Chirality is a fundamental feature underpinning complexity within the natural world. Efforts aimed at replicating chiral emergence and propagation observed in Nature have led to significant advances in synthetic chemistry,...
This work builds interpolative models to predict surfactant interface behavior for firefighting foam formulations on ethanol and gasoline fuels by combining design of experiment methodologies with molecular dynamic simulations.
The activity–dipolar ratio governs active superparamagnetic chaining: low ratios sustain power-law growth, intermediate ratios arrest growth at finite chain sizes, and high ratios destabilize doublets and suppress aggregation.
Alkyl chain length is commonly regarded as a passive determinant of lipophilicity in bioactive molecules. Here, we demonstrate that it functions instead as a molecular switch that reprograms supramolecular organization and transport-related behavior in dialkyl 2-(((4-acetamidophenyl)amino)propan-2-yl)phosphonates. A combined experimental-theoretical investigation was performed on the diethyl derivative (compound I) and the dibutyl derivative (compound II) using single-crystal and powder X-ray diffraction, DFT (omega B97X-D/6-31G*), DLS, NMR, IR/ATR spectroscopy, UV-vis absorption, fluorescence spectroscopy, HRMS, thermal analysis, QSAR, ADMET, and in vitro cytotoxicity studies. Structural analyses revealed that compound I forms hydrogen-bonded dimers, whereas compound II assembles into cooperative tetramers through complementary P=O & ctdot;H-N (amine) and C=O & ctdot;H-N (amide) interactions. Excellent agreement between experimental and calculated structural parameters validated the proposed supramolecular models. DLS, QSAR, and frontier orbital analyses showed that tetramer formation substantially increases molecular size, anisotropy, accessible surface area, and polarizability. Although compound II exhibits a slightly smaller HOMO-LUMO gap, both compounds remain electronically stable, with the reduced gap reflecting enhanced electronic delocalization rather than increased chemical reactivity. Thermal and ATR studies demonstrated that alkyl chain elongation decreases crystal packing efficiency and promotes hydrogen-bond reorganization without compromising the stability of the phosphonate core. Both compounds exhibited low cytotoxicity (IC50 > 100 mu M), indicating good biological tolerance. While monomer-based ADMET predictions suggest a shift from absorption-favored behavior in compound I to permeability-limited characteristics in compound II, these models do not fully capture the persistence of the experimentally verified tetrameric assembly. Collectively, the results establish alkyl chain length as a supramolecular design parameter governing assembly state, intermolecular interactions, and potential delivery pathways in phosphonate-based systems.
This review establishes a synergistic framework combining molecular design, formulation, and manufacturing to balance energy-sensitivity–manufacturability tradeoffs, with reproducibility and stability as critical as performance.
In this study, we systematically investigated the effects of the crystal phase (alpha, beta, and gamma) and crystallinity of Ga2O3 on the photocatalytic degradation activity toward perfluorooctanoic acid (PFOA). Among the tested catalysts, beta-Ga2O3 exhibited the highest degradation activity, and even within the same beta phase, the activity varied markedly depending on the crystallinity. Degradation experiments using various scavengers showed that the reaction proceeds predominantly via direct oxidation by photogenerated holes, while & centerdot;OH radicals contribute very little to the degradation. The superior activity of highly crystalline beta-Ga2O3 is attributed to synergistic effects of suppressed charge-carrier recombination, improved hole-utilization efficiency associated with a relatively low amount of surface hydroxyl groups, and a surface coordination environment favorable for substrate adsorption. These findings demonstrate that controlling the crystal structure and crystallinity is crucial for improving the performance of Ga2O3 photocatalysts for PFOA degradation.
Adsorption-based separation using metal-organic frameworks (MOFs) represents a promising, energy-efficient alternative to conventional separation technologies. While the vast design space of MOFs allows for precise property tuning to specific applications, the sheer scale of the design space renders exhaustive experimental testing impractical. Consequently, materials discovery relies on large-scale computational screening. Current screening workflows typically employ Grand-Canonical Monte Carlo (GCMC) simulations to predict adsorption properties; however, the high computational cost of GCMC often limits the number of materials screened. In this work, we introduce a process-level screening paradigm leveraging the computational speed of GPU-accelerated 3D classical density functional theory (cDFT). Validation against state-of-the-art GCMC simulations demonstrates that cDFT accurately predicts process key performance indicators (KPIs). The KPIs calculated via cDFT generally deviate by less than 5% from GCMC results while reducing computational costs by two to four orders of magnitude. Leveraging this increased efficiency, we screen the publicly available CoRE MOF 2025 database for methane/nitrogen separation using a temperature-swing adsorption process. By evaluating performance across a wide range of feed compositions, from industrial gas streams to dilute methane sources, the study identifies MOF candidates with robust performance profiles. The entire screening of over 5000 MOFs, requiring 460 000 adsorption calculations, was completed in only five days using two GPUs. The work establishes cDFT as a reliable, high-throughput approach for process-informed material discovery.
Molecular equivariant transformer (MET) learns symmetry-aware, charge-guided representations that capture geometric and electronic structure, enabling data-efficient and interpretable molecular property prediction across diverse tasks.
Accurate predictive thermodynamic models are essential tools for the computational design of new molecules. Models based on COnductor-like Screening Models (COSMO), such as COSMO-SAC and COSMO-RS, are well-suited for this purpose but require computationally expensive quantum mechanical (QM) calculations to generate key properties-the surface charge density distribution (p(σ)), the surface area (A) and the cavity volume (V)-for each molecule. Here we develop graph-based neural network surrogate models, based on a directed message passing neural network (DMPNN) and a graph convolutional network (GCN), to predict these molecular properties rapidly and accurately. We train the models on a dataset of over 16,000 compounds generated through an automated QM calculation pipeline. The DMPNN model outperforms GCN for probability of surface charge density prediction, while the GCN shows higher accuracy for surface area and cavity volume. We build on these strengths to propose a hybrid model, COSMO-NET. When applied to predict octanol-water partition coefficients, COSMO-NET achieves a mean absolute error (MAE) of 0.31 compared to 0.34 and 0.36 for DMPNN and GCN, respectively. These results demonstrate that machine learning surrogates can replace costly QM calculations while maintaining accuracy, supporting the discovery of new molecules and the evaluation of their performance.
José Rafael Bordin and Patrick S. Stayton introduce the Molecular Systems Design & Engineering themed issue on Molecular bioengineering: computational tools, smart materials, and therapeutic systems.
Soft materials have immense potential for engineering next-generation membranes that bridge design and sustainable applications, utilizing their flexibility, biocompatibility, conductivity, fouling resistance, selectivity, and responsiveness.
Norepinephrine (NE) is a vital biomarker for the diagnosis and monitoring of diseases, yet conventional detection methods are often limited by complex instrumentation and poor portability. Herein, we report the design and synthesis of a reaction-based fluorogenic probe that enables the sensitive and specific detection of NE through a distinct "turn-on" fluorescence response at an emission of similar to 548 nm. The probe exhibited a robust concentration-dependent response with a broad dynamic range from 0.05 mM to 2 mM with a limit of detection (LOD) of 3.20 mu M. In addition, the probe demonstrated remarkable selectivity toward NE over structurally similar catecholamines and amino acids, distinguishing it effectively from common biological interferents. To explore portable sensing applications, a cotton pad based platform was developed by immobilizing the probe on cotton pad substrates, which displayed a visible emission color transition from blue to yellow/green under UV illumination upon the interaction of increased NE concentrations. By integrating RGB analysis of emission colors with smartphones, quantitative and selective detection was achieved with linear responses and an LOD of 4.0 mu M. The work provides valuable insights into developing a simple, low-cost, and portable device for smartphone-assisted NE detection, with potential applicability for on-site monitoring, pharmaceutical quality control, and future development toward point-of-care applications after further validation in physiological samples.
In this work, two small-molecule acceptors (AQx-N3 and BO-N3) featuring 3rd-position branched inner side chains are developed. By comparing with the analogue acceptor AQx-2F, we elucidate the synergistic impact of central core substitution and side-chain branching position on the physicochemical and photoelectric properties. Comparative analysis reveals that quinoxaline core-substituted AQx-N3 exhibits a more planar molecular skeleton, upshifted energy levels, and stronger intrinsic crystallinity relative to the benzotriazole core-substituted BO-N3. Moreover, shifting the alkyl branching point outward from the 2nd-position in AQx-2F to the 3rd-position in AQx-N3 effectively minimizes steric hindrance and strengthens molecular core-to-core interactions, resulting in more ordered intermolecular pi-pi stacking and elevated charge carrier mobilities. When paired with the polymer donor D18, the robust crystallinity of AQx-N3, combined with its properly weakened thermodynamic miscibility with D18, promotes the formation of favorable phase separation active layer morphology and well-ordered molecular arrangement. As a result, the D18:AQx-N3-based device achieves a power conversion efficiency (PCE) of 19.66%, which outperforms the devices based on AQx-2F (PCE = 19.02%) and BO-N3 (PCE = 18.11%).
Hydrophilization of small-molecule NIR-II fluorophores empowers their applications in biological imaging with high signal-to-background ratio. Aggregation properties should be tuned to afford enhanced photophysical properties.
Contaminated water is a critical issue today, as it is a major cause of serious health problems. Diclofenac (DCF), a widely used NSAID prescribed for pain and inflammation, is highly...
The drying-based process of enzyme-free self-assembly of fibrinogen to fabricate fibrillar biomaterials is highly dependent on the type and relative amount of used salts, and is thus often rationalised on the basis of electrostatic double-layer interactions. However, using mass-loss and turbidity data, we show here that the critical salt concentration thresholds above which rapid fiber growth occurs is well above the limit of DLVO theory, pointing towards complete electrostatic shielding beyond a tight ionic Stern layer. We further show, by means of mass-loss SEM-EDS analysis, selective retention of sodium cations, particularly when in association with phosphate anions, whereas chloride ions are largely removed after washing the formed fibres. Using the fibrinogen D domain (Fg-D) as a representative protein model, we perform all-atom molecular dynamics simulations to understand the composition and structure of the Stern layer in chloride and phosphate salt environments promoting fibre formation. We find that both Na+ ions and monohydrogen and dihydrogen phosphate anions display strong and persistent binding to charged basic and acidic residues, respectively, forming a tightly bound Stern layer. In stark contrast, Cl- ions exhibit only transient interactions with the protein, maintaining a highly diffusive behaviour. The immobilization of ions in the Stern layer is due to the ion-chelation ability of neighbouring amino acids, concomitant with the formation of extended and poorly diffusing hydration structures. As a result, the electrostatic potential at the layer's edge is strongly modulated or even inverted depending on the local protein/salt/water features. Under these conditions, the protein-protein binding ability is not dependent on long-range electrostatic double-layer interactions, but on the precise matching of mutually facing and interpenetrating Stern-layer regions. These findings provide a physicochemical basis for designing enzyme-free fibrinogen materials with controllable fibrillar architectures and offer new opportunities for biomaterial development using tailored ionic environments.
Reactive polymer blends often undergo phase separation while their molecular constitution is still evolving. In such systems, stability limits and coexistence boundaries are not fixed properties of the initial mixture, but change with reaction progress, temperature, conversion, or another imposed protocol variable. This makes the interpretation and prediction of reaction-induced phase separation challenging, particularly when the components are polydisperse and the coexisting phases may differ in both composition and molecular distribution. Here we present PhaseTime, a Python framework for computing time- or protocol-dependent phase diagrams of reactive polydisperse polymer blends within Flory-Huggins-type thermodynamics. The framework combines evolving molecular-distribution models with calculations of spinodal curves, critical points, binodal curves, and cloud/shadow curves. It supports temperature- and composition-dependent interaction parameters, monodisperse reaction-dependent approximations, and polydisperse Flory-Stockmayer distributions. The framework also includes parameter fitting, diagnostic, and plotting tools through both a command-line interface and a Python API. Representative calculations demonstrate idealized phase-diagram topologies, including UCST, LCST, combined UCST/LCST, hourglass, and closed-loop behavior, as well as fitting to literature data and reaction-dependent phase behavior in monodisperse and polydisperse systems. The resulting phase diagrams provide quasi-equilibrium thermodynamic references for interpreting reaction-induced phase separation and for comparison with spatially resolved models in the fast-demixing limit.
Membrane separation is an effective strategy for treating oil/water emulsions, in which surface chemistry and microstructure play decisive roles. In this perspective, we summarize advances in oil/water emulsion separation membranes from the standpoint of surface molecular engineering. We first elucidate the interfacial physicochemical principles and molecular-level design parameters that govern surface wettability and capillary behavior. Strategies for constructing superhydrophilic/underwater superoleophobic membranes via organic and organic-inorganic molecular engineering are then systematically summarized. Beyond conventional symmetric designs, we discuss the concept of Janus membranes and their distinct demulsification-based separation mechanisms, together with emerging antifouling strategies that extend beyond traditional wettability control. Finally, key challenges in this field are highlighted to identify opportunities for next-generation membranes compatible with practical operation.
Tissue regeneration is a complex biological process requiring the coordinated regulation of antibacterial, antioxidant, adhesive, and angiogenic responses, which demands multifunctional biomaterials capable of addressing multiple healing pathways simultaneously. In this study, a gelatin-based multifunctional hydrogel is developed by first conjugating tannic acid onto gelatin chains to form a gelatin-tannic acid polymer, followed by zinc-ion-mediated crosslinking to generate a hydrogel network. Gel formation is primarily driven by metal-phenolic coordination between zinc ions and tannic acid, while additional interactions between tannic acid and gelatin contribute to network stability. The hydrogel is fabricated through a single step mixing process, enabling rapid gelation within approximately 10 seconds. The resulting hydrogel exhibits a storage modulus of approximately 300 Pa and a uniform porous microstructure, providing sufficient structural integrity. Strong wet tissue adhesion up to 14 kPa is achieved, together with tannic-acid-derived antioxidant activity. Zinc incorporation further imparts effective antibacterial performance against both Gram-positive and Gram-negative bacteria and significantly enhances endothelial angiogenic activity through sustained ion release. In vivo full-thickness wound studies confirm accelerated cutaneous tissue regeneration. This study establishes a coordination-driven strategy for engineering gelatin-based hydrogels with tunable structure and multifunctional performance.
The feasible structural manipulation, extended conjugation networks and good chemical stability of covalent organic frameworks (COFs) render them suitable as promising nonlinear optical (NLO) materials. However, the high symmetry of COFs diminishes their second order NLO properties. By combining the polarity of azulene with the good pi-conjugation of porphyrin, a series of two-dimensional azulene-porphyrin based COFs with various types of connections (C 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 C, CN, NN and BN bonds) are designed in the present work. The NN and CN connected azulene-porphyrin based COF exhibits a strong second harmonic generation (SHG) response, and the coordination of Zn further enhances the SHG responses. The mechanism of such enhancement by introducing heteroatoms and metals is scrutinized. The microscopic understanding of the correlation between the electronic structures and NLO properties facilitates the design and experimental exploration of novel COFs with exceptional NLO performance.
