Kinetic modeling of NH3-selective catalytic reduction (SCR) over a mildly hydrothermally aged commercial Cu-SSZ-13 catalyst
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High-energy associated with slow absorption-desorption kinetics remains a major challenge of all chemisorption-based CO2 capture technologies, particularly during solvent regeneration. A novel catalytic sorption system, based on aqueous piperidine (C5H10-NH) activated by a deep eutectic solvent (DES), was developed in this research to overcome the challenge.
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A new computational diagnostic method for pressure-induced compressibility is proposed by projecting its local contribution to the chemical explosive mode (CEM) in the chemical explosive mode analysis (CEMA) framework. The new method is validated for the study of detonation development during the deflagration-to-detonation transition (DDT) process. The flame characteristics are identified through the quantification of individual CEM contributions of chemical reaction, diffusion, and pressure-induced compressibility. Numerical simulations are performed to investigate the DDT processes in a stoichiometric hydrogen-air mixture. A Godunov algorithm, fifth-order in space, and third-order in time are used to solve the fully compressible Navier-Stokes equations on a dynamically adapting mesh. A single-step, calibrated chemical diffusive model (CDM) described by Arrhenius kinetics is used for energy release and conservation between the fuel and the product. The new diagnostic method is first applied to onedimensional (1D) canonical flame configurations followed by two-dimensional (2D) simulations of DDT in an obstructed channel where different detonation initiation scenarios are examined using the new CEMA projection formulation. Detailed examinations of the idealized configuration of detonation initiation through shock focusing mechanism at a flame front are also studied using the new formulation. A comparison of the currently proposed CEMA projection and the original formulation by the authors suggests that including the pressure-induced compressibility is essential for the use of CEMA in DDT process. The results also show that the new formulation of CEMA projection can successively capture the detonation initiation through either a gradient mechanism or a direct initiation mechanism, and therefore can be used as an effective local analytical tool for the computational diagnostics of detonation initiation in a DDT process. It was found that detonation development is characterized by a strong contribution of chemistry role to the CEM which is pivotal to the initiation of detonation. The role of compressibility is found enhanced at the edge of the detonation front where diffusion was found to have minimal effects on detonation development.
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Spinel LiNi 0.5 Mn 1.5 O 4 offers both the high-rate, low-cost and safety advantages of LiFePO 4 and the high energy density of LiNiₓMnᵧCo₁₋ₓ₋ᵧO₂ and LiNiₓCoᵧAlzO₂ cathodes. However, the large operating voltage of these materials induces electrolyte oxidation, which degrades the interface and drives Mn dissolution. These reactions are further exacerbated at high rates due to temperature rise. In this study, we discover that ammoniacal treatment followed by annealing introduces a high density of oxygen vacancies in the “near-surface region” of LiNi 0.5 Mn 1.5 O 4 particles. These vacancies release electrons changing the oxidation state of Mn and suppressing its tendency to oxidize the electrolyte. Further, these vacancies enhance the electrode’s electronic conductivity (by ∼3-fold) and Li + diffusivity (by ∼2-fold) greatly improving charge transport, especially when operated at high rates. This results in an across-the-board improvement in self-discharge, specific capacity, energy density, rate capability, coulombic efficiency and cycling stability. When cycled at ∼200 mA g −1 , the capacity fade averaged over 3000 cycles for the surface vacancy-enriched material is ∼0.0167% per cycle compared to an order of magnitude higher fade rate for the baseline material. In conclusion, these findings reveal the potential of targeted surface oxygen vacancy doping to develop cobalt-free and high energy density cathodes that tolerate fast charging and deliver improved cycle life.
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Brown rot fungi utilize iron as part of a chelator-mediated Fenton (CMF) reaction during wood biodegradation. Research suggests these fungi reduce Fe 3+ to promote oxygen radical generation resulting in depolymerization of the wood cell wall. High levels of Mn are also found in wood decayed by brown rot fungi. However, little is known about the oxidation states of Fe and Mn during the decay process. X-ray absorption near edge spectroscopy (XANES) can be used to examine metal oxidation states and coordination chemistry. In this work, XANES experiments were conducted on wood decayed by Gloeophyllum trabeum over 2–8 weeks with results showing that Mn 2+ and Fe 3+ predominated for metal oxidation states. However, Fe 2+ was present at sites of greater fungal growth In certain cases, the μXANES measurements showed that the fraction of Fe 2+ in the wood samples was as high as 50%. Localized areas of reduced iron corresponded with areas of greater fungal hyphal mass which is in agreement with how brown rot fungi decay wood via the CMF reaction. The limited change in oxidation state of Mn observed in wood with active fungal activity suggests that the role of manganese in CMF biodegradation chemistry should be further explored.
It is challenging to formulate complex physical phenomena that occur in a manufacturing process, particularly when the available data are limited, rendering conventional data-driven approaches ineffective. This study aims to predict humping onset in high-speed laser welding by introducing a novel framework, namely text-to-equations generative pre-trained transformer (T2EGPT). This method leverages the capabilities of large language models (LLMs), in combination with sparse experimental data and enriched literature data, to derive an interpretable and generalizable equation for predicting humping initiation. By capturing key correlations among physical parameters, T2EGPT generates a compact and dimensionless expression that accurately predicts hump formation. The equation reveals that humping arises from the interplay between inertia-driven backward melt flow and capillary-driven surface stabilization, where inertial forces drive molten metal backward and capillary forces resist surface deformation. Furthermore, compared to traditional data-driven models, T2EGPT demonstrates enhanced predictive accuracy and cross-material transferability. More broadly, this study highlights the potential of LLMs to integrate textual information with data-driven discovery, enabling the extraction of physical laws in data-scarce scientific domains.