化学最新文献

Gas flooding plays a crucial role in enhanced oil recovery; however, the underlying microscopic mechanisms, especially those related to interfacial changes, remain unclear. Based on a realistic geometric model of porous media, this study employs the level-set method to simulate the oil displacement processes of N₂ flooding, CO₂ immiscible flooding, CO₂ miscible flooding, and foam flooding at the microscale. The oil–gas interface is tracked throughout the simulations to compare and analyze the displacement behaviors of different gases. First, a dynamic simulation of the gas flooding process is conducted, analyzing the variations in pressure, velocity, and remaining oil volume fraction over time. Second, the effects of factors such as injection rate, foam gas–liquid ratio, and surface tension on oil displacement efficiency are investigated. Finally, the displacement performance of N₂ flooding, CO₂ flooding, and foam flooding is compared under specific conditions to examine the oil recovery mechanisms associated with different gases. The results indicate that the remaining oil volume fractions after N₂ flooding and CO₂ immiscible flooding are approximately 30%. Increasing the injection rate of N₂ and CO₂ can improve early-stage displacement performance and slightly enhance oil recovery. In contrast, the remaining oil volume fractions after CO₂ miscible flooding and foam flooding are about 10%. The optimal foam flooding effect is achieved at a gas–liquid ratio of 3 : 1 and a surface tension of 0.02, which corresponds to the lowest remaining oil volume fraction. Furthermore, the inlet pressure declines rapidly during N₂ and CO₂ immiscible flooding, resulting in a relatively low final pressure. In comparison, the inlet pressure decreases more gradually during CO₂ miscible flooding and foam flooding, maintaining a certain pressure level by the end of the process.

Bayesian optimization (BO) enables data-efficient optimization of complex chemical reactions by balancing exploration and exploitation in large, mixed-variable parameter spaces. This review provides an accessible introduction for chemists wishing to adopt BO, outlining the fundamentals of surrogate models, acquisition functions, and key mathematical concepts. Practical considerations are emphasized, including kernel design, representation of categorical variables, and strategies for multi-objective and batch optimization. Applications are comprehensively surveyed across experimental scales, from high-throughput platforms to automated flow reactors and larger-scale processes. Finally, emerging directions such as transfer learning and data reuse are discussed in the context of accelerating optimization campaigns and enabling more generalizable, data-driven strategies in chemistry.

Due to asphaltene's complex structure and its status as the most polar and surfactant component, it is extremely challenging to investigate the interaction between asphaltenes and wax molecules. In this study, we employ molecular dynamics methods to investigate the interaction mechanisms between asphaltenes including the isolated-island-type and the archipelago type and wax molecules with carbon numbers of 20, 25, and 30. By analyzing the aggregation rates, cluster sizes, distributions, and interaction energies of asphaltenes and wax molecules during the simulation process, we were able to elucidate that the branched structures of the isolated-island-type asphaltenes and the aromatic ring structures of the archipelago type asphaltenes play a dominant role in their interactions with wax molecules, which leads to a diffusion coefficient for island-type asphaltenes that is about 15% higher than that for archipelago-type asphaltene. In addition, as the wax molecules (C20, C25, and C30) aggregated, they tend to form elliptical structures, which exhibit affinity with the aromatic ring structure of asphaltenes. However, when wax molecules with C20, C25, and C30 are mixed and aggregated, the resulting ellipse-like structures have short chains at their ends due to varying wax molecule lengths. In this case, these short chains are attracted to the branched structures of asphaltenes, thereby enhancing the interaction between asphaltenes and waxes.

Per- and polyfluoroalkyl substances (PFASs) are persistent pollutants with shifting pollution characteristics toward short-chain and structurally diverse variants, posing great challenges for precise analysis. Pretreatment, as a rate-limiting step, lacks systematic guidance for technology selection. This review makes a unique contribution by constructing a "target substance characteristics-sample matrix-detection requirements" three-dimensional matching framework, systematically categorizing pretreatment technologies into solvent-based, adsorption-based, and advanced types, and dissecting their mechanisms, advantages, limitations, and applicability. Solvent-based technologies excel in long-chain PFASs analysis in simple matrices, while adsorption-based methods enable efficient enrichment of short-chain PFASs via functional materials, and advanced technologies meet green and complex matrix demands. Background contamination control strategies are also emphasized. Current challenges include inefficient short-chain enrichment, high material costs, and underdeveloped on-site devices. Critical needs involve the development of functional solvents, artificial intelligence-driven adsorbents, standardized protocols, and full-process PFASs-free systems. This review provides actionable references for PFASs analysis optimization.

The analysis of amino acids in biological samples is challenged by their high polarity, low ionization efficiency, and lack of chromophores, which limit detection by LC-UV or LC-MS. Derivatization is widely used to improve retention, sensitivity, and detection. In this study, derivatization agents based on pyridine, quinoline, and isoquinoline positional isomers scaffolds with COCl, SO₂Cl, and NHS ester reactive groups were systematically evaluated using deuterium-labeled amino acids to assess stability, derivatization efficiency, chromatographic behavior, and MS response. NHS ester-based agents were found to exhibit superior stability, maintaining activity for over 1 year, whereas carbonyl chlorides and sulfonyl chloride-based agents were highly reactive but less stable. NHS ester of isoquinoline-6-carboxylic acid (6-CiQ-NHS) was identified as the most effective agent, combining rapid derivatization, strong MS signal, and stability. LC-MS analysis demonstrated excellent linearity (R² ≥ 0.995), low nanomolar detection limits (0.23-6.33 nM), and separation of isomeric amino acids (e.g., isoleucine/leucine). 6-CiQ-NHS is proposed as a practical derivatization approach for amino acid quantification and provides a framework for the rational design of future agents.

Carnosic acid (CA), a diterpene abundant in rosemary, is well known for its antioxidant activity but is chemically unstable in plasma. During sample handling it rapidly undergoes autoxidation, which often makes its quantification unreliable. We developed an LC-MS/MS method that combines a simple acetonitrile-NaCl salting-out extraction with the use of antioxidant additives. Extraction conditions-solvent composition, salt ratio, and aqueous pH-were optimized using design of experiments and response surface methodology. In parallel, several antioxidants were tested for their ability to stabilize CA during preparation. The final protocol, using 100% acetonitrile, 100% NaCl, and acidic aqueous phase (pH < 1.5), together with pyrogallol as an additive, provided consistent recoveries above 80% and matrix effects within 15%. Linearity was excellent (r > 0.99) across the calibration range, and the method showed low quantification limits (5 nmol/L for CA; 1 nmol/L for its metabolites). Accuracy and precision met regulatory criteria, and reproducibility was achieved using podocarpic acid as an internal standard. In a small mouse study, the approach was able to follow CA in plasma after oral administration, with a peak observed at 0.25 h. By combining stabilization with a straightforward extraction, this work offers a practical way to quantify CA and related compounds in plasma. The strategy may also be useful for other polyphenols that share similar instability issues and could support future pharmacokinetic or bioavailability studies.

The construction of C-C bonds is a pivotal transformation in organic synthesis. Traditional Ullmann type and Hurtley reactions for constructing C(sp³)-C(sp²) bonds rely on organometallic reagents or substrates with active methylene units. These requirements significantly limit their practical applicability. Herein, employing two readily available organic halides, we report a ligand-enabled, electrochemical copper-catalyzed cross-electrophile coupling. Although Cu is the first metal reported for C-C bond construction, cross-electrophile coupling nowadays is dominated by Ni. This work demonstrates that efficient cross-electrophile coupling can be realized by electrochemical Cu catalysis. Our protocol is applicable to a wide range of propargyl bromides and (hetero)aryl iodides or bromides, delivering the desired products in good yields with high cross-selectivity. The use of an orthodimethylamine-substituted diamine ligand is crucial for promoting the reaction and suppressing cathodic Cu deposition. We attribute this effect to an intramolecular H···N H-bonding, which facilitates hyperconjugation between the NMe₂ moiety and the nitrogen atom coordinated to the Cu center. Mechanistic studies indicate that the reaction follows a radical pathway, contrasting with the S_N2 pathway reported previously. This work establishes a foundation for electrochemical Cu-catalyzed cross-electrophile coupling and provides a new paradigm for Cu-catalyzed C-C bond formation via radical intermediates.