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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development and validation of a quantitative proton NMR method for the analysis of pregnenolone

Pregnenolone (PREG) is an endogenous steroid frequently sold as an over-the-counter dietary supplement touted to promote neurological and immunological health. While the PREG dietary supplement is added to the diet for health benefits, there are no FDA approved PREG drugs. However, compounded PREG drug products are available to U.S. patients. The FDA works with state regulatory authorities on the oversight of compounding activities, including developing 503A and 503B lists of bulk substances that compounders are permitted to use. PREG is one of the substances publicly nominated to be included on the 503B list. Compounded hormone therapies such as those using PREG are of interest given the lack of standardization in compounded drug products which may increase the possibility of underdosing, overdosing, or contamination. However, no USP monograph currently exists to evaluate the quality of PREG drug substance or product. To address knowledge gaps and assist in quality control, a simple and rapid quantitative proton nuclear magnetic resonance spectroscopy (qNMR) method for the identification and assay of PREG in different types of PREG products was developed and validated. PREG samples were characterized using 1D 1 H and 2D 1 H– 13 C HSQC NMR spectra. The qNMR assay method (taking approximately 10 min per NMR spectrum) was validated for precision, accuracy, specificity, robustness and linearity per ICH Q2(R1) guidance. The method was validated in a range from 0.032 to 3.2 mg/mL. As a proof of concept, seven PREG bulk substance samples, three tablet and two capsule PREG dietary supplements were assessed by the qNMR analytical procedure. NMR data from all tested samples met the expected criteria for identification and assay. The results demonstrate the potential of qNMR for the quality assessment of different types of PREG samples.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A method for generating quantitative vapor-phase infrared spectra of solids: results for phenol, camphor, menthol, syringol, dicyclopentadiene and naphthalene

Here, a method is presented to generate quantitative vapor-phase infrared spectra from substances that naturally occur as solids with moderate volatility. The solid is gravimetrically dissolved into a solvent that has few infrared spectral features, typically CS 2 and CCl 4 separately. The solution is flowed at a constant rate from a linearly pumped syringe into a metered stream of nitrogen carrier gas regulated by a mass flow controller. The analyte/solvent mix is flash vaporized by volatilizing the solution across a heated stainless-steel surface as it emanates from the syringe tip. The N 2 gas-solution mixture is flowed into a long-path White cell thermostatted at a desired temperature, the long optical path compensating for the modest analyte mixing ratio. A composite spectrum is generated from typically ten or more 760-Torr pressure-broadened spectra over the 600 to 6500 cm -1 spectral range at 0.1 cm -1 spectral resolution. The solid analytes reported here using this novel technique include dicyclopentadiene, menthol, syringol, phenol, camphor, and naphthalene.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Discovery of Proteoforms Associated With Alzheimer's Disease Through Quantitative Top-Down Proteomics

The complex nature of Alzheimer's disease (AD) and its heterogenous clinical presentation has prompted numerous large-scale - omic analyses aimed at providing a global understanding of the pathophysiological processes involved. AD involves isoforms, proteolytic products, and posttranslationally modified proteins such as amyloid beta (Aβ) and microtubule-associated protein tau. Top-down proteomics directly measures these species and thus, offers a comprehensive view of pathologically relevant proteoforms that are difficult to analyze using traditional proteomic techniques. Here, we broadly explored associations between proteoforms and clinicopathological traits of AD by deploying a quantitative top-down proteomics approach across frontal cortex of 103 subjects selected from the ROS and MAP cohorts. The approach identified 1213 proteins and 11,782 proteoforms, of which 154 proteoforms had at least one significant association with a clinicopathological phenotype. One important finding included identifying Aβ C-terminal truncation state as the key property for differential association between amyloid plaques and cerebral amyloid angiopathy. Furthermore, various N-terminally truncated forms of Aβ had noticeably stronger association with amyloid plaques and global cognitive function. Additionally, we discovered six VGF neuropeptides that were positively associated with cognitive function independent of pathological burden. The database of brain cortex proteoforms provides a valuable context for functional characterization of the proteins involved in AD and other late-onset brain pathologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Color as a tool for quantitative analysis of heterogeneous polymer degradation

Polymers are widely used industry, but degrade due to environmental conditions, such as elevated temperature or radiation. The degradation of polymers is dominated oxidation in oxygenated environments. Such oxidation is commonly referred to as thermal-oxidation or photo-oxidation and in many situations is heterogeneous through thickness of the polymer. While heterogeneous oxidation of polymers is a common phenomenon, the effect on the lifetime of polymers is not well understood as methods to evaluate such oxidation are time consuming and challenging to implement. Here we show a novel method for quantitative evaluation of heterogeneous oxidation in polymers using a fast and accurate color approach. The proposed color approach was observed to improve the efficiency of obtaining heterogenous oxidation profiles in half the time of common approaches, such as indention or Fourier transform infrared spectroscopy. Furthermore, the method was found to produce trends similar to that observed using common approaches and the applicability of the method was verified by mapping oxygen concentration through thickness using energy dispersive X-ray spectroscopy. Lastly, the utility of the proposed method was demonstrated through the generation of an oxidation design space, which is envisioned to assist with designing experiments towards estimation of polymer lifetime. Finally, we anticipate this work to be a starting point for further evaluation of heterogeneous oxidation in polymers using optical properties.

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

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

We report x-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu 2 Te and its substoichiometric counterpart, Cu 1.43 Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu 1.43 Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Shrub and sedge rhizosphere communities display distinct affinities toward exudates and soil organic matter degradation: a quantitative stable isotope probing analysis

Warming temperatures are accelerating permafrost thaw and changing tundra vegetation, where woody shrubs are displacing sedges. Shrubs, such as Betula nana, and sedges, such as Eriophorum vaginatum, exhibit distinct life strategies including unique root-associated, or rhizosphere microbial communities. As permafrost thaws it unlocks previously unavailable carbon and nutrient sources resulting in deeper roots and a translocation of rhizosphere communities. Because permafrost microbial communities contain lower diversity and biomass than rhizosphere communities, the coalescence of rhizosphere and permafrost microbial communities could alter soil organic matter (SOM) degradation rates and increase greenhouse gas emissions. To identify metabolic strategies across distinct rhizosphere and permafrost microbial communities we conducted an isotope tracing incubation experiment. We inoculated thawed permafrost with shrub and sedge rhizosphere communities while adding exudates or water daily and compared this to an uninoculated control. After 46 days, we spiked samples with 18O enriched water or 13C enriched exudates and measured isotope incorporation into microbial DNA with quantitative stable isotope probing (qSIP). Our results indicate that exudate additions had little effect on uninoculated permafrost communities but the addition of exudates and rhizosphere inoculants had a compounding effect on respiration rates. We found that soils inoculated with shrub rhizosphere communities contained a mixture of exudate and SOM degraders while soils inoculated with sedge rhizosphere communities contained mainly SOM degraders. Finally, we found that individual microbial taxa exhibited maximum growth rates under specific combinations of microbial inoculant communities and exudate addition treatments. Our results reveal that microbial niches are strongly influenced by substrate preferences and community context, and suggest that a reduction in sedges and an expansion of shrubs may provide a mechanism by which permafrost carbon losses are mitigated through corresponding shifts in microbial communities and their substrate preferences.

Schaefer, Sean R [Natural Resources and Earth Syst↗
DOE OSTI2026

Classifying biophysical subpopulations of insulin secretory granules using quantitative whole-cell structure analysis

Pancreatic beta cells contain insulin secretory granules (ISGs), organelles where proinsulin is converted into insulin. As ISGs mature, they undergo extensive biophysical remodeling, producing a spectrum of subpopulations with heterogeneous molecular and spatial characteristics. However, systematic methods to define ISG subpopulations remain underdeveloped. To address this gap in knowledge, we employed soft X-ray tomography (SXT), which can quantitatively measure the biochemical density of ISGs within whole beta cells. Using unsupervised clustering, we classified subpopulations based on molecular density, size, and spatial positioning. Across different insulin secretory stimuli, we observed shifts toward mature and releasable subtypes, demonstrating that exogenous signals can dynamically remodel ISG subpopulation distributions. We extended this methodology to primary beta cells characterized using volume electron microscopy (vEM). Integrating subpopulations from SXT and vEM uncovered insights inaccessible by a single method in isolation. This strategy establishes a framework for defining therapeutic approaches aimed at enriching physiologically beneficial ISG subpopulations.

dense-core granules↗
DOE OSTI2026

Quantitative DNA Stable Isotope Probing Identifies Active Microorganisms Assimilating Volatile Fatty Acids in Full-Scale Enhanced Biological Phosphorus Removal Processes

Enhanced biological phosphorus removal (EBPR) systems often rely on exogenous carbon sources, such as volatile fatty acids (VFAs), to achieve higher P removal. Here, we employed DNA quantitative stable isotope probing (qSIP) using two VFAs, acetate and propionate, in cyclic anaerobic/aerobic incubations to assess their effects on P cycling and microbial activity with biomass from two full-scale EBPR water resource-recovery facilities that utilize VFA addition. We found that anaerobic VFA uptake preferences differed within known groups of PAOs, such as Candidatus Accumulibacter and Tetrasphaera-affiliated members (e.g., Ca. Phosphoribacter), between the two biomasses. The combination of qSIP with metagenomics identified isotopically labeled phages that were linked to active PAOs, highlighting their potential roles in modulating EBPR community composition and activity. The highest levels of anaerobic labeling from acetate were in genomes belonging to Saccharimonadales and Rickettsiales, which are generally host-associated with bacteria and eukaryotes, respectively. Furthermore, this finding highlights the possibility of cross-feeding between PAO hosts and their parasites or predators, as well as the role of so-far uncharacterized organisms participating in carbon cycling under EBPR conditions. Collectively, these results expand our understanding of the ecological interactions involved in communities anaerobically uptaking VFAs and cycling P that are central to EBPR.

Polyphosphate accumulating organisms↗
DOE OSTI2025

Role of Electron Correlation beyond the Active Space in Achieving Quantitative Predictions of Spin-Phonon Relaxation

Single-molecule magnets (SMMs) are promising candidates for molecular-scale data storage and processing due to their strong magnetic anisotropy and long spin relaxation times. However, as temperature rises, interactions between electronic states and lattice vibrations accelerate spin relaxation, significantly limiting their practical applications. Recently, ab initio simulations have made it possible to advance our understanding of phonon-induced magnetic relaxation, but significant deviations from experiments have often been observed. The description of molecules’ electronic structure has been mostly based on complete active space self-consistent field (CASSCF) calculations, and the impact of electron correlation beyond the active space remains largely unexplored. In this study, we provide the first systematic investigation of spin-phonon relaxation in SMMs with post-CASSCF multiconfigurational methods, specifically CAS followed by second-order perturbation theory and multiconfiguration pair-density functional theory. Taking Co(II)- and Dy(III)-based SMMs as case studies, we analyze how electron correlation influences spin-phonon relaxation rates across a range of temperatures, comparing theoretical predictions with experimental observations. Our findings demonstrate that post-CASSCF treatments make it possible to achieve quantitative predictions for Co(II)-based SMMs. For Dy(III)-based systems, however, accurate predictions require consideration of additional effects, underscoring the urgent necessity of further advancing the study of the effects of electronic correlation in these complex systems.

Energy↗
DOE OSTI2020

Probing Sulfur Chemical and Electronic Structure with Experimental Observation and Quantitative Theoretical Prediction of Kα and Valence-to-Core Kβ X-ray Emission Spectroscopy

An extensive experimental and theoretical study of the Kα and Kβ high-resolution X-ray emission spectroscopy (XES) of sulfur-bearing systems is presented here. This study encompasses a wide range of organic and inorganic compounds, including numerous experimental spectra from both prior published work and new measurements. Employing a linear-response time-dependent density functional theory (LR-TDDFT) approach, strong quantitative agreement is found in the calculation of energy shifts of the core-to-core Kα as well as the full range of spectral features in the valence-to-core Kβ spectrum. The ability to accurately calculate the sulfur Kα energy shift supports the use of sulfur Kα XES as a bulk-sensitive tool for assessing sulfur speciation. The fine structure of the sulfur Kβ spectrum, in conjunction with the theoretical results, is shown to be sensitive to the local electronic structure including effects of symmetry, ligand type and number, and, in the case of organosulfur compounds, to the nature of the bonded organic moiety. This agreement between theory and experiment, augmented by the potential for high-access XES measurements with the latest generation of laboratory-based spectrometers, demonstrates the possibility of broad analytical use of XES for sulfur and nearby third-row elements. The effective solution of the forward problem, i.e., successful prediction of detailed spectra from known molecular structure, also suggests future use of supervised machine learning approaches to experimental inference, as has seen recent interest for interpretation of X-ray absorption near-edge structure (XANES).

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

Confirmation of PNNL Quantitative Infrared Cross-Sections for Isobutane

The Pacific Northwest National Laboratory (PNNL) gas-phase database is a compilation of quantitative experimental (5, 25, 50 °C) infrared spectra of ca. 500 molecules, designed for in situ, standoff or remote sensing of gases and vapors at or near atmospheric pressure. The data are characterized by calibration on both the wavenumber and intensity axes. Recent papers have called into question the PNNL intensity values for isobutane, (2-methylpropane, HC(CH 3 ) 3 ) suggesting discrepancies of 30 to 40%. In this study we re-measure and re-examine the intensity values of isobutane using both similar and alternate methods to those used to generate the original PNNL database spectra. Here, indirect confirmation from literature data of homologous molecules and direct confirmation from new results confirm that for many band integrals across the isobutane spectrum, the original PNNL data are indeed accurate to within the reported 3% experimental uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantitative Specifications to Avoid Degradation during E-Beam and Induced Current Microscopy of Halide Perovskite Devices

Degradation due to electron beam exposure has posed a challenge in the use of electron microscopy to probe halide perovskite materials and devices. Herein, the interaction between the electron beam and the perovskite across acceleration voltages and at low probe currents is investigated in a scanning electron microscope (SEM) by monitoring the electron-beaminduced current (EBIC) response in perovskite solar cells in a planview configuration. SEM probe conditions are identified where dozens of repeated scans over a single region of the perovskite solar cell induce minimal electronic degradation. Overall, the induced current response of the perovskite device is found to strongly depend upon the beam condition: rapid decay occurs at high beam powers, the current activates at the lowest beam powers, and a newfound quasi-steady response is revealed at intermediate beam conditions. A quantitative window for the successful conduction of e-beam studies with minimal electronic degradation is revealed by evaluating induced current response over a wide range of perovskite devices, which invites broader use of SEM-based characterization techniques, including EBIC, as powerful techniques for correlative microscopy investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗
DOE OSTI2022

Near-Quantitative Predictions of the First-Shell Coordination Structure of Hydrated First-Row Transition Metal Ions Using K-Edge X-ray Absorption Near-Edge Spectroscopy

Understanding the solvation structure of transition metal ions is important due to its broad impact on the kinetics, stability, and reactivity in a variety of applications in geochemistry, biochemistry, energy storage, and environmental chemistry. Using water as the common ligand, we study the X-ray absorption pre-edge and near-edge spectra at the K-edge of a near-complete series of hydrated first-row transition metal ions with d-orbital occupancy ranging from d 2 to d 10 . Starting with optimized structures that were derived from an explicit solvation treatment at the density functional theory (DFT) level, we then compute the pre-edge X-ray absorption spectra at the metal ion K-edge at the time dependent density functional theory (TDDFT) and restricted active-space second-order perturbation theory (RASPT2) levels of theory. TDDFT calculations provide accurate results for spectra that are dominated by single excitations, while significant improvements were obtained with RASPT2 calculations that correctly distinguish between singly and doubly excited states, where relevant, with quantitative accuracy compared with experiment. We analyze and assign the dierent pre-edge features for each metal ion in order to reveal the impact of the variations in the d orbital occupancy on the rst-shell coordination environment. For completeness, we also report the lowest energy ligand-field d-d transitions for all the transition metal aqua ions considered in this study using complete active space second order perturbation theory (CASPT2).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Multiplexed Quantitative Analysis of Germline Single Amino Acid Variants by Targeted Proteomics in Nondepleted Human Plasma

Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings.

genetics↗
DOE OSTI2020

Quantitative Effects of Disorder on Chemically Modified Amorphous Carbon Electrodes

Real materials are disordered. This disorder influences the properties of these materials and the chemical processes that occur at their interfaces. Gaining a molecular-level understanding of the underlying physical manifestations caused by disordered materials is crucial to unraveling and ultimately controlling the efficiency and performance of these materials in a range of energy-related devices. This understanding necessitates measurement techniques through which disorder can be detected, quantified, and monitored. However, such quantitative measurements are notoriously difficult, as effects often average out in ensemble measurements. In this work, we describe how a combination of electrochemical and spatially resolved surface spectroscopy measurements illuminate a molecular-level picture of disorder in materials. Using amorphous carbon as an intrinsically disordered material, we covalently attached a monolayer of ferrocene. Interfacial electron transfer across the amorphous carbon–ferrocene interface is highly sensitive to disruptions of order. Here, by systematically varying linker properties and surface loadings, the influence of lateral interactions between nonuniformly distributed ferrocene headgroups on ensemble electrochemical measurements is demonstrated. Electrochemical and imaging data collectively indicate that conformational flexibility of the ferrocene moieties provides a mechanism to elude repulsive and unbalanced lateral interactions, while rigid linkages provide direct information about the underlying disorder of the material. This study is the first of its kind to quantify and visualize molecular disorder and heterogeneity with an experimental model accessed through ensemble measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantitative Analysis of Origin of Lithium Inventory Loss and Interface Evolution over Extended Fast Charge Aging in Li Ion Batteries

During the extreme fast charging (XFC) of lithium-ion batteries, lithium inventory loss (LLI) and reaction mechanisms at the anode/electrolyte interface are crucial factors in performance and safety. Determining the causes of LLI and quantifying them remain an essential challenge. We present mechanistic research on the evolution and interactions of aging mechanisms at the anode/electrolyte interface. We used NMC 532 /graphite pouch cells charged at rates of 1, 6, and 9 C up to 1000 cycles for our investigation. The cell components were characterized after cycling using electrochemical measurements, inductively coupled plasma optical emission spectroscopy, 7 Li solid-state nuclear magnetic resonance spectroscopy, and high-performance liquid chromatography/mass spectrometry. The results indicate that cells charged at 1 C exhibit no Li plating, and the increase of SEI thickness is the dominant source of the Li loss. In contrast, Li loss in cells charged at 9 C is related to the formation of the metallic plating layers (42%) the SEI layer (38.1%) and irreversible intercalation into the bulk graphite (19%). XPS analysis suggests that the charging rate has little influence on the evolution of SEI composition. The interactions between competing aging mechanisms were evaluated by a correlation analysis. In conclusion, the quantitative method established in this work provides a comprehensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, forecasting SEI failure scenarios, and assessing the XFC lithium-ion battery capacity fade.

25 ENERGY STORAGE↗
DOE OSTI2024

Quantitative Determination of Metal Ion Adsorption on Cellulose Nanocrystals Surfaces

Nanocellulose is a bio-based material that holds significant potential in the field of water purification. Of particular interest is their potential use as a key sorbent material for the removal of metal ions from solution. However, the structure of metal ions adsorbed onto cellulose surfaces is not well understood. The focus of this work is to determine quantitatively the three-dimensional distribution of metal ions of different valencies surrounding negatively charged carboxylate functionalized cellulose nanocrystals (CNCs) using anomalous small-angle X-ray scattering (ASAXS). These distributions can affect the water and ionic permeability in these materials. The data show that increasing the carboxylate density on the surface of the CNCs from 740 to 1100 mmol/kg changed the nature of the structure of the adsorbed ions from a monolayer into a multilayer structure. The monolayer was modeled as a Stern layer around the CNC nanoparticles, whereas the multilayer structure was modeled as a diffuse layer on top of the Stern layer around the nanoparticles. Within the Stern layer, the maximum ion density increases from 1680 to 4350 mmol of Rb + /(kg of CNC) with the increase in the carboxylate density on the surface of the nanoparticles. Additionally, the data show that CNCs can leverage multiple mechanisms, such as electrostatic attraction and the chaotropic effect, to adsorb ions of different valencies. By understanding the spatial organization of the adsorbed metal ions, the design of cellulose-based sorbents can be further optimized to improve the uptake capacity and selectivity in separation applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗