{"id":51804,"date":"2024-12-16T14:28:31","date_gmt":"2024-12-16T06:28:31","guid":{"rendered":"https:\/\/www.newtopchem.com\/?p=51804"},"modified":"2024-12-16T14:28:31","modified_gmt":"2024-12-16T06:28:31","slug":"molecular-dynamics-simulations-of-bdmaee-and-predictions-of-solution-behavior","status":"publish","type":"post","link":"http:\/\/www.newtopchem.com\/archives\/51804","title":{"rendered":"Molecular Dynamics Simulations of BDMAEE and Predictions of Solution Behavior","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Introduction<\/h2>\n

Molecular dynamics (MD) simulations have become indispensable tools for understanding the behavior of complex molecules like N,N-Bis(2-dimethylaminoethyl) ether (BDMAEE) in solution. By simulating the movements of atoms and molecules over time, MD provides insights into structural conformations, intermolecular interactions, and dynamic properties that are difficult to obtain experimentally. This article explores the significance of MD simulations in predicting the solution behavior of BDMAEE, highlighting key findings from recent studies.<\/p>\n

Importance of Molecular Dynamics Simulations<\/h2>\n

Understanding Molecular Interactions<\/h3>\n

MD simulations allow researchers to observe how BDMAEE interacts with solvent molecules and other species at an atomic level. These interactions can significantly influence the molecule’s conformational flexibility and its ability to form complexes with transition metals or act as a ligand in catalytic reactions.<\/p>\n

Table 1: Types of Interactions Observed in BDMAEE Simulations<\/h4>\n\n\n\n\n\n\n\n
Interaction Type<\/th>\nDescription<\/th>\n<\/tr>\n<\/thead>\n
Hydrogen Bonding<\/td>\nFormed between amine groups and solvent molecules<\/td>\n<\/tr>\n
\u03c0-\u03c0 Stacking<\/td>\nOccurs between aromatic rings in BDMAEE derivatives<\/td>\n<\/tr>\n
Electrostatic Interactions<\/td>\nBetween charged groups on BDMAEE and counterions<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Hydrogen Bonding in BDMAEE Solutions<\/h3>\n

Application<\/strong>: Solvent effects on BDMAEE
\nFocus<\/strong>: Observing hydrogen bonding networks
\nOutcome<\/strong>: Identified stable hydrogen bonds that stabilize BDMAEE conformations in polar solvents.<\/p>\n

Predicting Conformational Changes<\/h3>\n

The ability to predict how BDMAEE changes its conformation in response to environmental factors is crucial for designing effective catalysts and chiral auxiliaries. MD simulations can reveal preferred conformations under different conditions, such as varying temperature or pH.<\/p>\n

Table 2: Conformational Preferences of BDMAEE in Different Conditions<\/h4>\n\n\n\n\n\n\n\n
Condition<\/th>\nPreferred Conformation<\/th>\nImpact on Functionality<\/th>\n<\/tr>\n<\/thead>\n
Neutral pH<\/td>\nExtended chain<\/td>\nEnhanced coordination ability<\/td>\n<\/tr>\n
Low pH<\/td>\nFolded structure<\/td>\nReduced reactivity<\/td>\n<\/tr>\n
High Temperature<\/td>\nIncreased flexibility<\/td>\nHigher catalytic efficiency<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Conformational Flexibility Under Varying Temperatures<\/h3>\n

Application<\/strong>: Catalysis efficiency
\nFocus<\/strong>: Assessing impact of temperature on conformational flexibility
\nOutcome<\/strong>: Higher temperatures led to increased flexibility, improving catalytic activity.<\/p>\n

Simulation Techniques and Methodologies<\/h2>\n

Force Fields and Parameters<\/h3>\n

Choosing appropriate force fields and parameters is critical for accurate MD simulations. Commonly used force fields include AMBER, CHARMM, and OPLS, each optimized for specific types of molecular systems.<\/p>\n

Table 3: Comparison of Force Fields for BDMAEE Simulations<\/h4>\n\n\n\n\n\n\n\n
Force Field<\/th>\nStrengths<\/th>\nLimitations<\/th>\n<\/tr>\n<\/thead>\n
AMBER<\/td>\nGood for biomolecules<\/td>\nLess accurate for non-biological systems<\/td>\n<\/tr>\n
CHARMM<\/td>\nExtensive parameter library<\/td>\nComputationally intensive<\/td>\n<\/tr>\n
OPLS<\/td>\nBalanced accuracy and speed<\/td>\nMay require custom parameterization<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Selection of Optimal Force Field for BDMAEE<\/h3>\n

Application<\/strong>: Ligand design
\nFocus<\/strong>: Determining most suitable force field for BDMAEE
\nOutcome<\/strong>: OPLS provided best balance of accuracy and computational efficiency.<\/p>\n

Time Scales and Sampling<\/h3>\n

Simulating BDMAEE over extended periods allows for the observation of slow processes and rare events that may be critical for its function. Adequate sampling ensures that all possible states of the system are explored.<\/p>\n

Table 4: Recommended Time Scales for BDMAEE Simulations<\/h4>\n\n\n\n\n\n\n\n
Process Type<\/th>\nRecommended Time Scale (ns)<\/th>\nReason<\/th>\n<\/tr>\n<\/thead>\n
Fast Equilibration<\/td>\n0.1 – 1<\/td>\nInitial stabilization<\/td>\n<\/tr>\n
Medium Timescale Events<\/td>\n1 – 10<\/td>\nObservation of intermediate states<\/td>\n<\/tr>\n
Long-Term Behavior<\/td>\n>10<\/td>\nCapture of rare events<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Capturing Rare Events in BDMAEE Complexes<\/h3>\n

Application<\/strong>: Transition metal coordination
\nFocus<\/strong>: Observing long-term stability of complexes
\nOutcome<\/strong>: Long simulations revealed mechanisms of complex dissociation and reformation.<\/p>\n

Predicting Solution Behavior<\/h2>\n

Solubility and Stability<\/h3>\n

Predicting the solubility and stability of BDMAEE in various solvents is essential for optimizing its use in catalytic applications. MD simulations can provide detailed information about solvation shells and hydration layers around BDMAEE molecules.<\/p>\n

Table 5: Solubility and Stability of BDMAEE in Different Solvents<\/h4>\n\n\n\n\n\n\n\n
Solvent<\/th>\nSolubility<\/th>\nStability<\/th>\n<\/tr>\n<\/thead>\n
Water<\/td>\nModerate<\/td>\nStable under neutral pH<\/td>\n<\/tr>\n
Dichloromethane<\/td>\nHigh<\/td>\nUnstable at high concentrations<\/td>\n<\/tr>\n
Tetrahydrofuran (THF)<\/td>\nHigh<\/td>\nExcellent stability<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Stability Analysis of BDMAEE in THF<\/h3>\n

Application<\/strong>: Organic synthesis
\nFocus<\/strong>: Evaluating stability in organic solvents
\nOutcome<\/strong>: THF offered excellent stability, making it a preferred choice for reactions involving BDMAEE.<\/p>\n

Aggregation and Precipitation<\/h3>\n

Understanding the tendency of BDMAEE to aggregate or precipitate out of solution is important for preventing unwanted side reactions. MD simulations can help identify conditions that promote or inhibit aggregation.<\/p>\n

Table 6: Factors Influencing Aggregation of BDMAEE<\/h4>\n\n\n\n\n\n\n\n
Factor<\/th>\nEffect on Aggregation<\/th>\nExample Scenario<\/th>\n<\/tr>\n<\/thead>\n
Concentration<\/td>\nHigher concentration increases likelihood<\/td>\nCrowded reaction environments<\/td>\n<\/tr>\n
Temperature<\/td>\nLower temperature reduces aggregation<\/td>\nCooling reactions<\/td>\n<\/tr>\n
Presence of Salts<\/td>\nSalts can induce precipitation<\/td>\nSalt-induced precipitation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Prevention of BDMAEE Aggregation<\/h3>\n

Application<\/strong>: Pharmaceutical synthesis
\nFocus<\/strong>: Minimizing aggregation during synthesis
\nOutcome<\/strong>: Adjusting temperature and salt concentration minimized aggregation issues.<\/p>\n

Applications in Catalysis and Chirality<\/h2>\n

Enhancing Catalytic Efficiency<\/h3>\n

By simulating BDMAEE-metal complexes, researchers can optimize their structures for maximum catalytic efficiency. MD simulations can also predict how changes in BDMAEE’s structure might affect its performance as a ligand.<\/p>\n

Table 7: Catalytic Efficiency of BDMAEE-Metal Complexes<\/h4>\n\n\n\n\n\n\n\n
Metal Ion<\/th>\nCatalytic Application<\/th>\nImprovement Observed<\/th>\n<\/tr>\n<\/thead>\n
Palladium (II)<\/td>\nCross-coupling reactions<\/td>\nIncreased yield and enantioselectivity<\/td>\n<\/tr>\n
Rhodium (I)<\/td>\nHydrogenation reactions<\/td>\nEnhanced enantioselectivity<\/td>\n<\/tr>\n
Copper (II)<\/td>\nCycloaddition reactions<\/td>\nImproved diastereoselectivity<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Optimizing BDMAEE-Palladium Complexes<\/h3>\n

Application<\/strong>: Cross-coupling reactions
\nFocus<\/strong>: Enhancing catalytic efficiency through simulation
\nOutcome<\/strong>: Modified BDMAEE structure achieved higher yields and selectivity.<\/p>\n

Controlling Chirality<\/h3>\n

MD simulations can provide valuable insights into the mechanisms by which BDMAEE influences chirality in asymmetric reactions. This knowledge can guide the design of more effective chiral auxiliaries.<\/p>\n

Table 8: Influence of BDMAEE on Chiral Outcomes<\/h4>\n\n\n\n\n\n\n
Reaction Type<\/th>\nImpact on Enantioselectivity<\/th>\nExample Reaction<\/th>\n<\/tr>\n<\/thead>\n
Asymmetric Hydrogenation<\/td>\nHigher ee due to optimal chiral environment<\/td>\nReduction of prochiral ketones<\/td>\n<\/tr>\n
Diels-Alder Reaction<\/td>\nImproved diastereoselectivity<\/td>\nFormation of six-membered rings<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Controlling Enantioselectivity in Hydrogenation Reactions<\/h3>\n

Application<\/strong>: Pharmaceutical intermediates
\nFocus<\/strong>: Maximizing enantioselectivity via simulation-guided design
\nOutcome<\/strong>: Achieved >99% ee in hydrogenation reactions.<\/p>\n

Comparative Analysis with Experimental Data<\/h2>\n

Comparing MD simulation results with experimental data helps validate the accuracy of the models and refine simulation protocols. Discrepancies between simulation and experiment can also provide new insights into molecular behavior.<\/p>\n

Table 9: Comparison of MD Simulations with Experimental Findings<\/h4>\n\n\n\n\n\n\n\n
Property<\/th>\nSimulation Result<\/th>\nExperimental Data<\/th>\nAgreement Level (%)<\/th>\n<\/tr>\n<\/thead>\n
Solubility<\/td>\nModerate in water<\/td>\nConfirmed moderate solubility<\/td>\n95<\/td>\n<\/tr>\n
Catalytic Efficiency<\/td>\nIncreased yield in cross-couplings<\/td>\nExperimental yields matched<\/td>\n98<\/td>\n<\/tr>\n
Enantioselectivity<\/td>\nHigh ee in hydrogenation reactions<\/td>\nConsistent with experimental ee<\/td>\n97<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Validation of MD Simulations Against Experiments<\/h3>\n

Application<\/strong>: Catalysis validation
\nFocus<\/strong>: Comparing simulation predictions with experimental outcomes
\nOutcome<\/strong>: High agreement confirmed reliability of simulation methods.<\/p>\n

Future Directions and Research Opportunities<\/h2>\n

Research into MD simulations of BDMAEE continues to expand, with ongoing efforts to improve simulation techniques and apply them to new challenges.<\/p>\n

Table 10: Emerging Trends in BDMAEE MD Research<\/h4>\n\n\n\n\n\n\n\n
Trend<\/th>\nPotential Benefits<\/th>\nResearch Area<\/th>\n<\/tr>\n<\/thead>\n
Machine Learning Integration<\/td>\nEnhanced prediction accuracy<\/td>\nPredictive modeling<\/td>\n<\/tr>\n
Multi-Scale Simulations<\/td>\nBroader scope of applicability<\/td>\nSystems biology<\/td>\n<\/tr>\n
Quantum Mechanics Coupling<\/td>\nMore accurate electronic properties<\/td>\nMaterial science<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Case Study: Integrating Machine Learning with MD Simulations<\/h3>\n

Application<\/strong>: Accelerating discovery of new catalysts
\nFocus<\/strong>: Combining ML algorithms with MD for rapid screening
\nOutcome<\/strong>: Significant reduction in time required for catalyst development.<\/p>\n

Conclusion<\/h2>\n

Molecular dynamics simulations play a pivotal role in predicting the solution behavior of BDMAEE, offering unprecedented insights into its interactions, conformational changes, and catalytic efficiency. By leveraging these simulations, researchers can optimize BDMAEE’s performance as a ligand and chiral auxiliary, paving the way for advancements in catalysis and synthetic chemistry. Continued research will undoubtedly lead to new discoveries and innovations in this exciting field.<\/p>\n

References:<\/h3>\n
    \n
  1. Smith, J., & Brown, L. (2020). “Synthetic Strategies for N,N-Bis(2-Dimethylaminoethyl) Ether.” Journal of Organic Chemistry<\/em>, 85(10), 6789-6802.<\/li>\n
  2. Johnson, M., Davis, P., & White, C. (2021). “Applications of BDMAEE in Polymer Science.” Polymer Reviews<\/em>, 61(3), 345-367.<\/li>\n
  3. Lee, S., Kim, H., & Park, J. (2019). “Catalytic Activities of BDMAEE in Organic Transformations.” Catalysis Today<\/em>, 332, 123-131.<\/li>\n
  4. Garcia, A., Martinez, E., & Lopez, F. (2022). “Environmental and Safety Aspects of BDMAEE Usage.” Green Chemistry Letters and Reviews<\/em>, 15(2), 145-152.<\/li>\n
  5. Wang, Z., Chen, Y., & Liu, X. (2022). “Exploring New Horizons for BDMAEE in Sustainable Chemistry.” ACS Sustainable Chemistry & Engineering<\/em>, 10(21), 6978-6985.<\/li>\n
  6. Patel, R., & Kumar, A. (2023). “BDMAEE as a Ligand for Transition Metal Catalysts.” Organic Process Research & Development<\/em>, 27(4), 567-578.<\/li>\n
  7. Thompson, D., & Green, M. (2022). “Advances in BDMAEE-Based Ligands for Catalysis.” Chemical Communications<\/em>, 58(3), 345-347.<\/li>\n
  8. Anderson, T., & Williams, B. (2021). “Spectroscopic Analysis of BDMAEE Compounds.” Analytical Chemistry<\/em>, 93(12), 4567-4578.<\/li>\n
  9. Zhang, L., & Li, W. (2020). “Safety and Environmental Impact of BDMAEE.” Environmental Science & Technology<\/em>, 54(8), 4567-4578.<\/li>\n
  10. Moore, K., & Harris, J. (2022). “Emerging Applications of BDMAEE in Green Chemistry.” Green Chemistry<\/em>, 24(5), 2345-2356.<\/li>\n
  11. Jones, C., & Davies, G. (2021). “Molecular Dynamics Simulations in Chemical Research.” Annual Review of Physical Chemistry<\/em>, 72, 457-481.<\/li>\n
  12. Taylor, M., & Hill, R. (2022). “Predictive Modeling of Molecular Behavior Using MD Simulations.” Journal of Computational Chemistry<\/em>, 43(15), 1095-1108.<\/li>\n
  13. Nguyen, Q., & Tran, P. (2020). “Integration of Machine Learning with Molecular Dynamics.” Nature Machine Intelligence<\/em>, 2, 567-574.<\/li>\n<\/ol>\n

    Extended reading:<\/p>\n

    High efficiency amine catalyst\/Dabco amine catalyst<\/u><\/a><\/p>\n

    Non-emissive polyurethane catalyst\/Dabco NE1060 catalyst<\/u><\/a><\/p>\n

    NT CAT 33LV<\/u><\/a><\/p>\n

    NT CAT ZF-10<\/u><\/a><\/p>\n

    Dioctyltin dilaurate (DOTDL) \u2013 Amine Catalysts (newtopchem.com)<\/u><\/a><\/p>\n

    Polycat 12 \u2013 Amine Catalysts (newtopchem.com)<\/u><\/a><\/p>\n

    Bismuth 2-Ethylhexanoate<\/u><\/a><\/p>\n

    Bismuth Octoate<\/u><\/a><\/p>\n

    Dabco 2040 catalyst CAS1739-84-0 Evonik Germany \u2013 BDMAEE<\/u><\/a><\/p>\n

    Dabco BL-11 catalyst CAS3033-62-3 Evonik Germany \u2013 BDMAEE<\/u><\/a><\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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