醇胺化合物捕捉二氧化碳研究:從理論方法到分子動力學模擬

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(1)國立臺灣師範大學化學系 Department of Chemistry, National Taiwan Normal University. 碩士論文. 醇胺化合物捕捉二氧化碳研究:從理論方法到分子動力學模擬 Theoretical study of CO2 capture by alcoholamine system from methodology to dynamic simulation. 指導教授: 蔡明剛博士研究生: 黎學謙中華民國一百零一年六月. 1.

(2) Content Content ........................................................................................................................... 2 Figure Content ............................................................................................................... 4 Table Content ................................................................................................................. 5 中文摘要........................................................................................................................ 7 English Abstract ............................................................................................................. 9 Assessment of Dispersion-Improved Exchange-Correlation Functionals for the Simulation of CO2 Binding by Alcoholamines............................................................ 11 Abstract ................................................................................................................ 12 Introduction .......................................................................................................... 13 Methodology ........................................................................................................ 16 C1 Database ................................................................................................. 16 ab initio Electronic Structure Calculation .................................................... 17 DFT Functionals .......................................................................................... 20 Discussion ............................................................................................................ 21 Converging ab initio References by Using MP2/ATZ Optimized Geometries ...................................................................................................................... 21 Sensitivity of the Selection of MP2/ATZ Optimized Geometries ............... 22 XC Functional Comparison Using MP2/ATZ Optimized Geometry ........... 23 XC Functional Comparison Using DFT Optimized Geometry ................... 26 Bimoelcular and Trimolecular Reaction Mechanism Test ........................... 31 Conclusion ........................................................................................................... 33 Acknowledgement ............................................................................................... 35 Notes .................................................................................................................... 35 References ............................................................................................................ 36 Supplementary Materials ..................................................................................... 39 The Chain Length Effect of Alcohol Group to CO2 Capture by Alcoholamines: A First-Principle Molecular Dynamic Simulation Comparison ...................................... 54 Abstract ................................................................................................................ 55 Introduction .......................................................................................................... 55 Computational Details ......................................................................................... 58 Results and Discussion ........................................................................................ 59 Monomer and dimer properties of MEA and MPA...................................... 59 Energetics of the chemical absorption pathway ........................................... 60 Bulk simulation of MEA and MPA liquids .................................................. 62 CO2 concentration [CO2] effect ................................................................... 63 Inter-solvent hydrogen bond effect on CO2 absorption ............................... 65 2.

(3) Conclusion ........................................................................................................... 66 Acknowledgments................................................................................................ 67 Reference ............................................................................................................. 67 Supplementary Materials ..................................................................................... 70. 3.

(4) Figure Content Assessment of Dispersion-Improved Exchange-Correlation Functionals for the Simulation of CO2 Binding by Alcoholamines............................................................ 11 Figure 1. Schematic representation of CO2 capturing process by MEA.............. 14 Figure 3. Local minimum structure of DEA dimer at MP2 level. ....................... 30 Figure S1. Local minimum structure of DEA dimer at M06-2X levels............... 40 The Chain Length Effect of Alcohol Group to CO2 Capture by Alcoholamines: A First-Principle Molecular Dynamic Simulation Comparison ...................................... 54 Figure 1. Graphical representation of (a) MEA, (b) MEA2, (c) MPA, and (d) MPA2 where α-γ denotes the position of C in respect to amino group. ............... 60 Figure 2. Relative energy of the gas-phase optimization at the various fixed N-C (of CO2) distance.................................................................................................. 62 Figure 3. Radial distribution function of N-N for pure MEA and MPA liquid phase simulations. ................................................................................................ 64 Figure 4. Radial distribution function of N-N of (MEA)8, 1 CO2 in (MEA)8, and 4 CO2 in (MEA)8 solutions, respectively. ............................................................ 64 Figure 5. Radial distribution function of N-C (of CO2) of 1 CO2, and 4 CO2 in (MEA)8 and (MPA)8 solutions ............................................................................. 65 Figure 6. Radial distribution function of N-O (of OH) of 1 CO2, and 4 CO2 in (MEA)8 and (MPA)8 solutions ............................................................................. 66 Figure S1. Relative energy of the gas-phase optimization at the various fixed NC (of CO2) distance .............................................................................................. 70 Figure S2. N-N radical distribution function comparison of bulk MEA liquid using 27 and 8 molecule models. ......................................................................... 71 Figure S3. N-O (of OH) radical distribution functional of (MEA)27 and (MPA)27 at 400K. ................................................................................................................ 71. 4.

(5) Table Content Assessment of Dispersion-Improved Exchange-Correlation Functionals for the Simulation of CO2 Binding by Alcoholamines............................................................ 11 Table 1. Binding energy (kcal/mole) of complexes a-l1 at the various basis set extrapolations. ...................................................................................................... 22 Table 2. Comparison of the optimized geometries at MP2/ATZ, MP2/ADZ, and CCSD/ADZ .......................................................................................................... 23 Table 3. Overall performance (kcal/mol) of the tested 15 functionals ................ 25 Table 4. Statistical analysis of the relative binding energy in kcal/mol of the subcategories using MP2/ATZ optimized geometries. ........................................ 25 Table 5. Statistical analysis of the relative binding energy in kcal/mol of complexes a-l using the optimized geometry at the theory level......................... 28 Table 6. Statistical analysis of the relative binding energy in kcal/mol of the subcategories using the optimized geometries of the corresponding theory level. .............................................................................................................................. 28 Table 7. Geometry analysis (Å ) and binding energy (kcal/mol) using the corresponding optimized geometries. .................................................................. 29 Table 8. The calculated relative barrier and reaction energy for the bimolecular CO2-MEA reaction (in kcal/mol) using PCM model. .......................................... 32 Table 9. The calculated barrier and reaction energy for the trimolecular CO2(MEA)2 reaction (in kcal/mol) using PCM model. ............................................. 33 Table S1. Binding energy (in kcal/mol) of complexes a-l using MP2/ATZ optimized geometries. .......................................................................................... 41 Table S2. Binding energy (in kcal/mol) of complexes a-l using the corresponding DFT optimized geometries. ................................................................................. 42 𝐶𝐵𝑆(𝑇𝑍 → 𝑄𝑍). Table S3. Summary of ∆𝐸𝐶𝐶𝑆𝐷(𝑇). 𝐶𝐵𝑆(𝐷𝑍→𝑄𝑍). and ∆𝐸𝐶𝐶𝑆𝐷(𝑇). calculations where the. values of ∆E are in kcal/mol. ............................................................................... 50 The Chain Length Effect of Alcohol Group to CO2 Capture by Alcoholamines: A First-Principle Molecular Dynamic Simulation Comparison ...................................... 54 Table 1. Summary of NPA charge, dipole moment, and binding energy (BE) for monomeric and dimeric MEA and MPA, respectively. ....................................... 60 Table 2. The percentage of the categorized hydrogen bond (HB) interactions. ... 66 Table S1. NPA charges of each fragment for the constrained optimization scan along bimolecular CO2…MEA pathway shown in Figures 2 and S1. .................. 72 Table S2. NPA charges of each fragment for the constrained optimization scan along bimolecular CO2…MPA pathway shown in Figures 2 and S1 .................. 73 5.

(6) Table S3. NPA charges of each fragment for the constrained optimization scan along trimolecular CO2…(MEA)2 pathway shown in Figures 2 and S1............... 74 Table S4. NPA charges of each fragment for the constrained optimization scan along trimolecular CO2…(MPA)2 pathway shown in Figures 2 and S1. .............. 75. 6.

(7) 中文摘要自人類經濟活動蓬勃發展，石化燃料的大量使用，導致二氧化碳的排放量大增，進而引發極端氣候。在國際上目前提出的碳捕捉與碳封存。乙基醇胺(monoethanolamine)、二乙基醇胺(diethanolamine)、三乙基醇胺(triethanolamine) 為目前商用的碳捕捉劑，主要用於火力發電廠所產生出的二氧化碳。這類捕捉劑與二氧化碳的反應機制目前仍然未知，過去的研究透過理論計算的方法提出可能的反應機制，然而不同的理論方法對於反應路徑的預測有不同，故在本論文的研究起始於理論方法的分析，進而利用分子動力學的模擬，來提出新的捕捉劑設計策略。在理論方法的研究中提出了一個含有 12 對幾何優化過後的 C1 資料庫以測試 15 個密度泛涵理論方程。這 15 個密度泛涵理論方程已被發表改善電子交換項針對遠距離凡德瓦力。測試的標準是利用∆CCSD(T) 並經過方均根(RMS)統計後判定。在這個階段的研究中發現，ωB97 、ωB97X、ωB97XD 系列很適合計算涵有強凡德瓦力的氫鍵系統。BLYP-D 適合用在胺類化合物與二氧化碳吸附的組合。所以在之後的研究中使用 BLYP-D 來計算分子動力學反應。過去設計捕捉劑的策略是藉由提高胺的級數，來增加其親核性。工業製程是藉由環氧乙烷通入氨氣合成乙基醇胺、二已基醇胺、和三乙基醇胺，並藉由反應條件的控制而調整溶液中化合物的比例。然而三級胺類對二氧化碳的吸附能力並未優於二級胺類，因此重新思考朝向增長碳鏈來作為設計方向。正丙醇胺(npropanolamine)在計算結合能和電荷分析上，都明顯優於乙醇胺。為了解二氧化碳與捕捉劑在動力學上的影響，分別利用分子動力學模擬乙醇胺、丙醇胺 20%和 50%的無水混合二氧化碳溶液，連續模擬 12 萬步。模擬中發現正丙醇胺在徑向分配含數上明顯優於乙醇胺，其中發現溶液中氫鍵環境有顯著的影響。氫鍵環境越高的乙醇胺系統，會不利於捕捉劑對二氧化碳的吸附；而氫鍵數量較低的丙醇胺系統對二氧化碳的吸附有顯著的提升。 7.

(8) 在本篇研究中探討ωB97 泛涵方程適用於強凡德瓦力的氫鍵系統，BLYP-D 可用於捕捉劑與二氧化碳反應的計算。分子動力學的研究探討新的分子設計策略的有效性，吸附能力的提升以及其氫鍵對於碳捕捉的影響。. 關鍵字: 二氧化碳捕捉、計算化學、密度泛函理論、二乙基醇胺. 8.

(9) English Abstract As large carbon emission is rapidly growing in recent years, the concentration of carbon dioxide in the air highly growing. CCS is an abbreviation for carbon dioxide capture and storage that the one of solution to decrease the concentration of carbon dioxide to compensate the effect of what global worming brings. MEA (monoethanolamine), DEA (diethanolamine), and TEA (triethanolamine) are commercial compounds for reducing carbon dioxide emission as post combustion methods in fire power plant. However, in the past research, many theoretical studies have different insight of capture mechanism. Especially the existence of zwitterion; therefore, the study is starting from how to choose DFT functional, and then to use the molecular dynamic method to find out the details of CO2 capture by alcoholamines; therefore, developing a new compounds for CO2 capture is our goal. First, A C1 database comprising 12 bounded complex is used for testing 15 dispersion improved DFT functionals including from GGA, hybrid-GGA, meta-GGA, GGA with empirical dispersion. The standard in the comparison is choosing the delta CCSD(T) scheme that the energy is extrapolated from aTZ to aQZ, and the small basis set is aDZ. The result shows that ωB97 series are good at the hydrogen bond with high dispersion system, and BLYP-D is an appropriate functional to calculate the alcohomolamine bounded carbon dioxide. Secondly, the traditional strategy of alcoholamine design is increasing the degree of amine that MEA, DEA, and TEA are the products of the reaction between ethylene oxide and ammonia in industry. MPA is another way to increase the carbon chain of alcoholamine compounds. In molecular dynamic study, the performance of new molecule has been improved that the binding energy and the proportion of CO2 absorbed by MPA on radial distribution function diagram. 9.

(10) The first study conclude that ωB97 can be used in this system and the BLYP-D has top performance on absorption complex; moreover, the strategy of increasing carbon chain indeed improve the performance of the efficiency of CO2 capture.. Keyword: Carbon dioxide capture、Computational chemistry、Density functional theory、 Diethanolamine. 10.

(11) Assessment of Dispersion-Improved ExchangeCorrelation Functionals for the Simulation of 𝐂𝐎𝟐 Binding by Alcoholamines Hsueh-Chien Li,1 Jeng-Da Chai,2,* and Ming-Kang Tsai1,* 1. Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan 2 Department of Physics, Center for Theoretical Sciences, and Center for Quantum Science and Engineering, National Taiwan University, Taipei 10617, Taiwan To whom correspondence should be addressed. E-mail: [email protected] and [email protected]. 11.

(12) Abstract In this study, 12 bound complexes were selected to construct a database for testing 15 dispersion-improved exchange-correlation (XC) functionals, including hybrid Generalized Gradient Approximation (GGA), GGA modified using the Grimme’s pairwise strategy, and double hybrid XC functionals, for specifically characterizing the CO2 binding by alcoholamines. Bound complexes were selected based on the characteristics of their hydrogen bonds, dispersion, and electrostatic (particularly between the positive charge of CO2 and the lone pair of N of alcoholamines) interactions. The extrapolated binding energy from the aug-cc-pVTZ (ATZ) to aug-ccpVQZ (AQZ) basis set at the ∆CCSD(T) level was used as the reference for the XC functional comparison. M06-2X produced the optimal agreement if the optimized geometries at MP2/ATZ level were chosen for all the test bound complexes. However, ωB97XD, ωB97, and M06-L were preferred if the corresponding DFT optimized geometries were adapted for the benchmark. Simple bimolecular and trimolecular reaction between CO2 and monoethanolamines simulated using PCM model also confirmed that ωB97, ωB97X, and ωB97XD qualitatively reproduced the energetics of MP2 level. The inconsistent performance of the tested XC functionals, observed when using MP2 or DFT optimized geometries, raised concerns regarding using the singlepoint ab initio correction combined with DFT optimized geometry, particularly for determining the nucleophilic attack by alcoholamines to CO2.. Keywords: CO2 Capture, Density Functional Theory, Alcoholamines. 12.

(13) Introduction. Capturing and storing CO2 is crucial for ameliorating global climate change. Using alcoholamines, such as monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA), is one of the commercially available technologies involving solvent-based processes to immobilize the CO2 produced by fossil-fuel power stations.1 Alcoholamines are typically mixed with water to form carbamate solutions in the capturing process. The fundamental physics of CO2 binding by alcoholamines is to take advantage of the electrostatic interaction between the lone pair on the N of alcoholamines, which undergoes a nucleophilic attack, and the partial-positively charged C of CO2. Many experimental and theoretical studies have focused on elucidating the reaction mechanism of the MEA/CO2 system to understand the dynamics and kinetics of the CO2 capturing process, and ultimately reduce the operational cost of the solvent-based CO2-capture technology.2-14. A schematic representation of the CO2 binding moiety is shown in Figure 1. From the classical intermolecular interaction perspective, the bound complex (or a physically absorbed complex) is formed by the quadrupole-dipole interaction. The alpha-C of alcoholamines can be further polarized as CO2 approaches, in addition to the intrinsic polarization resulting from the electron-withdrawing effect of N due to the formation of zwitterion. The structures of the transition state and zwitterion (Figure 1) can be 13.

(14) intuitively considered as the chemical bond formation through the orbital overlapping between the p orbital of MEA and the LUMO of CO2, based on the quantum perspective. Accurately describing the CO2 capture process by alcoholamines is therefore a challenging theoretical task. The physical-absorbed complex can be simply described by weak-electrostatic and dispersion interactions. As the alkylation of the N of alcoholamine increases, the significance of the dispersion interaction also increases. Moreover, the chemical-bound zwitterionic species is formed by a bonding orbital consisting of the bent-𝜋 ∗ orbital of CO2 and the σ-character lone-pair electron of N. The lack of appropriate description for the dispersion interactions, as well as the lowlying unoccupied orbital along the CO2 absorption reaction coordinate, could produce controversial results when interpreting the experimental observations.. Figure 1. Schematic representation of CO2 capturing process by MEA. Many theoretical works have focused on outlining the reaction mechanism of the capturing process. Three types of mechanisms were summarized by Xie et al., including the (1) zwitterion mechanism,2, 4, 13 (2) single-step mechanism,9, 10 and (3) carbamic. 14.

(15) acid reaction mechanism.12 A sophisticated effort to differentiate the various proposed mechanisms by simulating the explicit solvation effects, dynamics of the intermolecular proton transfer process, and the various substitution effects to N of alcoholamines has been attempted by several studies in the past,13, 15-20 but is beyond the scope in this study. In this study, we aimed to search for a simple and accessible method at the Density Functional Theory (DFT) level to describe CO2–alcoholamine interactions based on the design of a three-category database (de noted as C1). The partition of the C1 was meant to examine the selected XC functionals separately in the category of NH3 and MEA, DEA, and CO2-binding. The presence of NH3, MEA, and DEA also accounted for the basicity resulted from the alkylation effect on N of alcoholmines, and triethanolamine (TEA) was omitted due to its slow CO2 uptake.21. The ideal description of CO2-alcoholamines interactions using the ab initio methods and triple-zeta quality basis set could be accessible for the bimolecular reaction like CO2 and MEA. More substitution on N of amines or even accounting the micro-solvation effect would be computationally impractical at such levels. Therefore, finding suitable XC functionals to provide qualitative description of the important intermediate and minimum structures would be useful for the future design the CO2 scrubbers computationally. The assessments of XC function for the non-covalent interactions have been documented in the past,22-25 and the S22 and S66 databases were 15.

(16) commonly used for the benchmark evaluation.26, 27 These databases consist of a wide range of non-covalent and biologically important complexes, and show most of the essential physics of the intermolecular interactions in nature. However, in this study, we aimed to search an optimal method for the specific application of CO2 binding by alcoholamines. As a consequence, we systematically developed a testing database including 12 bound complexes ranging from pure hydrogen-bond (HB) complexes, mixed HB, and dispersion complexes, to dispersion-dominated complexes. Fifteen functionals reported as being able to improve dispersion interaction were chosen for the benchmark tests; these functionals included 2 GGA modified by the Grimme’s semiempirical function, 11 hybrid GGA functionals, and 2 perturbed GGA functionals. The details of the database design, as well as the ab initio reference data, are presented in Section II. The results and discussion are presented in Section III. The conclusion is summarized in Section IV.. Methodology C1 Database Nine hydrogen-bond (HB) interacting complexes were included in the C1 database (Figure 2). These complexes included pure HB complexes such as (H2O)2 and (H2O)NH3, mixed HB and dispersion-bound complexes of MEA and DEA with H2O and NH3, and dispersion-dominant bound complexes such as (MEA)2, (MEA)DEA, and 16.

(17) (DEA)2. In addition, the binding interactions of CO2 with NH3, MEA, and DEA were also included. The C1 database was constructed chemical-intuitively, accounting for the electronic effect resulting from the various levels of alkylation on the N of alcoholamines, the consequent intermolecular HB interactions, and the CO2 binding. Moreover, the structures of the testing complexes were chosen based on their potential as stable intermediates along the reaction coordinate of the capturing process. In contrast to the conventional S22 database, the dispersion interaction resulting from the π electrons was omitted in the C1 database. Moreover, the σ electron was well represented and well suited for studying CO2 binding resulting from the nucleophilic attack of alcoholamines. The largest complex in C1 was 36 atoms, whereas the largest complex in S22 was 28 atoms. ab initio Electronic Structure Calculation MP2/aug-cc-pVTZ (ATZ) optimized geometries were used to conduct all the ab initio reference calculations, where ATZ included 4s3p2d1f basis functions for the second row elements. Both ΔCCSD(T) and MP2 at the complete basis set limit (CBS) were adopted to calculate the binding energies of complexes a-l listed in C1 database. The CBS was approximated using the extrapolation from aug-cc-pVTZ to aug-ccpVQZ. The formula of the ΔCCSD(T) scheme adopted in this study was listed as Eq. 1:28-30. 17.

(18) ∆CCSD(T). ∆ECBS. MP2 = ∆ECBS + (∆E CCSD(T) − ∆E MP2 )small basis set. (1). where each ΔE represents the binding energy with the basis set superposition error (BSSE) correction. The superscript of ΔE denotes the theory level of the BSSE calculations. Despite the counterpoise approach for correcting the binding energy for basis set incompleteness generally results in underestimated values, in particular for hydrogen bonded systems,25, 31 the tested cases in this study actually covered species from pure HB to dispersion-dominate complexes. The CBS limit at the ∆CCSD(T) level, ∆CCSD(T). denoted as ∆ECBS. MP2 , was approximated using ∆ECBS , and a correction term was. used to evaluate the difference between CCSD(T) and the MP2 energy using aug-cc∆CCSD(T). pVDZ basis set. As being introduced by Hobza et al., the ∆ECBS. scheme was. based on the assumption that the difference between the binding energies at CCSD(T) and MP2 levels converges faster than the binding energies themselves in respect to the MP2 quality of the basis set.28, 29 ∆ECBS adopted the formula in Eq. 2, where X and Y. represented aug-cc-pVTZ and aug-cc-pVQZ in the extrapolation, respectively.. MP2 ∆EXY = ∆EYHF +. ∆Ecorr ×n3X −∆Ecorr ×n3Y X Y n3X −n3Y. (2). The first term, ∆EYHF, of Eq. 2 was calculated at the HF/AQZ level, where ∆EXcorr or Y terms were the difference of the correlation energy in the binding energy calculation at the MP2 level using basis set X and Y, respectively. The correlation contribution was 18.

(19) further weighted by n, and n = 2, 3, and 4 if the basis set was aug-cc-pV(T or Q)Z, CCSD(T). respectively. Eq. 2 was also adopted to calculate ∆ETQ. CCSD(T). and ∆EDT. using. different ab initio method and basis set extrapolation, and ∆EXcorr or Y was adjusted consequently.. Figure 2. Graphical representation of C1 database, i.e. a is (H2O)2, b is NH3(H2O), c is MEA(H2O), d is MEA(NH3), e is DEA(H2O), f is DEA(NH3), g is (MEA)2, h is MEA(DEA), i is (DEA)2, j is NH3(CO2), k is MEA(CO2), and l is DEA(CO2).. 19.

(20) DFT Functionals Fifteen exchange-correlation (XC) functionals were chosen for testing the statistics error. Five functionals (CAM-B3LYP,32 LC-ωPBE,33-35 ωB97,36 ωB97X,36 and ωB97XD37) were long-range corrected by adding the Gauss error function in the exchange term. Four dispersion-improved functionals (BLYP-GD2,38, 39 B97-GD2,40 B3LYP-GD2,38, 39, 41 and B3LYP-GD3) were corrected using the Grimme’s D2 amd D3 dispersion, respectively.42,. 43. Four Minnesota series hybrid-GGA functionals were. included (M05-2X,44 M06-2X,45 M06-HF,46, 47 and M06-L48). For two double hybrid GGA functionals (B2PLYP and B2PLYPD), the PT2 scheme was included in the correlation calculation.49 All DFT calculations were performed using the Gaussian 09 package50 and NWChem.51 The geometries used for the BSSE calculations were optimized using the corresponding XC functional and ATZ basis set.. 20.

(21) Discussion Converging ab initio References by Using MP2/ATZ Optimized Geometries Four types of calculations including CCSD(T), △CCSD(T), and MP2, coupled with different basis set extrapolations (i.e., ATZ→AQZ and ADZ→ATZ) were adopted to determine the selection of ab initio reference data, and the results are summarized in Table 1. Only the six small complexes including (H2O)2, (H2O)(NH3), MEA(H2O), MEA(NH3), NH3(CO2), and MEA(CO2), i.e. a, b, c, d, j, and k, respectively, could be ∆CCSD(T). computed using all four extrapolation schemes. ∆ECBS. systematically but. marginally underestimated the binding energy of the small complexes (a-d) in CCSD(T). comparison with ∆ECBS CCSD(T). with ∆ECBS. ∆CCSD(T). . However, ∆ECBS. provided the optimal agreement. for the CO2 alcoholamine bound complexes (j-k). For the non-CO2 ∆CCSD(T). MP2 complexes (a-i), ∆ECBS predicted slightly stronger binding energy than ∆ECBS CCSD(T). and ∆EDT. . Given the interest of studying CO2 binding by alcoholamines, we, ∆CCSD(T). therefore, selected ∆ECBS. as the reference for comparison with the DFT results.. 21.

(22) Table 1. Binding energy (kcal/mole) of complexes a-l1 at the various basis set extrapolations.2. 1. 2.. CCSD(T). CCSD(T). ∆CCSD(T). MP2 ∆ECBS -5.02 -6.66 -8.07 -4.85 -8.25 -5.07 -4.56. A B C D E F G. ∆ECBS -5.37 -7.01 -7.93 -4.80 ─ ─ ─. ∆EDT -4.95 -6.52 -7.11 -4.77 -8.07 -4.91 -4.50. ∆ECBS -4.97 -6.48 -7.80 -4.73 -8.01 -4.87 -4.47. H I J. ─ ─ -3.15. ─ ─ -3.08. -5.62 -12.22 -3.10. -5.84 -12.92 -2.96. K L. -4.32 ─. -4.24 ─. -4.26 -4.26. -4.18 -4.39. Complexes. MP2/ATZ optimized geometries were used. Dashes denote the data are unavailable due to the computational expense.. Sensitivity of the Selection of MP2/ATZ Optimized Geometries To determine the accuracy of the MP2/ATZ optimized geometries, we also MP2 calculated ∆ECBS using the MP2/ADZ and CCSD/ADZ optimized geometries (Table. 2), and compared several selected interatomic distances. Geometrically, the MP2/ATZ optimized geometries were close to the CCSD/ADZ optimized geometries, without any relative interatomic distance being greater than 0.07 Å , whereas a negligible difference was observed when using the ADZ and ATZ basis sets at the MP2 level. However, the binding energy extrapolated using the MP2/ATZ optimized geometries was slightly closer to the energy extrapolated using the CCSD/ADZ optimized geometries. The energies extrapolated using the MP2/ADZ geometries were also in good agreement 22.

(23) with the ATZ basis set. Therefore, using MP2/ATZ optimized geometries provided a reasonable accuracy for the CCSD level, geometrically and energetically. Although an additional examination of the CCSD/ATZ optimized geometries would be preferred, computationally it would be too expensive or impractical.. Table 2. Comparison of the optimized geometries at MP2/ATZ, MP2/ADZ, and CCSD/ADZ Complexes. MP2/ATZ. MP2/ADZ. CCSD/ADZ. rON of (H2O)(NH3). 2.923. 2.938 (0.014). 2.980 (0.057). rNC of (NH3)(CO2). 2.937. 2.933 (-0.004). 2.950 (0.013). rNN of MEA(NH3). 3.208. 3.201 (-0.007). 3.269 (0.062). rNO of MEA(H2O). 2.868. 2.882 (0.013). 2.930 (0.062). rNC of MEA(CO2). 2.857. 2.861 (0.003). 2.896 (0.038). rNN of (MEA)2. 3.204. 3.208 (0.005). 3.270 (0.064). Complexes. MP2 1∆ECBS //MP2/ATZ. (H2O)(NH3). -6.66. -6.72 (-0.05). -6.61 (0.05). (NH3)(CO2). -2.96. -2.96 (0.00). -2.97 (-0.01). MEA(NH3). -4.85. -4.93 (-0.08). -4.88 (-0.03). MEA(H2O). -8.07. -8.15 (-0.08). -7.97 (0.10). MEA(CO2). -4.18. -4.22 (-0.04). -4.04 (0.14). (MEA)2. -4.56. -4.63 (-0.08). -4.61 (-0.05). MP2 MP2 1∆ECBS //MP2/ADZ 1∆ECBS //CCSD/ADZ. 1. Each column used MP2/ATZ, MP2/ADZ, and CCSD/ADZ optimized geometries, respectively. 2 The extrapolated binding energy was in kcal/mol and the intermolecular distance was in Å. 3. The parentheses denoted the relative binding energy and interatomic distance in respect to the values listed in the first column. XC Functional Comparison Using MP2/ATZ Optimized Geometry Table 3 summarizes the overall performance of the 15 functionals tested, using the 23.

(24) complexes a-l listed in Figure 2. The root mean square deviations (RMS) of 10 out of 15 functionals were observed to be less than 1.0 kcal/mol. The optimal approach was using M06-2X. The three functionals corrected using the Grimme’s dispersion approach (B3LYP-GD3, B97-GD2, and BLYP-GD2) also produced a good agreement, having RMS values of 0.56, 0.58, and 0.82, respectively. Nevertheless, the two doublehybrid functionals (B2PLYPD and B2PLYP) did not rank highly among these 15 cases. Complexes a-l can be further categorized into three subgroups, as summarized in Table 4. The first group, labeled "small molecule and MEA series", included a-d, g, j, and k; the second subgroup, labeled "DEA series", included complexes e, f, h, i, and l, where the dispersion interaction played a crucial role; and the last subgroup, called the CO2-bound series, contained complexes j-l. Both M06-2X and M05-2X were effective in all three sub-categories. Grimme’s dispersion correction showed the optimal agreement in the DEA series, particularly for B97-GD2 and B3LYP-GD3. The double hybrid functionals performed poorly in all subgroups, and their performance worsened in the dispersion-dominated DEA series. In the CO2-bound series, nine functionals (B3LYP-GD3, M05-2X, BLYP-GD2, B3LYP-GD2, ωB97XD, ωB97X, M06HF, M062X, and ωB97) were found to predict CO2 binding energy with RMS error less than 0.6 kcal/mol. Seven out of these nine functionals. overestimated the binding energy of. CO2 with alcoholamines, as shown by the negative value of the corresponding MSE. 24.

(25) ∆CCSD(T). Accounting ∆ECBS. gave slightly under-bound character of NH3(CO2) and CCSD(T). MEA(CO2) with respect to ∆ECBS. (Table 1), the over-bound character of these. seven functionals may provide an accurate description of the energy required for the CO2 capture by alcoholamines.. Table 3. Overall performance (kcal/mol) of the tested 15 functionals1 Methods. RMS. MSE. MAE. Methods. RMS. MSE. MAE. M06-2X 0.30 M06-L 0.45 B3LYP-GD3 0.56. -0.08 0.41 -0.45. 0.25 0.43 0.45. ωB97XD BLYP-GD2 B3LYP-GD2. 0.64 0.81 1.07. -0.33 -0.46 -0.84. 0.50 0.58 0.84. -0.47 0.06 -0.18 0.20 0.22. 0.51 0.45 0.50 0.48 0.39. B2PLYPD CAM-B3LYP LC-ωPBE B2PLYP. 1.46 2.06 2.54 3.72. 1.13 1.37 2.02 2.90. 1.13 1.47 2.02 2.90. ωB97 B97-D ωB97X M06HF M05-2X 1. 2.. 0.57 0.58 0.59 0.60 0.61. MP2/ATZ optimized geometries were used for complex a-l. RMS is the root mean square deviation. MSE stands for the mean signed error. MAE is mean absolute error. Table 4. Statistical analysis of the relative binding energy in kcal/mol of the subcategories using MP2/ATZ optimized geometries.1 2. Methods. Small Molecule and MEA series RMS MSE MAE Methods RMS. MSE. MAE. M06-2X M05-2X ωB97XD B3LYP-GD3 BLYP-D M06HF. 0.27 0.29 0.32 0.39 0.43 0.45. -0.13 -0.01 -0.12 -0.33 -0.19 0.13. 0.24 0.25 0.29 0.33 0.39 0.36. ωB97X B3LYP-GD2 ωB97 B2PLYPD CAM-B3LYP LC-ωPBE. 0.54 0.65 0.68 0.72 0.88 1.38. -0.48 -0.55 -0.66 0.64 0.55 1.23. 0.48 0.55 0.66 0.64 0.71 1.23. M06-L B97-D. 0.48 0.51. 0.46 0.32. 0.46 0.37. B2PLYP. 1.86. 1.68. 1.68. RMS. MSE. MAE. ωB97XD BLYP-D. 0.92 1.14. -0.64 -0.83. 0.79 0.85. 3. Methods. RMS MSE. DEA series MAE Methods. M06-2X ωB97. 0.33 0.36. 0.26 0.30. -0.02 -0.20. 25.

(26) M06-L. 0.41. 0.33. 0.38. B3LYP-GD2. 1.48. -1.23. 1.23. ωB97X B97-D B3LYP-GD3 M06HF M05-2X. 0.66 0.68 0.74 0.76 0.87. 0.25 -0.31 -0.63 0.30 0.55. 0.52 0.56 0.63 0.65 0.59. B2PLYPD CAM-B3LYP LC-ωPBE B2PLYP. 2.10 3.02 3.58 5.33. 1.81 2.52 3.12 4.62. 1.81 2.52 3.12 4.62. RMS. MSE. MAE. 4. Methods. RMS MSE. CO2 bound series MAE Methods. M05-2X 0.18 B3LYP-GD3 0.24 BLYP-D 0.27. -0.17 -0.21 0.24. 0.17 0.21 0.24. ωB97 M06-L B97-D. 0.53 0.59 0.76. -0.50 0.57 0.75. 0.50 0.57 0.75. B3LYP-GD2 0.29 ωB97XD 0.34 ωB97X 0.34. -0.25 0.33 -0.12. 0.25 0.33 0.32. B2PLYPD CAM-B3LYP LC-ωPBE. 1.07 1.54 2.10. 0.95 1.34 1.95. 0.95 1.34 1.95. -0.35 -0.48. 0.35 0.48. B2PLYP. 2.63. 2.38. 2.38. M06HF M06-2X 1. 2. 3. 4.. 0.36 0.49. MP2/ATZ optimized geometries were used for complex a-l. including complex a-d, g, j, and k. including complexes e, f, h, i , and l. including complexes j, k and l.. XC Functional Comparison Using DFT Optimized Geometry Table 5 lists the statistical analysis based on the relative binding energy calculated ∆CCSD(T). using the MP2/ATZ optimized geometry, compared with ∆ECBS. . The binding. energy at the DFT level was calculated using the geometry optimized for each corresponding functional. The performance of the tested functionals was substantially different from the previous analysis using MP2/ATZ geometries (Table 3). The reordering raised concerns about combing DFT optimized geometries and a singlepoint ab initio energetic correction. Only three functionals (ωB97X, ωB97, and M06L) were predicted to have RMS < 0.6 kcal/mol. Surprisingly, M06-2X, the optimal 26.

(27) functional listed in Table 3, dropped to almost the last position, with an RMS of 3.01 kcal/mol due to its different optimized geometry in the DEA series. The other two functionals treated using Grimme’s correction (B3LYP-GD3 and B97-GD2) also showed an acceptable agreement regarding to the ∆CCSD(T) level. The double hybrid functional still showed a considerable statistical deviation, even when using the corresponding optimized geometries. Similarly, dispersion-dominated series regardless using MP2 or DFT geometries were generally troublesome for the tested functionals. Only three functionals (ωB97, ωB97X, and M06-L) had RMS < 0.6 kcal/mol, and the double hybrid performed poorly in each of the subcategories. Nine functionals (M05-2X, BLYP-GD2, B3LYP-GD3, ωB97X, B3LYP-GD2, M06-2X, ωB97, ωB97XD, and B97-GD2) described the CO2 interaction with alcoholamines effectively. B97-GD2, as being performed strongly in small molecule and MEA subcategorie, produced RMS of 0.53 kcal/mol with the underbound character.. 27.

(28) Table 5. Statistical analysis of the relative binding energy in kcal/mol of complexes al using the optimized geometry at the theory level. Methods. RMS. MSE. MAE. Methods. RMS. MSE. MAE. M06-L ωB97X ωB97 M05-2X M06HF B3LYP-GD3 ωB97XD. 0.46 0.51 0.54 0.59 0.65 0.71 0.71. 0.36 -0.20 -0.47 0.20 0.06 -0.57 -0.34. 0.44 0.45 0.47 0.39 0.54 0.57 0.53. BLYP-D B3LYP-GD2 CAM-B3LYP B2PLYPD LC-ωPBE M06-2X B2PLYP. 1.27 1.28 1.68 1.74 2.21 3.02 3.03. -0.67 -0.99 1.15 0.76 1.81 -1.30 2.43. 0.91 0.99 1.26 1.34 1.81 1.36 2.43. B97-D. 0.76. -0.16. 0.54. 1.. ∆CCSD(T) ∆ECBS //MP2/ATZ. was used as the reference.. Table 6. Statistical analysis of the relative binding energy in kcal/mol of the subcategories using the optimized geometries of the corresponding theory level.1 2. Methods. RMS. Small Molecule and MEA series MSE MAE Methods RMS. M06-2X M05-2X ωB97XD. 0.23 0.30 0.39. -0.07 -0.04 -0.06. 0.19 0.26 0.33. ωB97 BLYP-D B3LYP-GD2. B3LYP-GD3 B97-D M06HF M06-L ωB97X. 0.44 0.44 0.46 0.48 0.53. -0.38 0.20 0.06 0.46 -0.47. 0.38 0.36 0.37 0.46 0.47. CAM-B3LYP LC-ωPBE B2PLYPD B2PLYP. MSE. MAE. 0.66 0.71 0.73. -0.60 -0.18 -0.64. 0.60 0.60 0.64. 0.79 1.27 1.47 1.61. 0.49 1.16 0.03 1.49. 0.67 1.16 1.02 1.49. RMS. MSE. MAE. 3. Methods. RMS. MSE. DEA series MAE Methods. ωB97 M06-L ωB97X M05-2X. 0.33 0.45 0.49 0.84. -0.27 0.23 0.18 0.53. 0.27 0.43 0.42 0.59. BLYP-D B3LYP-GD2 B2PLYPD CAM-B3LYP. 1.78 1.79 2.06 2.44. -1.35 -1.48 1.78 2.08. 1.35 1.48 1.78 2.08. M06HF B3LYP-GD3 ωB97XD B97-D. 0.85 0.97 1.00 1.05. 0.06 -0.82 -0.73 -0.65. 0.76 0.82 0.81 0.79. LC-ωPBE B2PLYP M06-2X. 3.08 4.28 4.67. 2.73 3.76 -3.01. 2.73 3.76 3.01. CO2 bound series MAE Methods. RMS. MSE. MAE. 4. Methods. RMS. MSE. 28.

(29) BLYP-D. 0.22. 0.09. 0.21. M06-L. 0.58. 0.56. 0.56. M05-2X B3LYP-GD3 ωB97X B3LYP-GD2 ωB97 M06-2X ωB97XD. 0.26 0.32 0.35 0.37 0.45 0.45 0.46. -0.25 -0.28 -0.15 -0.32 -0.35 -0.35 0.39. 0.25 0.28 0.33 0.32 0.35 0.38 0.39. B97-D M06HF B2PLYPD CAM-B3LYP LC-ωPBE B2PLYP. 0.59 0.64 1.03 1.39 1.91 2.21. 0.57 -0.54 0.92 1.22 1.79 2.03. 0.57 0.54 0.92 1.22 1.79 2.03. ∆CCSD(T). 1. 2. 3.. ∆ECBS //MP2/ATZ was used as the reference. including complex a-d, g, j, and k. including complexes e, f, h, i , and l.. 4.. including complexes j, k and l.. Table 7. Geometry analysis (Å ) and binding energy (kcal/mol) using the corresponding optimized geometries. 2 1. MP2 B2PLYPD B97-GD2 M06HF ωB97 BLYP-GD2 B3LYPGD2 ωB97XD M05-2X M06-L ωB97X B3LYPGD3 B2PLYP LC-ωPBE CAMB3LYP M06-2X. N13-N20 2O35-N13 2O15-O33. BE. RMS. MSE. MAE. 6.21E-03 2.69E-02 3.67E-02 6.27E-02 8.02E-02. -3.23E-03 -8.35E-03 -1.23E-02 5.91E-02 6.89E-02. 5.83E-03 2.58E-02 3.64E-02 5.91E-02 6.89E-02. 2.996 2.987 2.961 3.032 3.078 2.940. 2.846 2.841 2.830 2.804 2.910 2.820. 4.694 4.698 4.720 4.663 4.726 4.569. -12.22 -8.49 -14.36 -11.04 -12.28 -15.75. 2.951. 2.821. 4.564. -15.62 8.06E-02 -6.67E-02 6.67E-02. 3.032 3.110 3.091 3.095. 2.865 2.917 2.922 2.905. 4.879 4.860 4.419 4.991. -14.13 -10.54 -12.71 -11.30. 3.015. 2.849. 5.050. -13.99 2.05E-01 1.27E-01 1.27E-01. 3.104 3.153. 2.898 2.895. 5.448 5.451. -4.43 4.41E-01 3.05E-01 3.05E-01 -6.80 4.47E-01 3.21E-01 3.21E-01. 3.125. 2.895. 5.614. -7.86 5.37E-01 3.66E-01 3.66E-01. 3.200. 2.893. 2.964. -21.12 1.01E00 -4.93E-01 6.61E-01. 1.10E-01 1.24E-01 1.73E-01 1.84E-01. ∆CCSD(T). MP2/ATZ optimized geometry and BE was ∆ECBS 2 The interatomic distances are labeled in Figure 4. 1. 29. 8.00E-02 -1.17E-01 -0.35E-02 1.52E-01. 8.00E-02 1.17E-01 1.48E-01 1.52E-01.

(30) Figure 3. Local minimum structure of DEA dimer at MP2 level.. Table 7 summarized the statistical results of the various interatomic measurements, to show the consistency of the optimized geometries in each theoretical method. The DEA dimer was selected because the dominance of the dispersion interaction. The reference used the MP2/ATZ optimized geometry for the DEA dimer, and three interatomic distances (N13-N20, O35-N13, and O15-O33) were selected for the comparison (Figure 3). Although the three interatomic measurements could be simply considered as hydrogen-bonding interactions, the mutual interaction between the backbone alcohol groups was believed to substantially affect these interatomic distances. Thus, the description of the dispersion interaction of each functional could be examined in terms of geometrical variance from the MP2 reference. The results in Table 7 were sorted with respect to the size of the RMS of the DFT data, and all the DFT optimizations were started from the MP2 optimized geometry. B2PLYPD exhibited the optimal agreement with the MP2 geometry, but its binding energy 30.

(31) between two DEA molecules was considerably underestimated. The three functionals treated using Grimme’s D2 correction were within a 0.1Å deviation from the reference, whereas the corresponding DEA dimer binding energy was also in reasonable agreement. M06-2X (Figure S1) was observed to locate at a slightly different minimum in comparison with MP2 and other XC functionals, where additional HB was formed between O15-O33 and N13-N20 was elongated.. Bimoelcular and Trimolecular Reaction Mechanism Test Nine XC functionals (B3LYP-GD2, B3LYP-GD3, M05-2X, M06-2X, M06-HF, ωB97, ωB97X, ωB97XD, and BLYP-GD2) were selected to calculate the bimolecular CO2MEA and trimolecular CO2-(MEA)2 binding mechanism as described in Figure 1 and Figure S1 using ATZ basis set, compared with the results of MP2/ATZ. The solvent effect was approximated by the polarizable continuum solvation model (PCM) and the dielectric constant used 24.852 as ethanol. The calculated barriers and reaction energies were shown in Table 8-9. ωB97, ωB97X, and ωB97XD were found as the optimal choices and gave qualitatively agreement with the MP2 results in describing the transition state with less than 0.7 kcal/mol deviation, and acceptable accuracy at ~1.3 kcal/mol in estimating the reaction energy using the PCM approximation for the bimolecular and trimolecular reactions. Combining implicit solvent description and BLYP-GD2 was not able to locate the transition state and zwitterion along the 31.

(32) bimolecular reaction pathway but underestimated the barrier by more than 2.5 kcal/mol along the trimolecular reaction pathway. The inconsistent results of BLYP-GD2 may be inferred to the underestimated kinetics of CO2 absorption in dilute MEA solution but overestimated kinetics in liquid MEA in the condensed phase simulations. The description of explicit solvent molecule was absence in this study, and raised the importance of adopting first-principle molecular dynamic simulations to understand the complicated chemistry of CO2 capturing process by alcoholamines in the condense phase. Nonetheless, the current benchmark still provided a comprehensive comparison for the XC functional selection.. Table 8. The calculated relative barrier and reaction energy for the bimolecular CO2MEA reaction (in kcal/mol) using PCM model. 3.72 -7.75 -0.35 3.09 -1.43 1.45 2.80 2.58 2.44. MP2 ∆∆ETS 0.0 -4.18 -2.34 -1.75 -1.43 -1.26 -0.40 0.01 0.28. MP2 ∆∆ERec 0.0 -11.47 -4.07 -0.63 -0.39 -2.27 -0.92 -1.14 -1.28. ─. ─. ─. Theory. ∆ETS. ∆ERec. MP2 M06-HF M05-2X B3LYP-GD2 B3LYP-GD3 M06-2X ωB97XD ωB97X ωB97. 4.86 0.68 2.52 3.11 3.43 3.61 4.46 4.87 5.15. BLYP-GD22. ─. 1.. 2.. ∆ETS and ∆ERec are the barrier and reaction energy predicted at each theory MP2 MP2 level, respectively. ∆∆ETS and ∆∆ETS are the errors in respect to MP2 results. TS and zwitterion minimum structures of BLYP-GD2 using PCM model were not able to locate.. 32.

(33) Table 9. The calculated barrier and reaction energy for the trimolecular CO2-(MEA)2 reaction (in kcal/mol) using PCM model. -3.52 -19.00 -8.17 -3.80 -2.80 -5.63 -4.84. MP2 ∆∆ETS1 0.00 -2.60 -1.25 -1.80 -1.44 -0.65 -0.22. MP2 ∆∆E1−2 0.00 -13.31 -3.32 -0.34 0.50 -1.86 -0.71. MP2 ∆∆E1−3 0.00 -15.48 -4.65 -0.28 0.72 -2.12 -1.32. -4.73 -5.03 -1.48. 0.49 0.67 -2.56. -0.51 -1.04 0.85. -1.21 -1.52 2.04. Theory. ∆ETS1. ∆E1−2. ∆E1−3. MP2 M06-HF M05-2X B3LYP-GD2 B3LYP-GD3 M06-2X ωB97XD. 2.70 0.10 1.45 0.90 1.26 2.05 2.49. -3.30 -16.61 -6.63 -3.64 -2.80 -5.16 -4.34. ωB97X ωB97 BLYP-GD2. 3.19 3.37 0.14. -3.81 -4.01 -2.45. 1.. ∆ETS1, ∆E1−2, and ∆E1−3 are the barrier and reaction energy in respect to the bound complex (min1). ∆∆ExMP2 , x = TS1, 1-2, and 1-3 are the errors in respect to MP2 results.. Conclusion. We built a specific but systematic database for describing the potential interactions involved in the CO2 capture by alcoholamines, including hydrogen bond, dispersion, and electrostatic (positive charge of CO2 vs. the lone pair of N of alcoholamines) interactions. Using the proposed database, we tested 15 XC dispersion-improved XC functionals to determine the optimal choice for the future theoretical characterization of the CO2 capturing process. We used the binding energy at ∆CCSD(T) level extrapolated from ATZ to AQZ level as a reference. Using MP2/ATZ-optimized geometry, we determined that M06-2X was the optimal choice, whereas the other eight XC functionals also had RMS < 0.7 kcal/mol in the overall comparison. 33.

(34) Using the corresponding DFT optimized geometries significantly changed the ranking order, indicating that combining DFT optimized geometries with an ab initio single-point correction could be questionable. ωB97X was the optimal option, followed by ωB97 and M06-L; all three functionals had RMS < 0.6 kcal/mol. M06HF and B3LYP-GD3 were the next two optimal options, having RMS of 0.67 and 0.71 kcal/mol, respectively. Three out of 15 functionals (ωB97, ωB97X, and M06-L) described the dispersion-dominated DEA series effectively, and had RMS < 0.6 kcal/mol. Additionally, using DFT optimized geometries, we identified nine functionals (M052X, BLYP-GD2, B3LYP-GD3, ωB97X, B3LYP-GD2, M06-2X, ωB97, ωB97XD, and B97-GD2) as being suitable (RMS < 0.6 kcal/mol) for describing CO2-alcoholamine interactions. Furthermore, by filtering out the XC functional selection through comparison of the crucial geometrical descriptors listed in Table 7 (the criteria was chosen as < 0.1 Å ), we identified B97-GD2, BLYP-GD2, and ωB97 as the recommended choices for characterizing the process of CO2 capture by alcoholamine. ωB97 is available in most computational chemistry packages, and is suitable for simulating intermolecular interactions. B97-GD2 and BLYP-GD2 are available for the condensed-phase simulations. B97-GD2 performed strongly in the inter-alcoholamine interaction, and BLYP-GD2 produced an effective description of the CO2-alcoholamine interaction despite both functionals over-estimated the DEA dimer binding energy. 34.

(35) Simple bimolecular and trimolecular reaction tests between CO2 and MEA using PCM model also confirmed ωB97, ωB97X, and ωB97XD qualitatively reproduced the energetics at MP2 level. Considering the different ranking in terms of root-mean-square error for the two comparisons (using MP2 or DFT optimized geometries), the inconsistent results of the tested XC functionals raised concerns about using the conventional procedure of singlepoint ab initio electronic structure correction in combination with DFT optimized geometries, particularly for describing the CO2 binding by alcoholamines.. Acknowledgement. The authors are grateful for the financial support provided by the National Science Council (Grants 101-2113-M-003-003-MY2 and 101-2112-M-002-017-MY3). JDC was also supported by the National Taiwan University (Grant Nos. 99R70304, 101R891401, and 101R891403), and the National Center for Theoretical Sciences of Taiwan. Computational resources were provided by the National Center for High Performance Computing.. Notes. The binding energies (in kcal/mol) of complexes a-l, obtained using MP2/ATZ, and 35.

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(39) Assessment of Dispersion-Improved ExchangeCorrelation Functionals for the Simulation of 𝐂𝐎𝟐 Binding by Alcoholamines Supplementary Material Hsueh-Chien Li,1 Jeng-Da Chai,2,* and Ming-Kang Tsai1,* 1. Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan 2 Department of Physics, Center for Theoretical Sciences, and Center for Quantum Science and Engineering, National Taiwan University, Taipei 10617, Taiwan. To whom correspondence should be addressed. E-mail: [email protected] and [email protected]. 39.

(40) Figure S1. Local minimum structure of DEA dimer at M06-2X levels.. 40.

(41) Table S1. Binding energy (in kcal/mol) of complexes a-l using MP2/ATZ optimized geometries.. ωB97 ωB97X ωB97XD LC-ωPBE CAM-B3LYP B3LYP-GD2. a -5.69 -5.63 -4.98 -4.39 -5.23 -5.32. b -7.34 -7.32 -6.85 -6.08 -6.79 -7.13. c -8.60 -8.44 -8.10 -6.69 -7.54 -8.92. d -5.38 -5.21 -5.08 -3.37 -3.93 -5.41. e -8.61 -8.49 -8.74 -6.71 -7.34 -9.28. f -5.26 -5.07 -5.29 -3.01 -3.50 -5.59. B3LYP-GD3 BLYP-D B97-D. -5.25 -4.93 -4.31. -7.01 -6.81 -6.32. -8.51 -8.54 -7.81. -5.01 -5.12 -4.73. -8.95 -8.93 -8.47. -5.16 -5.27 -4.96. M06-2X M05-2X M06HF M06-L B2PLYP B2PLYPD. -5.12 -5.19 -4.76 -4.58 -4.34 -4.76. -6.59 -6.63 -6.45 -6.23 -5.64 -6.17. -7.99 -7.91 -8.06 -7.37 -5.87 -7.17. -4.60 -4.50 -4.10 -4.31 -2.76 -3.97. -8.12 -8.05 -8.41 -7.44 -5.57 -7.19. -4.81 -4.58 -4.62 -4.44 -2.14 -3.78. g. h. i. j. k. l. ωB97 ωB97X ωB97XD LC-ωPBE CAM-B3LYP B3LYP-GD2 B3LYP-GD3 BLYP-D B97-D M06-2X M05-2X. -4.81 -4.56 -4.88 -2.23 -2.81 -5.24 -4.69 -5.01 -4.64 -4.20 -3.88. -5.63 -5.28 -6.32 -2.45 -2.93 -6.57 -5.94 -6.27 -6.00 -5.27 -4.80. -11.96 -10.92 -13.93 -5.88 -6.67 -14.99 -13.45 -14.47 -13.46 -12.04 -10.47. -3.76 -3.57 -2.86 -2.11 -2.64 -3.16 -3.17 -2.73 -2.39 -3.47 -3.35. -4.85 -4.45 -3.89 -2.33 -3.02 -4.50 -4.47 -3.97 -3.36 -4.74 -4.44. -4.51 -3.97 -3.88 -1.33 -1.94 -4.70 -4.61 -4.21 -3.63 -4.86 -4.33. M06HF M06-L B2PLYP B2PLYPD. -3.62 -4.09 -1.43 -3.23. -4.80 -5.21 -1.20 -3.84. -10.90 -12.33 -2.50 -8.41. -3.35 -2.48 -2.05 -2.70. -4.58 -3.53 -1.97 -3.36. -4.73 -3.89 -0.47 -2.70. 41.

(42) Table S2. Binding energy (in kcal/mol) of complexes a-l using the corresponding DFT optimized geometries.. ωB97 ωB97X ωB97XD LC-ωPBE CAM-B3LYP B3LYP-GD2. a -5.71 -5.64 -4.98 -4.40 -5.27 -5.41. b -7.34 -7.30 -6.84 -6.09 -6.83 -7.25. c -8.58 -8.37 -8.06 -6.67 -7.54 -9.05. d -5.37 -5.15 -5.09 -3.35 -3.89 -5.43. e -8.55 -8.38 -8.62 -6.65 -7.36 -9.46. f -5.29 -5.09 -5.36 -3.20 -3.70 -5.68. B3LYP-GD3 BLYP-D B97-D M06-2X M05-2X M06HF M06-L B2PLYP B2PLYPD. -5.29 -5.14 -4.46 -5.13 -5.19 -4.78 -4.58 -4.35 -4.75. -7.08 -7.12 -6.49 -6.58 -6.61 -6.47 -6.20 -5.66 -6.18. -8.58 -8.99 -8.07 -7.98 -7.86 -8.26 -7.34 -6.00 -7.18. -5.05 -3.72 -4.63 -4.57 -4.53 -4.26 -4.34 -2.87 NA. -9.00 -9.46 -8.73 -8.09 -7.97 -8.78 -7.35 -5.72 -7.18. -5.29 -5.58 -5.12 -4.90 -4.58 -4.76 -4.60 -2.61 -3.83. ωB97. g -4.87. h -5.71. i -12.28. j -3.84. k -4.33. l -4.51. ωB97X ωB97XD LC-ωPBE CAM-B3LYP B3LYP-GD2 B3LYP-GD3 BLYP-D B97-D M06-2X M05-2X. -4.55 -4.86 -2.59 -3.09 -5.36 -4.74 -5.19 -4.77 -4.26 -3.90. -5.29 -6.46 -3.04 -3.49 -6.79 -6.08 -6.51 -6.11 -11.03 -4.83. -11.30 -14.13 -6.80 -7.86 -15.62 -13.99 -15.75 -14.36 -21.12 -10.54. -3.61 -2.89 -2.12 -2.66 -3.20 -3.21 -2.79 -2.47 -3.57 -3.40. -4.47 -3.51 -2.48 -3.12 -4.56 -4.55 -4.11 -3.53 -4.22 -4.57. -4.00 -4.06 -1.64 -2.18 -4.82 -4.71 -4.44 -3.91 -4.89 -4.40. M06HF M06-L B2PLYP B2PLYPD. -3.62 -4.11 -2.10 -3.21. -4.85 -5.27 -2.33 -3.80. -11.04 -12.71 -4.43 -8.49. -3.60 -2.48 -2.09 -2.72. -4.39 -3.57 -2.33 -3.37. -5.25 -3.89 -1.11 -2.77. 42.

(43) Optimized Cartesian coordinate (in Å ) of all complexes of C1 Database at MP2/ATZ a. (H2O)2 O H H O H H. -1.51139800 -0.55881500 -1.91607100 1.38638600 1.73992800 1.73505900. -0.00295600 -0.00033800 0.01852700 0.00220900 -0.76695200 0.75473500. -0.12206000 0.05332900 0.74870900 0.11201500 -0.34564000 -0.37603700. b. NH3(H2O) N H H. -1.37577400 -1.70581100 -1.63464800. 0.02221200 -0.28116100 -0.69850600. 0.00002300 0.90799100 -0.66274500. H O H H. -1.90371100 1.54474400 0.58228200 1.93435300. 0.85058500 -0.10600700 0.04997800 0.77167200. -0.24481300 -0.00011400 -0.00033800 0.00066200. c. MEA(H2O) N H. 0.83508500 1.18009000. 1.32273500 2.12662500. 0.18645200 -0.32449000. H C C H H H H O H O. 0.72788100 -0.46579500 -0.91052100 -0.34532900 -1.24962100 -0.20924000 -0.92368900 -2.22034700 -2.44646500 2.28339300. 1.62301300 0.90246900 -0.36744500 0.70029200 1.65641200 -1.17350100 -0.20584600 -0.66032900 -1.55389900 -1.13700500. 1.14928000 -0.35453900 0.33976100 -1.41787900 -0.24171700 0.12069900 1.42383100 -0.14107300 0.13333900 -0.09552400. H H. 1.93981100 3.23449300. -0.22721200 -1.03650100. 0.01909200 -0.18587600. d. MEA(NH3) N H. -0.06462000 -0.06364900. 1.30115300 2.30868900 43. -0.55658200 -0.66114600.

(44) H. 0.33024300. 0.91933600. -1.40964600. H H N H C C H H H H. -1.95022100 -3.08829500 -2.61016900 -3.31193600 0.79888700 0.87644700 0.36210000 1.81599100 1.48142900 -0.12816500. 0.17897600 -0.86199500 -0.54151900 -0.09285400 0.90360300 -0.60325600 1.26585300 1.30354900 -0.90916000 -1.01836600. -0.20349700 -0.74579300 0.08742700 0.66338600 0.55942300 0.61638900 1.49107300 0.48364900 1.47405700 0.70928400. O H. 1.49018200 1.30256500. -1.03084600 -1.96678100. -0.60612500 -0.72314900. e. DEA(H2O) C C H H H H C. -2.43300000 -1.21506200 -2.41183100 -2.42069500 -1.27452000 -1.21163300 1.21487800. -0.06993600 -0.73233600 -0.18920000 0.99794300 -1.81617600 -0.55629400 -0.73241600. 0.35237300 -0.25510400 1.44147800 0.12500200 -0.09849100 -1.33180100 -0.25512200. C H H H H N H O H O. 2.43287300 1.27425300 1.21145600 2.42068200 2.41166200 -0.00007100 -0.00006900 -3.57405200 -4.36023400 3.57387800. -0.07012100 -1.81626400 -0.55635400 0.99774900 -0.18934900 -0.14668200 -0.30522100 -0.71497300 -0.24921900 -0.71530100. 0.35236200 -0.09852500 -1.33181500 0.12495500 1.44147100 0.30873000 1.31280400 -0.20490700 0.09584800 -0.20485600. H O H H. 4.36008700 0.00056500 -0.00014400 0.00022100. -0.24945000 2.68498300 2.84186100 1.70792900. 0.09568000 -0.09757800 -1.04563700 -0.00040500. 44.

(45) f. DEA(NH3) N H H N H C C H H H. -0.09342300 -0.19134800 0.50706400 1.02607400 0.94426600 -1.31398800 -2.50835000 -1.24953000 -1.46503600 -3.41429400. -0.04690700 -0.81765300 1.91709700 2.79438800 3.12335800 0.04511100 0.19560900 0.92232100 -0.83368300 0.33601200. 0.15165500 -0.50319700 -0.44816300 -0.51126700 -1.46558000 0.93798700 0.02829300 1.58758600 1.58155300 0.62430400. H O H. -2.36519300 -2.59248400 -3.25442900. 1.06566300 -1.00114900 -0.87490900. -0.61762700 -0.74729200 -1.43374200. C C H H H H O H. 1.07722500 2.29246600 0.94169100 1.25115600 3.17501100 2.44017800 2.04280700 2.71537700. -0.30075500 -0.51282300 -1.17682600 0.56889100 -0.65209300 0.36297300 -1.68329700 -1.73633400. 0.98151800 0.11296800 1.63193000 1.62007800 0.74303900 -0.52134900 -0.67218100 -1.35766300. H. 0.54982900. 3.47553200. 0.06731600. -1.77075600 -2.04681700 -1.89971500 -2.62465100 -2.09411500 -2.58214700 -3.67517500. 1.72820200 2.62051800 1.80908300 0.65833200 -0.67982100 0.69051800 0.74022400. 0.27185800 -0.11996300 1.27441900 -0.25752600 0.21130800 -1.34570600 0.03779000. -1.10259500 -2.01266500 -3.02581400 -2.60777300 1.03729200 0.24558600. -0.84576400 -0.67554400 -1.67181400 -2.53312600 0.48201200 1.09279200. -0.21338400 1.30508300 -0.21709000 -0.12759200 -0.63639200 -0.44622600. g. (MEA)2 N H H C C H H H H O H N H. 45.

(46) H. 1.69597600. 0.99421700. -1.21186000. C C H H H H O H. 1.69266200 2.89458600 0.99080700 2.02426000 3.33361500 2.58530200 3.83213100 4.52415900. 0.12701400 -0.74184400 -0.44068600 0.99094700 -1.08980200 -1.60320400 0.06057700 -0.51385500. 0.61769700 0.34000200 1.23293000 1.20715800 1.27941100 -0.25726900 -0.38384400 -0.72446700. N H H. -1.34367400 -2.14721500 0.59212100. 0.06624700 0.15532000 -0.38729500. 0.11023100 -0.50486000 -0.61299700. N H C C H H H H. 1.56550900 1.99618600 -1.24520000 -1.10256400 -0.35926300 -2.11567300 -0.95566700 -0.23764200. -0.69090700 -0.46331200 1.26797900 2.48191800 1.18848900 1.40328000 3.37495600 2.35082800. -0.57283600 -1.46217800 0.92447800 0.04003700 1.55993200 1.58269800 0.65387900 -0.61569600. O H C C H H H H O H. -2.30603900 -2.17931200 2.26341200 3.72841800 1.84353900 2.18369300 4.23636900 3.82944900 4.27197900 5.15051400. 2.58372500 3.24921700 0.04140100 -0.32039300 -0.24248900 1.13370100 0.14977500 -1.40594300 0.15371600 -0.22214600. -0.72290800 -1.40586000 0.47776400 0.47330800 1.44575100 0.38975100 1.32012000 0.54623800 -0.76250600 -0.87145000. C C H H H H. -1.50765700 -1.69304700 -2.36072500 -0.60399000 -1.75586400 -0.83948000. -1.13623900 -2.33564100 -1.06522100 -1.28303500 -3.24360100 -2.41476700. 0.91465800 0.01825100 1.60483800 1.51230000 0.62418900 -0.65814100. h. MEA(DEA). 46.

(47) O. -2.90813600. -2.12775600. -0.70771700. H. -2.95249000. -2.77675800. -1.41621400. -0.91182400 0.08247000 -1.30478600 -0.40798400 -0.46361400 0.60015500 2.14784000. 2.02579800 2.22682600 1.00492500 2.15772000 2.29313900 3.17796800 1.35470700. 0.95484100 -0.16965800 0.91428100 1.91559700 -1.12136600 -0.02874400 -1.09091100. C H H. 3.10305600 1.79003200 2.69453600. 0.18629800 1.50650600 2.25391900. -1.07272300 -2.11969800 -0.79577200. H H N H O H O H. 3.97562400 3.42371200 1.05593200 0.57387600 -1.95494200 -2.55586100 2.41236500 3.01350100. 0.42041900 -0.00766800 1.14368500 0.26770400 2.98119300 2.91601100 -0.95359500 -1.70415100. -1.68834800 -0.04632600 -0.15157100 -0.38429300 0.77362500 1.52228900 -1.60225500 -1.55555200. H N C C H H H H C C. 0.04474300 -0.30691600 0.31307000 0.49104100 1.30301600 -0.27755200 -0.44408400 0.69739800 -1.76202100 -2.33540800. -1.87545500 -1.52396000 -2.27166700 -1.41706100 -2.59964200 -3.16491100 -0.88566000 -2.07062900 -1.59423000 -0.73315300. -1.04751000 -0.16346000 0.93598800 2.18275300 0.61747500 1.18656600 2.40030500 3.03170700 -0.14517900 -1.25006300. H H H H O H. -2.12129200 -2.14217300 -2.03921700 -1.94009000 -3.75145300 -4.13927800. -1.22004000 -2.61918800 0.30830700 -1.06517500 -0.87893100 -0.25785600. 0.81559900 -0.25250600 -1.09958200 -2.21673000 -1.20251200 -1.82672000. i. (DEA)2 C C H H H H C. 47.

(48) O. 1.58256900. -0.52170700. 2.09564400. H. 1.39857200. 0.14489000. 1.39824000. j. NH3(CO2) N H H C O O H. -1.99585400 -2.33773100 -2.39643600 0.94105400 0.94355400 0.98841300 -2.39691400. -0.01672300 0.93627400 -0.45782700 0.00512400 1.17539700 -1.16391500 -0.48397900. 0.00001200 -0.01514100 0.81877300 0.00000600 0.00000600 0.00000700 -0.80385400. k. MEA(CO2) N. 0.22347900. 1.20788300. -0.27534900. H H C C H H H H. -0.08640500 0.71878200 1.15396100 1.73476300 0.60669900 1.98073400 2.38162300 0.92495900. 2.15689300 1.22303300 0.77602800 -0.56463500 0.66527800 1.47525700 -0.92752900 -1.27918600. -0.10263000 -1.16050300 0.76852100 0.39219000 1.70548600 0.93513900 1.19502400 0.23184700. O H C O O. 2.48422300 2.73629400 -2.14257000 -2.69509300 -1.64327700. -0.36793600 -1.22936900 -0.37130600 0.14005500 -0.91895600. -0.81090000 -1.15653500 -0.00589300 -0.90065000 0.90177600. l. DEA(CO2) C C H. 2.42992700 1.20897400 2.41216300. -0.48845800 -1.06560100 -0.74163700. -0.43643100 0.24603300 -1.50237100. H H H C C H. 2.42055300 1.28734900 1.18825700 -1.20939100 -2.43012000 -1.28805500. 0.60083900 -2.16049800 -0.73035300 -1.06535600 -0.48781400 -2.16023300. -0.34621500 0.25352000 1.28400000 0.24586000 -0.43666600 0.25321900. 48.

(49) H. -1.18869000. -0.73021700. 1.28385900. H H N H O H O H C O. -2.42052000 -2.41225600 -0.00011300 -0.00007600 3.57236500 4.35972200 -3.57277700 -4.35999000 0.00031300 -0.00077300. 0.60146100 -0.74078200 -0.58227700 -0.90548400 -1.05156500 -0.65502700 -1.05079700 -0.65395100 2.15680500 1.80615300. -0.34621300 -1.50265500 -0.41147600 -1.37470000 0.20003200 -0.18502800 0.19951500 -0.18552200 0.23863100 1.35631400. O. 0.00141200. 2.56792700. -0.85729000. 49.

(50) CCSD(T). CCSD(T). Table S3. Summary of ∆ECBS(TZ→QZ) and ∆ECBS(DZ→TZ) calculations where the values of ∆E are in kcal/mol. E[CCSD(T)] ∆E[CCSD(T)] a. (H2O)2 (aVQZ) -152.73528 -76.36377 -76.36360 (aVTZ) -152.69296 -76.34276 -76.34259 (aVDZ) -152.55616 -76.27490 -76.27429. b. NH3(H2O) (aVQZ) -132.86967 -76.36348 -56.49589 (aVTZ) -132.83341 -76.34251 -56.48088 (aVDZ) -132.70998 -76.27434 -56.42660. c MEA(H2O) (aVQZ) -286.51050 -210.13471 -76.36343. E[HF]. ∆E[HF]. E2. -4.960. -152.13679 -3.640 -0.59848 -1.320 -76.06563 -0.29814 -76.06537 -0.29824. -4.778. -152.12609 -3.602 -0.56687 -1.176 -76.06031 -0.28246 -76.06005 -0.28254. -4.374. -152.08813 -3.616 -0.46803 -0.758 -76.04141 -0.23349 -76.04096 -0.23333. -6.468. -132.29610 -4.481 -0.57357 -1.987 -76.06499 -56.22397. CCSD(T). ∆E2 ∆ECBS(TZ→QZ). CCSD(T). ∆ECBS(DZ→TZ). -5.06. -4.95. -6.60. -0.29849 -0.27192. -6.283. -132.28717 -4.472 -0.54624 -1.811 -76.05969 -0.28282 -56.22035 -0.26053. -5.669. -132.25366 -4.429 -0.45632 -1.240 -76.04066 -0.23369 -56.20594 -0.22066. -7.761. -285.27521 -4.297 -1.23530 -3.464 -209.20366 -0.93104 -76.06469 -0.29873 50. -6.52. -7.93.

(51) (aVTZ) -286.43504 -76.34263 -210.08044 (aVDZ) -286.17555 -76.27496 -209.88986. -7.514. -285.25691 -4.283 -1.17813 -3.231 -76.05944 -0.28319 -209.19065 -0.88979. -6.733. -285.18824 -4.300 -0.98731 -2.433 -76.04075 -0.23421 -209.14064 -0.74922. -4.695. -265.43129 -1.709 -1.20901 -2.987 -56.22375 -0.27206 -209.20481 -0.93219. -4.534. -265.41465 -1.691 -1.15617 -2.843 -56.22015 -0.26077 -209.19181 -0.89088. -3.999. -265.35015 -1.712 -0.97455 -2.287 -56.20563 -0.22076 -209.14179 -0.75015. -7.707. -438.22924 -3.932 -1.81187 -3.775 -76.05929 -0.28349 -362.16368 -1.52236. -6.892. -438.12497 -3.969 -1.52079 -2.923 -76.04065 -0.23476 -362.07799 -1.28137. -4.654. -418.38815 -1.004 -1.79123 -3.650 -362.16645 -1.52453 -56.22010 -0.26088. -7.85. d MEA(NH3) (aVQZ) -266.64030 -56.49581 -210.13701 (aVTZ) -266.57082 -56.48091 -210.08268 (aVDZ) -266.32470 -56.42639 -209.89194 e DEA(H2O) (aVTZ) -440.04111 -76.34278 -363.68605 (aVDZ) -439.64576 -76.27541 -363.35936 f. DEA(NH3) (aVTZ) -420.17938 -363.69098 -56.48098. 51. -4.80. -4.77. -8.07. -4.91.

(52) (aVDZ) -419.79773 -363.36453 -56.42670 g. (MEA)2 (aVTZ) -420.17022 -210.08060 -210.08282 (aVDZ) -419.78838 -209.89022 -209.89222. j. NH3(CO2) (aVQZ) -244.89007 -56.49579 -188.38938 (aVTZ) -244.82603 -56.48070 -188.34064 (aVDZ) -244.61657 -56.42606 -188.18635. k MEA(CO2) (aVQZ) -398.53313 -210.13701 -188.38942 (aVTZ) -398.42998 -210.08258 -188.34099. -4.078. -418.28821 -1.029 -1.50953 -3.049 -362.08086 -1.28368 -56.20571 -0.22099. -4.265. -418.38312 -0.227 -1.78710 -4.037 -209.19066 -0.88994 -209.19210 -0.89072. -3.727. -418.28327 -0.249 -1.50511 -3.478 -209.14075 -0.74947 -209.14212 -0.75010. -3.071. -243.94617 -1.836 -0.94389 -1.235 -56.22394 -0.27185 -187.71931 -0.67007. -2.940. -243.92997 -1.817 -0.89606 -1.123 -56.22031 -187.70676. -243.86938 -1.818 -0.74719 -0.790 -56.20573 -0.22033 -187.66075 -0.52560. -4.204. -396.92645 -1.472 -1.60668 -2.732. -4.022. -3.15. -3.08. -0.26039 -0.63388. -2.608. -209.20504 -187.71906. -4.50. -4.32. -0.93197 -0.67036. -396.90096 -1.450 -1.52903 -2.572 -209.19202 -0.89056 -187.70663 -0.63436 52. -4.24.

(53) (aVDZ) -398.08434 -209.89167 -188.18707 l. -3.513. DEA(CO2) (aVTZ) -552.03373 -188.34145 -363.68594 (aVDZ) -551.55300 -188.18840 -363.35927. -396.80499 -209.14191 -187.66074. -1.27934 -2.046 -0.74975 -0.52633. -549.86991 0.363 -2.16382 -4.341 -187.70663 -0.63482 -362.16386 -1.52208 -3.342. -549.73869 0.355 -1.81431 -3.697 -187.66110 -0.52731 -362.07816 -1.28111. 53. -4.25.

(54) The Chain Length Effect of Alcohol Group to CO2 Capture by Alcoholamines: A First-Principle Molecular Dynamic Simulation Comparison Hsueh-Chien Li, Ming-Kang Tsai* Department of Chemistry, National Taiwan Normal University, 11677 Taipei, Taiwan. *To. whom correspondence should be addressed.. E-mail: [email protected]. 54.

(55) Abstract Monoethanolamine (MEA) and mono-n-propanolamine (MPA) solutions were investigated for 25% and 50% CO2 loading at 400 K by performing implicit solvation calculations and First-Principle Molecular Dynamic (FPMD) simulations using BLYPD functional. Improved CO2 absorption performance was observed for the MPA solution compared with that of the MEA solution, along both bimolecular and trimolecular pathways. The additional CH2 in MPA provided an additional polarization site to balance the two electron-withdrawing groups (NH2 and OH), thus reducing the electronic repulsion during the formation of charge-separated zwitterionic intermediates; the addditional CH2 also caused the solvent to become more polar, which faciliate the stabilization of zwitterionic intermediates. The addition of CO2 to the MEA and MPA solutions led to a reduced-hydrogen-bonding solution environment being favored. In addition, the probability of identifying the intermolecular hydrogen bond (HB) precursors (MEA)2 and (MPA)2 for the subsequent trimolecular pathway decreased. Therefore, the bimolecular pathway plays a more crucial role than the trimolecular pathway does. Besides a low-HB environment was preferred for CO2 uptake, the corresponding HB contribution was determined primarily from OH…N, and the contribution became more dominant as the CO2 loading increased. The occurrence of such HB interactions blocking the lone pair of N of alcoholamines hindered CO2 uptake as well.. Introduction CO2 can be chemically activated using three approaches: (1) the central C serves as a Lewis acid and reacts with electron-rich metals or nucleophilic organic compounds; (2) O serves as a Lewis base and reacts with electron-deficient metals and organic electrophiles; or (3) the C=O double bond can donate or accept electrons through the formation of π-complexes. One of the most crucial applications in which approach (1) is adopted is the use of amines as a CO2 scrubber in the petroleum industry to separate CO2 from natural gas1 or capture CO2 from flue gas emitted from coal-fired power plants.2-6 As summarized by Yang et al. and the reference therein, the efficiency of amines in a given capturing operation depends on the partical pressure of CO2.7 A higher level of alkylation of N in the amino moiety, for example, from primary alkanolamines to tertiary alkanolamines makes the corresponding solvents more suitable for interaction with CO2 molecules at high partial pressures in the stream.8, 9 In practice, a mixture of these solvents is used; the solvents are even blended with water, 55.