Molecular Systems Design & Engineering最新文献

Water-lean solvents have emerged as alternatives to conventional aqueous amines for CO₂ capture, although there is delicate balance between achieving high CO₂ loadings while maintaining sufficiently low viscosity. In this work, we present the advantages of serinol as a framework for designing single component water-lean solvents which meet these criteria. Starting from commercially available glycidyl ethers or epichlorohydrin, several symmetric and non-symmetric 1,3-diether-2-amino molecules were synthesized and thoroughly studied. Spectroscopic analyses (¹³C NMR and FTIR) confirmed chemical reactions between CO₂ and the serinol-based water-lean solvents. CO₂ absorption studies showed these solvents had high loading capacities with positive indications for stability and recyclability. The serinol-based molecules have low viscosities in their neat states (1–4 cP at 30 °C) with viscosities as low as 28 cP at 30 °C in highly CO₂-rich states. Furthermore, based on choice of functional groups, serinol-based molecules also show potential as switchable solvents that transition from hydrophobic to hydrophilic upon reaction with CO₂. Our molecular-level simulations reveal how CO₂ binding alters H-bonding networks, reduces free volume, and dramatically increases viscosity with increasing levels of complexation, mirroring the trends observed experimentally. The simulation data also support the observed switchable solvent behavior by elucidating the structural reorganization and dynamic constraints induced by CO₂ loading.

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

Group contribution (GC) models are powerful, simple, and popular methods for property prediction. However, the most accessible and computationally efficient GC methods, like the Joback and Reid (JR) GC models, often exhibit severe systematic bias. Furthermore, most GC methods do not have uncertainty estimates associated with their predictions. The present work develops a hybrid method for property prediction that integrates GC models with Gaussian process (GP) regression. Predictions from the JR GC method, along with the molecular weight, are used as input features to the GP models, which learn and correct the systematic biases in the GC predictions, resulting in highly accurate property predictions with reliable uncertainty estimates. The method was applied to six properties: normal boiling temperature (T_b), enthalpy of vaporization at T_b (ΔH_vap), normal melting temperature (T_m), critical pressure (P_c), critical molar volume (V_c), and critical temperature (T_c). The CRC Handbook of Chemistry and Physics was used as the primary source of experimental data. The final collected experimental data ranged from 485 molecules for ΔH_vap to 5640 for T_m. The proposed GCGP method significantly improved property prediction accuracy compared to the GC-only method. The coefficient of determination (R²) values of the testing set predictions are ≥0.85 for five out of six and ≥0.90 for four out of six properties modeled, and compare favorably with other methods in the literature. T_m was used to demonstrate one way the GCGP method can be tuned for even better predictive accuracy. The GCGP method provides reliable uncertainty estimates and computational efficiency for making new predictions. The GCGP method proved robust to variations in GP model architecture and kernel choice.

Atmospheric water harvesting (AWH) using metal–organic frameworks (MOFs) offers a promising route to address freshwater scarcity in arid and off-grid environments. Yet, the structural and chemical factors that govern MOF performance remain insufficiently understood. Here, we combine high-throughput Grand Canonical Monte Carlo (GCMC) simulations with interpretable machine learning to study the structure–property relationships driving water uptake in MOFs. A chemically and structurally diverse set of 2600 frameworks was selected from the ARC-MOF database, and water uptake capacities were computed at 100% and 30% relative humidity. Among several regression models, Light Gradient Boosting Machine (LGBM) achieved the highest predictive accuracy. SHapley Additive exPlanations (SHAP) and correlation analyses identified adsorption energetics, local electrostatics (oxygen and hydrogen partial charges, metal electronegativity), and framework density as the dominant factors, with geometry acting as a secondary modulator. To provide an explicit analytical form for rapid screening and hypothesis generation, we constructed a second-order polynomial regression model using the top SHAP-ranked features. These results advance the fundamental understanding of water adsorption in MOFs and establish a scalable, data-driven framework for the rational design of high-performance materials for AWH.

In tight oil exploitation, the heterogeneity of reservoirs has a crucial influence on the oil sweep volume and oil displacement efficiency. This study employs molecular dynamics simulations to investigate the oil displacement behavior in water flooding and the impact of the heterogeneity of oil reservoirs. By simulating single nanochannels with sizes ranging from 4 nm to 8 nm and double channels with combined sizes ranging from 4–5 nm to 4–8 nm, the effects of reservoir heterogeneity on oil displacement processes are analyzed. Results show that in single nanochannel systems, a larger nanochannel size significantly reduces threshold injection pressure, thereby lowering the resistance for water entry and enhancing oil displacement efficiency. In double-channel systems, water preferentially enters the larger-sized nanochannel, and the efficiency of oil displacement in the smaller-sized channel is constrained. The oil displacement processes of single and double nanochannel systems are discussed in detail, including the threshold injection pressure, atom number distribution, oil displacement efficiency, and nanochannel size effect. This work establishes a theoretical foundation for understanding microscale displacement mechanisms in heterogeneous tight oil reservoirs and offers practical guidance for optimizing development strategies in low-permeability reservoirs.

Indoleamine 2,3-dioxygenase is an immunomodulatory enzyme that shows great promise when delivered exogenously as a protein therapeutic. However, IDO activity is under complex redox control, mediated in part by multiple cysteine residues within its primary sequence. We have characterized three IDO mutants in which solvent-accessible cysteine residues were mutated to chemically-similar serine residues, “IDO_C4S4” with C112S, C159S, C206S, and C308S mutations and “IDO_C5S3” with C112S, C159S, and C308S mutations based on prior reports that C206 is necessary for catalytic function, and IDO_C0S8, in which all cysteine residues were mutated to serines. IDO_C0S8 was expressed in poor yield and demonstrated less than 1% activity when compared to wild-type IDO. In contrast, IDO_C4S4 and IDO_C5S3 demonstrated robust enzymatic activity, though IDO_C5S3 had a slower V_max than wild-type and IDO_C4S4. Computational predictions and experimental measurements suggested a high degree of structural similarity between the wild-type IDO and IDO_C4S4, with subtle perturbation of α-helical content for IDO_C5S3. The structure of IDO_C0S8 was predicted to be significantly different than that of wild-type IDO. IDO_C4S4 and IDO_C5S3 were more stable than wild-type IDO over time at physiological, ambient, and reduced temperatures, likely due to diminished oxidation of the mutant IDO forms. Based on the increased V_max and robust thermal stability of IDO_C4S4, we fused it to the anchoring moiety galectin 3, to evaluate its effectiveness in a mouse model of psoriasis. The IDO_C4S4-galectin-3 fusion blunted the rate and severity of disease as compared to wild-type IDO-galectin-3 fusion. When compared to historical data with Cys-Ala IDO mutants, this study highlights the importance of employing amino acid substitution according to similarity in isosteric and isostructural shape to advance IDO as an immunomodulatory therapeutic.

Shaping supramolecular hydrogels formed using low molecular weight gelators (LMWGs) into architecturally complex and multifunctional materials is a significant challenge. Here, we introduce a strategy to mechanically twist multiple 1D supramolecular gel filaments (gel noodles) into robust, multifunctional, and stimuli-responsive structures. Twisting introduced mechanical interlocking, which in two identical filaments yielded marginal improvement in tensile performance, while compositionally distinct gel noodles exhibited up to ∼25% increase in strength due to effective load redistribution and frictional contact. However, twisting three or more filaments reduced mechanical strength, likely due to high internal strain and the formation of misaligned bundles, an effect consistent with stochastic failure propagation in twisted fibre assemblies. These results highlight the dual nature of intertwining multiple noodles: it can reinforce or compromise mechanical robustness depending on geometry and filament interactions. Despite this, twisting chemically distinct noodles enabled the formation of robust structures with spatially separated functionalities, such as photoresponsiveness, while maintaining structural integrity. This modular strategy offers a tunable platform for engineering hierarchical materials with potential for future application-specific studies.

Some phosphonium-based ionic liquids exhibit LCST phase transition behaviour in water. This study demonstrates that host–guest interactions obtained by adding an α-cyclodextrin host increased their LCST phase transition temperature (T_LCST). NMR analysis confirmed the complex formation between the phosphonium cation and α-cyclodextrin that drives the drastic change of T_LCST.

In Cy3–DNA conjugates, cyanine dye–nucleobase interactions can alter Cy3 fluorescence intensity, potentially causing significant errors in fluorescence-based bioanalytical assays. We quantify the error due to Cy3 fluorescence intensity enhancement with reference to free Cy3, when bonded to hybridized and single stranded DNA oligonucleotides, with the help of a normalised parameter Δ_H. Homo-nucleotide probes with adenine repeats having purine bases adjacent to Cy3 showed high accuracy in hybridization (p-value ≤ 0.0005) and mismatch (p-value ≤ 0.0001) detection. The Δ_H was 100% for Cy3–adenine (A), 70% for Cy3–thymine (T), 80% for Cy3–guanine (G) and 60% for Cy3–cytosine (C) at 488 nm excitation. Compared to free Cy3 having single exponential decay, Cy3–DNA conjugation resulted in slower, excitation-dependent double exponential decay of Cy3 fluorescence, indicating non-radiative kinetic steps. The fluorescence lifetime of Cy3 in single stranded and hybridized Cy3–A increased by an excitation-dependent factor of ∼3, in comparison with the single stranded and hybridized counterparts of the other probes. The sensitivity to detect single nucleotide mismatches, quantified by the accuracy parameter α, was highest for Cy3–A and Cy3–G probes, with maximum α = 0.66 (66%) at 488 nm. In GC-rich hetero-nucleotide sequences, an adjacent purine in the complementary strand yielded maximum fluorescence enhancement, with a systematic decrease in fluorescence intensity upon shifting the position of G from Cy3, illustrating the single base distance sensitivity of fluorescence enhancement.

Carbon capture is a priority strategy for reducing CO₂ emissions and mitigating climate change. Adsorption-based technologies offer significant potential to reduce imposed parasitic energy, and metal–organic frameworks (MOFs) are considered a promising class of adsorbents for this purpose. In this review, targeting carbon capture using MOFs, we explore materials screening approaches using material-level properties (e.g., CO₂ working capacity and CO₂/N₂ selectivity) and process-level performance indicators (e.g., CO₂ purity and energy consumption), with an emphasis on the incorporation of process-level considerations into screening workflows. We also highlight recent advancements of data-driven property and process models in accelerating large-scale materials screening. Next, we review diverse materials design approaches, shifting from open-loop exhaustive search to closed-loop targeted discovery. Finally, we discuss the challenges associated with experimental databases, active materials discovery, and simultaneous material and process design, with perspectives proposed to accelerate the materials discovery for industrial carbon capture applications.