Computational Physics (United States)

ABSTRACT We present HECATEv2, the second release of the Heraklion Extragalactic Catalogue (HECATE), an all-sky, value-added galaxy catalogue comprising 204 733 galaxies from the Hyper Lyon-Meudon Extragalactic Database (HyperLEDA) with recession velocity $\lt 14\, 000 \, \rm {km\, s^{-1}}$ (D $\lesssim$ 200 Mpc). This release focuses on qualitative upgrades of the provided information while maintaining the same parent galaxy sample as HECATEv1. Improvements include a new cosmology-based distance framework, expanded and homogenized optical and mid-infrared (IR) photometry from SDSS-DR17/NSA, PS1-DR2, and All Wide-field Infrared Survey Explorer (AllWISE), and new quality-control flags for stellar contamination, incorrect photometry, and coordinate inconsistencies. We also extend the galaxy-size coverage and derive stellar population parameters for a substantially larger fraction of the sample. Star formation rates ($\mathrm{SFR}$) and stellar masses (${{M}_{\star }}$) are now available for $\gt 70$ per cent of galaxies using updated mid-IR/optical calibrations that account for stellar population age and dust attenuation, while gas-phase metallicities are derived for $\sim 90~{{\ \rm per\ cent}}$. Activity classifications are provided for $\gt 50$ per cent of galaxies based on spectroscopic and/or photometric diagnostics, and supermassive black hole (SMBH) masses for $\sim 86~{{\ \rm per\ cent}}$. In terms of $L_{B}$, $L_{Ks}$, $\mathrm{SFR}$, and ${{M}_{\star }}$, HECATEv2 is among the most complete local-universe catalogues with spectroscopic redshifts. We also provide spatial completeness maps as a function of distance and luminosity, highlighting variations across the sky. Compared to other catalogues (e.g. GLADE+ and NED-LVS), HECATEv2 offers broader (optical, near- and far-IR photometry, metallicity, and activity classifications) or comparable (mid-IR photometry, $\mathrm{SFR}$, and ${{M}_{\star }}$) coverage, making it a robust reference for studies of SMBH–host galaxy connections, gravitational-wave and high-energy transient hosts, population analyses, and rare galaxy subpopulations.

Two-dimensional (2D) boron-based materials have gained increasing interest due to their exceptional physicochemical properties and potential technological applications. In this way, borospherene, a 2D Boron-based fullerene-like lattice (2D–B40), is explored due to its potential for capturing and detecting toxic gases, such as CO, NO, NH3, and SO2. Therefore, density functional theory (DFT) simulations were carried out to explore the adsorption energy and the distinct interaction regimes, where CO exhibits weak physisorption (−0.16 eV), while NO (−2.24 eV), NH3 (−1.47 eV), and SO2 (−1.51 eV) undergo strong chemisorption. Bader charge analysis reveals significant electron donation from 2D–B40 to NO and electron acceptance from SO2. These interactions cause measurable shifts in work function, with SO2 producing the most significant modulation (ΔΦ = +14.6%). Remarkably, ab initio molecular dynamics simulations (AIMD) reveal spontaneous SO2 decomposition at room temperature, indicating dual functionality for both sensing and environmental remediation. Compared to other boron-based materials, such as χ3-borophene, β12-borophene, and B40 fullerene, 2D–B40 exhibits superior gas affinity, positioning it as a versatile platform for the detection and capture of toxic gases. Our findings therefore contribute to the advancement of research on low-dimensional boron-based materials aimed at multifunctional applications.

Far from being passive barriers, viral membranes actively influence the mechanical properties and biological activity of viruses. Their lipid–protein composition forms a responsive interface that impacts how viruses assemble, remain stable, and interact with their surroundings. While the behavior of lipids and proteins in cellular membranes is well-described, their specific contributions within viral systems remain underexplored, largely due to nanoscale complexity and experimental limitations involved. Here, coarse-grained molecular dynamics simulations of the Zika virus were analyzed to characterize lipid organization inside the virus. Lipid selectivity is strongly influenced by helix residue composition and depth of insertion into the membrane. The amphipathic EH-3 helix, for instance, preferentially coordinates with POPC but also establishes localized contacts with POPS through Lys and Ser residues, reflecting a balance between hydrophobic and electrostatic interactions. In contrast, the transmembrane ET-2 helix, dominated by hydrophobic residues, displays reduced lipid selectivity, with only peripheral serines showing a modest preference for POPS. Across the viral envelope, POPE contributes less to residue-specific coordination, while POPS participates in polar interactions that modulate the environment near positively charged residues. By shedding light on how lipids contribute to the architecture of the viral envelope and membrane, this work offers insights that deepen the understanding of viral particle integrity, guiding target membrane-dependent processes in antiviral design.

In conventional semiconductors, electron–phonon coupling (EPC) is weaker than phonon–phonon (ph–ph) scattering and plays little role in lattice thermal conductivity (κL). We show, using first-principles calculations combined with machine-learning force fields (MLFF), that EPC can instead dominate phonon transport in monolayer penta-graphene under carrier doping. The absence of mirror symmetry allows direct coupling of flexural (ZA) phonons to carriers, and tuning the Fermi level into the Van Hove singularity strongly enhances the electronic density of states and amplifies EPC. Consequently, κL is suppressed by nearly 64% (from 266 to ∼96 W m−1 K−1 at room temperature) and exhibits a weakened temperature dependence (from T−1.4 to T−1.0). This EPC-driven suppression of heat transport enhances the thermoelectric figure of merit by more than threefold. These findings open a pathway to engineer heat conduction in low-symmetry carbon allotropes via controlled doping.

We present a literature-validated computational framework for predicting synergistic mechanical and barrier properties in biodegradable food packaging materials through hierarchical integration of three distinct crosslinking mechanisms in biopolymer networks. The composite architecture combines: (1) chemical crosslinking via citric acid thermal esterification (∆σ_chem = 20 MPa), (2) enzymatic crosslinking via transglutaminase-catalyzed isopeptide bond formation (∆σ_enz = 8 MPa), and (3) ionic crosslinking via calcium-mediated alginate egg-box coordination (∆σ_ionic = 6 MPa), with hierarchical reinforcement from exfoliated montmorillonite nanoplatelets (aspect ratio 100-300). Employing interpenetrating polymer network (IPN) theory with conservative synergy enhancement factors (α = 2.0-2.8), we predict tensile strength of 32.4 ± 1.8 MPa-representing 6.2-fold enhancement versus pure starch baseline (5.2 ± 0.8 MPa)-oxygen permeability of 318 ± 22 cc•mil/m²•day•atm (65% reduction via tortuous diffusion pathways), and complete aerobic biodegradation of 89.2 ± 2.1% mass loss within 120 days per ASTM D5338 protocol. The observed synergistic enhancement factor of 2.8× (measured 32.4 MPa versus interference-corrected additive prediction of 11.6 MPa) demonstrates non-additive performance characteristic of multi-mechanism hierarchical polymer architectures. This computational methodology, validated against 14 peer-reviewed experimental studies, establishes a framework for rational design of petroleum-plastic alternatives achieving mechanical properties exceeding low-density polyethylene (LDPE: 10-20 MPa) while maintaining environmental biodegradability under home composting conditions, addressing the sustainability challenge of 146 million metric tons annual global plastic production.

The rational design of metal-based anticancer agents is challenged by vast chemical diversity, limited experimental throughput, and the scarcity of standardized structure-activity relationship data.Here, we investigate the extent to which predictive machine learning models can be applied productively to ruthenium(II) polypyridyl complexes in the low-data regime.A dataset of 718 cytotoxicity measurements was curated from the literature and used to benchmark multiple molecular representations and regression architectures under increasingly stringent datasplitting strategies.We observe that both predictive performance and apparent chemical-space extrapolation depend strongly on the chosen representation, with diberent encodings yielding complementary predictions.Guided by an ensemble-based modeling strategy, we screened a large virtual library of ruthenium polypyridyl complexes and selected a small, chemically diverse subset for synthesis and biological evaluation.The experimentally measured cytotoxicities are in excellent agreement with model predictions, including the discrimination of pronounced activity diberences between closely related complexes.These results demonstrate that data-driven prioritization can support the discovery and fine-tuning of cytotoxic ruthenium polypyridyl complexes despite limited and heterogeneous training data, and provide a general framework for integrating predictive modeling into medicinal inorganic chemistry drug discovery.

Transition metal dichalcogenides (TMDs), particularly monolayer MoS2, have received increased attention in materials science and have been exploited in diverse applications, from photonics to catalysis. Defects in TMDs play a crucial role in modulating their properties, and understanding defect-induced dynamics is of great importance. This study investigates the dynamics of sulfur depletion in defective monolayer MoS2, which yields stable MoS monolayers. Various defect sizes, temperature regimes (300–1000 K), and substrate effects were investigated. Through comprehensive classical molecular dynamics (CMD) and ab initio molecular dynamics (AIMD) simulations, we elucidate the dynamics of sulfur vacancy formation in MoS2 lattices. After removal of all sulfur atoms from the top layer, several sulfur atoms from the bottom layer spontaneously migrate to the top layer as a response to increase structural stability, thus creating a MoSx alloy. These findings deepen our understanding of defect dynamics in TMDs, offering valuable insights into the controlled engineering of their properties for nanotechnology applications.