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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Epitaxial growth mechanism of heterogeneous catalytic oxidation of Mn(II) on manganite under oxic conditions
Manganese oxides are ubiquitous in soils and sediments, and their formation and transformation processes affect the migration and transformation of heavy metals and organic pollutants. Most previous work has been focused the oxidation of Mn(II) on Mn(IV) oxide surface, but it remains poorly understood how Mn(II) interacts with manganite under oxic conditions. Here, the interactions between manganite and dissolved Mn(II) within pH 5.0–9.0 under oxic conditions were systematically studied. The results indicated that adsorbed Mn(II) was directly oxidized to manganite on the surface, and the oxidation rate increased with increasing pH and oxygen partial pressure. Electrons are likely transferred in bulk manganite during the oxidation process of Mn(II). Although manganite induced Mn(II) oxidation and the epitaxial growth of bulk particles, there was no obvious change in mineral phase. Dissolved oxygen accelerated the growth of manganite in Mn(II) solution. Compared with the absence of manganite, the presence of manganite inhibited the formation of hausmannite from Mn(II) oxidation by oxygen in air. Furthermore, this study reveals the catalytic oxidation process and mechanism of Mn(II) on manganite surface, and improves the understanding of manganite crystal growth in supergene environments.
Formation and transformation of manganese(III) intermediates in the photochemical generation of manganese(IV) oxide minerals
As important natural oxidants and adsorbents, manganese (Mn) oxide minerals affect the speciation, bioavailability and fate of pollutants and nutrient elements. It was found that birnessite-type Mn(IV) oxide minerals can be formed in the presence of NO 3 – and solar irradiation. However, the photochemical formation and transformation processes from Mn 2+ to Mn(IV) oxide minerals remain unclear. In this work, the Mn(IV) oxide minerals were confirmed to be photochemically formed mainly due to the disproportionation of Mn(III) intermediates generated from the oxidation of Mn 2+ in the presence of NO 3 – under UV light irradiation. The oxidation rate of Mn 2+ to Mn(IV) oxide minerals decreased with increasing initial Mn 2+ concentration due to the lower disproportionation rate. The increase in NO 3 – concentration, pH and temperature promoted Mn 2+ photochemical oxidation. The photochemical formation rate of Mn(IV) oxide minerals increased with increasing ligand concentrations at low ligand concentrations. Ligands affected the formation of Mn(IV) oxide minerals by promoting the formation and reducing the reactivity of Mn(III) intermediates. Altogether, this work reveals the important role of Mn(III) intermediates in the formation of natural Mn oxide minerals.
Can the multi-walled carbon nanotubes be used to alleviate the phytotoxicity of herbicides in soils?
Herbicides are commonly used globally. However, residual herbicides in soils for ages often result in phytotoxicity and serious yield loss to subsequent crops. In this paper, the multi-walled carbon nanotubes (MWCNTs) were utilized to amend the herbicide polluted soil, and the adsorption performance of herbicides to MWCNTs amended soil was studied. Results indicate efficient alleviation of herbicide-induced phytotoxicity to rice and tobacco due to MWCNTs amendment. When 0.4% MWCNTs were applied, the concentration of sulfentrazone that inhibited the same rice height by 50% (IC 50 ) increased to more than 3 times that of pure soil. When the MWCNTs were used to alleviate the phytotoxicity of quinclorac to tobacco, the MWCNTs not only alleviated the phytotoxicity of quinclorac but also promoted the growth of tobacco. Furthermore, the MWCNTs amended soil significantly increased the adsorption of herbicide to soil than biochar. The soil microbial analysis shows that MWCNTs had no significant effect on soil microbial community diversity, but the long-term exposure to MWCNTs could change the structure of the soil microbial community. Above all, our results highlighted the potential implication of the MWCNTs to ensure crop production by promoting crop growth and reducing the residual bioavailability of herbicides.
Dynamic Covalent Synthesis of Crystalline Porous Graphitic Frameworks
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Breaking the trade-off between selectivity and adsorption capacity for gas separation
It is generally recognized that porous solids (sorbents) with high selectivity and high adsorption capacity offer potential for energy-efficient gas separations. Unfortunately, there is generally a trade-off between capacity and selectivity, which represents a roadblock to the utility of sorbents in key industrial processes. For example, acetylene (C 2 H 2 ), an important fuel and chemical intermediate, is produced with CO 2 as an impurity, and the similar physicochemical properties of C 2 H 2 and CO 2 mean that most sorbents are poorly selective. Hybrid ultramicroporous materials (HUMs) are candidates for gas separations as they exhibit benchmark selectivity for several key gas pairs. Unfortunately, existing HUMs are handicapped by low capacity. Here, we report a new HUM, SIFSIX-21-Ni, that addresses the trade-off between selectivity and capacity that has plagued sorbents, as its high uptake and high selectivity renders it the new benchmark for C 2 H 2 /CO 2 separation performance.
Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents
Covalent organic frameworks (COFs) have emerged as a versatile material platform for such applications as chemical separations, chemical reaction engineering, and energy storage. Their inherently low mechanical stability, however, frequently renders existing methods of pelletization ineffective, contributing to pore collapse, pore blockage, or insufficient densification of crystallites. Here, we present a process for the shaping and densifying of COFs into robust centimeter-scale porous monoliths without the need for templates, additives, or binders. This process minimizes mechanical damage from shear-induced plastic deformation and further provides a network of interparticle mesopores that we exploit in accessing analyte capacities above those achievable from the intrinsic COF structure. Using a lattice-gas model, we accurately capture the monolithic structure across the mesoporous range and tie pore architecture to performance in both gas-storage and -separation applications. Collectively, these results represent a substantial step in the practical applicability of COFs and other mechanically weak porous materials.
Rare, convergent antibodies targeting the stem helix broadly neutralize diverse betacoronaviruses
Humanity has faced three recent outbreaks of novel betacoronaviruses, emphasizing the need to develop approaches that broadly target coronaviruses. Here, we identify 55 monoclonal antibodies from COVID-19 convalescent donors that bind diverse betacoronavirus spike proteins. Most antibodies targeted an S2 epitope that included the K814 residue and were non-neutralizing. However, 11 antibodies targeting the stem helix neutralized betacoronaviruses from different lineages. Eight antibodies in this group, including the six broadest and most potent neutralizers, were encoded by IGHV1-46 and IGKV3-20. Crystal structures of three antibodies of this class at 1.5–1.75-Å resolution revealed a conserved mode of binding. COV89-22 neutralized SARS-CoV-2 variants of concern including Omicron BA.4/5 and limited disease in Syrian hamsters. Collectively, these findings identify a class of IGHV1-46/IGKV3-20 antibodies that broadly neutralize betacoronaviruses by targeting the stem helix but indicate these antibodies constitute a small fraction of the broadly reactive antibody response to betacoronaviruses after SARS-CoV-2 infection.
A scalable exponential-DG approach for nonlinear conservation laws: With application to Burger and Euler equations
In this work, we propose an Exponential DG framework for partial differential equations. We decompose 7 governing equations into linear and nonlinear parts to which we apply the discontinuous Galerkin 8 (DG) spatial discretization. In particular, we construct the linear part using Jacobian that effectively 9 capture stiff characteristics in the system. The former is integrated analytically, whereas the latter 10 is approximated. This approach i) is stable with a large Courant number (Cr > 1); ii) supports 11 high-order solutions both in time and space; iii) is computationally favorable compared to IMEX 12 DG methods with no preconditioner; iv) becomes comparable to explicit RKDG methods on uniform 13 mesh and beneficial on non-uniform grid for Euler equations; v) is scalable in a modern massively 14 parallel computing architecture due to its explicit nature of exponential time integrators and com15 pact communication stencil of DG method. Numerical results demonstrate the performance of our 16 proposed methods through various examples. We also discuss the stability and convergence analysis 17 for our exponential DG scheme in the context of Burgers equation.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.
A model-constrained discontinuous Galerkin Network (DGNet) for compressible Euler equations with out-of-distribution generalization
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An adaptive and stability-promoting layerwise training approach for sparse deep neural network architecture
This work presents a two-stage adaptive framework for progressively developing deep neural network (DNN) architectures that generalize well for a given training data set. In the first stage, a layerwise training approach is adopted where a new layer is added each time and trained independently by freezing parameters in the previous layers. We impose desirable structures on the DNN by employing manifold regularization, sparsity regularization, and physics-informed terms. We introduce a ε – δ – stability-promoting concept as a desirable property for a learning algorithm and show that employing manifold regularization yields a ε – δ stability-promoting algorithm. Further, we also derive the necessary conditions for the trainability of a newly added layer and investigate the training saturation problem. In the second stage of the algorithm (post-processing), a sequence of shallow networks is employed to extract information from the residual produced in the first stage, thereby improving the prediction accuracy. Numerical investigations on prototype regression and classification problems demonstrate that the proposed approach can outperform fully connected DNNs of the same size. Moreover, by equipping the physics-informed neural network (PINN) with the proposed adaptive architecture strategy to solve partial differential equations, we numerically show that adaptive PINNs not only are superior to standard PINNs but also produce interpretable hidden layers with provable stability. As a result, we also apply our architecture design strategy to solve inverse problems governed by elliptic partial differential equations.
Optical image analysis for graphene layer detection: Enhanced green channel methodology
Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.
Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration
Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.