Search NASA⌕ Search

SEARCH · Search NASA

Results for “binary isotherm model”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

22 records · Page 2

A newly proposed isotherm model to predict Cs exchange with crystalline silicotitanate in tank waste simulants

The Zheng Anthony Miller (ZAM) computer model, a multicomponent ion exchange model used to predict the exchange of Group I metals onto crystalline silicotitanate (CST), has historically been used to predict Cs distribution coefficients from Hanford and Savannah River Site (SRS) tank waste simulants. Comparison of experimentally determined Cs distribution coefficients from tank waste simulants with ZAM isotherm model predictions indicate overprediction of Cs and K distribution coefficients for simple and complex simulants with the engineered form of CST. Additionally, recent changes in chemical composition/manufacturing of IONSIV TM R9140-B have resulted in increased Cs capacity from high-salt, highly alkaline solutions. Here, this work served to assess different isotherm models and refine equilibrium parameters to develop a model that can be applied to Hanford and SRS tank waste Cs removal efforts. Toward this goal, the Campbell Westesen Peterson (CWP) model was developed. This model utilized the experimentally determined Cs capacity, and simplified ZAM equilibria expressions to include only the binary substitution of Cs + or K + on the Na + sites. Equilibrium constants for these equations were refined using experimentally determined distribution coefficients. Overall, the CWP model significantly improved our ability to predict both Cs and K loading capacity from complex matrices.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Grain boundary segregation beyond the dilute limit: Separating the two contributions of site spectrality and solute interactions

Solute segregation at grain boundaries (GBs) is known to have a profound impact on material properties, and as such is becoming routinely used as an element in alloy design. Beyond the dilute limit, the extent of solute GB segregation is known to be concentration dependent. Using hybrid Monte Carlo/Molecular Statics simulations of Mg segregation in Al, in this work we decouple the two contributions to this composition dependence: (i) spectrality of atomic environments at the boundary and (ii) solute-solute interactions. Although only contribution (ii) is typically considered in the literature, we argue that both contributions are equally important to understand concentration dependence and correctly quantify GB solute segregation in a binary alloy. Finally, a thermodynamic segregation isotherm is outlined that accounts for both the spectrality of grain boundary sites and solute-solute interactions. Unlike classical isotherms like those of McLean or Fowler-Guggenheim, which can be successfully fitted to GB segregation data only over a limited range of composition and temperature, our proposed model is shown to be accurate across the composition-temperature space.

36 MATERIALS SCIENCE↗

An automated multi-component gas adsorption system (MC GAS)

The knowledge gap on adsorption of complex mixtures in the literature relative to single component data represents a persistent obstacle to developing accurate process models for adsorption separations. The collection of mixed gas adsorption data is an imminent need for improved understanding of the behavior of adsorbent systems in these diverse adsorption applications. Current approaches to understanding mixture adsorption using predictive theories based on pure component adsorption experiments often fail to capture the behavior of more complex, non-ideal systems. In this work, we present an automated volumetric instrument for the measurement of mixed gas adsorption isotherms. This instrument was validated by comparison to other in-house instruments and data available in the literature, and the binary adsorption measurements were found to be thermodynamically consistent. The automation of this instrument allows for rapid collection of high-quality mixture adsorption data.

47 OTHER INSTRUMENTATION↗

Arsenic stabilization performance of a novel starch-modified Fe-Mn binary oxide colloid

Arsenic (As) is an environmentally hazardous contaminant and can be a serious threat to human health. In China, remediation needs for a large number of As-contaminated sites renders a strong demand for efficient remedial reagents and cost effective approaches. In this study, a novel starch-modified Fe-Mn binary oxide (SFM), an amorphous colloidal material, has been synthesized as a remedial reagent and its As stabilization performance has been evaluated. A set of laboratory batch experiments were carried out by directly adding SFM of different dosages into three contaminated soils. Results demonstrated that SFM could transform As in soil from non-specifically and specifically sorbed fractions to the more stable form bounded to amorphous iron hydrous oxides, thus reducing the As concentration in TCLP leachates by 42.0-93.2%. Results from adsorption tests and microscopic analysis indicated that the interactions between SFM and As are mainly controlled by adsorption, oxidation, and precipitation processes. SFM has abundant surface hydroxyl groups, with excellent adsorption properties for both As(V) and As(III), with the maximum adsorption capacities of 160.63 and 284.64 mg/g respectively at pH 7.0. The adsorption process closely fitted pseudo second-order kinetics and Freundlich isotherm model. SFM could increase soil Eh and oxidize As(III) to As(V), which facilitated the As stabilization in soilColloidal iron-based material directly used for stabilization in As contaminated soils is reported here for the first time. Starch modification improves both the reactivity and mobility of SFM in soil. Our findings propose an efficient and convenient reagent for As remediation in soil.

Yan, Xiulan↗