Molecular mass dependence of point-to-set correlation length scale in polymers

2012 ◽  
Vol 137 (8) ◽  
pp. 084904 ◽  
Author(s):  
Ben Hanson ◽  
Victor Pryamitsyn ◽  
Venkat Ganesan
Keyword(s):  
Molecular Mass ◽  
Correlation Length ◽  
Length Scale ◽  
Mass Dependence ◽  
Set Correlation

Molecular Mass Dependence of Interfacial Tension in Complex Coacervation

2021 ◽  
Vol 126 (23) ◽  
Author(s):  
Debra J. Audus ◽  
Samim Ali ◽  
Artem M. Rumyantsev ◽  
Yuanchi Ma ◽  
Juan J. de Pablo ◽  
...  
Keyword(s):  
Interfacial Tension ◽  
Molecular Mass ◽  
Mass Dependence ◽  
Complex Coacervation

scholarly journals The Anomalies of Hyaluronan Structures in Presence of Surface Active Phospholipids—Molecular Mass Dependence

Polymers ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 273 ◽  
Author(s):  
Piotr Bełdowski ◽  
Tomasz Andrysiak ◽  
Aleksandra Mreła ◽  
Zenon Pawlak ◽  
Wayne Augé ◽  
...  
Keyword(s):  
Molecular Mass ◽  
Mass Dependence ◽  
Surface Active

scholarly journals Forecast Divergences of a Global Wave Model

2005 ◽  
Vol 133 (8) ◽  
pp. 2148-2162 ◽  
Author(s):  
Diana J. M. Greenslade ◽  
Ian R. Young
Keyword(s):  
Correlation Length ◽  
Surface Wind ◽  
Wave Model ◽  
Surface Wind Speed ◽  
Length Scale ◽  
Similar Distribution ◽  
Background Error ◽  
Latitudinal Dependence ◽  
Wave Models ◽  
Seasonal Signal

Abstract One of the main limitations to current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. One method that may be used to determine background errors is the “NMC method.” This method examines the forecast divergence component of the background error growth by considering differences between forecasts of different ranges valid at the same time. In this paper, the NMC method is applied to global forecasts of significant wave height (SWH) and surface wind speed (U10). It is found that the isotropic correlation length scale of the SWH forecast divergence (LSWH) has considerable geographical variability, with the longest scales just to the south of the equator in the eastern Pacific Ocean, and the shortest scales at high latitudes. The isotropic correlation length scale of the U10 forecast divergence (LU10) has a similar distribution with a stronger latitudinal dependence. It is found that both LSWH and LU10 increase as the forecast period increases. The increase in LSWH is partly due to LU10 also increasing. Another explanation is that errors in the analysis or the short-range SWH forecast propagate forward in time and disperse and their scale becomes larger. It is shown that the forecast divergence component of the background error is strongly anisotropic with the longest scales perpendicular to the likely direction of propagation of swell. In addition, in regions where the swell propagation is seasonal, the forecast divergence component of the background error shows a similar strong seasonal signal. It is suggested that the results of this study provide a lower bound to the description of the total background error in global wave models.

scholarly journals A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data, using a length scale computed from airborne lidar measurements

2021 ◽  
Author(s):  
David F. Baker ◽  
Emily Bell ◽  
Kenneth J. Davis ◽  
Joel F. Campbell ◽  
Bing Lin ◽  
...  
Keyword(s):  
Correlation Length ◽  
Error Model ◽  
Airborne Lidar ◽  
Length Scale ◽  
Correlation Model ◽  
Correlation Lengths ◽  
New Model ◽  
The Mean ◽  
Error Correlation ◽  
Along Track

Abstract. To check the accuracy of column-average dry air CO2 mole fractions (XCO2) retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (~3 km2) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ~6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause nonphysical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives – measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each XCO2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead.

scholarly journals Molecular mass dependence of hyaluronan detection by sandwich ELISA-like assay and membrane blotting using biotinylated hyaluronan binding protein

Glycobiology ◽  
2013 ◽  
Vol 23 (11) ◽  
pp. 1270-1280 ◽  
Author(s):  
H. Yuan ◽  
M. Tank ◽  
A. Alsofyani ◽  
N. Shah ◽  
N. Talati ◽  
...  
Keyword(s):  
Molecular Mass ◽  
Binding Protein ◽  
Sandwich Elisa ◽  
Mass Dependence ◽  
Hyaluronan Binding

scholarly journals Calculating an error correlation length scale from MFLL-OCO-2 column-average CO2 differences and using it to average OCO-2 data

2021 ◽  
Author(s):  
David F Baker ◽  
Emily Bell ◽  
Kenneth J Davis ◽  
Joel F Campbell ◽  
Bing Lin ◽  
...  
Keyword(s):  
Correlation Length ◽  
Length Scale ◽  
Error Correlation
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