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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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Important Roles of Thiols in Methylmercury Uptake and Translocation by Rice Plants
We report the bioaccumulation of the neurotoxin methylmercury (MeHg) in rice is a significant concern due to its potential risk to humans. Thiols have been known to affect MeHg bioavailability in microorganisms, but how thiols influence MeHg accumulation in rice plants remains unknown. Here, we investigated effects of common low-molecular-weight thiols, including cysteine (Cys), glutathione (GSH), and penicillamine (PEN), on MeHg uptake and translocation by rice plants. Results show that rice roots can rapidly take up MeHg, and this process is influenced by the types and concentrations of thiols in the system. The presence of Cys facilitated MeHg uptake by roots and translocation to shoots, while GSH could only promote MeHg uptake, but not translocation, by roots. Conversely, PEN significantly inhibited MeHg uptake and translocation to shoots. Using labeled 13 Cys assays, we also found that MeHg uptake was coupled with Cys accumulation in rice roots. Moreover, analyses of comparative transcriptomics revealed that key genes associated with metallothionein and SULTR transporter families may be involved in MeHg uptake. These findings provide new insights into the uptake and translocation of MeHg in rice plants and suggest potential roles of thiol attributes in affecting MeHg bioavailability and bioaccumulation in rice.
Machine Learning in Environmental Research: Common Pitfalls and Best Practices
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High-Efficiency Recovery of Acetic Acid from Water Using Electroactive Gas-Stripping Membranes
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Ca-Based Layered Double Hydroxides for Environmentally Sustainable Carbon Capture
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Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery
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The Impact of Clean Grid Transition on Wastewater Sector Greenhouse Gas Emissions
This study examines the spatial and temporal impacts of the U.S. clean energy grid transition on related greenhouse gas (GHG) emissions from the wastewater treatment industry. By analyzing data from 17,156 water resource recovery facilities (WRRFs) and state-specific grid decarbonization scenarios, the results project a 60% reduction in Scope 2 emissions by 2050, driven by the national shift to renewable energy. However, regional disparities are prominent, with northeastern and western states achieving the most significant reductions, while the Ohio Valley and Rockies are likely to experience higher emissions due to the reliance on fossil fuels. This study offers the first assessment of clean grid impacts on the WRRFs and highlights the need for targeted, region-specific strategies across different emission scopes. This research provides insights for policymakers and stakeholders in the wastewater sector, emphasizing the critical role of grid decarbonization in achieving GHG reduction goals.
Structure–Transport Relationships of Water–Organic Solvent Co-transport in Carbon Molecular Sieve (CMS) Membranes
We explore the effects of the carbon molecular sieve (CMS) microstructure on the separation performance and transport mechanism of water–organic mixtures. Specifically, we utilize PIM-1 dense films and integrally skinned asymmetric hollow fiber membranes as polymer precursors for the CMS materials. The PIM-1 membranes were pyrolyzed under several different pyrolysis atmospheres (argon, carbon dioxide, and diluted hydrogen gas) and at multiple pyrolysis temperatures. Detailed gas physisorption measurements reveal that membranes pyrolyzed under 4% H 2 and CO 2 had broadened ultramicropore distributions (pore diameter <7 Å) compared to Ar pyrolysis, and pyrolysis under CO 2 increased ultramicropore volume and broadened micropore distributions at increased pyrolysis temperatures. Gravimetric water and p-xylene sorption and diffusion measurements reveal that the PIM-1-derived CMS materials are more hydrophilic than other CMS materials that have been previously studied, which leads to sorption-diffusion estimations showing water-selective permeation. Water permeation in the vapor phase, pervaporation, and liquid-phase hydraulic permeation reveal that the isobaric permeation modes (vapor permeation and pervaporation) are reasonably well predicted by the sorption-diffusion model, whereas the hydraulic permeation mode is significantly underpredicted (>250×). Conversely, the permeation of p-xylene is well predicted by the sorption-diffusion model in all cases. The collection of pore size analysis, vapor sorption and diffusion, and permeation in different modalities creates a picture of a combined transport mechanism in which water–under high transmembrane pressures–permeates via a Poiseuille-style mechanism, whereas p-xylene solutes in the mixture permeate via sorption-diffusion.
Correction for “Structure–Transport Relationships of Water–Organic Solvent Co-transport in Carbon Molecular Sieve (CMS) Membranes”
There were two important typos identified in the reported units of water flux under reverse osmosis conditions.
Peroxide-Templated Assembly of a Trimetal Neodymium Complex Single-Molecule Magnet
In this study, we present a tri-metal neodymium complex with two notable qualities. Firstly, the assembly of the complex is templated by peroxide derived from atmospheric oxygen. Secondly, the bulk material behaves as a super-paramagnet, implying that the individual complexes are molecular magnets. The peroxide-templated assembly is possible due to the confluence of the high oxophilicity of neodymium along with the use of an azeotropic distillation synthesis method, which excludes water but admits oxygen. SQUID magnetometer measurements show an extremely high magnetic susceptibility as well as a lack of remanence.
Relationship among the Crystal Structure, Texture, and Macroscopic Properties of Tetragonal (Pb,La)(Zr,Ti)O 3 Ferroelectrics Investigated by In Situ High-Energy Synchrotron Diffraction
In situ diffraction investigations have played an important role in experimentally revealing the mechanism of piezoelectric and ferroelectric properties. In this study, a pure tetragonal ferroelectric ceramic of La-doped PbZr 0.5 Ti 0.5 O 3 (LaPZT50) was investigated to eliminate the complex influence of phase coexistence. The electric field evolutions of the crystal structure, domain switching, and lattice deformation of the tetragonal phase have been revealed by in situ high-energy synchrotron X-ray diffraction. Furthermore, we found that the crystal structure of LaPZT50 is quite stable, showing a negligible change in the Pb–O bond length, unit cell volume, and spontaneous polarization upon application of an in situ external electric field. Importantly, the maximum macroscopic polarization of tetragonal LaPZT50 is defined by the 111-oriented grains. As determined by the intensity difference, the switching of non-180° domains plays a more significant role in contributing to the macroscopic strain than lattice deformation. These results further imply that the phase coexistence around the morphotropic phase boundary facilitates domain wall motion in the tetragonal phase and improves the ferroelectric and piezoelectric properties.
High-Pressure and High-Temperature Synthesis and In Situ High-Pressure Synchrotron X-ray Diffraction
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Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers
Here, we develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces by using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed length bonds, with configurations updated through adaptive nonlocal Monte Carlo moves. This proposed method enables precise calculation of a polymer’s response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing the polymer conformation.
Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning
Here, we present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.
Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction
Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.