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When multiple reaction steps occur before thermal equilibration, kinetic energy from one reaction step can influence overall product distributions in ways that are not well predicted by transition state theory. An understanding of how the structural features of mechanophores, such as substitutions, affects reactivity, product distribution, and the extent of dynamic effects in the mechanochemical manifolds is necessary for designing chemical reactions and responsive materials. We synthesized two tetrafluorinated [4]-ladderanes with fluorination on different rungs and found that the fluorination pattern influenced the force sensitivity and stereochemical distribution of products in the mechanochemistry of these fluorinated ladderanes. The threshold forces for mechanochemical unzipping of ladderane were decreased by alpha-fluorination and increased by gamma-fluorination; these changes correlated to the different stabilizing or destabilizing effects of fluorination patterns on the first transition state. Using ab initio steered molecular dynamics (AISMD), we compared the product distributions of synthesized and hypothetical ladderanes with different substitution patterns. These calculations suggest that fluorination on the first two bonds of ladderane gives rise to a larger fraction of dynamic trajectories and a larger fraction of E alkene prod-uct through a mechanism resulting from larger momentum because of the greater atomic mass of fluorine. Fluorination on the third and fourth rungs instead gives a larger fraction of E alkene product primarily due to electronic effects. These com-bined experimental and computational studies of the mechanochemical unzipping of fluorinated ladderanes provide an example of how relatively simple substituents can affect the extent of non-statistical dynamics, and thus mechanochemical outcomes. 

A series of archaeal bipolar glycerol dialkyl glycerol tetraether (GDGT) lipids were synthesized. Structural properties of self-assembled structures of GDGTs were studied and related to functional behavior such as membrane fusion. 

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH 2 NH 2 + ). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI 2 MD) software for the cis – trans isomerization of trans -hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for trans -hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the cis – trans isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023–0.032 eV). The neural network photodynamics simulations of trans -hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the cis -product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding syn -product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns. 

Abstract Archaeal glycerol dibiphytanyl glycerol tetraethers (GDGT) are some of the most unusual membrane lipids identified in nature. These amphiphiles are the major constituents of the membranes of numerous Archaea, some of which are extremophilic organisms. Due to their unique structures, there has been significant interest in studying both the biophysical properties and the biosynthesis of these molecules. However, these studies have thus far been hampered by limited access to chemically pure samples. Herein, we report a concise and stereoselective synthesis of the archaeal tetraether lipid parallel GDGT‐0 and the synthesis and self‐assembly of derivatives bearing different polar groups. 

Abstract Archaeal glycerol dibiphytanyl glycerol tetraethers (GDGT) are some of the most unusual membrane lipids identified in nature. These amphiphiles are the major constituents of the membranes of numerous Archaea, some of which are extremophilic organisms. Due to their unique structures, there has been significant interest in studying both the biophysical properties and the biosynthesis of these molecules. However, these studies have thus far been hampered by limited access to chemically pure samples. Herein, we report a concise and stereoselective synthesis of the archaeal tetraether lipid parallel GDGT‐0 and the synthesis and self‐assembly of derivatives bearing different polar groups.