Nuclear analysis at NBS and NIST

Lindstrom, Richard M.

doi:10.1007/s10967-018-6192-7

Cited by 9 publications

(5 citation statements)

References 99 publications

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“…Also, as larger droplets are also expected to settle faster at the bottom of the measurement container, the obtained sizes should not be confused with the average sizes for the entire samples, which would also require measurement of CNF-stabilized PFC5 droplets present in the bulk. The density of liquid PFC5 is 1.69 g cm –3 (1 atm), 1.63 g cm –3 (1 atm), and 1.578 cm –3 (1.209 atm) at 4, 20, and 35 °C, respectively …”

Section: Resultsmentioning

confidence: 99%

Unravelling the Acoustic and Thermal Responses of Perfluorocarbon Liquid Droplets Stabilized with Cellulose Nanofibers

et al. 2019

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The attractive colloidal and physicochemical properties of cellulose nanofibers (CNFs) at interfaces have recently been exploited in the facile production of a number of environmentally benign materials, e.g. foams, emulsions, and capsules. Herein, these unique properties are exploited in a new type of CNF-stabilized perfluoropentane droplets produced via a straightforward and simple mixing protocol. Droplets with a comparatively narrow size distribution (ca. 1–5 μm in diameter) were fabricated, and their potential in the acoustic droplet vaporization process was evaluated. For this, the particle-stabilized droplets were assessed in three independent experimental examinations, namely temperature, acoustic, and ultrasonic standing wave tests. During the acoustic droplet vaporization (ADV) process, droplets were converted to gas-filled microbubbles, offering enhanced visualization by ultrasound. The acoustic pressure threshold of about 0.62 MPa was identified for the cellulose-stabilized droplets. A phase transition temperature of about 22 °C was observed, at which a significant fraction of larger droplets (above ca. 3 μm in diameter) were converted into bubbles, whereas a large part of the population of smaller droplets were stable up to higher temperatures (temperatures up to 45 °C tested). Moreover, under ultrasound standing wave conditions, droplets were relocated to antinodes demonstrating the behavior associated with the negative contrast particles. The combined results make the CNF-stabilized droplets interesting in cell-droplet interaction experiments and ultrasound imaging.

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Section: Resultsmentioning

confidence: 99%

Unravelling the Acoustic and Thermal Responses of Perfluorocarbon Liquid Droplets Stabilized with Cellulose Nanofibers

et al. 2019

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“…From Table 1 in Allison and Truhlar, it is possible, using standard heats of formation, to calculate the parameters α 1 , α 2 , and u * needed to perform a correction to TST based on the Eckart model. − In order to evaluate this correction, we used the approach by Johnston and Heicklen as modified by Brown . Integration of the transmission coefficient over energy was performed using the Mathematica NIntegrate function .…”

Section: Computational Detailsmentioning

confidence: 99%

“…Integration of the transmission coefficient over energy was performed using the Mathematica NIntegrate function . In addition to the three parameters just mentioned, we also tabulated β, the skew angle for the reaction, as well as ω e (B–C), the harmonic frequency of the B–C bond in the reaction A + BC → AB + C, available from the NIST WebBook . A summary of the parameters used in this study can be found in Table S1.…”

Section: Computational Detailsmentioning

confidence: 99%

“…For isoergic reactions, e.g., H + H 2 and D + H 2 , the necessary information for the evaluation of κ ECK is available in Table of Allison and Truhlar; in these cases, α 1 = α 2 , and for isotopologue variations of isoergic reactions, the values of α 1 and α 2 are unchanged, but ω im is different. For nonisoergic reactions, the data in that table were supplemented using heats of formation data from NIST . With this information in hand, κ ECK was obtained numerically following the approach by Johnston and Heicklen as modified by Brown .…”

mentioning

confidence: 99%

“…One is the three Eckart parameters just mentioned and given by eqs –. Additional descriptors are the skew angle β, given in Table 1 of Allison and Truhlar, as well as ω e (B–C), the harmonic frequency of the B–C molecule in the reaction A + BC → AB + C, available from the NIST WebBook . These descriptor sets are given in Table , which we discuss in detail below.…”

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confidence: 99%

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A Machine Learning Approach for Prediction of Rate Constants

Houston

Nandi

Bowman

2019

J. Phys. Chem. Lett.

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We report a machine learning approach to train and predict bimolecular thermal rate constants over a large temperature range. The approach uses Gaussian process (GP) regression to evaluate the difference between accurate quantum results and Eckart-corrected conventional transition state theory, mostly for collinear reactions. Training is done on a database of rate constants for 13 reaction/potential surface combinations, and testing is performed on a set of 39 reaction/potential surface combinations. Averaged over all test reactions, the GP method is within 80% of the accurate answer, whereas transition state theory (TST) is only within 330% and Eckart-corrected TST (ECK) is within 110%. In the tunneling region, GP is generally (with a few exceptions) more accurate and sometimes much more accurate. In the high-temperature recrossing region, GP is significantly more accurate than either TST or ECK, neither of which addresses the possibility of recrossing. The GP predictions for the 3D reactions O(3P) + H2, OH + H2, O(3P) + CH4, and H + CH4, for which accurate quantum results are available, provide further encouragement to the machine learning approach.

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INAA with anticoincidence counting significantly reduces interferences from the 554.3-keV photopeak of 82Br to allow reliable measurements of nanogram levels of arsenic in solid biological materials via the 559.1-keV photopeak of 76As

Zhang

Chatt

2018

J Radioanal Nucl Chem

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Nuclear analysis at NBS and NIST

Cited by 9 publications

References 99 publications

Unravelling the Acoustic and Thermal Responses of Perfluorocarbon Liquid Droplets Stabilized with Cellulose Nanofibers

Unravelling the Acoustic and Thermal Responses of Perfluorocarbon Liquid Droplets Stabilized with Cellulose Nanofibers

A Machine Learning Approach for Prediction of Rate Constants

INAA with anticoincidence counting significantly reduces interferences from the 554.3-keV photopeak of 82Br to allow reliable measurements of nanogram levels of arsenic in solid biological materials via the 559.1-keV photopeak of 76As

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