Mesoscale Simulation of Asphaltene Aggregation

Asphaltenes constitute a heavy aromatic crude oil fraction with a propensity to aggregate and precipitate out of solution during petroleum processing. Aggregation is thought to proceed according to the Yen-Mullins hierarchy, but the molecular mechanisms underlying mesoscopic assembly remain poorly understood. By combining coarse-grained molecular models parametrized using all-atom data with high-performance GPU hardware, we have performed molecular dynamics simulations of the aggregation of hundreds of asphaltenes over microsecond time scales. Our simulations reveal a hierarchical self-assembly mechanism consistent with the Yen-Mullins model, but the details are sensitive and depend on asphaltene chemistry and environment. At low concentrations asphaltenes exist predominantly as dispersed monomers. Upon increasing concentration, we first observe parallel stacking into 1D rod-like nanoaggregates, followed by the formation of clusters of nanoaggregates associated by offset, T-shaped, and edge–edge stacking. Asphaltenes possessing long aliphatic side chains cannot form nanoaggregate clusters due to steric repulsions between their aliphatic coronae. At very high concentrations, we observe a porous percolating network of rod-like nanoaggregates suspended in a sea of interpenetrating aliphatic side chains with a fractal dimension of ∼2. The lifetime of the rod-like aggregates is described by an exponential distribution reflecting a dynamic equilibrium between coagulation and fragmentation

A Study of the Morphology, Dynamics, and Folding Pathways of Ring Polymers with Supramolecular Topological Constraints Using Molecular Simulation and Nonlinear Manifold Learning

Ring polymers are prevalent in natural and engineered systems, including circular bacterial DNA, crown ethers for cation chelation, and mechanical nanoswitches. The morphology and dynamics of ring polymers are governed by the chemistry and degree of polymerization of the ring and intramolecular and supramolecular topological constraints such as knots or mechanically interlocked rings. In this study, we perform molecular dynamics simulations of polyethylene ring polymers at two different degrees of polymerization and in different topological states, including a trefoil knot, catenane state (two interlocked rings), and Borromean state (three interlocked rings). We employ nonlinear manifold learning to extract the low-dimensional free energy surface to which the structure and dynamics of the polymer chain are effectively restrained. The free energy surfaces reveal how the degree of polymerization and topological constraints affect the thermally accessible conformations, chiral symmetry breaking, and folding and collapse pathways of the rings and present a means to rationally engineer ring size and topology to stabilize particular conformational states and folding pathways. We compute the rotational diffusion of the ring in these various states as a crucial property required for the design of engineered devices containing ring polymer components

Rhodium(III)-Catalyzed Site-Selective C–H Alkylation and Arylation of Pyridones Using Organoboron Reagents

In this study we developed a method for the pyridine-directed, rhodium-catalyzed, site-selective C–H alkylation and arylation of pyridones using commercially available trifluoroborate reagents. This simple and versatile transformation proceeded smoothly under relatively mild conditions with perfect site selectivity. The coupling groups in the boron reagents can be extended to primary alkyl, benzyl, and cycloalkyl. Moreover, direct C–H arylation products could also be obtained under similar conditions

Coarse-Grained Molecular Simulation and Nonlinear Manifold Learning of Archipelago Asphaltene Aggregation and Folding

Asphaltenes constitute the heaviest aromatic component of crude oil. The myriad of asphaltene molecules falls largely into two conceptual classes: continentalpossessing a single polyaromatic coreand archipelagopossessing multiple polyaromatic cores linked by alkyl chains. In this work, we study the influence of molecular architecture upon aggregation behavior and molecular folding of prototypical archipelago asphaltenes using coarse-grained molecular dynamics simulation and nonlinear manifold learning. The mechanistic details of aggregation depend sensitively on the molecular structure. Molecules possessing three polyaromatic cores show a higher aggregation propensity than those with two, and linear archipelago architectures more readily form a fractal network than ring topologies, although the resulting aggregates are more susceptible to disruption by chemical dispersants. The Yen–Mullins hierarchy of self-assembled aggregates is attenuated at high asphaltene mass fractions because of the dominance of promiscuous parallel stacking interactions within a percolating network rather than the formation of rodlike nanoaggregates and nanoaggregate clusters. The resulting spanning porous network possesses a fractal dimension of 1.0 on short length scales and 2.0 on long length scales regardless of the archipelago architecture. The incompatibility of the observed assembly behavior with the Yen–Mullins hierarchy lends support that high-molecular weight archipelago architectures do not occur at significant levels in natural crude oils. Low-dimensional free energy surfaces discovered by nonlinear dimensionality reduction reveal a rich diversity of metastable configurations and folding behavior reminiscent of protein folding and inform how intramolecular structures can be modulated by controlling asphaltene mass fraction and dispersant concentration

Mesoscale Simulation and Machine Learning of Asphaltene Aggregation Phase Behavior and Molecular Assembly Landscapes

Asphaltenes constitute the heaviest fraction of the aromatic group in crude oil. Aggregation and precipitation of asphaltenes during petroleum processing costs the petroleum industry billions of dollars each year due to downtime and production inefficiencies. Asphaltene aggregation proceeds via a hierarchical self-assembly process that is well-described by the Yen–Mullins model. Nevertheless, the microscopic details of the emergent cluster morphologies and their relative stability under different processing conditions remain poorly understood. We perform coarse-grained molecular dynamics simulations of a prototypical asphaltene molecule to establish a phase diagram mapping the self-assembled morphologies as a function of temperature, pressure, and <i>n</i>-heptane:toluene solvent ratio informing how to control asphaltene aggregation by regulating external processing conditions. We then combine our simulations with graph matching and nonlinear manifold learning to determine low-dimensional free energy surfaces governing asphaltene self-assembly. In doing so, we introduce a variant of diffusion maps designed to handle data sets with large local density variations, and report the first application of many-body diffusion maps to molecular self-assembly to recover a pseudo-1D free energy landscape. Increasing pressure only weakly affects the landscape, serving only to destabilize the largest aggregates. Increasing temperature and toluene solvent fraction stabilizes small cluster sizes and loose bonding arrangements. Although the underlying molecular mechanisms differ, the strikingly similar effect of these variables on the free energy landscape suggests that toluene acts upon asphaltene self-assembly as an effective temperature

Chemical Resolution of <i>C</i>,<i>N</i>‑Unprotected α‑Substituted β‑Amino Acids Using Stable and Recyclable Proline-Derived Chiral Ligands

We report the first purely chemical method for the resolution of <i>C</i>,<i>N</i>-unprotected racemic α-substituted β-amino acids (β²-AAs) using thermodynamically stable and recyclable chiral proline-derived ligands. The ligands and racemic β²-AAs along with Ni­(II) could form a pair of Ni­(II) complex diastereoisomers with a desirable diastereoselectivity (dr up to 91:9). Enantiomerically pure <i>C</i>,<i>N</i>-unprotected β²-AAs could be obtained by simple hydrolysis of an isolated favored Ni­(II) complex. The method featured unique versatility compared with enzymatic approaches and characterized by its broad synthetic generality, good stereochemical outcome, and mild reaction conditions, thus making it a powerful supplement in the field of chemical resolution of β²-AAs

Asymmetric Synthesis of Chiral Heterocyclic Amino Acids via the Alkylation of the Ni(II) Complex of Glycine and Alkyl Halides

An investigation into the reactivity profile of alkyl halides has led to the development of a new method for the asymmetric synthesis of chiral heterocyclic amino acids. This protocol involves the asymmetric alkylation of the Ni­(II) complex of glycine to form an intermediate, which then decomposes to form a series of valuable chiral amino acids in high yields and with excellent diastereoselectivity. The chiral amino acids underwent a smooth intramolecular cyclization process to afford the valuable chiral heterocyclic amino acids in high yields and enantioselectivities. This result paves the way for the development of a new synthetic method for chiral heterocyclic amino acids

Quantitative Polymerase Chain Reaction Primer Sequences.

<p>Quantitative Polymerase Chain Reaction Primer Sequences.</p

Histology scores in different groups of mice models.

<p>Histology scores in different groups of mice models.</p

Hepatic iron, fibrogenic markers and alcohol-metabolizing enzymes in human advanced ALD.

<p><b>(A)</b> Representative H/E section of the human de-identified advanced ALD liver. Hepatic <b>(B)</b> iron and <b>(C)</b> hydroxyproline contents, and <b>(D)</b> collagen I (COL I), TGFβ, TNFα, ADH and ALDH1 mRNA expression. The mRNA expression of the respective molecules was normalized with that of β-actin.</p