−2330. (30) Howell, J.; Roesing, M.; Boucher, D. A Functional Approach to Solubility Parameter Computations. J. Phys. Chem. B 2017, 121, 4191−4201. (31) Hansen, C. M. Appendix A: Table A.2. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 485−505. (32) Hansen, C. M. Chapter 13: Applications − Barrier Polymers. Hansen Solubility Parameters A User’s Handbook; 2nd ed.; CRC Press: Boca Raton, 2007; pp 243−258.

(33) Abbott, S.; Hansen, C. M.; Yamamoto, H. Chapter 1: The Minimum Possible Theory (simple introduction). Hansen solubility parameters in practice - complete with eBook, software and data., 5th ed. [Online]; 2015; pp 10−14. http://www.hansen-solubility.com.

13252

DOI: 10.1021/acs.jpcc.9b00154 J. Phys. Chem. C 2019, 123, 13246−13252