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Article

Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex

by
Xin Wen
1,
Kaipai Ren
1,
Wenzhen Zhang
1,
Guangli Zhou
2,* and
Yi Luo
1,3,*
1
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
2
School of Chemistry and Chemical Engineering, Linyi University, Linyi 276000, China
3
PetroChina Petrochemical Research Institute, Beijing 102206, China
*
Authors to whom correspondence should be addressed.
Inorganics 2024, 12(6), 172; https://doi.org/10.3390/inorganics12060172
Submission received: 2 May 2024 / Revised: 9 June 2024 / Accepted: 13 June 2024 / Published: 19 June 2024
(This article belongs to the Special Issue Feature Papers in Organometallic Chemistry 2024)
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Abstract

:
This study aimed to investigate the insertion reaction of the polar monomers mediated by the cationic rare earth metal complex [(C5H5)Sc(NMe2CH2C6H4-o)]+ utilizing a combination of density functional theory (DFT) calculations and multivariate linear regression (MLR) methods. The chain initiation step of the insertion reaction could be described by the poisoning effect and the ease of monomer insertion, which could be represented via the DFT-calculated energy difference between σ- and π-coordination complexes (ΔΔE) and insertion energy barrier (ΔG≠), respectively. The results indicate that ΔΔE and ΔG≠ can be predicted by only several descriptors using multiple linear regression methods, with a root mean squared error (RMSE) of less than 2.5 kcal/mol. Furthermore, the qualitative analysis of the MLR models provided effective information on the key factors governing the insertion reaction chain initiation.

Graphical Abstract

1. Introduction

The polymers synthesized via the insertion reactions of polar monomers demonstrate excellent surface properties, solvent resistance, and adhesion [1,2,3,4,5]. Consequently, these reactions are critically important and have attracted significant research interest [6,7,8,9,10,11,12,13,14]. Research indicates that rare earth metal complexes display remarkable chain initiation and stereoselectivity in these reactions, particularly with functionalized styrene monomers, functionalized α,ω-dienes, and allyl methacrylate, among other polar monomers [15,16,17,18,19,20,21]. Regarding the extensively researched insertion reaction of polar monomers, it encounters specific challenges due to the simultaneous presence of C=C bonds and polar functional groups. These groups can heteroatomically coordinate with monomers, potentially poisoning the Lewis-acidic metal centers within the complexes. Currently, in the research on polar monomer insertion reaction, a significant amount of successful experimental work has been carried out. Hou et al. [22,23,24,25,26] demonstrated that cationic half-sandwich rare earth metal aminobenzyl complexes are highly effective in catalyzing the insertion reactions of halogenated or amino-containing styrene monomers. Cui et al. [27,28,29,30,31] developed constrained-geometry-configuration rare-earth metal complexes that effectively catalyze the insertion reactions of methoxystyrene monomers, ethyl vinyl ether, and other polar monomers, showing high catalytic activity and stereoselectivity. Currently, the impact of ligands on the yield, activity, and selectivity of organometallic catalysts has gradually been studied, and the selection of relevant descriptors has also been gradually introduced [32,33,34,35]. However, compared with the abundant experimental data, systematic studies on the microfactors affecting the poison effects and insertion energy barriers of various polar monomers catalyzed by rare-earth metals, incorporating DFT calculations and MLR, remain scarce [23,36,37,38,39,40,41].
Our group [42,43] has sequentially investigated both the homopolymerization of ethylene and its copolymerization with polar monomers using palladium and nickel complexes. The results suggest that the geometric parameters and electron occupancies function as molecular descriptors, effectively explaining the factors influencing the chain initiation of ethylene homopolymerization and copolymerization with polar monomers.
To expand upon our previous research, we conducted a thorough investigation into the chain initiation of insertion behavior of various polar monomers, including halogenated monomers, ether and ketone monomers, and polar styrene monomers. This analysis employed a half-sandwich scandium aminobenzyl complex to determine the factors influencing the chain initiation of polar monomer insertion (Scheme 1). The chain initiation of these polar monomers in insertion reactions is influenced by both the poisoning effect and the ease of monomer insertion. These effects can be quantified by the energy differences between π-complex and σ-complex (ΔΔE(π-σ)) and the insertion barriers of the monomers (ΔG≠), respectively. We performed DFT calculations and multiple linear regression analysis (MLR) on both ΔΔE(π-σ) and ΔG≠ to evaluate their impacts on the chain initiation of the insertion reactions.

2. Results and Discussion

To systematically explore the factors influencing the chain initiation of polar monomer insertion, this study examined 45 different polar monomers, as illustrated in Figure 1. Initially, the energies of the σ-complexes and π-complexes formed by these monomers coordinating with the active species were calculated, as shown in the SI named poisoning effect.xyz. When ΔΔE(π-σ) > 0, heteroatom coordination is favored, suggesting a potential poisoning tendency for the scandium complex. Using DFT, the coordination energies for each polar monomer were calculated. It was observed that certain polar monomers tend to form stable σ-complexes with metal centers. The lower energy of these σ-complex structures results in a larger ΔΔE(π-σ), signifying a strong poisoning effect from these monomers.
The poisoning abilities of the aforementioned 45 monomers were calculated, and the calculated results are shown in Table S1. The results revealed that monomers of type ester polar monomers and methylene-spaced polar monomers in groups 1–15 exhibited stronger poisoning capabilities toward the metal center, while those of type functionalized styrene monomers showed weaker poisoning abilities. To further investigate the reasons behind the poisoning effects, we developed a multivariate linear regression (MLR) model for the 15 polar monomers (1–15) that exhibited higher ΔΔE(π-σ) values. The structures of these monomers are illustrated in Figure S1. It was crucial to select appropriate steric and electronic descriptors to establish a linear relationship model via MLR. Initially, we considered the electronic effects of C=C bonds and heteroatoms coordinating with the metal center and calculated the NPA charge (QX, QCα, QCβ), NMR chemical shifts (NMRX, NMRCα, NMRCβ), and the frontier orbital energies of the monomer (LUMO, HOMO) descriptors using Gaussian 16 program. Subsequently, we addressed the steric effects arising from various substituents in polar monomers, selecting sterimol parameters using the molecular modeling pro software (B1, B5, L). B1 and B5 are the minimal and maximal dimensions perpendicular to the ligand length, respectively, and L is the ligand length. The bond lengths (Cα=Cβ, Cβ-R) and bond angle (Cα-Cβ-R) were retrieved using Gaussian as descriptors. Consequently, 15 potentially relevant descriptors were chosen based on DFT-optimized monomer structures, as shown in Figure 2. The values of these descriptors, along with ΔΔE(π-σ), are compiled in Tables S1 and S2.
Further univariate correlation analysis was performed on data from polar monomers to examine the relationship between individual descriptors and ΔΔE(π-σ). The correlations were visualized using Matlab software, resulting in a correlation color map, as displayed in Figure 3. The numerical values within each colored block within the map show the Pearson correlation coefficient between the descriptors and ΔΔE(π-σ), as well as among the descriptors themselves. The strength of the correlation is represented by the absolute value of the correlation coefficient (|R|), ranging from 0 to 1, where values closer to 1 indicate stronger correlations. The last line of data specifically highlights the correlation between ΔΔE(π-σ) and various descriptors. The analysis identified that the three descriptors most strongly correlated with ΔΔE(π-σ) were QCα (|R| = 0.74), NMRCβ (|R| = 0.76), and L (|R| = 0.74). Given that the maximum univariate correlation between the descriptors and ΔΔE(π-σ) reached only 0.76, further multivariate linear regression analyses were subsequently conducted.
Prior to model training, the values of the selected descriptors were normalized. Subsequently, descriptors were defined as independent variables and ΔΔE(π-σ) as the dependent variable. Stepwise regression analysis was employed to eliminate irrelevant descriptors and to construct a multivariate linear regression model, as illustrated in Figure 4. In stepwise regression analysis, the p-value is usually used to test the independent variables added at each step and to assess the significance of their coefficients. The magnitude of the p-value is typically used to determine whether to retain the independent variable in the model. The closer the p-value is to 0, the higher the level of significance, indicating that the observed coefficient is statistically significant. According to Figure 4, it is evident that the p-values of descriptors NMRCβ, B5, and L were 0.000, 0.003, and 0.000, indicating significant influences of these three descriptors on the poisoning effect. Therefore, a multivariate linear regression model based on these variables for ΔΔE(π-σ) was established. The corresponding equation was constructed as follows:
ΔΔEpre = 27.9 − 21.7NMRCβ + 8.9B5 − 13.0L
It is important to highlight that these models utilize coefficients to reflect the significance of the descriptors. Positive and negative coefficients indicate positive and negative correlations, respectively. This result suggests that a larger NMRCβ value enhances the coordination capability of the Cβ atom with the metal center, favoring the formation of stable complexes and consequently resulting in a smaller ΔΔE(π-σ), indicating a weaker poisoning effect. Similarly, the maximum dimension perpendicular to the ligand length (B5) shows a positive correlation, while the ligand length (L) shows a negative correlation. This result indicates that as the L increases and, simultaneously, the B5 decreases, the coordination capacity between the heteroatom and the metal center weakens, thereby reducing the poisoning effect. Furthermore, according to the corresponding equation, the model predicted values ΔΔE(π-σ)pre for each monomer could be obtained, as shown in Figure 4a.
To evaluate the model’s fitting performance, a correlation fitting was conducted between the DFT-computed ΔΔE(π-σ) values and the model-predicted values, as depicted in Figure 5a. This analysis demonstrated a high coefficient of determination (R2 = 0.88), indicating the model’s robust performance. Additionally, with Root Mean Square Deviation (RMSD) and Mean Absolute Error (MAE) values of 1.63 kcal/mol and 1.94 kcal/mol, respectively, the model proves to be relatively stable. To further minimize the risk of overfitting, a fivefold cross-validation was performed, as illustrated in Figure 5b, resulting in an R2 of 0.86, with RMSE = 2.50 kcal/mol and MAE = 2.11 kcal/mol, confirming that the model does not exhibit overfitting.
To assess the extrapolation capability of the model, external data sources distinct from the initial data sets were employed for comparison and validation, as depicted in Figure 6. Five polar monomers (Figure 6a), labeled as 1′–5′ and not included in the initial training set, were selected for this external validation, and the parameter values for the three descriptors were compiled in Table S3. As shown in Figure 6b, the model demonstrated robust performance on these monomers, with an R2 of 0.91, an RMSE of 1.89 kcal/mol, and an MAE of 2.22 kcal/mol, indicating strong extrapolation capabilities.
After validating the stability of our model through various means, we can conclude that the poisoning ability of polar monomers increases as their NMR and L become more negative and B5 becomes more positive. Therefore, in future polymerization processes involving polar monomers, the poisoning capability of monomers toward metal centers can be roughly estimated by testing only these few variables.
While certain polar monomers may poison the metal center in insertion reactions, other polar monomers are capable of undergoing such reactions without significant disruption. The insertion energy barriers for the remaining 30 monomers (16–45), which demonstrate a weaker poisoning effect, have been computed. The monomer coordinate structures of the four insertion paths are compiled in the SI. The structures of these monomers are depicted in Figure S2, the calculated results for the insertion barriers are shown in Table S4, and the coordinate structures are organized in the Supporting Information. DFT calculations were performed on various conformations of each coordination complex to ascertain the insertion barriers ΔG≠ (1,2-re, 1,2-si, 2,1-re, and 2,1-si), with the insertion reaction mechanism illustrated in Figure 7a. A higher ΔG≠ value indicates greater difficulty in initiating the insertion reaction process. For example, monomer No.16, as shown in Figure 7b, had its coordination energy, insertion barriers, and product energy calculated separately for the four insertion pathways. The computational results reveal that among these pathways, the 2,1-si pathway exhibits the lowest barrier, at just 8.6 kcal/mol, while the barriers for the remaining pathways are 15.0 kcal/mol, 12.3 kcal/mol, and 18.1 kcal/mol, respectively. This highlights that, from a kinetic perspective, the 2,1-si pathway is the most favorable for monomer insertion mediated by the active species.
Analogously, the favorable insertion behaviors of the other 29 polar monomers (17–45) were calculated using DFT, with the results listed in Tables S4 and S5.
Similarly, a stepwise regression analysis was conducted on these 30 monomers based on their ΔG≠ values and the previously selected 15 descriptors through multivariate linear regression (MLR) analysis, resulting in Equation (2).
ΔG≠pre = 6.55 + 4.4Qα + 6.8NMRCβ + 7.3HOMO
As depicted in Equation (2) and Figure 8a, the multivariate linear regression model identified the top three descriptors with the highest positive correlations: QCα, NMRCβ, and HOMO. The HOMO descriptor emerged as the most influential in the model, indicating that a higher HOMO energy value increases the monomer’s susceptibility to electron loss. Additionally, increases in QCα and NMRCβ strengthen the coordination ability between the Cα=Cβ bond and the metal center. The combined influence of these descriptors leads to a decrease in the energy of the π-complex, thereby increasing the insertion energy barrier for the monomer. Subsequent linear fitting demonstrated a robust correlation between the predicted and calculated ΔG≠ values across the 30 datasets, as shown in Figure 7b, confirming a satisfactory correlation with an R2 of 0.87, an RMSE of 0.96 kcal/mol, and an MAE of 0.80 kcal/mol.
We also conducted a fivefold cross-validation, as shown in Figure 9a, which resulted in an R2 of 0.82, with an RMSE of 1.17 kcal/mol and an MAE of 0.95 kcal/mol. This indicates that the model is robust and not subject to overfitting. Furthermore, to assess the model’s extrapolation capabilities, we selected an additional set of 10 monomers (6′–15′), as depicted in Figure 9b,c. The parameter values for the descriptors are summarized in Table S6. Notably, the model exhibited an impressive R2 of 0.88, along with an RMSE of 1.36 kcal/mol and an MAE of 1.22 kcal/mol, thereby confirming its strong extrapolation ability. These theoretical analyses provide further validation for the model’s rationale, reinforcing the reliability of the constructed multiple linear regression equation.
After confirming the stability of our model through a variety of methods, we can infer that the insertion energy barrier for chain initiation of polar monomers escalates as their QCα, NMRCβ, and HOMO increases. Consequently, in the forthcoming insertion reaction incorporating polar monomers, the insertion energy barrier for chain initiation toward metal centers can be approximately gauged by examining these select variables.

3. Computational Details

All DFT computations were carried out utilizing the Gaussian 16 software package [44]. All geometry optimizations and frequency calculations were performed using the B3LYP functional [45,46,47], which includes the D3 version of Grimme’s empirical dispersion correction [48,49]. The SDD basis set [50,51,52] was applied for Sc, and the 6-31G(d) basis set was used for nonmetal atoms. To achieve more accurate relative energies, single-point energies were calculated using the M06 functional [53] in combination with a larger basis set (SDD basis set for Sc and 6-311+G (d, p) for the remaining atoms) and the PCM model [54], which was used to account for the solvation effect of toluene. The generation of correlation color maps and the execution of multivariate linear regression analysis were carried out using Matlab software [55]. Furthermore, sterimol parameters were computed on the basis of optimized structures by using the Molecular Modeling Pro software [56].

4. Conclusions

In summary, we employed a combined strategy of DFT calculations and multivariate linear regression (MLR) analysis to investigate the chain initiation of polar monomer insertion reaction mediated by a scandium complex. The poisoning effect and the ease of insertion of polar monomer, which is concerned with insertion reaction chain initiation, have been investigated, and the MLR models were constructed, respectively. It was found that the electronic descriptors NMR of Cβ (NMRCβ) and steric descriptors sterimol parameters B5 and L play an important role in the poisoning effect. In the case of the ease of monomer insertion described by the insertion energy barrier, it was observed that electronic descriptors NPA charge of Cα (QCα), NMR of Cβ (NMRCβ), and frontier orbital energy (HOMO) jointly played a role, while the impact of steric hindrance was nonsignificant. The external validation results indicate that such predictive models demonstrate a certain degree of extrapolation capability. These results demonstrated that the combination of DFT calculations and MLR analysis could serve as an effective strategy to investigate polar monomer insertion reaction systems mediated by the Sc complex. It is expected that the results will hopefully offer some useful hints for the development of a more efficient rare earth metal complex in the polymerization of polar monomers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/inorganics12060172/s1, Figure S1: 15 sets polar monomers for the analysis for the poisoning effect (ΔΔE). Figure S2. 30 sets polar monomers for the analysis for the ease of monomer insertion (ΔG≠). Table S1: Electronic descriptors and ΔΔE for 1-15 polar monomers (free energies in kcal/mol). Table S2. Steric descriptors and ΔΔE for 1-15 polar monomers (free energies in kcal/mol). Table S3. Descriptors and ΔΔE for 1’-5’ polar monomers (free energies in kcal/mol). Table S4. Electronic descriptors and ΔG≠ for 16-45 polar monomers (free energies in kcal/mol). Table S5. Steric descriptors and ΔG≠ for 16-45 polar monomers (free energies in kcal/mol). Table. S6. Descriptors and ΔG≠ for 6’-15’ polar monomers (free energies in kcal/mol).; Optimized Cartesian coordinates of all stationary points together with their single-point energies (a.u.) in solution and the imaginary frequencies (cm−1) of transition states (XYZ).

Author Contributions

Conceptualization, X.W., G.Z. and Y.L.; Methodology, X.W., G.Z. and Y.L.; Software, K.R.; Formal analysis, K.R.; Investigation, X.W.; Data curation, X.W., G.Z. and Y.L.; Writing—original draft, X.W.; Writing—review & editing, X.W., K.R., W.Z., G.Z. and Y.L.; Supervision, W.Z., G.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NSFC (Nos. 22071015) and the Open Funding Project of Key Laboratory of Functional Polymer Materials, Ministry of Education, Nankai University (KLFPM202301). The authors also thank the Network and Information Center of Dalian University of Technology for part of computational resources.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author/s.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dong, J.; Hu, Y. Design and synthesis of structurally well-defined functional polyolefins via transition metal-mediated olefin polymerization chemistry. Coord. Chem. Rev. 2006, 250, 47–65. [Google Scholar] [CrossRef]
  2. Rünzi, T.; Mecking, S. Saturated polar-substituted polyethylene elastomers from insertion polymerization. Adv. Funct. Mater. 2014, 24, 387–395. [Google Scholar] [CrossRef]
  3. Dai, S.; Chen, C. Palladium-catalyzed direct synthesis of various branched, carboxylic acid-functionalized polyolefins: Characterization, derivatization, and properties. Macromolecules 2018, 51, 6818–6824. [Google Scholar] [CrossRef]
  4. Na, Y.; Dai, S.; Chen, C. Direct synthesis of polar-functionalized linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE). Macromolecules 2018, 51, 4040–4048. [Google Scholar] [CrossRef]
  5. Sui, X.; Hong, C.; Pang, W.; Chen, C. Unsymmetrical α-diimine pall adium catalysts and their properties in olefin (co)polymerization. Mater. Chem. Front. 2017, 1, 967–972. [Google Scholar] [CrossRef]
  6. Huang, J.; Liu, Z.; Cui, D.; Liu, X. Precisely Controlled Polymerization of Styrene and Conjugated Dienes by Group 3 Single-Site Catalysts. ChemCatChem 2018, 10, 42–61. [Google Scholar] [CrossRef]
  7. Liu, B.; Qiao, K.; Fang, J.; Wang, T.; Wang, Z.; Liu, D.; Xie, Z.; Maron, L.; Cui, D. Mechanism and Effect of Polar Styrenes on Scandium-Catalyzed Copolymerization with Ethylene. Angew. Chem. Int. Ed. 2018, 57, 14896–14901. [Google Scholar] [CrossRef]
  8. Liu, D.; Wang, M.; Wang, Z.; Wu, C.; Pan, Y.; Cui, D. Stereoselective Copolymerization of Unprotected Polar and Nonpolar Styrenes by an Yttrium Precursor: Control of Polar-Group Distribution and Mechanism. Angew. Chem. Int. Ed. 2017, 56, 2714–2719. [Google Scholar] [CrossRef]
  9. Wang, T.; Liu, D.; Cui, D. Highly Syndioselective Coordination (Co)Polymerization of ortho-Fluorostyrene. Macromolecules 2019, 52, 9555–9560. [Google Scholar] [CrossRef]
  10. Liu, D.; Yao, C.; Wang, R.; Wang, M.; Wang, Z.; Wu, C.; Lin, F.; Li, S.; Wan, X.; Cui, D. Highly Isoselective Coordination Polymerization of Ortho-Methoxystyrene with beta-Diketiminato Rare-Earth-Metal Precursors. Angew. Chem. Int. Ed. 2015, 54, 5205–5209. [Google Scholar] [CrossRef]
  11. Xu, T.; Liu, J.; Lu, X. Highly Active Half-Metallocene Yttrium Catalysts for Living and Chemoselective Polymerization of Allyl Methacrylate. Macromolecules 2015, 48, 7428–7434. [Google Scholar] [CrossRef]
  12. Liu, Z.; Liu, B.; Zhao, Z.; Cui, D. Chemo- and Stereoselective Polymerization of Polar Divinyl Monomers by Rare-Earth Complexes. Macromolecules 2021, 54, 3181–3190. [Google Scholar] [CrossRef]
  13. Yan, C.; Liu, Z.; Xu, T. Regioselective, stereoselective, and living polymerization of divinyl pyridine monomers using rare earth catalysts. Polym. Chem. 2020, 11, 2044–2052. [Google Scholar] [CrossRef]
  14. Xu, P.; Wu, L.; Dong, L.; Xu, X. Chemoselective Polymerization of Polar Divinyl Monomers with Rare-Earth/Phosphine Lewis Pairs. Molecules 2018, 23, 360. [Google Scholar] [CrossRef]
  15. Nishiura, M.; Guo, F.; Hou, Z. Half-Sandwich Rare-Earth-Catalyzed Olefin Polymerization, Carbometalation, and Hydroarylation. Acc. Chem. Res. 2015, 48, 2209–2220. [Google Scholar] [CrossRef]
  16. Nishiura, M.; Hou, Z. Novel polymerization catalysts and hydride clusters from rare-earth metal dialkyls. Nat. Chem. 2010, 2, 257–268. [Google Scholar] [CrossRef]
  17. Wang, C.; Luo, G.; Nishiura, M.; Song, G.; Yamamoto, A.; Luo, Y.; Hou, Z. Heteroatom-assisted olefin polymerization by rare-earth metal catalysts. Sci. Adv. 2017, 3, e1701011. [Google Scholar] [CrossRef]
  18. Chen, J.; Gao, Y.; Wang, B.; Lohr, T.; Marks, T. Scandium-catalyzed self-assisted polar co-monomer enchainment in ethylene polymerization. Angew. Chem. Int. Ed. 2017, 56, 15964–15968. [Google Scholar] [CrossRef]
  19. Yamamoto, A.; Nishiura, M.; Oyamada, J.; Koshino, H.; Hou, Z. Scandium-Catalyzed Syndiospecific Chain-Transfer Polymerization of Styrene Using Anisoles as a Chain Transfer Agent. Macromolecules 2016, 49, 2458–2466. [Google Scholar] [CrossRef]
  20. Guo, F.; Nishiura, M.; Koshino, H.; Hou, Z. Cycloterpolymerization of 1,6-Heptadiene with Ethylene and Styrene Catalyzed by a THF-Free Half-Sandwich Scandium Complex. Macromolecules 2011, 44, 2400–2403. [Google Scholar] [CrossRef]
  21. Wang, H.; Zhao, Y.; Nishiura, M.; Yang, Y.; Luo, G.; Luo, Y.; Hou, Z. Scandium-Catalyzed Regio- and Stereoselective Cyclopolymerization of Functionalized α,ω-Dienes and Copolymerization with Ethylene. J. Am. Chem. Soc. 2019, 141, 12624–12633. [Google Scholar] [CrossRef]
  22. Fu, T.; Jiang, L.; Sun, H.; Hou, Z.; Guo, F. Scandium-catalyzed stereoselective block and alternating copolymerization of diphenylphosphinostyrenes and isoprene. Polym. Chem. 2022, 13, 3498–3505. [Google Scholar]
  23. Guo, F.; Jiao, N.; Jiang, L.; Li, Y.; Hou, Z. Scandium-Catalyzed Syndiospecific Polymerization of Halide-Substituted Styrenes and Their Copolymerization with Styrene. Macromolecules 2017, 50, 8398–8405. [Google Scholar] [CrossRef]
  24. Yamamoto, A.; Nishiura, M.; Yang, Y.; Hou, Z. Cationic Scandium Anisyl Species in Styrene Polymerization Using Anisole and N,N-Dimethyl-o-toluidine as Chain-Transfer Agents. Organometallics 2017, 36, 4635–4642. [Google Scholar] [CrossRef]
  25. Wang, H.; Wu, X.; Yang, Y.; Nishiura, M.; Hou, Z. Co-syndiospecific Alternating Copolymerization of Functionalized Propylenes and Styrene by Rare-Earth Catalysts. Angew. Chem. Int. Ed. 2020, 59, 7173–7177. [Google Scholar] [CrossRef]
  26. Shi, Z.; Guo, F.; Li, Y.; Hou, Z. Synthesis of Amino-Containing Syndiotactic Polystyrene as Efficient Polymer Support for Palladium Nanoparticles. J. Polym. Sci. Part A Polym. Chem. 2015, 53, 5–9. [Google Scholar] [CrossRef]
  27. Pan, Y.; Rong, W.; Jian, Z.; Cui, D. Ligands Dominate Highly Syndioselective Polymerization of Styreneby Using Constrained-geometry-configuration Rare-earth Metal Precursors. Macromolecules 2012, 45, 1248–1253. [Google Scholar] [CrossRef]
  28. Wang, Z.; Liu, D.; Cui, D. Statistically Syndioselective Coordination (Co)polymerization of 4-Methylthiostyrene. Macromolecules 2016, 49, 781–787. [Google Scholar] [CrossRef]
  29. Liu, D.; Wang, R.; Wang, M.; Wu, C.; Wang, Z.; Yao, C.; Liu, B.; Wan, X.; Cui, D. Syndioselective coordination polymerization of unmasked polar methoxystyrenes using a pyridenylmethylene fluorenyl yttrium precursor. Chem. Commun. 2015, 51, 4685–4688. [Google Scholar] [CrossRef]
  30. Zhong, Y.; Wu, Y.; Cui, D. Highly Syndiotactic Coordination (Co)polymerization of para-Methylselenostyrene. Macromolecules 2021, 54, 1754–1759. [Google Scholar] [CrossRef]
  31. Jiang, Y.; Li, S.; Cui, D. Synthesis of Poly(Ethyl vinyl ether) with high molecular weight by rare-earth metal cationic catalysts. Polymer 2021, 226, 123790. [Google Scholar] [CrossRef]
  32. Harper, K.; Vilardi, S.; Sigman, M. Prediction of catalyst and substrate performance in the enantioselective propargylation of aliphatic ketones by a multidimensional model of steric effects. J. Am. Chem. Soc. 2013, 135, 2482–2485. [Google Scholar] [CrossRef]
  33. Mougel, V.; Santiago, C.; Zhizhko, P.; Bess, E.; Varga, J.; Frater, G.; Sigman, M.; Copéret, C. Quantitatively analyzing metathesis catalyst activity and structural features in silica-supported tungsten imido−alkylidene complexes. J. Am. Chem. Soc. 2015, 137, 6699–6704. [Google Scholar] [CrossRef]
  34. Sigman, M.; Harper, K.; Bess, E.; Milo, A. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Acc. Chem. Res. 2016, 49, 1292–1301. [Google Scholar] [CrossRef]
  35. Durand, D.; Fey, N. Computational ligand descriptors for catalyst design. Chem. Rev. 2019, 119, 6561–6594. [Google Scholar] [CrossRef]
  36. Zhao, Y.; Luo, G.; Wang, X.; Kang, X.; Cui, D.; Hou, Z.; Luo, Y. DFT studies on the polymerization of Functionalized styrenes catalyzed by rare-earth-metal complexes: Factors affecting C-H activation relevant to step-growth polymerization. Organometallics 2018, 37, 3210–3218. [Google Scholar] [CrossRef]
  37. Zhao, Y.; Luo, G.; Kang, X.; Guo, F.; Zhu, X.; Zhen, R.; Hou, Z.; Luo, Y. “C-H…π Interaction” regulates the stereoselectivity in olefin polymerization. Chem. Commun. 2019, 55, 6689–6692. [Google Scholar] [CrossRef]
  38. Yang, J.; Yu, Y.; Qu, J.; Luo, Y. Effects of nucleophilic ligands on the chain initiation efficiency of polar monomer polymerizations catalyzed by 2-methoxyethylaminobis(phenolate)yttrium complexes: A DFT study. Dalton Trans. 2017, 46, 16993–16999. [Google Scholar] [CrossRef]
  39. Shi, X.; Nishiura, M.; Hou, Z. Simultaneous chain-growth and step-growth polymerization of methoxystyrenes by rare-earth catalysts. Angew. Chem. Int. Ed. 2016, 55, 14812–14817. [Google Scholar] [CrossRef]
  40. Guo, F.; Jiang, L.; Diao, K.; Hou, Z. Stereoselective copolymerization of 4-(N,N-diphenylamino)styrene and isoprene by a C5H5-ligated scandium catalyst: Synthesis of amino-functionalized crystalline styrenic thermoplastic elastomers. Polym. Chem. 2020, 11, 1314–1320. [Google Scholar]
  41. Chai, Y.; Wu, C.; Liu, D.; Run, M.; Cui, D. Self-assisted stereospecific polymerization of unmasked polar 4-methylthio-1-butene. Sci. China Chem. 2019, 62, 761–766. [Google Scholar] [CrossRef]
  42. Zhao, W.; Liu, Z.; Zhao, Y.; Luo, Y. Multivariate Linear Regression Models to Predict Monomer Poisoning Effect in Ethylene/Polar Monomer Copolymerization Catalyzed by Late Transition Metals. Inorganics 2022, 10, 26. [Google Scholar] [CrossRef]
  43. Lu, H.; Kang, X.; Yu, H.; Zhang, W.; Luo, Y. Using a single complex to predict the reaction energy profile: A case study of Pd/Ni-catalyzed ethylene polymerization. Dalton Trans. 2023, 52, 14790–14796. [Google Scholar] [CrossRef]
  44. Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Petersson, G.A.; Nakatsuji, H.; et al. Gaussian 16, Revision A.03; Gaussian, Inc.: Wallingford, CT, USA, 2016. [Google Scholar]
  45. Lee, C.; Yang, W.; Parr, R.G. Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 1988, 37, 785–789. [Google Scholar] [CrossRef]
  46. Becke, A.D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 1988, 38, 3098–3100. [Google Scholar] [CrossRef]
  47. Becke, A.D. A new mixing of Hartree Fock and local density functional theories. J. Chem. Phys. 1993, 98, 1372–1377. [Google Scholar] [CrossRef]
  48. Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010, 132, 154104. [Google Scholar] [CrossRef]
  49. Grimme, S.; Ehrlich, S.; Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. J. Chem. Phys. 2011, 32, 1456–1465. [Google Scholar] [CrossRef]
  50. Dolg, M.; Wedig, U.; Stoll, H.; Preuss, H. Energy-adjusted ab initio pseudopotentials for the first row transition elements. J. Chem. Phys. 1987, 86, 866–872. [Google Scholar] [CrossRef]
  51. Schwerdtfeger, P.; Dolg, M.; Schwarz, W.; Bowmaker, G.; Boyd, P. Relativistic effects in gold Chemistry. I. Diatomic gold compounds. J. Chem. Phys. 1989, 91, 1762–1774. [Google Scholar] [CrossRef]
  52. Andrae, D.; Haußermann, U.; Dolg, M.; Stoll, H.; Preuss, H. Energy-adjusted ab initio pseudopotentials for the second and third row transition elements. Theor. Chim. Acta 1990, 77, 123–141. [Google Scholar] [CrossRef]
  53. Zhao, Y.; Truhlar, D. Benchmark energetic data in a model system for Grubbs II metathesis catalysis and their use for the development, assessment, and validation of electronic structure methods. J. Chem. Theory Comput. 2009, 5, 324–333. [Google Scholar] [CrossRef]
  54. Ittel, S.; Johnson, L.; Brookhart, M. Late-metal catalysts for ethylene homo-and copolymerization. Chem. Rev. 2000, 100, 1169–1204. [Google Scholar] [CrossRef]
  55. Guo, J.; Minko, Y.; Santiago, C.; Santiago, C.; Sigman, M. Developing comprehensive computational parameter sets to describe the performance of pyridine-oxazoline and related ligands. ACS Catal. 2017, 7, 4144–4151. [Google Scholar] [CrossRef]
  56. Cramer, R., III. Quantitative drug design. Annu. Rep. Med. Chem. 1976, 11, 301–310. [Google Scholar]
Inorganics 12 00172 sch001
Scheme 1. Insertion reaction and poisoning effect mechanism of polar monomers meditated by rare-earth metal complex.
Scheme 1. Insertion reaction and poisoning effect mechanism of polar monomers meditated by rare-earth metal complex.
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Inorganics 12 00172 g001
Figure 1. Several different polar monomer structures. The heteroatoms coordinated to the metal center in the polar monomer are marked in pink.
Figure 1. Several different polar monomer structures. The heteroatoms coordinated to the metal center in the polar monomer are marked in pink.
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Figure 2. The definition of atoms and substituents, along with schematic representation of descriptors.
Figure 2. The definition of atoms and substituents, along with schematic representation of descriptors.
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Figure 3. Correlation color map: the last line corresponds to the single-parameter correlations of the ΔΔE(π-σ).
Figure 3. Correlation color map: the last line corresponds to the single-parameter correlations of the ΔΔE(π-σ).
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Figure 4. (a) The structures of 15 set polar monomers, along with the blue representing the calculated ΔΔE(π-σ) and the pink representing the predicted ΔΔE(π-σ)pre. (b) Analysis of 15 monomer structures using stepwise regression analysis of ΔΔE(π-σ), including coefficients and p-values. The blue horizontal lines represent the three descriptors with the highest correlation, namely NMRCβ, B5, and L.
Figure 4. (a) The structures of 15 set polar monomers, along with the blue representing the calculated ΔΔE(π-σ) and the pink representing the predicted ΔΔE(π-σ)pre. (b) Analysis of 15 monomer structures using stepwise regression analysis of ΔΔE(π-σ), including coefficients and p-values. The blue horizontal lines represent the three descriptors with the highest correlation, namely NMRCβ, B5, and L.
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Figure 5. (a) Plot of computed vs. predicted ΔΔE(π-σ) using the multivariate linear regression model. (b) Linear fitting plot for fivefold cross-validation method.
Figure 5. (a) Plot of computed vs. predicted ΔΔE(π-σ) using the multivariate linear regression model. (b) Linear fitting plot for fivefold cross-validation method.
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Figure 6. (a) Five polar monomers for external verification. (b) Plot of computed vs. predicted ΔΔE(π-σ) for external verification.
Figure 6. (a) Five polar monomers for external verification. (b) Plot of computed vs. predicted ΔΔE(π-σ) for external verification.
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Figure 7. (a) Mechanism of the monomer insertion. (b) Computed energy profiles for rare-earth metal complex mediated various insertion manners of vinyl chloride (energies in kcal/mol).
Figure 7. (a) Mechanism of the monomer insertion. (b) Computed energy profiles for rare-earth metal complex mediated various insertion manners of vinyl chloride (energies in kcal/mol).
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Figure 8. (a) Application of stepwise regression analysis of ΔG≠. (b) Plot of computed vs. predicted ΔG≠ for external verification.
Figure 8. (a) Application of stepwise regression analysis of ΔG≠. (b) Plot of computed vs. predicted ΔG≠ for external verification.
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Figure 9. (a) Linear fitting plot for fivefold cross-validation method. (b) Plot of computed vs. predicted ΔG≠ for external verification. (c) Ten polar monomers for external verification.
Figure 9. (a) Linear fitting plot for fivefold cross-validation method. (b) Plot of computed vs. predicted ΔG≠ for external verification. (c) Ten polar monomers for external verification.
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Wen, X.; Ren, K.; Zhang, W.; Zhou, G.; Luo, Y. Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex. Inorganics 2024, 12, 172. https://doi.org/10.3390/inorganics12060172

AMA Style

Wen X, Ren K, Zhang W, Zhou G, Luo Y. Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex. Inorganics. 2024; 12(6):172. https://doi.org/10.3390/inorganics12060172

Chicago/Turabian Style

Wen, Xin, Kaipai Ren, Wenzhen Zhang, Guangli Zhou, and Yi Luo. 2024. "Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex" Inorganics 12, no. 6: 172. https://doi.org/10.3390/inorganics12060172

APA Style

Wen, X., Ren, K., Zhang, W., Zhou, G., & Luo, Y. (2024). Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex. Inorganics, 12(6), 172. https://doi.org/10.3390/inorganics12060172

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