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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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27 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High permeability sub-nanometre sieve composite MoS 2 membranes

Two-dimensional membranes have gained enormous interest due to their potential to deliver precision filtration of species with performance that can challenge current desalination membrane platforms. Molybdenum disulfide (MoS 2 ) laminar membranes have recently demonstrated superior stability in aqueous environment to their extensively-studied analogs graphene-based membranes; however, challenges such as low ion rejection for high salinity water, low water flux, and low stability over time delay their potential adoption as a viable technology. Here, we report composite laminate multilayer MoS 2 membranes with stacked heterodimensional one- to two-layer-thick porous nanosheets and nanodisks. These membranes have a multimodal porous network structure with tunable surface charge, pore size, and interlayer spacing. In forward osmosis, our membranes reject more than 99% of salts at high salinities and, in reverse osmosis, small-molecule organic dyes and salts are efficiently filtered. Finally, our membranes stably operate for over a month, implying their potential for use in commercial water purification applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance enhancement of aqueous ionic liquids with lower critical solution temperature (LCST) behavior through ternary mixtures

Thermally responsive ionic liquids (ILs) exhibit liquid–liquid phase separation when mixed with water and heated above a lower critical solution temperature (LCST), resulting in a water-rich (WR) and an IL-rich (ILR) phase. These binary IL–water mixtures can be employed in a variety of thermodynamic processes such as forward osmosis (FO) desalination, for which two solution properties are desirable: low phase separation temperature and high osmotic strength (osmolality). However, these two properties are interlinked, with ILs that exhibit higher osmotic strengths typically requiring higher phase separation temperatures. This behavior tends to arise from the hydrophilicity of the IL cations, which enhances osmotic strength while also elevating the phase separation temperature. In this work, we highlight a pathway to overcome this tradeoff by developing ternary IL mixtures (two ILs with varying cation hydrophilicity mixed with water), which lowers the phase separation temperature while maintaining and even enhancing the osmotic strength of the solution. We characterize the mixing behavior (osmolality, phase separation temperature, WR phase purity, and WR to ILR phase mass ratio) of four ILs as a function of their concentration in solution. We find that an enhancement of up to 81.6% in the osmolality with a concomitant reduction of up to 15.4% in the phase separation temperature can be achieved using this approach. The ternary mixture is also shown to improve the phase separation kinetics by nearly 95% compared to the binary mixture. Overall, this work highlights a new pathway to improve the performance of LCST ILs for water and energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improving the Economic Viability of Biological Utilization of Coal Power Plant CO 2 by Improved Algae Productivity and Integration with Wastewater Treatment

The overall goal of this project was to improve the cost-effectiveness of systems using algae to capture and beneficially utilize CO 2 from the flue gas of coal-fired power plants. Algal biomass cultivation is an attractive alternative for carbon capture and utilization because it can uptake more CO 2 per land area than any other photosynthetic crop. Also, the resulting biomass can be used to make valuable large-market commodity products, such as biofuels and animal feed. However, the use of algal carbon capture has been limited by several factors including: (1) high costs for growing algae (>$\$1,300$/ton), (2) large land requirements for algae cultivation, and (3) environmental concerns about the large amount of water and nutrient inputs needed for growing algae. This project has taken direct aim at resolving these key limitations and demonstrated several viable approaches to improve the economic and environmental feasibility of algal carbon capture. In particular, this project demonstrated a significant increase in average annual algal productivity from baseline values of 8 g/m 2 -day to above 20 g/m 2 -day. This supports a much lower projected cost of algae production (<$\$500$/ton) and reduces land requirements by more than 50%. Additionally, this project confirmed the ability to grow multiple algae species with wastewater nutrients, which facilitates the potential for substantial new revenue streams for algae cultivation based on tipping fees for nutrient laden wastewater. This study highlighted that there is more potential value in wastewater nutrient treatment credits than in the primary commodity products made from algae. The use of wastewater inputs also mitigates the concerns associated with the large amount of water needed for growing algae. The algal biomass grown for this project using wastewater and simulated flue gas inputs were characterized for potential use as an animal feed ingredient. The algae had a protein content in the range of 35-40%, which is between some common commodity animal feed products— distiller’s grains (~25-30% protein) and soybean meal (45-50% protein). In-vitro digestion tests using cattle rumen fluids, showed that this algae biomass was best suited for use as “bypass protein”, which is digested in the animal intestine. Based on the protein content and amino acid profile of the algae grown in this study, it was estimated to have market value in the range of $\$190$-$\$280$/ton. There is potential for higher value feed products if the algae content of omega-3 fatty acids or antioxidants is accounted for. This project also did small scale demonstration of forward osmosis algae dewatering and nanofiltration of hydrothermal liquefaction aqueous products, which were shown to improve the cost and energy balance for producing algal products. When all the improvements demonstrated in this study were included in techno-economic analysis, it showed there was a viable pathway to profitable algal CO 2 utilization with a payback period in the range of six to ten years.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Systems and methods for water desalination using thermo-responsive ionic liquids regenerated by solar energy

This disclosure provides systems, methods, and apparatus related to water desalination. In one aspect, a method includes generating a diluted draw solution using forward osmosis. Wastewater is on a first side of an osmotic membrane and a draw solution is on a second side of the osmotic membrane. The draw solution comprises a mixture of water and an ionic liquid. Water in the wastewater diffuses across the osmotic membrane to the draw solution to form the diluted draw solution. The diluted draw solution is heated using a photonic heater to a temperature above a lower critical solution temperature (LCST) of the ionic liquid to phase separate the diluted draw solution into the ionic liquid and treated water.

Kostecki, Robert↗
DOE OSTI2021

TOUGHREACT V4.12

TOUGHREACT V4.12 is a 3-D reactive transport code that handles to non-isothermal, multiphase/multicomponent fluid flow, heat transport, aqueous and gaseous species advection-diffusion, and equilibrium/kinetic water-gas-rock-biological reactions. TOUGHREACT V4.12 is based on TOUGHREACT V3.3, with several new features including: an option to compute activity coefficients using the Pitzer ion interaction model; implementation of ECO2N V2.0 for temperatures up to 300 C; options to simulate forward and reverse osmosis, including 2D Poiseuille flow; addition of EOS8 and revisions for EOS5 and EWASG with H2(g); addition of gas species decay, various new output formats and time step control options; many bug fixes.

Spycher, Nicolas↗
DOE OSTI2022

Theoretical Pathway toward Improved Reverse Osmosis Membrane Selectivity for Neutral Solutes: Inspiration from Gas Separations

We report highly rigid membrane materials with tailored structures have exhibited permeabilities and selectivities that exceed the polymer upper bound in gaseous and organic solvent separations for challenging mixtures containing species that are similar in size and shape. One potential question is whether such membrane materials can maintain meaningful guest diffusivities in situations where the microporous spaces are essentially full of guest molecules. Here, we use a simplified transition state theory approach to estimate the diffusivity of water and small organics within a microporous membrane. The transition state theory model is parameterized using experimental values from zeolites and carbon molecular sieve materials found in the literature. We demonstrate the differences in transport and Maxwell–Stefan diffusivities based on guest species loading with different isotherm behaviors. These calculations theorize a path forward for highly selective reverse osmosis membranes for aqueous phase separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2020

Measuring Neutrino Oscillations in the NOvA and CHIPS Detectors.

NOvA is a long baseline neutrino experiment which uses muon neutrinos produced by the NuMI beam at the Fermi National Accelerator Laboratory to measure muon neutrino disappearance and electron neutrino appearance, among other analyses. The Far/Near Extrapolation analysis framework (FNEX) was used to measure muon neutrino disappearance using the events observed by NOvA while the NuMI beam was in Forward Horn Current configuration (i.e. this analysis uses neutrino data, not antineutrino data). The values of the parameters measured in this analysis are sin 2 &theta; 23 - 0.499$^{+0.098}_{-0.069}$ and Δm$^2_{32}$ - 2.454$^{+0.108}_{0.119}$ &times; 10 -3 eV, which is consistent with the results of NOvA’s main muon neutrino disappearance analysis using the experiment’s usual framework, known as CAF. The CHIPS detector is a water Cherenkov detector currently in construction. It will also use the NuMI beam to perform muon neutrino disappearance and electron neutrino appearance analyses. The necessary water clarity for a detector with a diameter of 25 m and a height of 6 m is discussed, as well as the results of testing water filtration systems. The filtration that will be used for the detector is a 10 µm carbon block filter and a 0.5 µm filter in series, with no reverse osmosis or ultraviolet sterilisation being used. This provides an attenuation length of 16.63 ± 0.25 m for light of 532 nm wavelength (green), so light with a wavelength of 405 nm (violet) would have an attenuation length of 133±2 m. The greatest possible distance between a PMT and any point in the detector’s volume is approximately 26 m, so 133 m is sufficient for the detector to measure Cherenkov light produced anywhere in the detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗