Search Results (1,441)

The Jinjia oilfield in Shengli oilfield is a typical ultra-sensitive reservoir characterized by high crude oil viscosity, poor fluidity, high clay content, and weak cementation. During development, oil-sand mixtures readily plug pore throats. Various development methods including water flooding and thermal recovery have been implemented, yet severe problems persist: inability to inject, failure to displace, and lack of capacity to produce. To address these challenges, a functional microbial mineral-modified desensitization-chemical viscosity-reduction dual-effect agent, a self-growing gel dispersion profile control agent, and a low-damage deep acidizing system were developed. Laboratory experiments clarified the enhanced oil recovery mechanism of the bio-chemical desensitization and viscosity-reduction system. Results indicate that the desensitization and viscosity-reduction system can inhibit clay swelling, with the anti-swelling improvement rate of core permeability reaching 56%. Chemical viscosity reduction enabled heavy oil to “flow effectively,” achieving a viscosity reduction rate of 98.9% after adsorption. The profile control agent dispersed and migrated, then stably adsorbed onto particle surfaces to plug high-permeability channels, demonstrating strong anti-scouring capability and effectively suppressing channeling flow. In the composite system, bio-chemical desensitization and viscosity reduction synergistically enhanced mobility control, achieving an oil recovery factor of 56.5%, representing a 26.3% increase over post-water-flooding viscosity-reduction flooding. After two pilot well groups in the Jinjia oilfield were converted from water flooding to bio-chemical desensitization and viscosity-reduction composite flooding, single-well oil production capacity increased by 2.8-fold, water cut decreased by 12%, and both development performance and economic benefits were significantly improved—transforming the situation from “increasing water without increasing oil” to “increasing both liquid and oil production.” The research findings provide important reference value for the effective development of ultra-sensitive reservoirs. Full article

Ionic liquids, as green gas solubility media, have great potential for applications in carbon capture, industrial waste gas purification, and other fields. However, the massive combination of anions and cations makes their screening extremely difficult. Machine learning can break through the bottleneck of traditional experiments and simulations and achieve high-throughput prediction of gas solubility in ionic liquids. This article provides a systematic review of the research progress of machine learning in predicting the gas solubility performance of ionic liquids. The classification and modeling process of machine learning, the construction and performance of machine learning prediction models for the solubility of gases such as CO₂, H₂S, NH₃, SO₂, N₂O and others in ionic liquids were analyzed and summarized. The progress and existing problems of machine learning application for gas capture in ionic liquids and the future development direction are discussed, in order to provide assistance and theoretical reference for the directional design and industrial application of ionic liquids. Full article

This study employs a multiscale theoretical approach to systematically investigate the thermal stability, phase transition characteristics, and pyrolysis product distributions of four long-chain n-alkanes ranging from n-dodecane to n-pentadecane (C12–C15). At the electronic structure level, density functional theory calculations reveal that with increasing chain length, the HOMO–LUMO gap narrows monotonically from 8.87 eV to 8.77 eV and global softness increases, indicating enhanced electronic responsiveness to thermal perturbation. Molecular electrostatic potential analysis shows decreasing surface potential variance and 100% nonpolar surface area across all species, confirming that intermolecular interactions are exclusively governed by London dispersion forces. At the condensed-phase level, semiempirical quantum-based molecular dynamics (xTB-MD) simulations at 3500 K over 6 ps trajectories reveal qualitative chain-length-dependent initial bond-breaking patterns: C2 species appear prominently among early fragments for C12–C15 systems, with medium-sized fragments (C3, C4) becoming increasingly prevalent and C1 species relatively less prominent as chain length grows. This work provides an integrated “electronic structure-condensed phase transition-pyrolysis kinetics” perspective, offering precise theoretical insights and critical benchmark data for the pyrolysis mechanisms of long-chain n-alkanes. Full article

In this study, a new series of (E)-4-((2-(2-(4-amino-3-methyl-5-oxo-4,5-dihydro-1H-1,2,4-triazol-1-yl)acetyl)hydrazono)methyl)phenyl 4-halogenobenzenesulfonates (3a–3d), where 3a = F, 3b = Cl, 3c = Br, and 3d = I, were successfully synthesized via a straightforward synthetic route. The structures of the obtained compounds were fully characterized and confirmed by spectroscopic techniques, including FT-IR, ¹H NMR, and ¹³C NMR, as well as LC-MS/MS analysis. 1,2,4-triazole-based hydrazone derivatives (3a–3d) were investigated using IR and NMR spectroscopy and DFT calculations. Intermolecular interactions, HOMO-LUMO, dipole moment, polarization, first-order hyperpolarizability, and molecular electrostatic potential studies on the molecules were examined. The HOMO and LUMO energy gap study supports the charge transfer probability in the molecules. These were conducted to investigate the reactivity and stability of heterocyclic molecules in bioactivity analysis. Electron density mapping within the molecular electrostatic potential plot and electrostatic potential representation within the iso-surface plot evaluated the concept of charge distribution in the molecule as nucleophilic reactions and electrophilic regions. The predicted nonlinear optical (NLO) properties of the molecules are much greater than those of urea. The results obtained from these investigations collectively provide evidence that the molecules possess nonlinear optical applications. Novel triazole–hydrazone-functionalized aryl sulfonate derivatives (3a–3d) were evaluated for their anticancer potential against a panel of brain and non-brain cancer cell lines. Compound 3b exhibited the most favorable overall biological profile, displaying potent activity against SH-SY5Y neuroblastoma (GI = 7.59 μM) and U87MG glioblastoma cells (GI = 13.85 μM), together with the lowest toxicity toward normal FL fibroblasts (GI = 62.02 μM). Compounds 3c and 3d demonstrated remarkable potency against IDHmut-U87 glioma cells (GI = 3.87 and 3.27 μM, respectively), although their selectivity toward cancer cells was limited. DNA degradation studies revealed substantial fragmentation, particularly in C6 and SH-SY5Y cells, while migration assays indicated reduced cellular motility. Molecular docking studies identified compound 3b as the strongest PI3Kα binder, supporting a possible. In addition, the antimicrobial activities of compounds 3a–3d were evaluated against selected Gram-positive and Gram-negative bacteria as well as Candida species using the broth microdilution method. The compounds exhibited measurable antimicrobial effects with MIC values ranging from 156 to 625 µg/mL, showing moderate growth inhibition against the tested microorganisms. Although the observed activity was lower than that of the reference antimicrobial agents, the results indicate that these triazole–hydrazone derivatives possess a detectable level of antimicrobial activity and provide a basis for further structural optimization. Collectively, the results suggest that compound 3b represents the most promising lead structure due to its balanced combination of potency, selectivity, and predicted target engagement. Molecular docking was performed to evaluate the binding potential of newly synthesized triazole derivatives (3a–3d) against PI3Kα. The docking protocol was validated by re-docking alpelisib, yielding an RMSD of 0.64 Å. Among the tested compounds, 3b showed the most favorable binding energy (−9.94 kcal/mol) and estimated Ki value (52.13 nM), consistent with its superior in vitro activity. Its interactions with key PI3Kα residues, including Val851, Ser854, Met922, and Asp933, support a stable binding mode within the ATP-binding pocket. In silico ADME and toxicity analyses suggested acceptable drug-likeness characteristics, absence of major hepatotoxic, mutagenic, and carcinogenic liabilities, and moderate predicted acute toxicity profiles. These findings suggest that 3b is the most promising derivative for further validation. Full article

This study investigates the mechanism of ether bridge formation on the edges of graphene quantum dots (GQDs) and evaluates its impact on their structural, electronic, and optical properties. Using density functional theory (DFT) coupled with Clar’s aromatic sextet rule, we analyzed different edge functionalization sites on a model nanographene. The kinetic parameters evaluated via the Eyring–Polanyi equation demonstrate that the stability of functional groups is fundamentally governed by the retention or migration of aromatic sextets. While epoxidation at thermodynamically favorable edge sites that form stable epoxy intermediates exhibits high kinetic stability with substantial activation barriers, alternative configurations directly relax during geometry optimization to minimize aromaticity disruption. Moreover, highly metastable epoxy intermediates convert to ether bridges via nearly barrierless pathways at ambient temperature. Simplified time-dependent DFT (sTD-DFT) calculations show that oxygen functionalization narrows the energy gap, yielding a distinct bathochromic shift into the visible range. Ultimately, Clar’s rule is established as a predictive tool for ether bridge formation, enabling the rational design of GQDs with tailored stability and optical properties for bioimaging and optoelectronic applications. Full article

Quantitative structure–activity relationship and quantitative structure–property relationship (QSAR/QSPR)-based molecular toxicity prediction provides an in silico strategy for prioritizing environmental contaminants when longer-duration bioassay data are sparse. However, many Simplified Molecular-Input Line-Entry System (SMILES)-based machine learning models treat exposure duration as an unconstrained numerical covariate and provide limited information on whether predictions are supported by the observed temporal domain. Here, we evaluated an applicability-domain-aware chemoinformatics framework that combines transformer-derived molecular representations with toxicokinetic-informed temporal encoding and evidential uncertainty estimation. The approach replaces conventional ${log}_{10}$-transformed time encoding with a bounded first-order toxicokinetic saturation feature and combines this representation with Deep Evidential Regression to support a joint chemical–temporal view of the QSAR/QSPR applicability domain. Using experimentally derived U.S. EPA Ecotoxicology Knowledgebase (ECOTOX) fish EC50 mortality records, models were trained on 48,728 acute-duration observations and evaluated retrospectively on 2090 temporally separated longer-duration observations. The combined toxicokinetic and evidential model reduced temporal extrapolation error relative to conventional time encoding while maintaining comparable within-domain validation performance. The learned population-level timescale converged to 221 ± 3 h, consistent with accumulation timescales extending beyond standard acute fish test durations. Epistemic uncertainty was positively associated with absolute prediction error across all 10 folds, suggesting that the uncertainty estimates retained sample-level information relevant to applicability-domain-aware molecular toxicity screening. Cross-species analyses further showed that model behavior depended on training time coverage, with greater convergence when available assays covered a larger fraction of the learned timescale. These results suggest that toxicokinetic-informed temporal encoding can improve uncertainty-aware QSAR/QSPR modeling of environmental contaminant toxicity and support prioritization of compounds for further testing, while complementing rather than replacing chronic bioassays. Full article

Two-dimensional heterostructures have attracted considerable attention in electrocatalytic hydrogen evolution due to their pronounced interfacial effects, tunable electronic properties, and large specific surface areas. In this work, two representative oxygen-terminated transition metal carbides (MXenes) and three typical transition metal dichalcogenides (TMDs) were selected to construct six heterostructures. Using first-principles density functional theory (DFT) calculations, their binding energies, structural stability, electronic structures, and HER catalytic performance were systematically investigated. The results showed that all heterostructures possessed good thermodynamic stability and favorable electronic properties. In particular, SnS₂/Ti₂CO₂, SnSe₂/Ti₂CO₂, SnTe₂/Ti₂CO₂, and SnTe₂/Zr₂CO₂ exhibited near-optimal hydrogen adsorption Gibbs free energy, indicating excellent HER activity. Moreover, the variation in Gibbs free energy of hydrogen adsorption from isolated monolayers to heterostructures could be effectively correlated with the work function difference. The predicted trends provided a useful descriptor for catalytic performance. Overall, this study provides theoretical insights into the rational design of efficient, advanced HER catalysts and contributes to the advancement of sustainable energy conversion technologies. As this work is based solely on first-principles calculations, the predicted catalytic activity of the heterostructure should be regarded as a theoretical prediction and awaits experimental confirmation. Full article

Background: Ferrocene-containing compounds have gained attention in medicinal chemistry due to their unique redox and structural properties. This study investigates ferrocene-based analogues of imatinib and nilotinib to define their binding determinants within the ABL1 kinase domain using an integrated in silico approach, in relation to their previously reported cytotoxic activity. Methods: Ligand geometries were optimized at the B3LYP/def2-TZVP level with D3(BJ) dispersion and SMD solvation. Molecular docking against ABL1 (PDB ID: 2HYY) was performed using Glide SP, validated by re-docking and enrichment screening. Docked poses were refined using MM-GBSA (Prime, VSGB 2.1/OPLS4). The most active compounds (9 and 15a), together with the inactive control 15e, were subjected to three independent 500 ns molecular dynamics simulations (Desmond, OPLS4), followed by trajectory analysis including RMSD, RMSF, radius of gyration, SASA, and polar surface area. Results: Compounds 9 and 15a maintained stable binding within the ATP-binding pocket despite lacking the canonical hinge interaction with Met318, indicating hinge-independent binding. Their binding was mainly driven by interactions with Asp381 (DFG motif) and cation–π contacts with Lys271. In contrast, the compound 15e showed unstable binding, increased conformational flexibility, reduced pocket burial, and loss of key stabilizing interactions. Active compounds also preserved stable P-loop dynamics, with Tyr253 engagement suggesting a role in loop stabilization. Compound 9 exhibited the most constrained and reproducible binding mode among all analogues. Conclusions: Ferrocene-based analogues can sustain stable ABL1 binding via non-classical interaction networks independent of hinge recognition. The clear distinction between active compounds and the inactive analogue 15e supports the robustness of the proposed binding mode and provides a structural basis for their reported cytotoxic activity. These findings support further experimental evaluation of ferrocene-containing scaffolds as potential BCR-ABL1 inhibitors. Full article

This study examines a series of organic–inorganic crystal structures containing the periodate anion (IO₄⁻) to clarify the nature of the anion–anion interactions that are frequently referred to as halogen bonds. Our analysis demonstrates that, in many cases, IO₄⁻ does not develop an electrophilic σ-hole on the iodine center, even in the presence of organic cations, and therefore cannot reliably function as a halogen-bond donor. In its discrete (0D) form, the anion retains its character as a Lewis base. In crystal structures where extended architectures are observed—such as one-dimensional chains, two-dimensional layers, or three-dimensional cage-like assemblies—these structures arise predominantly from strong coulombic interactions with surrounding cations, as the interaction between the anions is intrinsically repulsive in the gas phase. Hydrogen bonding, together with other noncovalent interactions including chalcogen, tetrel, and/or pnictogen bonding, plays a dominant role in stabilizing the anionic arrangements and governing their structural organization. Full article

Metronidazole is an antibiotic used to treat Helicobacter pylori, a bacterium responsible for chronic infections in humans that cause gastric inflammation, ulcers, and cancer. However, its long-term administration is limited by toxicity and increased resistance. In the search for more effective agents against H. pylori infection, molecular hybridization has now been applied to the synthesis of the new compound 3. Its structure connects the metronidazole moiety to pectolinarigenin, the latter obtained by acid hydrolysis of glycosylated flavonoids isolated from the plant Linaria reflexa Desf. The NOE effect supported the C-7 functionalization of 3, as evidenced by the energy-minimized DFT-calculated structure. The new molecule enriches the chemical space of known metronidazole–flavonoid analogs, among which the genistein derivative 2 was reported as the most active in inhibiting bacterial strains. The computational analysis of 2 and 3 compared with metronidazole as the reference has provided favorable data for both Absorption, Distribution, Metabolism, and Excretion (ADME) predictions and the probability of anti-H. pylori activity, besides rising docking evaluation on three specific targets and dynamics simulation as inhibitors of the flavodoxin enzyme. The results are promising for further in-depth biological investigation. Full article

The ATP-binding cassette transport protein Pdr5p is known to play a role in multidrug resistance in Saccharomyces cerevisiae by effluxing structurally diverse xenobiotics; yet the physicochemical determinants of substrate recognition remain poorly defined. To address this, density functional theory (DFT) calculations at the B3LYP-D3BJ/def2-SVP level were combined with machine learning to derive a predictive model of substrate recognition using a curated dataset of 66 compounds spanning 9 functional categories. A hybrid support vector machine (SVM) classifier achieved 96.3% accuracy (95% CI: 81.0–99.9%, Clopper–Pearson exact) in discriminating substrates from non-substrates under leave-one-out cross-validation. Feature importance analysis identified lipophilicity (LogP, F-score = 37.5) as the dominant descriptor, suggesting that membrane partitioning constitutes the initial recognition step. The HOMO–LUMO gap contributed secondarily (F-score = 12.4). Substrates were further distinguished by high frontier orbital focalization, with frontier orbital spread of 1.8–2.6%, compared to 4.18–7.22% for non-substrates. Notably, a model trained exclusively on Pdr5p data achieved 87% prediction accuracy when applied without retraining to the human P-glycoprotein (ABCB1) dataset, suggesting conserved physicochemical principles of substrate recognition across evolutionarily distant ABC transporters. These findings provide a quantum chemical framework for understanding and potentially predicting MDR transporter substrate specificity. Full article

The photophysical properties of the designed molecules were investigated by theoretical calculations. The introduction of thiophene units into the DABNA-1 core reduces both S₁ and T₁ energies, whereas the derived ∆E_ST values become larger. As revealed by normal mode analysis for all designed molecules, the designed molecule, including the S···S interaction, exhibits the lowest reorganization energy during the excitation and de-excitation. Vibrationally resolved emission spectra further show that S···S interaction plays a pivotal role in reducing the spectrum width. Comprehensively, it is evident that the S···S interaction is a useful chemical design strategy to suppress the k_nr and enhance the color purity for OLED emitter. Full article

In this study, the R452A refrigerant used in refrigerated trucks is investigated through a multiscale approach combining thermodynamic and molecular-level analyses. The performance of the vapor compression refrigeration system is evaluated using energy and exergy analyses to assess system efficiency and identify irreversibilities. At the molecular level, Density Functional Theory (DFT) is employed to investigate the electronic structure and bonding characteristics of refrigerant components. This approach enables a detailed understanding of molecular properties that influence macroscopic thermodynamic behavior in refrigeration systems. Full article

This study applied density functional theory (DFT) to investigate gas-sensitive devices based on Pt- and Pd-doped SnS₂ monolayers, exploring their adsorption and sensing performance on four characteristic gases generated under normal operating or fault conditions of transformer oil. The adsorption behaviors and underlying sensing mechanisms of four gases on pristine and modified SnS₂ were systematically elucidated. The results reveal that Pt/Pd incorporation triggers a transition from weak physisorption to robust chemisorption. Compared to intrinsic SnS₂, the decorated monolayers exhibit dramatically augmented adsorption energies and accelerated interfacial charge transfer for all target molecules. Crucially, noble metal modification fundamentally modulates the electronic structure of the SnS₂ lattice, endowing the material with exceptional recognition specificity for distinguishing different gas species. These theoretical insights establish Pt- and Pd-SnS₂ as highly promising candidates for advanced DGA sensors, providing a robust materials design strategy for the condition monitoring of critical electrical infrastructure. Full article

A comparative density functional theory (DFT) study was performed to elucidate the mechanistic details of Schiff base formation between 3-pyridinecarboxaldehyde and the three positional isomers of aminobenzoic acid (o-, m-, and p-). Both single proton transfer (SPT) and methanol-assisted double proton transfer (DPT) pathways were systematically investigated in the gas phase and within a polarizable continuum model (PCM) for methanol. All stationary points were optimized at the B3LYP/6-31G and 6-311++G(d,p) levels, and transition states were confirmed by vibrational frequency and intrinsic reaction coordinate (IRC) analyses. The results reveal that the DPT mechanism is consistently associated with significantly lower activation free energies compared to the direct SPT pathway, particularly in methanol, where solvent-mediated proton relay markedly stabilizes the transition states. The positional effect of the amino group influences both the electrostatic potential distribution and the activation barriers, with the para isomer exhibiting enhanced nucleophilicity and improved reaction efficiency. These findings provide detailed mechanistic insight into solvent-assisted proton transfer processes in Schiff base synthesis and highlight the cooperative role of hydrogen-bond networks in reducing energetic barriers. Full article