We present another step in our efforts to expand far-field imaging beyond the diffraction limit and beyond the limitations imposed by fluorescent labels. The model system we examine is based on the exploitation of entangled photon pairs at 810 nm generated by spontaneous parametric down conversion (SPDC) of a 405 nm source to resolve the separations of the line pairs of a series of two-dimensional objects. A ghost-imaging optical system directs signal and idler photons along two paths, one of which contains the objects. Images are constructed using a covariance analysis of the entangled pairs using typically 106 frames collected with an EMCCD detector that was calibrated to account for the number of entangled pairs of photons, which is necessary for an accurate determination of the resolution and its comparison with the classical method. Conservation of momentum requires that there is inversion symmetry between the transverse k vectors of the photons passing through the target and their entangled counterparts directed along the targetless path. Our covariance analysis not only addresses this symmetry but also that the width of the covariance peak is broader than a single pixel owing to the nature of SPDC and the details of the optical arrangement. Consideration of this broadening provides significant improvement of the image quality. The Heisenberg limit (HL) is achieved within experimental error: using the same optical alignment, the resolution obtained with entangled pairs of 810 nm photons is, within experimental error, twice that obtained with classical 810 nm photons. More importantly, the error bars of measurements with 810 nm entangled pairs are quantified by the Cramér-Rao lower bound (CRLB) and compared with those of the classical measurements. While other workers have done quantum imaging on nonpoint sources, to our knowledge we are the first group that not only has done the quantum imaging but also has compared the CRLB and the measured error of the quantum and classical measurements for a series of different-sized objects. (Similar comparisons have, however, been made for systems that can be approximated as point sources, e.g., determining the separation of two laser beams).

We quantify the excitonic coupling in the homodimer of the dimeric Venus fluorescent protein using a quantum-classical hybrid workflow. Because the anionic chromophore carries appreciable double-excitation character, we obtain its site energy from domain-based local-pair-natural-orbital similarity-transformed equation-of-motion coupled-cluster theory (DLPNO-STEOM-CCSD), which places the in-protein bright ππ* state at 523.90 nm, in good agreement with the experimental absorption maximum near 515 nm and validated against an explicit triples-corrected EOM-CCSD(fT) benchmark; single-excitation time-dependent density functional theory (TDDFT) blueshifts this state. Applying a transition-density coupling (TDC) formalism to the STEOM transition density and simulating the VenusA206 tandem-dimer construct (two β-barrels covalently joined by an inter-domain linker), the covalent tether holds the A206 interface docked throughout 1 ns of room-temperature dynamics. We obtain a thermally averaged coupling of J = 32.8 ± 1.6 cm-1 across the 1000-frame trajectory (Davydov splitting 2|J| = 65.6 ± 3.1 cm-1), representing a 19% near-field enhancement over the ensemble point-dipole estimate (JPDA = 27.6 ± 1.3 cm-1) at the 24.69 ± 0.32 Å chromophore centroid separation. Furthermore, we argue that a separation of time scales resolves the apparent theoretical tension between intermediate experimental excitonic couplings and the highly decoherent biological environment. While it has been hypothesized that the fluorescent protein β-barrel scaffold sustains coupling by shielding the chromophore from thermal fluctuations, we emphasize that the separation of time scales applies irrespective of the exact degree of environmental decoherence and dissipation. Collective photoexcitation imprints the Davydov splitting under optical-limit dielectric screening upon absorption, preceding bulk solvent relaxation and sub-picosecond environmental dephasing. To characterize the subsequent post-absorption evolution, we employ stochastic wavefunction simulations to model the transition from a delocalized exciton superposition to incoherent hopping between localized chromophore states.

Alkane molecules on water interfaces are known to preferentially align parallel to the interface. However, this trend has not been examined in a way that directly connects interfacial ordering with free energy profiles, particularly in distinguishing the role of carbon number from that of molecular topology in accessing interfacial configurations. Here, atomistic molecular dynamics simulations were used to investigate linear (n-C5, n-C6, n-C7, and n-C12) and cyclic (c-C5, c-C6, and c-C7) alkanes at aqueous interfaces. Interfacial density profiles, orientational order parameters, and free energy profiles along the surface-normal coordinate were analyzed using logarithmic mean-force dynamics (LogMFD). Linear alkanes show a pronounced increase in interfacial ordering, whereas cyclic alkanes exhibit the same qualitative tendency but a substantially weaker size dependence. The corresponding transfer free energy difference increases from about 26.7 to 49.9 kJ mol-1 for the linear series, compared to about 25.0 to 29.7 kJ mol-1 for the cyclic series. These results demonstrate clear topology-dependent interfacial ordering. Additional conformational analysis indicates that both series respond through reorientation: the end-to-end extension of the linear alkanes changes by less than 0.2% across the interface, and the ring-distortion metric of the cyclic alkanes changes by less than 1%.

Human CYP2E1 oxidizes both eicosapentaenoic acid (EPA, an ω-3 polyunsaturated fatty acid [PUFA]) and arachidonic acid (AA, an ω-6 PUFA) to bioactive lipid mediators, yet the mechanistic basis for its regio- and enantioselective preferences remains incompletely understood. Here, we use a combined molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) strategy to compare the oxidation of EPA and AA by human CYP2E1 at atomistic resolution. MD simulations indicate that EPA and AA occupy essentially the same CYP2E1 cavity but are stabilized through distinct interaction patterns. Compared with AA, EPA more frequently forms a hydrogen bond with A108 at the channel entrance and exhibits more uniform sampling of near-attack geometries in the ω-1, ω-2, and ω-3 regions. QM/MM free energy profiles show that ω-1S hydroxylation has the lowest barrier for both substrates, but that the barriers for the ω-2/ω-3 epoxidation pathways are close to that for ω-1S hydroxylation in EPA, rendering these pathways kinetically competitive. Energy decomposition analysis indicates that regioselectivity is largely determined by the QM energy term (ΔEQM), whereas enantioselectivity at ω-1 reflects small differences in packing and entropic cost between the pro-R and pro-S transition states in the confined active site. These results provide a coherent mechanistic picture of CYP2E1-catalyzed oxidation of ω-6 and ω-3 PUFAs and identify specific active-site residues as promising targets for tuning regio- and stereoselectivity in lipid oxidation.

Coarse-grained (CG) molecular dynamics (MD) simulations offer an efficient route to explore long-time-scale dynamics of knots in proteins and polymers. Despite extensive studies of knots using both atomistic (AA-) and CG-MD simulations, systematic comparisons of how coarse-graining alters knot relaxation mechanisms, intermediates, and time scales─particularly in melts and proteins─remain scarce. Here, we employ AA- and CG-MD simulations to investigate the relaxation of tight knots in single polyethylene (PE) chains, PE-polymer melts, and a trefoil-knotted protein. Across all systems, CG models accelerate knot relaxation and enhance global mobility while preserving the dominant topological relaxation mechanism observed in AA-MD simulations. In single chains, CG-MD simulations produce smooth, monotonic unknotting trajectories, whereas AA-MD simulations reveal transient structural intermediates and intermittent shape rearrangements within the knot core. In melts, CG dynamics remain faster and more homogeneous, while AA polymers exhibit kinetically trapped intermediates and sliding-dominated relaxation driven by topological confinement. For the knotted protein, CG-MD simulations enable knot loosening and conformational rearrangements inaccessible on standard AA-MD time scales due to kinetic trapping. Collectively, these findings establish that coarse-graining systematically accelerates topological relaxation by smoothing the effective energy landscape and suppressing short-lived metastable knot conformations, while the dominant relaxation pathway is preserved.

This study reports a systematic experimental and analytical workflow for evaluating push-pull heptamethine chromophores (PPH-phores) and their poled guest-host polymers for electro-optic (EO) applications. Attenuated total reflection (ATR) spectroscopy was used to accurately measure the optical birefringence and the DC/AC EO coefficients (r33 values) of poled films, establishing a direct correlation between molecular properties and bulk material responses. Sellmeier analysis of refractive-index dispersion quantified the nonresonant and low-energy resonance contributions and, combined with Kuzyk's two-component model, enabled reliable extraction of key parameters, including the order parameter (⟨P2⟩), dipole moment, and linear polarizability of PPH-phores. Incorporating the experimentally determined ⟨P2⟩, the rigid oriented gas model was applied to derive the first hyperpolarizability (β) of PPH-phore directly from measured r33 values. Across a comprehensive evaluation of 15 PPH-phores and 20 guest-host polymers, we identified an ultralarge dressed β of up to -34,000 × 10-30 esu and a linear polarizability of up to 9.31 × 10-22 cm3, the highest values reported to date for PPH-phores, which collectively translate into large r33 values in poled polymers. This integrated workflow accelerates structure-property relationship studies and facilitates the discovery and optimization of PPH-phores, supporting the scalable development of high-performance organic EO materials.

Machine learning interatomic potentials (MLIPs) can achieve near density-functional-theory (DFT) accuracy at force-field computational cost; however, long-time, large-scale molecular dynamics (MD) simulations often fail when trajectories sample local atomic environments that are underrepresented in the training set. Here, we employ an active-learning workflow to develop a robust neural network potential (NNP) for perfluorinated ionomer membranes (Nafion) across a wide range of hydration levels (λ). A reliable deep potential (DP) model is constructed through iterative data set expansion within active-learning loops. Specifically, off-equilibrium configurations are generated via nonequilibrium DPMD simulations and selected using an ensemble force-deviation criterion combined with a three-dimensional structural feature space augmented by minimum interatomic distances, which significantly enhances the DP model's robustness. The trained DP model enables stable MD simulations of large Nafion systems containing approximately 10,000-20,000 atoms for an extended duration of 31 ns. Our DPMD simulations reproduce the qualitative hydration dependence of density and yield self-diffusion coefficients of hydrogen atoms and hydronium ions in quantitative agreement with experimental values. Compared with previous ab initio MD and MLIP-MD studies, our simulations show improved agreement with experimental transport properties and remain predictive up to λ = 24, well beyond the training range (λ ≤ 13). This work provides an efficient and scalable approach for achieving stable, large-scale NNP-MD simulations of heterogeneous polymer electrolyte membranes and related disordered materials.

Deep eutectic solvents (DES) present a formidable challenge for property prediction due to the complex interplay of hydrogen bonding, Coulombic, dispersion, and coordination interactions that define their macroscopic behavior. Existing approaches─group contribution methods, COSMO-RS, and brute-force molecular dynamics─either lack transferability, require prohibitive computational cost, or obscure the physical origins of observed trends. Here we propose a graph-theoretic framework in which a DES mixture is represented as a directed, weighted, typed multigraph whose nodes are mixture components (or interaction sites) and whose edges encode pairwise interaction affinities categorized by physical type. We define a set of 11 physically interpretable graph invariants─comprising three interaction loads (S_HB, S_C, S_vdW), hydrogen-bond directionality (Δ_HB), weighted clustering coefficient (C_w), spectral radius (ρ(A)), algebraic connectivity (μ2), modularity (Q*), global efficiency (E_g), size asymmetry (Φ_V), and interaction entropy (H_int)─11 scalar descriptors that reduce to approximately four-five independent invariants for the two-node graphs used to represent binary DES (Section 9.5)─and propose closed-form equations (property closures) mapping these invariants to six thermophysical properties (density, viscosity, surface tension, melting point depression, conductivity, and refractive index) cross-validated against experimental data for 15 choline chloride-based DES, plus a heat-capacity closure that is proposed but not yet cross-validated owing to insufficient experimental data. Cross-validated performance meets the predefined target benchmark for density (R2 = 0.94, MAPE = 1.65%); performance for the other five properties falls below target at the current, heuristic level of parametrization, so the framework's primary demonstrated contribution at this stage is a physically transparent, mechanistically interpretable descriptor set rather than uniformly accurate prediction. The framework is designed to be parametrized from COSMO-RS interaction energies, quantum-mechanical cluster calculations, or molecular dynamics simulations, and its modular architecture enables systematic extension to ternary and higher-order systems, hydrated DES, and natural deep eutectic solvents (NADES). We discuss thermodynamic consistency constraints, uncertainty quantification strategies, and a cross-validation protocol for fitting the closure coefficients. This framework provides a computationally efficient, physically transparent bridge between molecular-level interaction data and engineering-scale property prediction for the rational design of task-specific DES.

Fluorescent eutectogels, as emerging smart materials that integrate the unique microenvironment, excellent temperature resistance, and eco-friendliness of deep eutectic solvents (DESs), have driven remarkable advancement in flexible optical materials and information encryption. Here we report a transparent fluorescent eutectogel prepared by incorporating the environmentally sensitive cationic luminophore 2,5-bis(2-pyridiniumyl)-1H-pyrrole bis(hexafluorophosphate) ([H3DPP]2+) into a glycerol/Zn(NO3)2·6H2O DES followed by in situ photopolymerization of acrylamide. The nitrate-containing DES strongly reshapes the photophysical behavior of [H3DPP]2+, producing a pronounced red shift and enhanced emission relative to glycerol or aqueous Zn(NO3)2 solution. The addition of monomer has little influence on the DES-regulated emission, whereas gel-network formation causes only moderate fluorescence attenuation while largely preserving the DES-defined emissive state. Spectroscopic analysis and DFT/TD-DFT calculations indicate that directional hydrogen-bonding interactions between NO3- and the protonated/pyrrolic N-H sites of [H3DPP]2+ modulate its electronic structure and optical transitions. The resulting eutectogel exhibits high visible transparency, tensile strength of 230 kPa, stretchability of 820%, adhesion to diverse substrates (0.25 MPa on PTFE), and excellent solvent retention, maintaining 95% of its solvent after storage at 70 °C for 9 days. The fluorescence also remains visible after prolonged storage at 25, 70, and -25 °C. Moreover, relying on the high transparency, good mechanical properties and stimulus-responsive fluorescence characteristics of this eutectogel, localized chemical writing, UV-triggered visualization and barcode digital reading can be realized. This work demonstrates that DES-regulated microenvironments offer a robust platform for tuning molecular emission and developing stable, stimuli-responsive fluorescent soft materials for visual anticounterfeiting and information encryption.

Protein folding is the process by which a polypeptide chain organizes into its three-dimensional structure through a balance of stabilizing and destabilizing interactions encoded by the sequence. A central question in protein biophysics is how thermodynamic factors guide a polypeptide toward its native folded state despite the rugged energy landscape and the competing influence of nonnative interactions. In many biomolecular processes, cooperativity provides a mechanism by which multiple weak interactions act collectively to generate a robust response. In the context of protein folding, such cooperative effects may arise when the formation of one native contact enhances the stability or likelihood of nearby native contacts, thereby promoting collective organization toward the folded state. At the same time, folding is opposed by the much larger number of non-native interactions, whose heterogeneity can introduce frustration and destabilize folding even when the average native bias favors the folded phase. The interplay of these competing effects in determining foldability remains unclear in statistical-mechanical models. Here, we address this problem using a one-dimensional spin-glass model of protein folding with explicit shared-residue cooperative interactions encoded through wedge-based motifs. We show that modest cooperative bias can stabilize folding even where the noncooperative system remains unfolded, whereas non-native energetic fluctuation suppresses folding and shifts the transition to higher cooperative strengths. We further find that partial cooperative coverage is sufficient to lower the folding threshold. Therefore, the model provides a mean-field framework for incorporating cooperative interaction strength into the native one-dimensional model of protein folding and for describing how local cooperativity reshapes the folding transition.

In the present study, we investigated the solvation behavior of a binary DIL-MIL mixture across varying water mole fractions using a multiscale approach combining MD simulations with DFT and AIM analyses. This strategy provided molecular-level insights into intermolecular interactions and clarified how hydration modulates both the microscopic structure and macroscopic properties, ultimately improving the performance of the studied DIL. Structural analysis showed that while the primary ion arrangement remains largely stable, hydration significantly reshapes local organization. At low water content, cation clustering, strong ion correlations, and small water domains dominate, with cations preferentially bonding to anions. Higher hydration disrupts these domains, weakens ion-water interactions, strengthens water-water and cation-anion hydrogen bonding, and yields a more homogeneous, bulk-like structure. Hydration markedly enhances ion mobility by weakening ion cages, reducing ion-ion correlations, and accelerating translational and rotational dynamics. DFT analysis further revealed that hydration decreases direct cation-anion binding energies while reinforcing overall stabilization through stronger hydrogen-bonding networks, consistent with increased electron density at bond critical points. These findings provide molecular-level insights into how water incorporation can be used to tune the structural and dynamical properties of DIL-based systems for electrolyte, solvent, catalytic, and other electrochemical applications.

Regulatory mandates have imposed strict limits on allowable sulfur content in petroleum fuels to minimize atmospheric pollutants. While conventional desulfurization methods are highly effective at removing simple, reactive sulfur compounds, e.g., thiols and sulfides, they struggle in the removal of refractory sulfur compounds that are aromatic, sterically hindered, or electronically stabilized. Ionic liquids (ILs) are particularly well-suited to extract these problematic compounds, e.g., thiophenes and their derivatives, given their complementary physical properties. However, choosing and optimizing ILs that suit industrial desulfurization requirements has been challenging. In this work, potentials of mean force simulations on a biphasic oil-IL system have been performed to investigate IL-based extractive desulfurization (EDS) efficiency by examining the transfer of two aromatic sulfur compounds, thiophene (TH) and dibenzothiophene (DBT), and an oxidized derivative (DBTO2) from the model oil dodecane into 12 unique ILs. The ILs were constructed using combinations of 1-alkyl-3-methylimidazolium [CnMIM] cations, where n = 2, 4, and 8 (i.e., ethyl, butyl, and octyl), respectively, with the anions: thiocyanate [SCN], tetrafluoroborate [BF4], hexafluorophosphate [PF6], and bis(trifluoromethylsulfonyl)amide [NTf2]. The study investigates how interfacial charge density, cation ordering, electrostatic interactions, π-π stacking, free-volume formation, hydrophobicity, and oil dissolution into the IL phase collectively influence sulfur compound extraction efficiency and selectivity, while also evaluating the enhanced thermodynamics associated with combined oxidative and extractive desulfurization (ODS-EDS). Detailed molecular-level characterization of the IL-oil interfacial environment is provided to elucidate the key structural and energetic features governing sulfur compound partitioning and extraction behavior in imidazolium-based ILs.

Sustaining viscoelasticity of surfactant solutions at subzero temperatures and across extreme pH regimes remains challenging, as freezing and harsh acidic or alkaline environments inhibit aggregate growth and retard structural relaxation. This study examines microstructural evolution and rheological response of C22-tailed amine oxide (C22AO) in a 50/50 (v/v) ethylene glycol-water mixture spanning pH 2.0-13.0 and 20 °C to -20 °C. Potentiometric titration identifies an apparent pKa at 6.8, with ζ-potential declining from +18.53 mV (pH 2.0) to +2.35 mV (pH 6.8) and -1.66 mV (pH 13.0). All formulations retain thermal stability, freezing near -42.0 °C. Microscopy and scattering confirm that pH dominates aggregate morphology in 0.5 wt % C22AO solutions. Acidic conditions (pH 2.0) yield heterogeneous lamellar domains with a 57.0 nm repeat distance at 20 °C, evolving into coexisting 62.8 and 48.3 nm spacings at -20 °C. Near neutrality (pH 6.8), compact lamellar bilayers shrink from 37.0 to 31.4 nm upon cooling, whereas alkaline conditions (pH 13.0) promote disordered, entangled wormlike micelles. All samples exhibit pronounced shear-thinning and elasticity-dominated rheology. At -20 °C across these three pH values, zero-shear viscosities reach 2.45 × 106, 1.96 × 106, and 1.10 × 106 mPa·s, with corresponding elastic moduli of 65, 10, and 28 Pa at 1 rad·s-1 and loss tangent values of 0.24, 0.40, and 0.75. These findings demonstrate that pH governs aggregate morphology, while subzero cooling strengthens molecular packing and decelerates structural relaxation, enabling robust thickening and marked viscoelasticity in cryogenic extreme-pH environments.