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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chemical Functional Groups Regulate Ion Concentrations and pHs in Nanopores

Understanding ion behaviors in functionalized nanopores is essential to deciphering reactions in both natural and engineered systems, such as sediments, biological ion channels, and membranes. While many efforts have shown the modified ion behaviors in the functionalized nanopores, a direct measurement and analysis to show how chemical functional groups affect ion concentrations in nanopores are critically needed. In this work, we present a plasmonic nanosensor that can measure the local concentrations of protons, anions (phosphate, nitrate, sulfate, and arsenate), and cations (mercury, lead, and copper) in functionalized nanopores, and we compare their concentrations in nanopores with the corresponding bulk concentrations. Notably, chemical functional groups induced ion concentrations differently in nanopores. In pristine nanopores and methyl- and phenyl-functionalized nanopores, we discovered an unexpected concurrence of an enhanced anion concentration and a suppressed cation concentration. In addition, the nanopore pH is dependent on bulk solution compositions and can be lower by 2.5 units, even when the bulk solution is well-buffered. In contrast, for hydrophilic (amine, thiol, and carboxyl) nanopores, pH depended on the p K a of the functional groups, and the heavy metal concentrations depended on chemical interactions with the functional groups. Our findings provide a better understanding of water chemistry in nanopores and can help precisely control ions in nanopores to benefit the design of membrane-based desalination techniques, CO 2 storage, and porous catalysts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

The Viability of Trajectory Analysis for Diagnosing Dynamical and Chemical Influences on Ozone Concentrations in the UTLS

The viability of trajectory analysis for diagnosing the interplay between chemistry and dynamics is investigated by comparing ozone mixing ratios modelled using air-parcel pathways to values observed along flight tracks during ATTREX (Airborne Tropical TRopopause EXperiment). Trajectories are initiated at the locations of ozone observations and tracked backward in time to their sources at termini of backward trajectories. The modelled values of ozone utilize 3-dimensional analysis fields from WACCM (Whole Atmosphere Community Climate Model) (a chemical-climate model with dynamical fields nudged towards MERRA (Modern-Era Retrospective Analysis and Research Applications) reanalysis) and ERA-interim (product of ECMWF - the European Centre for Medium-Range Weather Forecasts) to determine source mixing ratios with chemical production and loss terms derived from the ozone chemistry used in WACCM. A statistical base of modelled ozone is constructed with 6 trajectory platforms (adiabatic, diabatic, and kinematic forced by ERA-interim and MERRA), two chemical models (WACCM chemistry and no chemistry), and 4 trajectory lengths (5, 10, 20, and 30 days). Linear regression is employed to separate systematic errors from random errors and to characterize the impact of source mixing ratios, path length, vertical motion, and chemistry on modelled ozone errors. Errors in the analysis ozone fields are large, if not dominant, contributors to model error. Random errors are particularly large for point-by-point comparisons, however averaging over 800 km (75 minutes) flight segments substantially reduces random error and exposes systematic errors. Of the two analysis ozone data sets, WACCM, which incorporates detailed chemistry, provides the smaller systematic errors while ERA-interim, which has crude chemistry but assimilates observational data, has the smaller random errors. Of the different trajectory platforms, adiabatic calculations produce the smaller random errors (irrespective of the use of chemistry) but both vertical motion and chemistry are required to optimally reduce systematic errors. These results suggest that meaningful analysis of dynamical and chemical interactions that control ozone mixing ratios are viable on spatial scales larger than a few reanalysis grid spaces, that errors in the analyzed ozone data sets are large but not prohibitively so, and that vertical velocities and heating rates from reanalysis data, while problematic, contain useful information [on the ozone concentrations in the UTLS (Upper Troposphere/Lower Stratosphere)].

ozone↗
DOE OSTI2020

Overcoming Oxidation State-Dependent Spectral Interferences: Online Monitoring of U(VI) Reduction to U(IV) via Raman and UV–vis Spectroscopy

Analytical characterization of chemical processes is an essential component of both industrial processes and fundamental studies. Applicable techniques abound, but optical spectroscopy has the unique combination of being easily incorporated into on-line (in-situ) monitoring probes and can also provide abundant chemical information (concentration, oxidation state, speciation). However, this abundance of chemical information can make applying optical spectroscopy to complex chemical processes challenging. This is particularly evident in the bulk electrolysis of U(VI) to U(IV), an essential preparatory step in some nuclear fuel recycling schemes. Starting and product materials have interfering spectral signatures in both electronic and vibrational spectroscopies, making accurate and real-time analysis difficult to achieve. In this paper, we discuss the application of optical spectroscopy to the on-line monitoring of a bulk electrolysis process and the methods utilized to analyze data as well as characterize the efficiency and mechanism of U(IV) generation.

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

Bioactivity Profiling of Chemical Mixtures for Hazard Characterization

Abstract The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures. Here, we demonstrate the application of bioactivity profiling of environmental water samples using the sentinel and ecotoxicology model species Daphnia to reveal the biomolecular response induced by exposure to real-world mixtures. We exposed a Daphnia strain to 30 sampled waters of the Chaobai River and measured the gene expression response profiles. Using a multiblock correlation analysis, we establish correlations between chemical mixtures identified in 30 water samples with gene expression patterns induced by these chemical mixtures. We identified 80 metabolic pathways putatively activated by mixtures of inorganic ions, heavy metals, polycyclic aromatic hydrocarbons, industrial chemicals, and a set of biocides, pesticides, and pharmacologically active substances. Our data-driven approach discovered both known bioactivity signatures with previously described modes of action and new pathways linked to undiscovered potential hazards. This study demonstrates the feasibility of reducing the complexity of real-world mixture toxicity to characterize the biomolecular effects of a defined number of chemical components based on gene expression monitoring of the sentinel species Daphnia.

Engineering↗
NASA NTRS1999

Chemistry Experiments

The purpose of the chemistry component of the model comparison is to assess to what extent differences in the formulation of chemical processes explain the variance between model results. Observed concentrations of chemical compounds are used to estimate to what degree the various models represent realistic situations. For readability, the materials for the chemistry experiment are reported in three separate sections. This section discussed the data used to evaluate the models in their simulation of the source gases and the Nitrogen compounds (NO(y)) and Chlorine compounds (Cl(y)) species.

Brasseur, Guy↗
DOE OSTI2021

Chemical Interplay of Silicon and Graphite in a Composite Electrode in SEI Formation

In this study, we investigated the effect of the Si/graphite weight ratio in half-cells on the solid electrolyte interphase (SEI) layer's chemistry. The nominal concentrations of active materials were (wt % Si/wt % Gr) 15/73, 30/58, 60/28, and 80/0. The electrolyte in the cells consisted of either 1.2 M LiPF 6 in ethylene carbonate/ethyl methyl carbonate (3:7 by wt) or 1.2 M LiPF 6 in ethylene carbonate:ethyl methyl carbonate (3:7 by wt) + 10 wt % fluoroethylene carbonate. These coin cells were cycled five times at the C/10 rate. As expected, the addition of silicon to the electrode significantly increased the measured capacity. Examination of the aged composite material showed that the electrolyte influenced the concentration of chemical environments on the surface. Depth profiling revealed that these concentrations of surface environments changed with sputtering time. A statistics-of-mixtures model was used to deconvolute how silicon and graphite interacted during the formation of these species and how the interaction changed with depth.

25 ENERGY STORAGE↗
NASA NTRS2013

The XRD Amorphous Component in John Klein Drill Fines at Yellowknife Bay, Gale Crater, Mars

Drill fines of mudstone (targets John Klein and Cumberland) from the Sheepbed unit at Yel-lowknife Bay were analyzed by MSL payload elements including the Chemistry and Mineralogy (CheMin), APXS (Alpha Particle X-Ray Spectrometer), and Sample Analysis at Mars (SAM) instruments. CheMin XRD results show a variety of crystalline phases including feldspar, pyroxene, olivine, oxides, oxyhydroxides, sulfates, sulfides, a tri-octahedral smectite, and XRD amorphous material. The drill fines are distinctly different from corresponding analyses of the global soil (target Rocknest) in that the mudstone samples contained detectable phyllosilicate. Here we focus on John Klein and combine CheMin and APXS data to calculate the chemical composition and concentration of the amorphous component. The chemical composition of the amorphous plus smectite component for John Klein was calculated by subtracting the abundance-weighted chemical composition of the individual XRD crystalline components from the bulk composition of John Kline as measured by APXS. The chemical composition of individual crystalline components was determined either by stoichiometry (e.g., hematite and magnetite) or from their unit cell parameters (e.g., feldspar, olivine, and pyroxene). The chemical composition of the amorphous + smectite component (approx 71 wt.% of bulk sample) and bulk chemical composition are similar. In order to calculate the chemical composition of the amorphous component, a chemical composition for the tri-octahedral smectite must be assumed. We selected two tri-octahedral smectites with very different MgO/(FeO + Fe2O3) ratios (34 and 1.3 for SapCa1 and Griffithite, respectively). Relative to bulk sample, the concentration of amorphous and smectite components are 40 and 29 wt.% for SapCa1 and 33 and 36 wt.% for Griffithite. The amount of smectite was calculated by requiring the MgO concentration to be approx 0 wt.% in the amorphous component. Griffithite is the preferred smectite because the position of its 021 diffraction peak is similar to that reported for John Klein. In both cases, the amorphous component has low SiO2 and MgO and high FeO + Fe2O3, P2O5, and SO3 concentrations relative to bulk sample. The chemical composition of the bulk drill fines and XRD crystalline, smectite, and amorphous components implies alteration of an initially basaltic material under near neutral conditions (not acid sulfate), with the sulfate incorporated later as veins of CaSO4 injected into the mudstone.

Morris, Richard V.↗
NASA NTRS2019

Quantifying the Influence of Redox Conditions on the Seismic Properties of Olivine

A series of eight olivine specimens were fabricated by hot-pressing at 1200 degrees Celsius and 300 megapascals (MPa). Each hot-pressed specimen was then wrapped in Pt, Ni or NiFe foil to vary oxygen fugacity (fO2), and interrogated via forced torsional oscillation. Mechanical testing was conducted at 10 oscillation periods between 1 and 1000 seconds, at a confining pressure of 200 MPa, during a slow staged-cooling from a maximum temperature of 1200 degrees Celsius to room temperature. After mechanical testing, each specimen was axially sectioned and EBSD (Electron BackScatter Diffraction) was used for the determination of the representative grain size, and grain size distribution of each sample. In addition, each longitudinal section was mapped via FTIR (Fourier Transform InfraRed spectroscopy) to determine the spatial distribution and concentrations of chemically bound and molecular water. Amongst these specimens, chemically bound ‘water’ contents were observed to vary between 0 and 1150 atom ppm (parts per million) H/Si, and molecular water concentrations varied between 0 and 245 atom ppm H/Si. Our forced-oscillation results demonstrate that the measured magnitude of anelastic relaxation within the experimental ‘window’ of oscillation periods is unrelated to the water content. Rather, a relationship was observed between the magnitude of anelastic relaxation and the prevailing redox conditions, which is influenced by the choice of metal sleeving used during the mechanical test. Further, regardless of water content or metal sleeving, each specimen exhibits coupled variations in shear modulus and dissipation within the observational window, indicative of ‘high-temperature background’ behavior, that can be described by a Burgers-type model. During initial fitting of the Burgers models, the unrelaxed shear modulus at a reference temperature of 900 degrees Celsius (G (sub UR)) and the temperature derivative of the unrelaxed shear modulus (dG (sub U)/dT), were treated as adjustable parameters. For all Fe-bearing olivine samples (but not a hydrous and oxidized Fe-free sample) we observe deficits of G (sub UR), and increased values of dG (sub U)/dT, relative to the expected elastic (anharmonic) behavior of Fo90 olivine. This behavior is indicative of anelastic relaxation occurring at shorter periods than observable within the ‘window’ of oscillation periods used in the mechanical test. Moving towards a comprehensive seismologically applicable Burgers model, which includes this newly observed effect of redox conditions on anelastic relaxation, we will present our progress in reconciling truly anharmonic and elastic behavior of Fo (sub 90) olivine with our observed forced-oscillation data.

Cline, Christopher, II↗
NASA NTRS2019

The Influence of Redox Conditions on the Seismic Properties of Polycrystalline Olivine

Eight olivine specimens were fabricated by use of a solgel method and hot-pressing at 1200 degrees Centigrade and 300 megapascals (MPa) inside of welded Pt capsules. Each hot-pressed specimen was then recovered, precision ground, and wrapped in Pt, Ni or NiFe foil to vary oxygen fugacity (fO2) during the subsequent forced torsional oscillation measurements. Mechanical testing was conducted at 10 oscillation periods between 1 and 1000 seconds, at a confining pressure of 200 MPa, during a slow staged-cooling from a maximum temperature of 1200 degrees Centigrade down to room temperature. After mechanical testing, each specimen was axially sectioned and EBSD (Electron BackScatter Diffraction) was used for the determination of the representative grain size, and grain size distribution of each sample. In addition, each longitudinal section was mapped via FTIR (Fourier Transform Infrared Spectroscopy) to determine the spatial distribution and concentrations of chemically bound and molecular water. Amongst these eight specimens, chemically bound ‘water’ contents were observed to vary between 0 and 1150 atom parts per million (ppm) H/Si, and molecular water concentrations varied between 0 and 245 atom ppm H/Si. Our forced-oscillation results demonstrate that the measured magnitude of anelastic relaxation within the experimental ‘window’ of oscillation periods is unrelated to the water content. Rather, a relationship was observed between the magnitude of anelastic relaxation and the prevailing redox conditions, which is influenced by the choice of metal sleeving used during the mechanical test. Further, regardless of water content or metal sleeving, each specimen exhibits coupled variations in shear modulus and dissipation within the observational window, indicative of ‘high-temperature background’ behavior, that can be described by a Burgers-type model. During initial fitting of the Burgers models, the unrelaxed shear modulus at a reference temperature of 900°C (elastic unloading/reloading shear modulus G (sub UR)) and the temperature derivative of the unrelaxed shear modulus (dGU/dT), were treated as adjustable parameters. For all Fe-bearing olivine samples we observe deficits of G (sub UR) and increased values of dGU/dT, relative to the expected elastic (anharmonic) behavior for Fo (Forsterite content percentage) (sub 90) olivine. This behavior is indicative of anelastic relaxation occurring at shorter periods than observable within the ‘window’ of oscillation periods used in the mechanical test. Moving towards a comprehensive and seismologically applicable Burgers model, which includes this newly observed effect of redox conditions on anelastic relaxation, we will present our progress on reconciling truly anharmonic and elastic behavior of Fo (sub 90) olivine with our observed forced-oscillation data.

Cline, C. J., II↗
NASA NTRS2007

Multiple-Parameter, Low-False-Alarm Fire-Detection Systems

Fire-detection systems incorporating multiple sensors that measure multiple parameters are being developed for use in storage depots, cargo bays of ships and aircraft, and other locations not amenable to frequent, direct visual inspection. These systems are intended to improve upon conventional smoke detectors, now used in such locations, that reliably detect fires but also frequently generate false alarms: for example, conventional smoke detectors based on the blockage of light by smoke particles are also affected by dust particles and water droplets and, thus, are often susceptible to false alarms. In contrast, by utilizing multiple parameters associated with fires, i.e. not only obscuration by smoke particles but also concentrations of multiple chemical species that are commonly generated in combustion, false alarms can be significantly decreased while still detecting fires as reliably as older smoke-detector systems do. The present development includes fabrication of sensors that have, variously, micrometer- or nanometer-sized features so that such multiple sensors can be integrated into arrays that have sizes, weights, and power demands smaller than those of older macroscopic sensors. The sensors include resistors, electrochemical cells, and Schottky diodes that exhibit different sensitivities to the various airborne chemicals of interest. In a system of this type, the sensor readings are digitized and processed by advanced signal-processing hardware and software to extract such chemical indications of fires as abnormally high concentrations of CO and CO2, possibly in combination with H2 and/or hydrocarbons. The system also includes a microelectromechanical systems (MEMS)-based particle detector and classifier device to increase the reliability of measurements of chemical species and particulates. In parallel research, software for modeling the evolution of a fire within an aircraft cargo bay has been developed. The model implemented in the software can describe the concentrations of chemical species and of particulate matter as functions of time. A system of the present developmental type and a conventional fire detector were tested under both fire and false-alarm conditions in a Federal Aviation Administration cargo-compartment- testing facility. Both systems consistently detected fires. However, the conventional fire detector consistently generated false alarms, whereas the developmental system did not generate any false alarms.

Hunter, Gary W.↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
DOE OSTI2020

Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals

Time courses of compound concentrations in plasma are used in chemical safety analysis to evaluate the relationship between external administered doses and internal tissue exposures. This type of experimental data is rarely available for the thousands of non-pharmaceutical chemicals to which people may potentially be unknowingly exposed but is necessary to properly assess the risk of such exposures. In vitro assays and in silico models are often used to craft an understanding of a chemical’s pharmacokinetics; however, the certainty of the quantitative application of these estimates for chemical safety evaluations cannot be determined without in vivo data for external validation. To address this need, we present a public database of chemical time-series concentration data from 567 studies in humans or test animals for 144 environmentally-relevant chemicals and their metabolites (187 analytes total). All major administration routes are incorporated, with concentrations measured in blood/plasma, tissues, and excreta. We also include calculated pharmacokinetic parameters for some studies, and a bibliography of additional source documents to support future extraction of time-series. In addition to pharmacokinetic model calibration and validation, these data may be used for analyses of diferential chemical distribution across chemicals, species, doses, or routes, and for meta-analyses on pharmacokinetic studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy

The chemical content of dry, ground leaf material sampled from deciduous and conifer tree species from sites in Alaska, Wisconsin, and California was estimated using visible and shortwave IR spectroscopy. Seven chemical components - sugar, starch, protein, cellulose, total chlorophyll, lignin, and total nitrogen - were analyzed by wet chemical methods and their concentrations regressed against log 1/rho and first and second differences of log 1/rho (where rho is measured reflectance) at wavelengths selected by stepwise regression. Predictions of chemical concentrations based on cross validation suggest that this technique may be useful for extracting vegetation canopy biochemical information by remote sensing.

Card, Don H.↗
NASA NTRS1991

Small Business Innovations (Mass Microbalance)

Femtometrics of Costa Mesa, CA, developed the Model 200-1 SAW Mass Microbalance under a NASA Small Business Innovation Research (SBIR) contract with Langley Research Center. The product is described as "the next generation of aerosol mass microbalance technology," because a new type of sensor, the Surface Acoustic Wave (SAW) piezoelectric crystal, offers mass resolution two orders of magnitude greater than the Quartz Crystal Microbalance cascade impactor (QCM) (used at Langley since 1979 for collection and measurement of aerosol particles in the upper atmosphere). The Model 200-1 SAW Mass Microbalance, which provides a 400-fold increase in mass sensitivity per unit area over the QCM, can be used for real-time particle monitoring in clean rooms, measuring chemical vapors in very low concentrations, measuring target chemicals in the stratosphere and in industry as a toxic vapor monitor.

Source record↗
NASA NTRS1994

Process Analyzer

The ChemScan UV-6100 is a spectrometry system originally developed by Biotronics Technologies, Inc. under a Small Business Innovation Research (SBIR) contract. It is marketed to the water and wastewater treatment industries, replacing "grab sampling" with on-line data collection. It analyzes the light absorbance characteristics of a water sample, simultaneously detects hundreds of individual wavelengths absorbed by chemical substances in a process solution, and quantifies the information. Spectral data is then processed by ChemScan analyzer and compared with calibration files in the system's memory in order to calculate concentrations of chemical substances that cause UV light absorbance in specific patterns. Monitored substances can be analyzed for quality and quantity. Applications include detection of a variety of substances, and the information provided enables an operator to control a process more efficiently.

Source record↗
NASA NTRS2018

GMI-IPS: Processing & Visualization Software Used in ATom DC-8 Aircraft Studies

NASA's Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed.The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT compatible flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, and providing Python data structures for storing flight and model information, and more.The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.

Damon, M. R.↗