Molecular Structural Model Research Articles

Discovery and Characterization of BAY-184: A New Potent and Selective Acylsulfonamide-Benzofuran In Vivo-Active KAT6AB Inhibitor.

KAT6A and KAT6B genes are two closely related lysine acetyltransferases that transfer an acetyl group from acetyl coenzyme A (AcCoA) to lysine residues of target histone substrates, hence playing a key role in chromatin regulation. KAT6A and KAT6B genes are frequently amplified in various cancer types. In breast cancer, the 8p11-p12 amplicon occurs in 12-15% of cases, resulting in elevated copy numbers and expression levels of chromatin modifiers like KAT6A. Here, we report the discovery of a new acylsulfonamide-benzofuran series as a novel structural class for KAT6A/B inhibition. These compounds were identified through high-throughput screening and subsequently optimized using molecular modeling and cocrystal structure determination. The final tool compound, BAY-184 (29), was successfully validated in an in vivo proof-of-concept study.

Molecular Dynamic Simulations of the Physical Properties of Four Ionic Liquids

In this study, the molecular structure models of four ionic liquids were created, the reverse nonequilibrium molecular dynamics simulation (RNEMD) approach was used to predict their densities and viscosities, and their thermal conductivity was simulated using the non-equilibrium molecular dynamic simulation method (NEMD). The calculated results of ionic liquid densities were compared with the data in the literature; most of the variances are around 2.5%, and the maximum relative deviation was less than 6.27%; viscosity values were compared with the experimental data, with a maximum relative deviation of −8.96% and a minimum relative deviation of −2.72%. The simulated thermal conductivity has a good linear relationship with respect to temperature and pressure, which is in good agreement. This study provides a reference for molecular dynamics simulation to measure the physical properties of ionic liquids, which is important for the development of ionic liquids.

Exploring the graphitization transformation mechanism of deposited carbon in molten salt electrolysis: A novel insight from molecular structure models

Molten salt electrolysis graphitization is a novel method for the electrochemical graphitization, offering significant technological advantages. Researchers have shown keen interest in preparing graphite materials through molten salt strategies. However, there is still a lack of comprehensive research methodologies at the molecular scale for the removal of heteroatoms and rearrangement of carbon atoms in different amorphous carbon conversion systems. To address the above issue, the deposited carbon (DC, a coking byproduct) was converted into graphitized material (electrolysis products, EP) through the electrochemical method in this study. By processing Transmission Electron Microscope images with Python programming, aromatic skeleton structure information was quantitatively extracted. The evolution of the structure of DC was studied using Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. Based on information about elemental content, aromatic skeletal structure, and heteroatom functional groups, average chemical structure models were constructed for DC and EP, and relevant chemical bond parameters were statistically analyzed. Then, the influence of the electric field on the structural transformation of amorphous carbon in molten salts was investigated. The research results show that the electric field significantly affects the graphitization transformation process of amorphous carbon in molten salts, enhancing the interaction between calcium ions and DC molecules, increasing the diffusion coefficient of particles in the system, and promoting the formation of more sp2-hybridized carbon atoms, thus forming a more compact aromatic ring network. This study provides a new research method and molecular/atomic-level insights for a deeper understanding of the mechanism of graphitization transformation of amorphous carbon.

LoGAN: local generative adversarial network for novel structure prediction

Abstract The efficient generation and filtering of candidate structures for new materials is becoming increasingly important as starting points for computational studies. In this work, we introduce an approach to Wasserstein generative adversarial networks for predicting unique crystal and molecular structures. Leveraging translation- and rotation-invariant atom-centered local descriptors addresses some of the major challenges faced by similar methods. Our models require only small sets of known structures as training data. Furthermore, the approach is able to generate both non-periodic and periodic structures based on local coordination. We showcase the data efficiency and versatility of the approach by recovering all stable C5H12O isomers using only 39 C4H10O and C6H14O training examples, as well as a few randomly selected known low-energy SiO2 crystal structures utilizing only 167 training examples of other SiO2 crystal structures. We also introduce a filtration technique to reduce the computational cost of subsequent characterization steps by selecting samples from unique basins on the potential energy surface, which allows to minimize the number of geometry relaxations needed after structure generation. The present method thus represents a new, versatile approach to generative modeling of crystal and molecular structures in the low-data regime, and is available as open-source.

Novel homozygous missense variants in MED27 associated with neurodevelopmental disorder: Clinical and pathogenetic research

BackgroundNeurodevelopmental disorder with spasticity, cataracts, and cerebellar hypoplasia (NEDSCAC), induced by MED27 gene, is an autosomal recessive rare disorder characterized by widespread developmental delay with varying degrees of intellectual impairment. Other symptoms include limb spasticity, cataracts, and cerebellar hypoplasia. So far there have been limited reports on NEDSCAC. MethodsIn this study, we conducted genetic testing on a child presenting with developmental delay as the primary clinical feature. The genetic test results indicated the presence of novel homozygous missense variants c.74G > A, p.(Arg25His) in the MED27 gene. In vitro functional validation experiments, including plasmid construction and cell transfection, Western blotting, and molecular dynamics structural modeling, were performed on the MED27 Arg25His variant. ResultsThe results demonstrated a significant reduction in protein expression of MED27 Arg25His and indicated may weaken the interaction force between the MED27 subunit and MED14 subunit. ConclusionsThis study expands our understanding of MED27 gene variants and their associated clinical phenotypes. Additionally, it contributes to the investigation of the potential pathogenesis of NEDSCAC caused by MED27 gene variants.

Rescue of Familial Lecithin:Cholesterol Acyltranferase Deficiency Mutations with an Allosteric Activator.

Lecithin:cholesterol acyltransferase (LCAT) deficiencies represent severe disorders characterized by aberrant cholesterol esterification in plasma, leading to life-threatening conditions. This study investigates the efficacy of Compound 2, a piperidinyl pyrazolopyridine allosteric activator that binds the membrane-binding domain of LCAT, in rescuing the activity of LCAT variants associated with disease. The variants K218N, N228K, and G230R, all located in the cap and lid domains of LCAT, demonstrated notable activity restoration in response to Compound 2. Molecular dynamics simulations and structural modeling indicate that these mutations disrupt the lid and membrane binding domain, with Compound 2 potentially dampening these structural alterations. Conversely, variants such as M252K and F382V in the cap and α/β-hydrolase domain, respectively, exhibited limited or no rescue by Compound 2. Future research should prioritize in vivo investigations that would validate the therapeutic potential of Compound 2 and related activators in familial LCAT deficiency patients with mutations in the cap and lid of the enzyme. SIGNIFICANCE STATEMENT: Lecithin:cholesterol acyltranferase (LCAT) catalyzes the first step of reverse cholesterol transport, namely the esterification of cholesterol in high density lipoprotein particles. Somatic mutations in LCAT lead to excess cholesterol in blood plasma and, in severe cases, kidney failure. In this study, we show that recently discovered small molecule activators can rescue function in LCAT-deficient variants when the mutations occur in the lid and cap domains of the enzyme.

Molecular Structural Characteristics and 3D Model Reconstruction of Organic Matter in Longmaxi Formation Shale.

The establishment of molecular structure modeling is an important means to study the pore characteristics of shale organic matter and is significant for molecular-level simulations of gas storage and diffusion. Using 13C NMR, FTIR, and XPS combined with the split-peak fitting technique, the structural characteristics of the aromatic structure, aliphatic structure, and oxygen functional groups of kerogen from the shale of the Longmaxi Formation, Wuxi County, Chongqing Municipality, were quantitatively characterized. A macromolecular structure model of the kerogen was also constructed by using the 2D macromolecular structure model construction method in combination with elemental analysis experiments. The results showed that the 2D single-molecule structural model of the sample consisted of 2 benzenes, 2 naphthalenes, 1 anthracene, 5 pyrenes, 1 pyridine, and 1 pyrrole. The C skeleton types were 93 protonated arylons, 39 bridged arylons, 6 carboxylons, 5 alkyl-substituted carbons, 2 oxygen-substituted carbons, 4 methylene carbons, and 3 methylons. The established 2D molecular structure formula was C152H82O12N2. The final 3D macromolecular structure model consisted of 14 2D molecular structures (structural formula C2128H1148O168N28), with the density set to 1.77 cm3/g, compressed in a cubic cell with an edge length of 3.05 nm. Finally, the adsorption results showed that the experimental adsorption of CO2 adsorption was less than the simulated adsorption, completing the validation of the model. The above study provides a method for determining the molecular structure of kerogen in the Longmaxi Formation shale, which can guide the study of the pore structure characteristics of the Longmaxi Formation shale.

Optical Properties of H-Bonded Heterotriangulene Supramolecular Polymers: Charge-Transfer Excitations Matter.

H-bonded N-heterotriangulene (NHT) supramolecular polymers offer a nice playground to explore the nature and dynamics of electronic excitations in low-dimensional organic nanostructures. Here, we report on a comprehensive molecular modeling of the excited-state electronic structure and optical properties of model NHT stacks, highlighting the important role of intermolecular charge-transfer (CT) excitations in shaping their optical absorption and emission lineshapes. Most importantly, we show that the coupling between the local and CT excitations, modulated by the electric fields induced by the presence of polar amide groups forming H-bonded arrays along the stacks, significantly increases the resulting hybrid exciton bandwidth. We discuss these findings in the context of the efficient transport of singlet excitons over the μm length scale reported experimentally on individual self-assembled nanofibers with molecular-scale diameter.

Optimization of mechanical and safety properties by designing interface characteristics within energetic composites

The interfacial structure has an important effect on the mechanical properties and safety of the energetic material. In this work, a mesostructure model reflecting the real internal structure of PBX is established through image digital modeling and vectorization processing technology. The microscopic molecular structure model of PBX is constructed by molecular dynamics, and the interface bonding energy is calculated and transferred to the mesostructure model. Numerical simulations are used to study the influence of the interface roughness on the dynamic compression and impact ignition response of PBX, and to regulate and optimize the mechanical properties and safety of the explosive to obtain the optimal design of the surface roughness of the explosive crystal. The results show that the critical hot spot density of PBX ignition under impact loading is 0.68 mm−2. The improvement of crystal surface roughness can improve the mechanical properties of materials, but at the same time it can improve the impact ignition sensitivity and reduce the safety of materials. The optimal friction coefficient range for the crystal surface that satisfies both the mechanical properties and safety of PBX is 0.06–0.12. This work can provide a reference basis for the formulation design and production processing of energetic materials.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Engineering of the Phytase YiAPPA to Improve Thermostability and Activity and Its Application Potential in Dephytinization of Food Ingredients.

The aim of this study was to modify phytase YiAPPA via protein surficial residue mutation to obtain phytase mutants with improved thermostability and activity, enhancing its application potential in the food industry. First, homology modeling of YiAPPA was performed. By adopting the strategy of protein surficial residue mutation, the lysine (Lys) and glycine (Gly) residues on the protein surface were selected for site-directed mutagenesis to construct single-site mutants. Thermostability screening was performed to obtain mutants (K189R and K216R) with significantly elevated thermostability. The combined mutant K189R/K216R was constructed via beneficial mutation site stacking and characterized. Compared with those of YiAPPA, the half-life of K189R/K216R at 80°C was extended from 14.81 min to 23.35 min, half-inactivation temperature (T50 30) was increased from 55.12°C to 62.44°C, and Tm value was increased from 48.36°C to 53.18°C. Meanwhile, the specific activity of K189R/K216R at 37°C and pH 4.5 increased from 3960.81 to 4469.13 U/mg. Molecular structure modeling analysis and molecular dynamics simulation showed that new hydrogen bonds were introduced into K189R/K216R, improving the stability of certain structural units of the phytase and its thermostability. The enhanced activity was primarily attributed to reduced enzyme-substrate binding energy and shorter nucleophilic attack distance between the catalytic residue His28 and the phytate substrate. Additionally, the K189R/K216R mutant increased the hydrolysis efficiency of phytate in food ingredients by 1.73-2.36 times. This study established an effective method for the molecular modification of phytase thermostability and activity, providing the food industry with an efficient phytase for hydrolyzing phytate in food ingredients.

Toward the Reconciliation of Inconsistent Molecular Structures from Biochemical Databases.

Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, including metabolomics, systems biology, and drug discovery. No such database can be complete and it is often necessary to incorporate data from several sources. However, the molecular structure for a given compound is not necessarily consistent between databases. This article presents StructRecon, a novel tool for resolving unique molecular structures from database identifiers. Currently, identifiers from BiGG, ChEBI, Escherichia coli Metabolome Database (ECMDB), MetaNetX, and PubChem are supported. StructRecon traverses the cross-links between entries in different databases to construct what we call identifier graphs. The goal of these graphs is to offer a more complete view of the total information available on a given compound across all the supported databases. To reconcile discrepancies met during the traversal of the databases, we develop an extensible model for molecular structure supporting multiple independent levels of detail, which allows standardization of the structure to be applied iteratively. In some cases, our standardization approach results in multiple candidate structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternatives. As a case study, we applied StructRecon to the EColiCore2 model. We found at least one structure for 98.66% of its compounds, which is more than twice as many as possible when using the databases in more standard ways not considering the complex network of cross-database references captured by our identifier graphs. StructRecon is open-source and modular, which enables support for more databases in the future.

Properties of ethanol-based foamed asphalt binders using the molecular dynamics (MD) method

The molecular structure models of asphalt binder, ethanol additive, and ethanol-based foamed asphalt were constructed through the Molecular Dynamics (MD) method. The standard ethanol-based foamed asphalt model was employed to describe the modifier with its different compositions, including 10%, 20%, and 30% ethanol. The simulation calculations were done for the ethanol-based foamed asphalt molecular models under the NPT and NVT ensembles. The density, glass transition temperature, and radial distribution function of ethanol-based foamed asphalt molecular models were obtained to verify the rationalization of asphalt models and analyze the variation of density parameters with temperature and ethanol content for ethanol-based foamed asphalt molecular models. The results show that the simulated densities of the asphalt binder and three ethanol-based foamed asphalt molecular models remained constant with the increase of simulation steps. The simulated density values of basic and 10%-ethanol-based foamed asphalt molecular models are close to 0.9 g cm−3. The simulated densities of 20%-ethanol-based and 30%-ethanol-based foamed asphalt molecular models were 0.8 g cm−3 and 0.75 g cm−3. Meanwhile, the simulated density values of both asphalt binder and all ethanol-based foamed asphalt decreased with the increase in temperature and ethanol additive dosage. The glass transition temperatures of basic asphalt binder, 10%-ethanol-based, 20%-ethanol-based, and 30%-ethanol-based foamed asphalt occurred in the range of 275–295 K, 330–350 K, 330–350 K, and 320–340 K, respectively. In contrast, the glass transition temperature of ethanol-based foamed asphalt increased with the increase of ethanol additive dosage, indicating that adding ethanol additive significantly improved the high-temperature resistance of matrix asphalt. In the radial distribution function diagrams of all samples, the first strong peak appeared at 0.85–1.3 Å, and the second strong peak appeared at 1.95–2.35 Å. Moreover, both peaks increased with the increase of ethanol additive dosage, suggesting that the contact between ethanol molecules and asphalt molecules was closer with the rise of ethanol additive dosage.

3D molecular structural modeling and characterization of indium phosphide via irregularity topological indices

Indium phosphide (InP) is a binary semiconductor composed of indium and phosphorus. It has a zinc blende crystal structure, which is a type of cubic lattice structure. This lattice is composed of indium and phosphorus atoms arranged in a lattice of cube-shaped cells, with each cell containing four indium atoms and four phosphorus atoms. This lattice structure is the same for all materials with a zinc blende crystal structure and is the most common type of lattice structure in semiconductors. Indium phosphide (InP) has several chemical applications. It is commonly used as a dopant in the production of semiconductors, where it helps control the electrical properties of the material. InP is also utilized in the synthesis various indium-containing compounds, which can have applications in catalysts and chemical reactions. Additionally, InP nanoparticles have been investigated for their potential use in biomedical imaging and drug delivery systems. The topological characterization of 3D molecular structures can be performed via graph theory. In graph theory, the connections between atoms are represented as edges and the atoms themselves are represented as nodes. Furthermore, graph theory can be used to calculate the topological descriptors of the molecule such as the degree-based and reverse degree-based irregularity toplogical indices. These descriptors can be used to compare the topology of different molecules. This paper deals with the modeling and topological characterization of indium phosphide (InP)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{InP}})$$\\end{document} via degree-based and reverse irregularity indices. The 3D crystal structure of the InP is topologically modeled via partition of the edges, and derived closed form expressions for its irregularity indices. Our obtained results will be surely be helpful in investigating the QSPR/QSAR analysis as well as understanding the deep irregular behavior of the indium phosphide (InP)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{InP}})$$\\end{document}.

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

Molecular structure characterization of coal with different coalification degrees: a combined study from FT-IR, Raman, 13C NMR spectroscopy and modelling

While extensive research has been conducted on the evolution of coal's molecular structure during coalification, the heterogeneity and complexity of coal continue to impede the accurate and precise quantitative characterization of its structural evolution mechanism. In this study, the organic matter source, coal quality, carbon skeleton structure, and functional group occurrence of coal at different coalification stages have been determined using maceral identification, vitrinite reflectance, industrial quality, elemental analysis, and spectral analysis. Four molecular models have been created through computer-aided molecular design. The results indicate that the hydrogen atoms in the benzene ring structure reduce as coalification increases. The content of aliphatic compounds gradually decreases, while the length of aliphatic side chains first decreases (Ro,max<1.81%) and then increases (Ro,max>1.81%). Moreover, the chemical structure of coal tends to mature and stabilize, as the degree of coal crystallization increases, and the oxygen-containing groups gradually decrease. Therefore, aromatization increases with the condensation reaction dominating in the late stage of coalification. After intensive attempts to determine the chemical bond mode between aromatic carbon, aliphatic side chain, and oxygen functional group, the final molecular structure models are presented as C188H165O13N3 (F-1), C182H161O9N5 (J-1), C195H153O10N3 (T-1), and C193H129O7N3 (W-1). Furthermore, the mechanism of coalification is discussed based on the variation of the side chains and chemical bonds. According to the adsorption heat of functional groups, the methane adsorption capacity increases as the degree of coalification increases. The inflection point of methane adsorption capacity is at Ro,max>1.80%. This study provides a theoretical framework to understand the molecular structure evolution of coals at varying degrees of metamorphism. Additionally, it proposes a reliable method for characterizing coal molecular structures with high accuracy.

Preparation and Acoustic Performance of Porous Aerogel Composites of Graphene Oxide and Cellulose

In this study, sound insulation materials with a high sound absorption coefficient were prepared. In this paper, using cellulose (CEL) and graphene oxide (GO) as the main raw materials and epichlorohydrin as the cross-linker, the CEL-GO composite aerogels were prepared via lyophilisation. The structure, molecular bonding, and acoustic absorption mechanisms of the composite aerogel were characterised and analysed using SEM, FTIR, XRD, BET, and Raman. In addition, corresponding molecular structure models were constructed. The acoustic attenuation of the CEL-GO composite aerogel was measured using a standing wave tube acoustic attenuation tester. The results show that the chemical bond between the GO and CEL composite is established, and the addition of graphene makes the pores of the composite more advanced, which is more favorable for sound absorption, and the acoustic absorption coefficient can reach up to 0.87.

Molecular modeling of CO2 affecting competitive adsorption within anthracite coal

This study aimed to investigate the adsorption properties of CO2, CH4, and N2 on anthracite. A molecular structural model of anthracite (C208H162O12N4) was established. Simulations were performed for the adsorption properties of single-component and multi-component gases at various temperatures, pressures, and gas ratios. The grand canonical ensemble Monte Carlo approach based on molecular mechanics and dynamics theories was used to perform the simulations. The results showed that the isotherms for the adsorption of single-component CO2, CH4, and N2 followed the Langmuir formula, and the CO2 adsorption isotherm growth gradient was negatively correlated with pressure but positively correlated with temperature. When the CO2 injection in the gas mixture was increased from 1 to 3% for the multi-component gas adsorption, the proportion of CO2 adsorption rose from 1/3 to 2/3, indicating that CO2 has a competing-adsorption advantage. The CO2 adsorption decreased faster with increasing temperature, indicating that the sensitivity of CO2 to temperature is stronger than that of CH4 and N2. The adsorbent potential energies of CO2, CH4, and N2 diminished with rising temperature in the following order: CO2 < CH4 < N2.

Analysis of the microstructure of high viscosity components in Tahe ultra heavy oil based on the combination of ESI (±) FT-ICR/MS

In addressing the challenge of high viscosity in Xinjiang Tahe heavy oil, crucial for optimizing extraction processes, this study focuses on identifying and analyzing the microstructure of key components influencing oil viscosity. The aim is to uncover the underlying reasons for its high viscosity and guide research towards effective viscosity reduction techniques. Electrospray ionization (ESI) combined with fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) characterizes the molecular-level constituents contributing to oil viscosity. The investigation covers the composition and distribution of oxygen-containing, nitrogen-containing, and organometallic compounds, developing a molecular structure model for these critical viscosity-inducing components. Findings reveal Xinjiang Tahe extra heavy oil as a solid mass at room temperature, with significant viscosity reduction observed post-deasphalting, highlighting asphaltenes' pivotal role in viscosity. Analysis indicates a higher content of metal elements (such as V and Ni) in asphaltenes compared to the crude oil itself, with an abundance of porphyrin structures in asphaltenes markedly influencing oil viscosity. Cyclic, aromatic, and unsaturated compound structures are predominantly found in the asphaltenes of heavy oil. Through combined ESI (±) FT-ICR/MS analysis, the crude oil is determined to mainly comprise acidic compounds, non-basic nitrogen compounds, and basic nitrogen compounds, offering significant insight into heavy oil's complex chemical composition. Specifically, non-basic nitrogen compounds in Tahe crude oil, including alkylcarbazoles, alkylbenzocarbazoles, and alkylpolybenzocarbazoles, are identified as key components, with molecular carbon numbers ranging from C12 to C56 and a predominance near C27. Additionally, asphaltenes in Tahe heavy oil exhibit a higher double bond equivalence (DBE) than the crude oil, indicating a greater degree of condensation with an increased number of double bonds and heterocyclic structures. The significant difference in DBE values between asphaltenes and crude oil underscores their impact on viscosity. A detailed molecular structure model of the asphaltenes in Tahe extra heavy oil has been constructed based on FT-ICR/MS analysis, providing a foundational understanding for developing viscosity reduction strategies.

Features and insights for molecular structure of Chinese Taixi anthracite at atomic scales

This work presents insights into the molecular structure of Chinese Taixi anthracite (TXA) at atomic scales. The comprehensive analysis of materials characterizations and data fitting & calculations are performed to reveal microscopic characteristics and structural parameters of TXA. Then the planar and three-dimensional molecular structure models of TXA are con with the help of computer-aided molecular design and multi-scale molecular simulation calculations. The chemical structure characteristics of TXA are systematically analyzed, such as the carbon framework, main functional groups, and typical chemical bonding features. It is found that aromatic carbons are the main skeleton of organic matter, combined with some aliphatic carbons mainly in form of methyl, methylene and methine. Conceptualized single molecular description of TXA is determined to be C763H397O21N8. Representative two-dimensional and three-dimensional molecular structural models are also constructed to describe the apparent molecular structural features of TXA. Finally, molecular structural models are further verified by the elemental composition, nuclear magnetic resonance, Fourier transform infrared, and bond concentration of TXA. In addition, we summarized microstructure features of coal with various metamorphism. This work could provide theoretical guidance for understanding the microstructure of TXA and building the correlation between coal structure and functional properties to achieve efficient utilization of coal resources.