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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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NASA NTRS1998

Development of Curved-Plate Elements for the Exact Buckling Analysis of Composite Plate Assemblies Including Transverse Shear Effects

The analytical formulation of curved-plate non-linear equilibrium equations that include transverse-shear-deformation effects is presented. A unified set of non-linear strains that contains terms from both physical and tensorial strain measures is used. Using several simplifying assumptions, linearized, stability equations are derived that describe the response of the plate just after bifurcation buckling occurs. These equations are then modified to allow the plate reference surface to be located a distance z(c), from the centroid surface which is convenient for modeling stiffened-plate assemblies. The implementation of the new theory into the VICONOPT buckling and vibration analysis and optimum design program code is described. Either classical plate theory (CPT) or first-order shear-deformation plate theory (SDPT) may be selected in VICONOPT. Comparisons of numerical results for several example problems with different loading states are made. Results from the new curved-plate analysis compare well with closed-form solution results and with results from known example problems in the literature. Finally, a design-optimization study of two different cylindrical shells subject to uniform axial compression is presented.

McGowan, David M.↗

NASA NTRS1998

A Novel Approach for Adaptive Signal Processing

Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.

Chen, Ya-Chin↗

DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗

DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Designing high-performance superconductors with nanoparticle inclusions: Comparisons to strong pinning theory

One of the most promising routes for achieving high critical currents in superconductors is to incorporate dispersed, non-superconducting nanoparticles to control the dissipative motion of vortices. However, these inclusions reduce the overall superconducting volume and can strain the interlaying superconducting matrix, which can detrimentally reduce T c . Consequently, an optimal balance must be achieved between the nanoparticle density n p and size d. Determining this balance requires garnering a better understanding of vortex–nanoparticle interactions, described by strong pinning theory. Here, we map the dependence of the critical current on nanoparticle size and density in (Y 0.77 , Gd 0.23 )Ba 2 Cu 3 O 7-δ films in magnetic fields of up to 35 T and compare the trends to recent results from time-dependent Ginzburg–Landau simulations. We identify consistency between the field-dependent critical current J c (B) and expectations from strong pinning theory. Specifically, we find that J c ∝ B -α , where α decreases from 0.66 to 0.2 with increasing density of nanoparticles and increases roughly linearly with nanoparticle size d/ξ (normalized to the coherence length). At high fields, the critical current decays faster (~B -1 ), suggesting that each nanoparticle has captured a vortex. When nanoparticles capture more than one vortex, a small, high-field peak is expected in J c (B). Due to a spread in defect sizes, this novel peak effect remains unresolved here. Finally, we reveal that the dependence of the vortex creep rate S on nanoparticle size and density roughly mirrors that of α, and we compare our results to low-T nonlinearities in S(T) that are predicted by strong pinning theory.

36 MATERIALS SCIENCE↗

NASA NTRS1996

Efficient Low-Speed Flight in a Wind Field

A new software tool was needed for flight planning of a high altitude, low speed unmanned aerial vehicle which would be flying in winds close to the actual airspeed of the vehicle. An energy modeled NLP (non-linear programming) formulation was used to obtain results for a variety of missions and wind profiles. The energy constraint derived included terms due to the wind field and the performance index was a weighted combination of the amount of fuel used and the final time. With no emphasis on time and with no winds the vehicle was found to fly at maximum lift to drag velocity, V(sub md). When flying in tail winds the velocity was less than V(sub md), while flying in head winds the velocity was higher than V(sub md). A family of solutions was found with varying times of flight and varying fuel amounts consumed which will aid the operator in choosing a flight plan depending on a desired landing time. At certain parts of the flight, the turning terms in the energy constraint equation were found to be significant. An analysis of a simpler vertical plane cruise optimal control problem was used to explain some of the characteristics of the vertical plane NLP results.

Feldman, Michael A.↗

NASA NTRS2010

NASA Tech Briefs, June 2010

Topics covered include: Situational Awareness from a Low-Cost Camera System; Data Acquisition System for Multi-Frequency Radar Flight Operations Preparation; Mercury Toolset for Spatiotemporal Metadata; Social Tagging of Mission Data; Integrating Radar Image Data with Google Maps; Demonstration of a Submillimeter-Wave HEMT Oscillator Module at 330 GHz; Flexible Peripheral Component Interconnect Input/Output Card; Interface Supports Lightweight Subsystem Routing for Flight Applications; MMIC Amplifiers and Wafer Probes for 350 to 500 GHz; Public Risk Assessment Program; Particle Swarm Optimization Toolbox; Telescience Support Center Data System Software; Update on PISCES; Ground and Space Radar Volume Matching and Comparison Software; Web-Based Interface for Command and Control of Network Sensors; Orbit Determination Toolbox; Distributed Observer Network; Computer-Automated Evolution of Spacecraft X-Band Antennas; Practical Loop-Shaping Design of Feedback Control Systems; Fully Printed High-Frequency Phased-Array Antenna on Flexible Substrate; Formula for the Removal and Remediation of Polychlorinated Biphenyls in Painted Structures; Integrated Solar Concentrator and Shielded Radiator; Water Membrane Evaporator; Modeling of Failure for Analysis of Triaxial Braided Carbon Fiber Composites; Catalyst for Carbon Monoxide Oxidation; Titanium Hydroxide - a Volatile Species at High Temperature; Selective Functionalization of Carbon Nanotubes: Part II; Steerable Hopping Six-Legged Robot; Launchable and Retrievable Tetherobot; Hybrid Heat Exchangers; Orbital Winch for High-Strength, Space-Survivable Tethers; Parameterized Linear Longitudinal Airship Model; and Physics of Life: A Model for Non-Newtonian Properties of Living Systems.

Source record↗

DOE OSTI2020

Cation, Anion, and Radical Isomers of C4H4N: Computational Characterization and Implications for Astrophysical and Planetary Environments

Nitrogen-containing ions and molecules in the gas phase have been detected in non-Earth environments such as dark molecular clouds and more recently in the atmosphere of Saturn's moon Titan. These molecules may serve as precursors to larger heterocyclic structures that provide the foundation of complex biological molecules. On Titan, molecules of m/z 66 have been detected by the Cassini mission, and species of the empirical formula C 4 H 4 N may contribute to this signature. We have characterized seven isomers of C 4 H 4 N in anionic, neutral radical, and cationic states using density functional theory. Structures were optimized using the range-separated hybrid ωB97X-V with the cc-pVTZ and aug-cc-pVTZ basis sets. Anionic and radical C4H4N favor cyclic structures with aromatic and quasi-aromatic electron arrangements, respectively. Interestingly, ionization from the radical surface to the cation induces significant changes in structural stability, and the global minimum for positively charged isomers is CH 2 CCHCNH + , a pseudo-linear species reminiscent of cyanoallene. Select formation pathways to these structures from Titan's existing or postulated gas-phase species, reactions that are also relevant for other astrophysical environments, are discussed. By characterizing C 4 H 4 N isomers, we have identified energetically stable anionic, radical, and cationic structures that may be present in Titan's atmosphere and dark molecular clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

An entropy-based debiasing approach to quantifying experimental coverage for novel applications of interest in the nuclear community

This manuscript proposes a novel information-theoretic approach to the quantification of experimental relevance, i.e., coverage, to achieve optimal data assimilation results for nuclear engineering applications. Specifically, this work posits the need for a new metric, called coverage (q C ) of an application’s quantity of interest, i.e., eigenvalue or power peaking for an advanced reactor concept, defined herein as the theoretically maximum achievable reduction in the quantity’s uncertainty given measurements from a pool of experiments in a manner that is independent of the data assimilation procedure employed. Currently, reduction in a quantity’s uncertainty is strongly biased by the underlying assumptions of the assimilation procedure to account for the under-determined nature of such problems and the similarity criterion employed to identify relevant experiments. To address this challenge, this work has developed a coverage metric, q C , based on mutual information, which establishes a new conceptual framework for assessing coverage, one that is independent of the model parameters and responses degree of variations in both the experimental and application domains, i.e., linear vs non-linear, and their prior uncertainty distributions, i.e., Gaussian vs. non-Gaussian. The q C is an entropic measure capable of addressing coverage for general nonlinear problems with non-Gaussian uncertainties and inclusive of the measurement uncertainties from multiple experiments. Numerical experiments from manufactured analytical problems as well as a set of benchmarks from the ICSBEP handbook are employed to demonstrate its theoretical and practical performance as compared to the c k -based experiment selection methodology, commonly employed in the neutronic community. The manuscript then employs other well-known adaptations to existing data assimilation methodologies for nonlinear and non-Gaussian problems capable of achieving the coverage posited by q C .

Bayesian data assimilation↗

DOE OSTI2020

Major element composition controls rare earth element solubility during leaching of coal fly ash and coal by-products

Coal combustion ash and pre-combustion coal refuse are currently under consideration as potential sources of rare earth elements. One of the early steps in recovering REEs from coal by-products is often acid leaching, which can result in low pH leachates with complex aqueous chemistry. The aim of this work was to understand the connection between REE solubility, pH, and major elemental components of leachates for coal by-products. To accomplish this, we investigated the effects of solids concentration (i.e., pulp density) and pH adjustment on REE solubility in acid leachates of coal fly ashes from the Powder River Basin (PRB) and Appalachian Basin in the United States, and a coal processing refuse from the Southwestern U.S. For PRB ashes, the concentrations of soluble REEs generally increased with increasing pulp density; however, at pulp density values above 80–100 g/L, the soluble REE concentrations in the leachates were markedly lower. Similarly, the soluble concentrations of other major solutes (Fe, Al, Si) that leached from PRB fly ashes were also non-linear with pulp density. These major elements tended to reach maximum concentration values at 60–70 g/L pulp density. In contrast, for the Appalachian fly ashes and the coal by-product, soluble concentrations of REE and major elements in leachates increased linearly with pulp density. Chemical equilibria calculations of mineral saturation indices indicated that trends in soluble REE concentrations could be explained by saturation conditions for Fe and Al-(hydr)oxides and possibly sulfate minerals, but not lanthanide hydroxides. Furthermore, pH adjustment of the acid leachates showed that REEs and many major solutes were removed from solution at pH values above 4.5, also consistent with Fe- and Al-(hydr)oxide precipitation. These results highlight the importance of understanding the chemical composition of leachates when designing REE recovery processes for low-grade geologic feedstocks and that precipitation of hydr(oxide) or sulfate minerals of major elements rather than discreet formation of REE mineral phases could be used for process optimization.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Divertor detachment in the pre-fusion power operation phase in ITER during application of resonant magnetic perturbations

Detachment of the divertor plasma during application of resonant magnetic perturbation (RMP) fields is evaluated for hydrogen H-mode plasma during the first pre-fusion power operation (PFPO-1) phase in ITER by 3D plasma boundary modelling with EMC3–EIRENE. Plasma response effects from a linearized, resistive, single fluid MHD model are discussed, which includes partial screening of the externally applied field—but also field amplification near the separatrix. Furthermore, this field amplification is found to play a pivotal role for the magnetic footprint on the divertor targets, but is sensitive to model parameters. Extensions of the footprint beyond the straight portions of the ITER vertical divertor targets, optimized for high stationary heat flux handling, may be possible depending on the level of toroidal rotation in the plasma. Exhaust from the bulk plasma is guided by the helical corrugations (lobes) of the perturbed separatrix, and this results in an upstream heat flux that is distributed over these lobes with lower peak values than in the typical radial heat flux profiles seen in the absence of magnetic perturbations. As a consequence, an earlier onset (with respect to the upstream density) of detachment is found in the traditional strike zone when RMPs are applied, but secondary, non-axisymmetric strike locations appear—and those remain attached at temperatures above 10 eV. Neon seeding can mitigate these non-axisymmetric heat loads, but this becomes less efficient for large magnetic footprints.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2017

An Analysis of the Optimal Control Modification Method Applied to Flutter Suppression

Unlike basic Model Reference Adaptive Control (MRAC)l, Optimal Control Modification (OCM) has been shown to be a promising MRAC modification with robustness and analytical properties not present in other adaptive control methods. This paper presents an analysis of the OCM method, and how the asymptotic property of OCM is useful for analyzing and tuning the controller. We begin with a Lyapunov stability proof of an OCM controller having two adaptive gain terms, then the less conservative and easily analyzed OCM asymptotic property is presented. Two numerical examples are used to show how this property can accurately predict steady state stability and quantitative robustness in the presence of time delay, and relative to linear plant perturbations, and nominal Loop Transfer Recovery (LTR) tuning. The asymptotic property of the OCM controller is then used as an aid in tuning the controller applied to a large scale aeroservoelastic longitudinal aircraft model for flutter suppression. Control with OCM adaptive augmentation is shown to improve performance over that of the nominal non-adaptive controller when significant disparities exist between the controller/observer model and the true plant model.

aeroservoelasti↗

DOE OSTI2024

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage (CRADA Report)

NREL and Element 16 collaborated on sulfur thermal energy storage modeling using NREL’s high performance computing (HPC) resources to assist its application in industrial processes. Industrial process heat (IPH) accounts for ~70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately, 1500 TWht (~60% Terawatt hour thermal) of IPH demand is in the temperature range of 100-300°C. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. This HPC4EI project facilitated Element 16’s development of low-cost and high-impact molten sulfur TES for dispatchable IPH. The development of a high-fidelity model validated by experimental data and HPC simulations enabled the successful resolution of the complex interplay between fluid dynamics and heat transfer processes during transient operation of sulfur TES, overcoming the numerical challenges posed by the non-linear temperature-dependent physical properties of sulfur. The project helped accelerate Element 16’s molten sulfur TES product design and support its broad applications.

25 ENERGY STORAGE↗

DOE OSTI2023

Using the Stix finite element RF code to investigate operation optimization of the ICRF antenna on Alcator C-Mod

Abstract As the Ion Cyclotron Radio Frequency range (ICRF) heating becomes more favorable in fusion devices, the urgency of predicting and mitigating impurity generation that arises from it becomes more pressing. In the ICRF regime, rectified Radio Frequency (RF) sheaths are known to form at antenna and material edges that influence negative effects like sputtering and a decrease in heating efficiency. Methods to mitigate the formation of these RF sheaths through RF image currents cancellation have been experimentally studied. A power-phasing scan done on Alcator C-Mod in which the amount of power on the two inner straps ( P in ) versus the total 4 straps ( P tot ) was varied showed a minimization of enhanced potentials between P in / P tot ∼ 0.7–0.9 while impurities were minimized for P in / P tot ∼ 0.5–0.8. New capabilities in the realm of representing the RF sheath numerically now allow for these experiments to be simulated. Given the size of the sheath relative to the scale of the device, it can be approximated as a Boundary Condition (BC). A new parallelized cold-plasma wave equation solver called Stix implements a non-linear sheath impedance model BC formulated by Myra et al (2015 Phys. Plasmas 22 062507) through the method of finite elements using the MFEM library [ http://mfem.org ]. It is seen that Stix shows qualitative agreement with the measured C-Mod enhanced potentials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Spectroscopy of GR1 centers in synthetic diamonds

We report on the spectroscopic characterization, absorption saturation, and excited-state dynamics of GR1 centers in synthetic diamonds. The non-linear optical measurements reveal an efficient bleaching of the GR1 center’s ground level under ns-pulsed 633 nm excitation. The maxima of absorption and emission cross sections were estimated to be 4.5 × 10 −17 cm 2 and 9 × 10 −17 cm 2 at 630 nm and 780 nm, respectively. The radiative lifetime of the excited level was estimated to be 8.5 ns. The 658 nm probe kinetics uncovered relaxation of 1 T 2 excited level going predominantly to a metastable state with a lifetime of 220 μs. An induced absorption detected with the use of a highly concentrated diamond sample could be due to up-conversion and photoionization processes in additional impurity-vacancy center with absorption at the short-wavelength tail of GR1. The results presented here indicate that synthetic diamonds with GR1 centers could serve as near infrared gain media or passive Q-switchers for laser cavities over the 633–750 nm spectral range. Optimization of the center concentration and the crystal parameters are required to minimize the induced absorption at the lasing wavelengths.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Planning Paths Through Singularities in the Center of Mass Space

The center of mass space is a convenient space for planning motions that minimize reaction forces at the robot's base or optimize the stability of a mechanism. A unique problem associated with path planning in the center of mass space is the potential existence of multiple center of mass images for a single Cartesian obstacle, since a single center of mass location can correspond to multiple robot joint configurations. The existence of multiple images results in a need to either maintain multiple center of mass obstacle maps or to update obstacle locations when the robot passes through a singularity, such as when it moves from an elbow-up to an elbow-down configuration. To illustrate the concepts presented in this paper, a path is planned for an example task requiring motion through multiple center of mass space maps. The object of the path planning algorithm is to locate the bang- bang acceleration profile that minimizes the robot's base reactions in the presence of a single Cartesian obstacle. To simplify the presentation, only non-redundant robots are considered and joint non-linearities are neglected.

Doggett, William R.↗

NASA NTRS2002

Contactless conductivity detector for microchip capillary electrophoresis

A microfabricated electrophoresis chip with an integrated contactless conductivity detection system is described. The new contactless conductivity microchip detector is based on placing two planar sensing aluminum film electrodes on the outer side of a poly(methyl methacrylate) (PMMA) microchip (without contacting the solution) and measuring the impedance of the solution in the separation channel. The contactless route obviates problems (e.g., fouling, unwanted reactions) associated with the electrode-solution contact, offers isolation of the detection system from high separation fields, does not compromise the separation efficiency, and greatly simplifies the detector fabrication. Relevant experimental variables, such as the frequency and amplitude of the applied ac voltage or the separation voltage, were examined and optimized. The detector performance was illustrated by the separation of potassium, sodium, barium, and lithium cations and the chloride, sulfate, fluoride, acetate, and phosphate anions. The response was linear (over the 20 microM-7 mM range) and reproducible (RSD = 3.4-4.9%; n = 10), with detection limits of 2.8 and 6.4 microM (for potassium and chloride, respectively). The advantages associated with the contactless conductivity detection, along with the low cost of the integrated PMMA chip/detection system, should enhance the power and scope of microfluidic analytical devices.

NASA Center JPL↗