Molecular Systems Design & Engineering最新文献

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.

Selective adsorption of hazardous micropollutants from water remains a critical challenge in sustainable materials design. Herein, we demonstrate a combined computational–experimental approach to rationally engineer molecularly imprinted polymers for targeted porosity, using 2,4,6-trinitrotoluene as a model template. By simulating pre-polymerisation mixtures of monomers, crosslinkers, and solvent using molecular dynamics, we capture key template–monomer interactions and predict the resulting porosity of the final polymer network. Surface area and free volume predictions from simulations show excellent agreement with experimental nitrogen sorption data across varying solvent compositions. Our findings highlight a fundamental trade-off between imprinting efficiency (favoured in acetonitrile-rich environments) and porous structure (promoted by dimethyl sulfoxide). We validate that pre-polymerisation simulations alone can accurately guide formulations toward high-performance materials, opening new pathways for computationally-driven design of porous polymeric adsorbents.

Field-based simulations can be challenging in multi-component polymer systems and are highly sensitive to the choice of relaxation coefficients (λ) used in the field update algorithms. Judiciously chosen relaxation coefficients are critical for both the stability and convergence of field-based simulations, yet their selection is challenging when the number of unique chemical species in the system is large. In this work, we develop a new method to automatically and efficiently locate optimal relaxation coefficients in systems with large numbers of species. We begin by analyzing the effects of relaxation coefficients in two- and three-species systems and demonstrate that regions of high-performance are both narrow and system-specific. Based on these findings, we next develop a method based on Bayesian optimization that automatically locates relaxation coefficients that are stable and exhibit good performance. We demonstrate that our method is considerably faster than naive search methods and becomes particularly efficient as the system complexity increases. This work demonstrates that Bayesian optimization can be used to stabilize and accelerate field-based simulations that contain many different chemical species.

Conjugation elongation of squaraines is a potential approach to optimize their performance as nonlinear optical (NLO) chromophores and TiO₂-photosensitizers in dye-sensitized solar cells (DSCs). This study investigates the impact of integrating π-conjugated heteroaromatic spacers on the optoelectronic properties of four modeled unsymmetrical squaraine derivatives. Density functional theory (DFT) and time-dependent TD-DFT computations revealed the dual functionality of all four π-extended squaraine dyes, with the capability to sensitize TiO₂ for far-red light harvesting and amplify second-order NLO response at the molecular-level. Dye SQ-N incorporating an ethyl-dithienopyrrole π-spacer emerged as the optimal photosensitizer for TiO₂-based DSCs, exhibiting a hyperchromic S₁ transition and a light harvesting efficiency (LHE) of 98% at 697 nm (λ_max), the most thermodynamically driven electron injection (ΔG_inj), robust adsorption (E_ads) onto the TiO₂ nanocluster, enhanced orbital coupling (ΔE_oi) and hybridization between virtual molecular π* orbitals of SQ-N and 3d-orbitals of Ti atoms, in addition to superior charge transfer at the SQ-N–TiO₂ interface, under deep red-to-NIR photoexcitation. Conversely, the P-acetyl dithienophosphole oxide π-linker in SQ-P led to a sixfold enhancement in off-resonant hyperpolarizability (β₀) compared to the π-spacer-free parent dye, in addition to manifesting the maximal dynamic electro-optic Pockels (EOP) β₁₀₆₄ and β₁₄₆₀, second-harmonic generation (SHG) β₁₀₆₄ and hyper-Rayleigh scattering β₁₀₆₄. Analytical DFT-predicted SHG activity of the modeled dyes showed simultaneous potential for NIR-to-green and telecom E-band (1460 nm) to red light conversion. β scans demonstrated dual EOP and optical rectification functionality, while dyes SQ-N and SQ-Th further displayed significant sum/difference frequency generation (SFG/DFG) output at ω₁ ± ω₂. Polarization-resolved SHG analysis revealed a hybrid dipolar–octupolar NLO symmetry across all the dyes, with maximal harmonic intensity at Ψ = ±90°—a signature of synergistic dipole alignment and 3D charge delocalization.