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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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How to Minimize Faradaic Efficiency Error in Electrochemical CO 2 Reduction for Gas Products
Faradaic efficiency (FE) is an important metric for evaluating electrochemical processes, such as carbon dioxide reduction, that is often used for performance comparisons. Although the equation to calculate FE is well-known, details needed to yield accurate values are often overlooked, potentially leading to errors exceeding 100%. Avoiding any errors is crucial for drawing correct conclusions, especially during a catalysis optimization process. Factors with high potential for FE errors include incorrect mass flow rates and temperature values, imprecise gas chromatography calibrations, and uncorrected gas viscosities, while factors with less potential are also identified. In conclusion, this manuscript presents FE calculation guidelines intended for both newcomers and experts in the field of electrochemical fuel production.
Minimizing Interfacial Resistance between Polymer Electrolytes and Metal Electrodes Using Applied Current
Reducing the interfacial resistance between different phases in electrochemical systems is crucial for enabling practical applications. In this work, we proposed a process for reducing the interfacial resistance between polymer electrolytes and metal electrodes. Thus far in the literature, the lowest interfacial resistance reported in these systems is 15 Ω·cm2. In this study, assembled and preconditioned symmetric cells with lithium–indium alloy electrodes showed similar values. The current through the cell was increased in steps up to the limiting current. This resulted in a permanent decrease of the interfacial resistance to values as low as 1 Ω·cm2, a value that is comparable to that of optimized lithium-ion batteries. The proposed process is general, and it could be applied to any combination of polymer electrolytes and metal electrodes.
Minimization of Gibbs Energy in High-Pressure Multiphase, Multicomponent Mixtures through Particle Swarm Optimization
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Minimizing Toxicity and Optimizing Lubricity of Ionic Liquids for Eco-Friendly Lubrication
As much as 60 million liters of lubricating fluids end up in the environment annually, and thus, the environmental impact of lubricants is increasingly recognized in addition to meeting the rheological and tribological requirements. Although US Environmental Protection Agency-approved environmentally acceptable base fluids are available, there is a lack of additives that are both nontoxic and effective in wear protection. Here, this study reports the successful development of a new class of ionic liquids (ILs) with demonstrated significantly lower aquatic toxicity and superior friction reduction and wear protection capabilities compared with a commercial lubricant additive and some ILs reported in the literature. Specifically, ammonium phosphate and phosphonium phosphate ILs with four-carbon alkyls have been identified with balanced oil solubility, thermal stability, toxicity, and lubricity, which provide fundamental insights for future development of eco-friendly ILs.
Risk Minimization in Scale-Up of Biomass and Waste Carbon Upgrading Processes
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Higher Wood Density Lowers Feedstock Cost and Has Minimal Impact on Biomass Conversion to Biofuels
Poplar and other woody feedstocks have the potential to provide up to 200 million tons of biomass per year that can be converted to liquid fuels. Most forestry strategies that aim to increase biomass productivity per hectare rely on short rotation plantations of fast-growing varieties. The improvement of the wood density as a key trait itself has largely been overlooked. We evaluated natural variation in wood density across a population of genetically diverse Populus trichocarpa trees grown in a common garden. Wood density varies greatly within this population but is heritable; higher wood density was not systematically associated with reduced growth, challenging assumptions of a trade-off between wood density and biomass accumulation. Furthermore, denser wood led to significant improvements throughout the supply chain including lowering biomass production and transportation costs. Higher density did not correlate with changes in biomass composition. Density did not impact bioconversion in the two feedstock-to-fuel pipelines tested (pretreatment by ionic liquids and fermentation to bisabolene or soaking in aqueous ammonia and fermentation to ethanol) on a representative subset of poplars. These findings highlight wood density as a promising breeding target for accelerating the development of high-yielding, conversion-efficient bioenergy crops and as an avenue for increasing landuse efficiency and reducing biomass transportation cost.
Concerted Proton–Electron Transfer Minimizes Substituent Effects on Adsorbed Phthalocyanine Electrocatalysis
Molecularly modified electrodes (MMEs) are potent electrocatalysts, but few principles exist for their rational design. Electrocatalysis by soluble molecules depends strongly on substituents that tune the catalyst redox potential (E 1/2 ), but it is unclear if this parameter similarly impacts MME catalysis. Herein, we employ the hydrogen evolution reaction (HER) as a test case for comparing carbon-adsorbed cobalt phthalocyanine (CoPc/C) and cobalt hexadecafluoro-phthalocyanine (CoFPc/C). By correlating HER activity and voltammetric data to total Co surface concentration across a wide range of catalyst loadings, we find that only 5–25% of adsorbed Co sites contribute to the Co(II/I) redox wave and that this subpopulation poorly correlates with catalytic activity. Instead, in the low-loading limit, catalytic activity correlates linearly with the majority Co(II/I)-silent Co population, revealing per-site turnover frequency (TOF) values for HER. Despite a 230 mV difference in Co(II/I) redox potentials, CoPc/C and CoFPc/C display TOF values differing by less than a factor of 3 when compared over a wide potential range. Mechanistic studies point to an inner-sphere concerted proton–electron transfer step as rate-determining, suggesting that the Co–H bond dissociation free energy (BDFE) rather than the Co(II/I) E 1/2 is thermodynamically relevant. Computational studies indicate that the fluoro-substituents lead to compensatory changes in Co(II/I) E 1/2 and Co(I) basicity, leaving the Co–H BDFE largely unchanged between CoPc and CoFPc and thereby manifesting in similar catalytic rates. Furthermore, these results highlight the limited effect of E 1/2 -tuning on MME catalytic activity and motivate the development of methods to directly alter active site–substrate BDFE.
Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks
Not Available
Ultrastrong steel via minimal lattice misfit and high-density nanoprecipitation
Abstract not provided
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.
Hyperbolic enhancement of photocurrent patterns in minimally twisted bilayer graphene
Quasi-periodic moiré patterns and their effect on electronic properties of twisted bilayer graphene have been intensely studied. At small twist angle θ, due to atomic reconstruction, the moiré superlattice morphs into a network of narrow domain walls separating micron-scale AB and BA stacking regions. We use scanning probe photocurrent imaging to resolve nanoscale variations of the Seebeck coefficient occurring at these domain walls. The observed features become enhanced in a range of mid-infrared frequencies where the hexagonal boron nitride substrate is optically hyperbolic. Our results illustrate the capabilities of the nano-photocurrent technique for probing nanoscale electronic inhomogeneities in two-dimensional materials.
Machine learning assisted derivation of minimal low-energy models for metallic magnets
Abstract We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.
3D printed triply periodic minimal surfaces as advanced structured packings for solvent-based CO 2 capture
Advanced structured packing geometries fabricated with 3D printing were used for absorber CO 2 capture with a liquid solvent. Compared to conventional packing, they had greatly enhanced (90–140%) effective surface areas and comparable hydrodynamics.
“Glow ELISA”: sensitive immunoassay with minimal equipment and stable reagents
Glow enzyme-linked immunosorbent assay (glow ELISA) uses inexpensive and shelf-stable glow stick reagents to chemically excite fluorescent reporters, obviating the need for excitation light sources, filters, and complex optics.