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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗
DOE OSTI2024

Metadynamics investigation of lanthanide solvation free energy landscapes and insights into separations energetics

Lanthanide ion solvation chemistry in nonaqueous phases is key to understanding and developing effective separation processes for these critical materials. Due to the complexity and inherent disorder of the solution phase, a comprehensive picture of the solvated metal ion is often difficult to generate solely from conventional spectroscopic approaches and electronic structure calculations, particularly in the extractant phase. In this work, we use classical molecular dynamics (MD) simulation with an advanced sampling technique, metadynamics, supplemented by experimental spectroscopy and speciation analysis, to measure lanthanide solvation free energy landscapes. We define coordination-based collective variables to probe the entire range of solvation configurations in the organic phase of lanthanum (La), europium (Eu), and lutetium (Lu) nitrate salts bound with a commonly used extractant, N,N′-dimethyl, N,N′-dioctylhexylethoxymalonamide (DMDOHEMA). The known lanthanide extraction trend of La ≈ Eu > Lu is readily explained by the measured free energy surfaces, which show consistent DMDOHEMA coordination from La to Eu, followed by loss of DMDOHEMA coordination from Eu to Lu. These simulations suggest how ligand crowding at the metal center can control selectivity, in this case resulting in the opposite extraction trend as observed with other conventional extractants, where the enthalpic contribution from increasing lanthanide charge density across the series dominates the extraction energetics. We also find that the presence of inner-sphere water, verified by time-resolved fluorescence, diversifies the accessible solvation structures. As a result, understanding solvation requires consideration of an entire thermodynamic ensemble, rather than the single dominant lowest-energy structure, as is often considered out of necessity in interpretation of spectroscopic data or in electronic structure-based ligand design approaches. In general, we demonstrate how metadynamics uniquely enables investigation of complex, multidimensional solvation energetic landscapes, and how it can explain selectivity trends where extraction is controlled by more complex mechanisms than simple charge density-based selectivity.

Wang, Xiaoyu↗
DOE OSTI2020

Domain wall diode based on functionally graded Dzyaloshinskii–Moriya interaction

In this work, we present a general approach for studying the dynamics of domain walls in biaxial ferromagnetic stripes with functionally graded Dzyaloshinskii–Moriya interaction (DMI). By engineering the spatial profile of the DMI parameter we propose the concept of a diode, which implements filtering of domain walls of certain topological charge and helicity. We base our study on phenomenological Landau–Lifshitz–Gilbert equations with additional Zhang–Li spin-transfer terms using a collective variable approach. In the effective equations of motion the gradients of DMI play the role of a driving force which competes with current driving. All analytical predictions are confirmed by numerical simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the “feature importance” approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

Chemistry↗
DOE OSTI2025

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗
DOE OSTI2023

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics

The chemical equilibrium between self-ionized and molecular water dictates the acid–base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)–based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid–base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H 2 O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

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

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tuning water dissociation at oxide–electrolyte interfaces with electric fields

Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio–based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO 2 –electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 eÅ, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, we find that small electric field changes exert minor effects on individual reaction energy barriers but significantly influence the populations of local configurations associated with initial states that are most favorable for reactions. These findings elucidate the pronounced impact of electric fields on interfacial water dissociation and reveal a mechanism for electric-field-controlled chemical reactions.

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

Renormalized site density functional theory

Site density functional theory (SDFT) provides a rigorous framework for statistical mechanics analysis of inhomogeneous molecular liquids. The key defining feature of these systems is the presence of two very distinct interactions scales (intra- and inter-molecular), and as such proper description of both effects is critical to the accuracy of the calculations. Current SDFT applications utilize the same approximation scheme for both interaction motifs, which negatively impacts the results. Dual space methodology, used in this work, alleviates this issue by providing the flexibility of evaluating part of the interactions in traditional field representation. For molecular liquid this translates into retaining density representation for inter-molecular interactions but describing stiff intra-molecular remainder by more appropriate conventional field based methods. This opens the way to decouple analysis of the two interactions scales - the idea which is developed further in this work for the case of homogeneous reference approximation of inter-molecular interactions. We demonstrate that by defining new collective variables, the behavior of the original molecular liquid system at the inter-molecular level can be transformed to resemble that of an effective simple fluid mixture. The latter is linked to intra-molecular scale through renormalized interaction parameters. We illustrate this renormalization procedure for several types of diatomic liquids, showing that this approach cures many of the shortcomings of existing SDFT methods.

Chuev, Gennady N.↗
DOE OSTI2021

The Atmospheric Carbon and Transport (ACT)-America Mission

The Atmospheric Carbon and Transport (ACT)-America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. In this study, two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1,140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five 6-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair-weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO 2 and CH 4 fluxes and atmospheric GHG transport realizations. The ACT-America dataset and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise subcontinental GHG flux estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

2000-2001 California Statewide Household Travel Survey

The 2000-2001 California Statewide Household Travel Survey, conducted under the auspices of the California Department of Transportation, was conducted between October 2000 and December 2001 among households located in each of California's 58 counties. The purpose of the study was to update the statewide database of household socioeconomic and travel information and helped to refine travel estimates, models, and forecasts throughout California. The survey was an essential element in determining statewide and regional travel patterns. A total of 17,040 households participated in the survey, contributing household socioeconomic and travel data. Travel variables collected include trip times, mode, activity at location, origin and destination, and vehicle occupancy, among other travel-related data from 134,173 trips. Residents completed diary records of their daily travel over a 24-hour (weekday) or 48-hour period (Friday/Saturday or Sunday/Monday pair).

1Hz data↗
DOE OSTI2021

Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012

Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities. This data package includes ten csv files (five data files and their corresponding data dictionaries) and one file-level metadata excel file. Data files contains information about which plots were exposed to treatments related to drought and nitrogen, information about litter bags reciprocal transplants manipulation for water input and nitrogen, detail information about water addition, precipitation records, and litter variables collected. Data dictionary files include detail explanation for each column in the data files. The file-level metadata file describes each file mentioned above. All the analyses were done using the R software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Joint Modeling/Experimental Approach to Characterize Ionization and Fragmentation of SOA Molecules with CIMS

The Molecular Dynamics technique Collective variable hyperdynamics (CVHD) interfaced with density functional tight-binding at the GFN1-xTB and GFN2-xTB levels of electronic structure theory has been applied to the H + GAG positively charged peptide system to explore the fragmentation of this simple tripeptide under relatively low temperature/energy conditions. Unlike conventional chemical dynamics simulations which can only be performed up to hundreds of picoseconds when coupled with semi-empirical Hamiltonians, CVHD is able to capture the long-time dynamics of multiple proton hopping, the formation of reversible ring structures, and ester rearrangement of the H + GAG system prior to fragmentation. The CVHD method applied to H + GAG is also able to uncover alternative fragmentation pathways not considered previously such as glycine fragmentation on the N-terminal side of the charged peptide and new cyclic cationic species. These findings in the H + GAG case have implications for other peptide systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2023

LongBondEliminator: A Molecular Simulation Tool to Remove Ring Penetrations in Biomolecular Simulation Systems

We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2024

Transfer of NASA Technology to the DoD: Mitigating Motion Sickness with Autogenic Feedback Training Exercise

Motion sickness poses a significant safety risk, particularly in the context of aviation. Given its prevalence among aviators and its detrimental impact on performance, researchers have attempted to identify effective mitigation strategies for motion sickness. Currently, many of the existing interventions are pharmacological, and while effective, they present a problem due to their associated adverse side effects. The primary goals of the current research were 1) demonstrate the value of the application of Autogenic-Feedback Training Exercise (AFTE), a physiological training program developed by NASA to mitigate the impact of operational stressors such as motion sickness and spatial disorientation on human physiology and performance and 2) evaluate a new enhanced version of AFTE training software and demonstrate the ability to apply it remotely and train other personnel to administer it. AFTE combines principles of autogenic therapy, and biofeedback in training individuals to control their physiological reactions through a series of relaxation and arousal exercises. This study included twenty-six participants, 17 men and 9 women. On day 1 participants were tested in a rotating chair (pre-test) to determine their motion sickness tolerance (measured as minutes of rotation); days 2-5 consisted of four AFTE training sessions, each 30-min. in duration for a total of 2 hours; and on day 6 the participants were re-tested in the rotating chair. Results revealed a significant increase in motion sickness tolerance with AFTE on the post-test when compared to pre-test, participants had significantly lower symptom diagnostic scores post-test, and there was no significant gender effect. In addressing the first goal it was concluded that the modified 2-hour version of AFTE significantly improved motion sickness tolerance with participants experiencing fewer symptoms. A second goal of this research was to refine and transfer a NASA technology, software and methods for applications within DOD by training other personnel to administer AFTE. A comparison of NASA and NAMRU-D trainers on AFTE outcome for improving participants’ motion sickness tolerance revealed no significant difference indicating the successful transfer of methods to DOD. In addition, remote AFTE training of military aviators at distant sites was feasible. A third goal was to identify individual patterns of interoceptive abilities and related autonomic metrics able to predict stress response and training outcome. These data included measures of personality traits obtained from questionnaires and specific autonomic measures (e.g., heart rate variability) collected by NAMRU-D investigators. These results will be reported in a separate paper.

autonomic nervous system↗
NASA NTRS2022

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

John L. Schnase↗
NASA NTRS2004

Variability of collagen crosslinks: impact of sample collection period

Because of the variability of collagen crosslinks, their use as markers for bone resorption is often criticized. We hypothesized that the variability could be reduced by collecting urine for 24 hours (or longer) instead of using single voids, and by not normalizing to creatinine. Urine samples were collected from 22 healthy subjects during two or more 24-hour periods. Each 24-hour pool and each 2nd void of the day were analyzed for N-telopeptide (NTX), pyridinium (PYD), and deoxypyridinoline (DPD) crosslinks. Data were analyzed by using linear regression. For NTX, R2 for the two, 2nd-void samples (n = 38) was 0.55, whereas R2 for the two 24-hour pools was 0.51 or 0.52, expressed per day or per creatinine. For PYD and DPD, R2 for the 2nd-void samples was 0.26 and 0.18, R2 for the 24-hour pools expressed per day was 0.58 and 0.74, and R2 for the 24-hour pools expressed per creatinine was 0.65 and 0.76, respectively. Regression of the 2nd void and the corresponding 24-hour pool, expressed per day, yielded R2 = 0.19, 0.19, and 0.08, for NTX, PYD, and DPD, respectively (n = 76 each). For the 2nd-void sample and its corresponding 24-hour pool, expressed per creatinine, R2 = 0.24, 0.33, and 0.08, respectively. In a separate study, the coefficient of variation for NTX was reduced (P < 0.05) when data from more than one 24-hour collection were combined. Thus, the variability inherent in crosslink determinations can be reduced by collecting urine for longer periods. In research studies, the high variability of single-void collections, compounded by creatinine normalization, may alter or obscure findings.

Specimen Handling/methods↗