In this study, we reconstruct the dark energy (DE) as a Dirac-Born-Infeld (DBI) scalar field from the Hubble dataset (32 CC + 26 BAO) and the DESI dataset using the Gaussian process (GP). As the GP is a non-parametric and model-independent way to reconstruct a function and its derivative using the data, our reconstruction of the DE equation of state, the DE density parameter, and the potential does not assume any particular model of cosmology. Using Monte Carlo realizations of the GP-reconstructed expansion history, we derive a posterior estimate of the Hubble constant, obtaining H0=69.53±2.68 km s−1 Mpc−1. This method offers a fully model-independent estimate of H0, relying only on data and GP priors, and provides an unbiased intermediate value useful for reassessing the Planck-SH0ES tension. Using the reconstructed profiles of the scalar potential as a function of the field ϕ, along with their associated uncertainties, we perform a chi-square curve fitting procedure to assess the viability of four different scalar field potentials, such as Exponential, Power-law, Free Field (quadratic), and Higgs-like potential. This allows us to identify which potential best fits the reconstructed data. We also employ MCMC analysis to place quantitative constraints on the model parameters associated with each potential. Furthermore, we do a χ2 analysis for all four potentials and comment on the goodness of the fit for each of them. Finally, we discuss possible generalizations of our model-independent framework and outline the phenomenological implications of our findings.
Accurate kinetic modeling is essential for elucidating sorption mechanisms and optimizing pollutant removal; however, the traditional pseudo-first-order (PFO) and pseudo-second-order (PSO) models are often misapplied due to linearization artifacts and limited statistical validation. This study establishes a comprehensive, statistically rigorous framework that integrates nonlinear least-squares regression, multi-criteria error analysis, information-theoretic model selection, and jackknife resampling for uncertainty quantification in kinetic parameters. Eight models-PFO, PSO, pseudo-mixed-order fractional (PMOF), mixed 1,2-order (MOM), Ritchie second-order (RSO), Elovich, Bangham, and intraparticle diffusion (IPD)-were evaluated across 40 sorbent-sorbate systems involving heavy metals, precious metals, radionuclides, dyes, and emerging contaminants. Quantitatively, the PMOF model yielded the lowest mean average relative error (ARE approximate to 2.1 %), Marquardt's percent standard deviation (MPSD approximate to 4.2 %), and Akaike Information Criterion (AIC = 4.5-22), outperforming PSO (ARE approximate to 3.4 %, MPSD approximate to 6.5 %) and PFO (ARE approximate to 6.8 %, MPSD approximate to 11.8 %). Multi-criteria ranking confirmed the order PMOF > RSO > PSO > MOM approximate to Elovich > Bangham > IPD > PFO. Jackknife resampling, introduced here for the first time in sorption kinetics, revealed that datasets with fewer than eight points increased parameter uncertainty by >25 %, whereas dense early-time sampling reduced deviation to <5 %. These findings demonstrate that the PMOF model bridges diffusion- and surface-reaction-controlled regimes via a fractional constant, offering a reliable and interpretable framework for kinetic analysis in environmental separation processes. Finally, a user-friendly Excel-based nonlinear fitting tool was developed to automate model fitting and statistical evaluation.
Nurse plants can modify local microenvironments and influence the performance of co-occurring species in alpine ecosystems. While facilitative interactions between cushion plants and their beneficiaries have been widely studied, little is known about how alpine shrubs influence the balance between facilitation and competition across heterogeneous microhabitats. Such knowledge is increasingly important because shrub expansion is occurring in many alpine regions under climate warming. We examined how microhabitats formed by the creeping pine Pinus pumila affect the growth, leaf traits, and foliar chemistry of the dwarf shrub Vaccinium vitis-idaea on Mt. Norikura, Japan. We compared dense shrub thickets on slopes (MAT), isolated patchy shrubs on ridges (PATs), and open bare ridges without pine cover (OUTs). We quantified environmental conditions (light availability, wind velocity, soil moisture, and nutrient availability), leaf morphology (leaf mass per area; LMA), foliar chemistry (C, N, and P contents, δ13C, δ15N), and growth traits (shoot length and plant height) of V. vitis-idaea. MAT and PATs showed lower canopy openness and wind exposure than OUTs, while soil conditions were more strongly influenced by topography than by shrub presence. V. vitis-idaea exhibited greater shoot length, plant height, and foliar N under MAT, but higher LMA and δ13C in OUTs, indicating acclimation to high-irradiance and drier conditions. These results suggest that P. pumila simultaneously mitigates wind exposure and dry conditions while imposing light limitation, resulting in trait-specific responses of V. vitis-idaea. Growth traits reflected competitive responses to shading, whereas foliar δ13C indicated facilitative effects associated with improved water availability. Our findings highlight the ecological importance of non-cushion nurse plants in alpine ecosystems.
Understanding the nature of dark energy remains one of the central challenges in modern cosmology, motivating the exploration of modified gravity theories and quantum gravitational corrections. In this work, we explore the reconstruction of f(Q,T) gravity within the Barrow Holographic Dark Energy (BHDE) framework. Motivated by quantum gravitational effects, BHDE modifies the standard holographic dark energy via a deformed entropy-area relation characterized by the Barrow parameter delta. Adopting the form f(Q,T) = Q + h(T), we reconstruct the theory using three infrared cutoffs: the Hubble horizon, the future event horizon, and the Granda-Oliveros cutoff. Function h(T) exhibits distinct behaviors: power law, logarithmic, or exponential growth, depending on the cutoff and the Barrow parameter delta. Furthermore, we analyze the evolution of the effective Newton's constant to assess the viability of the reconstructed Q + h(T) model and its deviation from general relativity. The resulting models account for the late-time acceleration of the universe and allow for quintessence or phantom-like dynamics (dependence on delta), offering a viable framework for exploring dark energy and potential quantum gravity effects. In addition, the condition of the effective Newton's constant and the dark energy equation of state offer qualitative indications that our models may have the potential to alleviate the existing H0 and S8 tensions.
Efficient green hydrogen production from diverse water sources demands excellent catalysts that combine high activity, durability, and pH universality. Herein, we present a surface-engineered graphene mesosponge (GMS) uniformly decorated with ruthenium (Ru) nanoclusters as a robust electrocatalyst for the hydrogen evolution reaction (HER). The hierarchical GMS structure offers exceptional conductivity and mesoporosity, enabling nanoscale Ru dispersion and strong interfacial coupling. The obtained synergy of Ru50/GMS (optimized Ru deposition) delivers outstanding HER performance across pH range conditions, which provides overpotentials of ∼0.13 V and ∼0.061 V in acidic and alkaline electrolytes, respectively, close to those of the Pt electrode. Interestingly, Ru50/GMS achieves 10 mA cm-2 at only ∼0.39 V in neutral seawater, demonstrating robust operation under harsh, chloride-rich conditions. This performance is nearly 3-fold higher than that of pristine GMS, while sustaining accelerated kinetics and enhanced charge buffering. This is due to electronic modulation at the Ru-graphene interface via topological defects, which substantially optimized hydrogen adsorption and desorption, underpinning rapid reaction pathways. Furthermore, long-term operations confirm structural integrity and negligible catalyst degradation after 5000 cycles and 24 h at ultrahigh current density (-208 ± 10 mA cm-2), highlighting catalyst resilience for practical conditions. Therefore, this work demonstrates a scalable strategy for designing Ru-based catalysts on porous graphene supports, offering a compelling route for efficient, seawater-compatible green hydrogen production.
