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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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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Emergent actin flows explain distinct modes of gliding motility

During host infection, Toxoplasma gondii and related unicellular parasites move using gliding, which differs fundamentally from other known mechanisms of eukaryotic cell motility. Gliding is thought to be powered by a thin layer of flowing filamentous (F)-actin sandwiched between the plasma membrane and a myosin-covered inner membrane complex. How this surface actin layer drives the various gliding modes observed in experiments—helical, circular, twirling and patch, pendulum or rolling—is unclear. Here we suggest that F-actin flows arise through self-organization and develop a continuum model of emergent F-actin flow within the confines provided by Toxoplasma geometry. In the presence of F-actin turnover, our model predicts the emergence of a steady-state mode in which actin transport is largely directed rearward. Removing F-actin turnover leads to actin patches that recirculate up and down the cell, which we observe experimentally for drug-stabilized actin bundles in live Toxoplasma gondii parasites. These distinct self-organized actin states can account for observed gliding modes, illustrating how different forms of gliding motility can emerge as an intrinsic consequence of the self-organizing properties of F-actin flow in a confined geometry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learning Inference of Random Medium Properties

Earth materials are heterogeneous across a range of spatial scales, but the resolvability of small structures is limited by sparse data coverage, noise, bandlimitedness, and other difficulties. In practice, heterogeneities below a certain size cannot be recovered from seismic data except through statistical medium descriptions, which even then can be difficult to uniquely determine. To improve the characterization of such heterogeneities, we develop a novel supervised machine learning (ML) model that provides insight about the recoverability of statistical medium properties from elastic waveform data and succeeds despite cycle-skipping and other challenges well known from elastic waveform inversion. We demonstrate the approach using random media generated by superimposing self-affine random variations on homogeneous and layered background structures. After training on sparsely-recorded, high-frequency waveforms from hundreds of different random medium realizations, we show the ability of our ML model to recover correlation lengths and other statistical properties of interest to near-surface and crustal seismology, among other fields. For frequency passbands and spatial offsets encountered in seismology, Gaussian correlation lengths and the amplitude of the random variations relative to the background model are recovered even in challenging scenarios involving unknown medium parameters, complex crustal structures, and low signal-to-noise ratio. In comparison, von Kármán correlation lengths, which are related to larger-wavelength variations of the medium than Gaussian correlation lengths, are not as well recovered. These results provide one of the first and most systematic investigations of the recoverability of statistical properties of heterogeneities below the resolution limit of deterministic seismic tomography, and suggest practical ML strategies for high-frequency waveform seismology.

58 GEOSCIENCES↗
DOE OSTI2024

Atmospheric pressure plasmas interacting with wet and dry microchannels: reverse surface ionization waves

Atmospheric pressure plasma jets (APPJs) are increasingly being used to functionalize polymers and dielectric materials for biomedical and biotechnology applications. Once such application is microfluidic labs-on-a-chip consisting of dielectric slabs with microchannel grooves hundreds of microns in width and depth. The periodic channels, an example of a complex surface, present challenges in terms of directly and uniformly exposing the surface to the plasma. In this paper, we discuss results from computational and experimental investigations of negative APPJs sustained in Ar/N 2 mixtures flowing into ambient air and incident onto a series of microchannels. Results from two-dimensional plasma hydrodynamics modeling are compared to experimental measurements of electric field and fast-camera imaging. The propagation of the plasma across dry microchannels largely consists of a sequence of surface ionization waves (SIWs) on the top ridges of the channels and bulk ionization waves (IWs) crossing over the channels. The IWs are directed into electric field enhanced vertices of the next ridge. The charging of these ridges produce reverse IWs responsible for the majority of the ionization. The propagation of the plasma across water filled microchannels evolve into hopping SIWs between the leading edges of the water channels, regions of electric enhancement due to polarization of the water. Positive, reverse IWs follow the pre-ionized path of the initial negative waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A level-set immersed boundary method for reactive transport in complex topologies with moving interfaces

A simulation framework based on the level-set and the immersed boundary methods (LS-IBM) has been developed for reactive transport problems in porous media involving a moving solid-fluid interface. The interface movement due to surface reactions is tracked by the level-set method, while the immersed boundary method captures the momentum and mass transport at the interface. The proposed method is capable of accurately modeling transport near evolving boundaries in Cartesian grids. The framework formulation guarantees second order accuracy in space. Since the interface velocity is only defined at the moving boundary, an interface velocity propagation method is also proposed. The method can be applied to other moving interface problems of the “Stefan” type. Here, we validate the proposed LS-IBM both for flow and transport close to an immersed object with reactive boundaries as well as for crystal growth. Lastly, the proposed method provides a powerful tool to model more realistic problems involving moving reactive interfaces in complex domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Projecting Antarctica's contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2)

Abstract. The sea level contribution of the Antarctic ice sheet constitutes a large uncertainty in future sea level projections. Here we apply a linear response theory approach to 16 state-of-the-art ice sheet models to estimate the Antarctic ice sheet contribution from basal ice shelf melting within the 21st century. The purpose of this computation is to estimate the uncertainty of Antarctica's future contribution to global sea level rise that arises from large uncertainty in the oceanic forcing and the associated ice shelf melting. Ice shelf melting is considered to be a major if not the largest perturbation of the ice sheet's flow into the ocean. However, by computing only the sea level contribution in response to ice shelf melting, our study is neglecting a number of processes such as surface-mass-balance-related contributions. In assuming linear response theory, we are able to capture complex temporal responses of the ice sheets, but we neglect any self-dampening or self-amplifying processes. This is particularly relevant in situations in which an instability is dominating the ice loss. The results obtained here are thus relevant, in particular wherever the ice loss is dominated by the forcing as opposed to an internal instability, for example in strong ocean warming scenarios. In order to allow for comparison the methodology was chosen to be exactly the same as in an earlier study (Levermann et al., 2014) but with 16 instead of 5 ice sheet models. We include uncertainty in the atmospheric warming response to carbon emissions (full range of CMIP5 climate model sensitivities), uncertainty in the oceanic transport to the Southern Ocean (obtained from the time-delayed and scaled oceanic subsurface warming in CMIP5 models in relation to the global mean surface warming), and the observed range of responses of basal ice shelf melting to oceanic warming outside the ice shelf cavity. This uncertainty in basal ice shelf melting is then convoluted with the linear response functions of each of the 16 ice sheet models to obtain the ice flow response to the individual global warming path. The model median for the observational period from 1992 to 2017 of the ice loss due to basal ice shelf melting is 10.2 mm, with a likely range between 5.2 and 21.3 mm. For the same period the Antarctic ice sheet lost mass equivalent to 7.4 mm of global sea level rise, with a standard deviation of 3.7 mm (Shepherd et al., 2018) including all processes, especially surface-mass-balance changes. For the unabated warming path, Representative Concentration Pathway 8.5 (RCP8.5), we obtain a median contribution of the Antarctic ice sheet to global mean sea level rise from basal ice shelf melting within the 21st century of 17 cm, with a likely range (66th percentile around the mean) between 9 and 36 cm and a very likely range (90th percentile around the mean) between 6 and 58 cm. For the RCP2.6 warming path, which will keep the global mean temperature below 2 ∘C of global warming and is thus consistent with the Paris Climate Agreement, the procedure yields a median of 13 cm of global mean sea level contribution. The likely range for the RCP2.6 scenario is between 7 and 24 cm, and the very likely range is between 4 and 37 cm. The structural uncertainties in the method do not allow for an interpretation of any higher uncertainty percentiles. We provide projections for the five Antarctic regions and for each model and each scenario separately. The rate of sea level contribution is highest under the RCP8.5 scenario. The maximum within the 21st century of the median value is 4 cm per decade, with a likely range between 2 and 9 cm per decade and a very likely range between 1 and 14 cm per decade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Goethite and Hematite Nucleation and Growth from Ferrihydrite: Effects of Oxyanion Surface Complexes

The presence of oxyanions, such as nitrate (NO 3 – ) and phosphate (PO 4 3– ), regulates the nucleation and growth of goethite (Gt) and hematite (Hm) during the transformation of ferrihydrite (Fh). Our previous studies showed that oxyanion surface complexes control the rate and pathway of Fh transformation to Gt and Hm. However, how oxyanion surface complexes control the mechanism of Gt and Hm nucleation and growth during the Fh transformation is still unclear. Here, we used synchrotron scattering methods and cryogenic transmission electron microscopy to investigate the effects of NO 3 – outer-sphere complexes and PO 4 3– inner-sphere complexes on the mechanism of Gt and Hm formation from Fh. Our TEM results indicated that Gt particles form through a two-step model in which Fh particles first transform to Gt nanoparticles and then crystallographically align and grow to larger particles by oriented attachment (OA). In contrast, for the formation of Hm, imaging shows that Fh particles first aggregate and then transform to Hm through interface nucleation. This is consistent with our X-ray scattering results, which demonstrate that NO 3 – outer-sphere and PO 4 3– inner-sphere complexes promote the formation of Gt and Hm, respectively. These results have implications for understanding the coupled interactions of oxyanions and iron oxy-hydroxides in Earth-surface environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Near-continuum, hypersonic oxygen flow over a double cone simulated by direct simulation Monte Carlo informed from quantum chemistry

A large-scale, fully resolved direct simulation Monte Carlo (DSMC) computation of a non-equilibrium, reactive flow of pure oxygen over a double cone is presented. Under the simulated near-continuum conditions, the computational demands are shown to be significant because of the wide range of length scales that must be resolved. Therefore, robust grid adaption capabilities and efficient parallelization of the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) code that is utilized in this work are essential. The thermochemical and transport collision models were selected for efficiency and simplicity. First-principles data, obtained from the highly accurate direct molecular simulation method, were used to inform the collision models’ parameters. Importantly, because SPARTA implements molecular collision models using collision-specific energies, the resulting macroscopic relaxation rates were evaluated a posteriori via zero-dimensional heat bath simulations. The comparisons of surface properties, namely heat flux and pressure, show very close agreement with previous computational fluid dynamics (CFD) results. Differences with the measurements were found to be similar to the CFD simulations. The unresolved discrepancy with the measurements could be due to inconsistent free stream conditions with the actual experimental data or missing physical phenomena altogether, for example atomic and molecular oxygen electronically excited states, three-dimensional effects, or more complex gas–surface interactions. As shown in this work, the advantages of obtaining a DSMC particle solution for these flows reside in the method's ability to be directly informed from first principles and to seamlessly describe internal energy non-equilibrium for all modes. With the advent of exascale computing and beyond, particle methods will be an increasingly important tool to verify the validity of physical assumptions in reduced-order models via fully resolved, experimental-scale simulations, down to the level of molecular-level distributions.

Mechanics↗
DOE OSTI2024

Ca-dimers, solvent layering, and dominant electrochemically active species in Ca(BH4)2 in THF

Abstract Divalent ions (Mg, Ca, and Zn) are being considered as competitive, safe, and earth-abundant alternatives to Li-ion electrochemistry, but present challenges for stable cycling due to undesirable interfacial phenomena. We explore the formation of electroactive species in the electrolyte Ca(BH 4 ) 2 ∣THF using molecular dynamics coupled with a continuum model of bulk and interfacial speciation. Free-energy analysis and unsupervised learning indicate a majority population of neutral Ca dimers and monomers with diverse molecular conformations and an order of magnitude lower concentration of the primary electroactive charged species – the monocation, $${\rm{CaBH}}_{4}^{+}$$ CaBH 4 + – produced via disproportionation of neutral complexes. Dense layering of THF molecules within ~1 nm of the electrode surface strongly modulates local electrolyte species populations. A dramatic increase in monocation population in this interfacial zone is induced at negative bias. We see no evidence for electrochemical activity of fully-solvated Ca 2+ . The consequences for performance are discussed in light of this molecular-scale insight.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Transport and Assembly of Magnetic Surface Rotors**

This minireview focuses on recent advances with surface magnetic rotors, namely field-responsive spherical or anisotropic microparticles that translate close to, or are embedded in a confining surface. The application of external magnetic modulations allows these microscopic wheels to be remotely spun and steered while also tuning their interactions and inducing assembly from a collection of disordered, moving units. With optical microscopy one can observe and characterize the complex collective phenomena that emerge in dissipative colloidal systems driven far from equilibrium by external fields. From a technological point of view, magnetic surface rotors envisage implementation into microfluidic devices, and can be used for drug delivery, mixing as well as a model system for biological active matter.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The black hole interior from non-isometric codes and complexity

Quantum error correction has given us a natural language for the emergence of spacetime, but the black hole interior poses a challenge for this framework: at late times the apparent number of interior degrees of freedom in effective field theory can vastly exceed the true number of fundamental degrees of freedom, so there can be no isometric (i.e. inner-product preserving) encoding of the former into the latter. In this paper we explain how quantum error correction nonetheless can be used to explain the emergence of the black hole interior, via the idea of “non-isometric codes protected by computational complexity”. We show that many previous ideas, such as the existence of a large number of “null states”, a breakdown of effective field theory for operations of exponential complexity, the quantum extremal surface calculation of the Page curve, post-selection, “state-dependent/state-specific” operator reconstruction, and the “simple entropy” approach to complexity coarse-graining, all fit naturally into this framework, and we illustrate all of these phenomena simultaneously in a soluble model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Drivers of Dry Day Sensitivity to Increased CO 2

Persistent precipitation deficits are among the most impactful consequences of global warming. Here we focus on changes in the annual number of dry days (NDD) and in the annual maximum length of dry spells due to a quadrupling of atmospheric CO 2 . We use atmosphere-only simulations to decompose the projected changes into additive contributions. A fast adjustment leads to a global increase in NDD despite notable regional exceptions (e.g., South Asia and Sahel). The effect of the uniform component of the surface ocean warming is model-dependent but shapes the regional distribution of the NDD response in each model. Finally, the ocean warming pattern also contributes to large uncertainties, likely through contrasting changes in large-scale circulation. Our results thus highlight the complexity of the NDD response, with policy-relevant practical implications for mitigation and adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Theoretical and kinetic modeling study of hydrazine oxidation

The present work constitutes the first theoretical and kinetic modeling study of hydrazine oxidation, which may be important for burnout in ammonia-fueled combustion. The kinetics of the oxidation of N 2 H 4 , N 2 H 3 and tHNNH by molecular oxygen were investigated via a quantum chemistry/canonical transition state theory approach. Geometries and anharmonic frequencies were obtained with density functional theory, and energies from coupled cluster calculations (CCSD(T)) extrapolated to the infinite basis set limit, with corrections for core-valence electron correlation, scalar relativistic effects, and higher level correlation up to lambda coupled cluster, CCSDT(Q) Λ . The key reactions occurred on the N 2 H 4 O 2 potential energy surface, where the results indicated a fast reaction of N 2 H 3 with HO 2 via singlet adducts to yield tHNNH + H 2 O 2 and HNN(H)O + H 2 O, while reaction on the triplet surface proceeds via a bound complex followed by a tight, submerged barrier to yield N 2 H 4 + O 2 . The results were incorporated in a detailed reaction mechanism, which was used to interpret the shock tube results from Michel and Wagner (1965) on the effect of O 2 on hydrazine conversion at 1100–1400 K. The kinetic model captured qualitatively the observed behavior, but underestimated the reaction rate under oxidizing conditions. The hydrazine pyrolysis chemistry dominated conversion at reducing conditions and/or high temperature. At oxidizing conditions and intermediate temperatures (≲ 1400 K), reactions of N 2 -amines with HO 2 and O 2 were important for the oxidation rate.

Ab initio calculations↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mechanisms for the Generation of Complex Fracture Networks: Observations From Slant Core, Analog Models, and Outcrop

We use observations of hydraulic fractures in core, outcrop attributes of natural hydraulic fractures, and analogue models, to address how hydraulic fracture networks evolve. A slant core from the Wolfcamp Formation—an unconventional shale hydrocarbon reservoir in the Permian Basin of West Texas—collected within 18 m and 30 m of two hydraulically stimulated horizontal wells, provided an opportunity to examine hydraulic fractures directly. In approximately 183 m of core, 309 calcite-sealed natural opening-mode fractures and 375 hydraulic fractures were identified. Many hydraulic fractures in the core show complex morphology, including twist-hackle segmentation, diversion, and bifurcation; these structures most commonly develop at lithological bed boundaries and mechanical heterogeneities such as natural fractures and concretions. An outcrop of bed-parallel pavements in the Cretaceous Boquillas Formation in West Texas contains opening-mode fractures that likely formed by natural hydraulic fracturing. Fracture traces provide evidence of twist-hackle segmentation, and are typically associated with bed boundaries and preexisting bed-parallel stylolites. A laboratory study of hydraulic fracturing of 33 synthetic blocks of gypsum and hydrostone revealed fracture steps, diversions, twist hackles, and multiple overlapping fractures together with information on fracture growth directions. These complexities in the fracture network were dominantly nucleated at inclusions used to simulate pre-existing fractures, and as a result of mechanical heterogeneity introduced by the wellbore and perforations. Collectively, our results show that complex fracture networks are produced in hydraulic fracturing of self-sourced reservoir strata. Mechanical stratigraphic boundaries and other heterogeneities are likely to enhance fracture network complexity through the processes of segmentation, diversion, and bifurcation. These processes create multiple fracture strands, resulting in an increased number of hydraulic fractures over those initiated, thereby increasing total fracture surface area. Our study provides insight into hydraulic fracture network propagation, and has applications for evaluation, completion, production, and fracture modeling of unconventional reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

Wetlands are the single largest natural source of atmospheric methane (CH 4 ), contributing approximately 30% of total surface CH 4 emissions, and they have been identified as the largest source of uncertainty in the global CH 4 budget based on the most recent Global Carbon Project CH 4 report. High uncertainties in the bottom–up estimates of wetland CH 4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH 4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH 4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH 4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH 4 yr -1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH 4 yr -1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH 4 yr -1 ). Bottom–up estimates are subject to significant uncertainties (~80 Tg CH 4 yr -1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH 4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH 4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH 4 fluxes (e.g. FLUXNET-CH 4 ) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH 4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH 4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH 4 observations directed at wetlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Diffusion Monte Carlo Study of the Structure and Spectroscopy of H 3 O –

A potential energy surface for H 3 O – has been developed based on the NN+(MOB-ML) approach we developed for studies of complexes of OH – with two or three water molecules. Unlike those systems, H 3 O – has two low-energy isomers, H – ·H 2 O and OH – ·H 2 , which differ in energy by less than 2.5 kcal mol –1 , and which are separated by a barrier of roughly 4.5 kcal mol –1 . We find that by training the NN+(MOB-ML) model using structures based on diffusion Monte Carlo (DMC) simulations initiated in the two potential minima, we are able to obtain a potential surface that describes both isomers. Using these potentials, the structure and spectra of H 3 O – and its deuterated analogues are investigated using DMC. These calculations show that the ground state wave function for H 3 O – is mainly localized in the H – ·H 2 O minimum in the potential, with a small amount of the probability amplitude (<5%) in the region of the OH – ·H 2 minimum. The delocalization of the wave function into the secondary minimum is lowered by deuteration of the water molecule, while replacing H – with D – increases the isomerization due to the shortening of the average distance between the hydride ion and the hydrogen atom in water to which it is bound. Introducing one quantum of excitation in the H – ···H 2 O stretching vibration increases the amount of isomerization, while the isomerization decreases with additional excitation of this mode. Excitation of the free OH stretch in the water molecule also increases the amount of isomerization, while excitation of the out-of-plane bending vibration suppresses the isomerization. Furthermore, the effects of partial deuteration on the frequencies for these vibrations are also explored.

Chemical structure↗
DOE OSTI2021

High‐efficiency adsorption of various heavy metals by tea residue biochar loaded with nanoscale zero‐valent iron

Abstract Nanoscale zero‐valent iron (nZVI) loaded on tea residue biochar activated with phosphoric acid composites was synthesized. This adsorbent can efficiently adsorb multiple heavy metal ions such as Pb (II), Cr (III), Cu (II), and Ni (II) simultaneously. The adsorption process was found to follow the pseudo‐second‐order kinetic model. Equilibrium adsorption capacities reached 288.18, 162.34, 242.72, and 267.22 mg·g −1 for Pb(II), Cr(III), Cu(II), and Ni(II), respectively. The common interfering ions exhibited lower influence on its adsorption capacity. The adsorption mechanism includes reduction, ion exchange, surface complexation and precipitation. Furthermore, the waste adsorbent was converted into a catalyst in situ, which could catalyze the degradation of Congo red by establishing an electron transfer pathway. This research provides a new concept for the sustainable utilization of industrial tea residues and the harmless treatment of waste nZVI composite adsorbents.

Liu, Shuang↗