Molecular Systems Design & Engineering最新文献

Accurately predicting the density of organic compounds is essential in chemical engineering. This study develops a robust quantitative structure–property relationship (QSPR) model using a multiple linear regression (MLR) methodology, based on a comprehensive dataset of 5478 organic compounds and 23 866 data points to predict density over a broad temperature range (115.0 to 594.1 K). Notably, norm indices (NIs) are applied for QSPR modeling of organic compound density for the first time. The model demonstrates excellent predictive performance, with a squared correlation coefficient (R²) of 0.9953 and a mean absolute error (MAE) of 10.11 kg m⁻³. Rigorous internal, external, and extrapolation validations are applied to confirm the model's reliability, accuracy, and generalization. The model achieves an R² value of 0.9951 and a MAE of 9.31 kg m⁻³ in external validation, while in internal validation using leave-one-out cross-validation, the corresponding values are 0.9951 and 10.51 kg m⁻³, respectively. Extrapolation validation, a novel approach recently introduced, further confirms the model's extrapolation ability, with most descriptors achieving the root mean square error (RMSE) of the test set (EV) values well below the training set's standard deviation (σ₉₅ = 140.89 kg m⁻³), closely aligning with RMSE_test (model). The RMSE of forward test exhibits a significant increase for NI₈ and NI₂₇ when the extrapolation degree (ED) exceeds 0.02, which suggests that it is not recommended to apply these two NIs for extrapolation. Overall, the results validate the robustness and broad applicability of the ρ(NI,T)-QSPR model, confirming its reliability for organic compound density prediction in industrial applications.

According to the Food and Drug Administration (FDA), a drug is defined as a substance used for the mitigation, treatment, and therapy of a disease. In increasing the effectiveness of treatment, drugs need a carrier to produce a controlled delivery pattern. This study used microcrystalline cellulose-graft-poly(itaconic acid) copolymer as a drug carrier, while α-tocopherol was used as a drug model. The copolymer main chain is hydrophilic, while the side chains are hydrophobic. The amphiphilic structure can result in the formation of micelles. The success of copolymer synthesis was proven by the presence of a new peak in the infrared absorption band at 1645 cm⁻¹. The peak indicated the presence of a CO group of itaconic acid grafted onto the main chain of microcrystalline cellulose. The molecular process of carrying α-tocopherol can be observed based on the results of molecular dynamics simulations. The carriage of α-tocopherol is characterized by a copolymer radial distribution function (RDF) peak at a range of 0.5–0.9 nm and a decrease in the solvent-accessible surface area (SASA). The drug release data were modeled using the exponential model (first-order kinetic), the Weibull model (fractal-like first-order kinetic), and the diffusion-based Higuchi model.

Taking inspiration from natural systems, such as spider silk and mollusk nacre, that employ hierarchical assembly to attain robust material performance, we leveraged matrix–filler interactions within reinforced polymer–peptide hybrids to create self-assembled hydrogels with enhanced properties. Specifically, cellulose nanocrystals (CNCs) were incorporated into peptide–polyurea (PPU) hybrid matrices to tailor key hydrogel features through matrix–filler interactions. Herein, we examined the impact of peptide repeat length and CNC loading on hydrogelation, morphology, mechanics, and thermal behavior of PPU/CNC composite hydrogels. The addition of CNCs into PPU hydrogels resulted in increased gel stiffness; however, the extent of reinforcement of the nanocomposite gels upon nanofiller inclusion also was driven by PPU architecture. Temperature-promoted stiffening transitions observed in nanocomposite PPU hydrogels were dictated by peptide segment length. Analysis of the peptide secondary structure confirmed shifts in the conformation of peptidic domains (α-helices or β-sheets) upon CNC loading. Finally, PPU/CNC hydrogels were probed for their injectability characteristics, demonstrating that nanofiller–matrix interactions were shown to aid rapid network reformation (∼10 s) upon cessation of high shear forces. Overall, this research showcases the potential of modulating matrix–filler interactions within PPU/CNC hydrogels through strategic system design, enabling the tuning of functional hydrogel characteristics for diverse applications.

Water-based lubricants demonstrate significant development potential in machining and automotive manufacturing industries owing to their environmental friendliness, safety profile, and ease of cleaning. In this study, two eco-friendly amino acid-based ionic liquids (AAILs), N-ethyl-D-glucamine-2-(N-methyldodecanamido) acetate acid (EDG-LS) and N-octyl-D-glucamine-2-(N-methyldodecanamido) acetate (ODG-LS), were synthesized using 2-(N-methyldodecanamido) acetic acid and glucosamine as raw materials. When AAILs were employed as water-based lubrication additives, the physicochemical characteristics, tribological performances, and lubrication mechanisms of the lubricants were systematically evaluated. The results of the cast iron tests demonstrate that adding just 1 wt% of AAIL additives can significantly reduce the corrosion of water. Moreover, EDG-LS exhibits superior friction reduction (69.9% decrease) and anti-wear properties (91.4% reduction) compared to water. The combined influence of physically adsorbed films and tribochemical reaction layers endows AAILs with outstanding tribological performance. Additionally, two kinds of AAILs exhibit favorable biodegradability, with a biodegradation rate approaching 60%. This research provides theoretical insights for creating eco-friendly, biodegradable, and multifunctional water-based lubricant additives.

Developments related to large language models (LLMs) have deeply impacted everyday activities and are even more significant in scientific applications. They range from simple chatbots that respond to a prompt to very complex agents that plan, conduct, and analyze experiments. As more models and algorithms continue to be developed at a rapid pace, the complexity involved in building this framework increases. Additionally, editing these algorithms for personalized applications has become increasingly challenging. To this end, we present a modular code template that allows easy implementation of custom Python code functions to enable a multi-agent framework capable of using these functions to perform complex tasks. We used the template to build DynaMate, a complex framework for generating, running, and analyzing molecular simulations. We performed various tests that included the simulation of solvents and metal–organic frameworks, calculation of radial distribution functions, and determination of free energy landscapes. The modularity of these templates allows for easy editing and the addition of custom tools, which enables rapid access to the many tools that can be involved in scientific workflows.

The orientation of integral membrane proteins (IMPs) with respect to the membrane is established during protein synthesis and insertion into the membrane. After synthesis, IMP orientation is thought to be fixed due to the thermodynamic barrier for “flipping” protein loops or helices across the hydrophobic core of the membrane in a process analogous to lipid flip-flop. A notable exception is EmrE, a homodimeric IMP with an N-terminal transmembrane helix that can flip across the membrane until flipping is arrested upon dimerization. Understanding the features of the EmrE sequence that permit this unusual flipping behavior would be valuable for guiding the design of synthetic materials capable of translocating or flipping charged groups across lipid membranes. To elucidate the molecular mechanisms underlying flipping in EmrE and derive bioinspired design rules, we employ atomistic molecular dynamics simulations and enhanced sampling techniques to systematically investigate the flipping of truncated segments of EmrE. Our results demonstrate that a membrane-exposed charged glutamate residue at the center of the N-terminal helix lowers the energetic barrier for flipping (from ∼12.1 kcal mol⁻¹ to ∼5.4 kcal mol⁻¹) by stabilizing water defects and minimizing membrane perturbation. Comparative analysis reveals that the marginal hydrophobicity of this helix, rather than the marginal hydrophilicity of its loop, is the key determinant of flipping propensity. Our results further indicate that interhelical hydrogen bonding upon dimerization inhibits flipping. These findings establish several bioinspired design principles to govern flipping in related materials: (1) marginally hydrophobic helices with membrane-exposed charged groups promote flipping, (2) modulating protonation states of membrane-exposed groups tunes flipping efficiency, and (3) interhelical hydrogen bonding can be leveraged to arrest flipping. These insights provide a foundation for engineering synthetic peptides, engineered proteins, and biomimetic nanomaterials with controlled flipping or translocation behavior for applications in intracellular drug delivery and membrane protein design.

The low charge separation efficiency and slow water oxidation kinetics of bismuth vanadate (BiVO₄, BVO) limit its performance for solar water splitting. Here, a flame growth method has been developed to rapidly grow a nickel-based cocatalyst (NiO_x) on the surface of the worm-like BVO films. After 20 s flame growth, the NiO_x cocatalyst, which is comprised of Ni, NiO, and NiOOH, can be uniformly and rapidly synthesized. The NiO_x/BVO composite photoanode achieves a photocurrent density of 3.80 mA cm⁻² at 1.23 V vs. RHE in a neutral electrolyte, which is 6.67 times higher than that of the pristine BiVO₄. Under the assistance of polyacrylamide hydrogel coating, the photocurrent of the NiO_x/BVO photoanode can be well maintained at 62.26% after a 24 h long-term stability test. The performance improvement can be mainly attributed to the fact that the NiO_x layer reduces the resistance of the charge transfer and the energy barrier of the oxygen evolution reactions, and introduces a large number of oxygen vacancies. This research confirms that the flame growth of cocatalysts is an efficient method for preparing the cocatalytic layer on the nanostructure photoelectrode, which can well maintain the nanostructures.

Developing new negative emission technologies (NETs) to capture atmospheric CO₂ is necessary to limit global temperature rise below 1.5 °C by 2050. The technologies, such as direct air capture (DAC), rely on sorption materials to harvest trace amounts of CO₂ from ambient air. Deep eutectic solvents (DESs) and eutectic solvents (ESs), a subset of ionic liquids (ILs), are all promising new CO₂ sorption materials for DAC. However, the experimental design space for different DESs/ESs/ILs is vast, with the exact CO₂ complexation pathways difficult to elucidate; this creates significant limitations in rationally designing new materials with targeted CO₂ sorption energetics. Herein, the CO₂ complexation pathways for a structural library of different DES/ES components are computed using quantum chemical calculations (i.e., density functional theory). For the entire structure library, we report the energies of elementary CO₂ binding and proton transfer reactions as these reactions are fundamental in DAC within DESs and ESs. These elementary reactions are combined to generate CO₂ complexation pathways and calculate their free energies. The different elementary steps and reaction pathways demonstrate the range of CO₂ complexation free energies and the significance between CO₂ binding and proton transfer reactions. We also report the CO₂ complexation free energies with different functional groups around the CO₂ sorption site, supporting the concept of functionalization for tuning CO₂ complexation thermodynamics. Additionally, our findings suggest potential descriptors, such as proton affinity or pK_a, could be useful when identifying candidate species for ESs and predicting/rationalizing product distributions. Our work has implications for experimental synthesis, characterization, and performance evaluation of new DAC sorption materials.

A hybrid mesoscopic simulation approach combining multiple particle collision dynamics (MPCD) with molecular dynamics (MD) is employed to investigate the dynamic behaviors and conformational changes of semi-flexible [2]catenanes with varying ring sizes under steady shear flow conditions. Firstly, our study reveals an irregular linear relationship between the three-dimensional surface area of the rings and the shear rate, as evidenced by changes in the surface area of the semi-flexible [2]catenane. Through schematic observations, we find that the dynamic behaviors of [2]catenanes differ for varying ring sizes. Small rings exhibit tumbling motions, medium rings show slip-tumbling motions, while large rings undergo fold-slipping motions. Medium and large rings show shear thinning conformation changes. Secondly, we analyze the normal and diagonal angles of the two rings, demonstrating that the movements in both the shear direction and the gradient direction are complete but intermittent. Thirdly, we analyze how the relative displacement vector of the center of mass between the two rings in the [2]catenane changes over time. This analysis indication of the relative motion occurring between the two rings. We also find that within certain ranges of shear rate and ring size, the two rings of the [2]catenane twist into “8” shapes, rather than slip-tumbling and fold-slipping motions. These findings provide valuable insights for guiding the transport of catenane polymers in biological systems and for designing catenane polymeric materials for industrial applications.

Intrinsically disordered proteins (IDPs) yield solutions with tunable phase transition behavior and have been widely applied in designing stimuli-responsive materials. Understanding interactions between amino acid residues of the IDP sequence is critical to designing new IDP-based materials with selective phase behavior, assembly, and mechanical properties. The lack of defined structure for this class of proteins complicates accurate prediction of their molecular-scale behavior. In this review, recent progress is presented in the development and application of simulation methods to describe the behavior of IDPs. Results for elastin-like polypeptides (ELPs) and resilin-like polypeptides (RLPs) are highlighted, focusing on studies that compare simulation results with experimental findings.