Effect of MnS precipitation on solute equilibrium partition coefficients in high sulfur steel during solidification

2018 ◽  
Vol 33 (20) ◽  
pp. 3490-3500 ◽  
Author(s):  
Lintao Gui ◽  
Mujun Long ◽  
Huabiao Chen ◽  
Dengfu Chen ◽  
Huamei Duan ◽  
...  
Keyword(s):  
Partition Coefficients ◽  
Equilibrium Partition ◽  
Mns Precipitation ◽  
Image Position

Abstract

Understanding interactions of organic nitrates with the surface and bulk of organic films: implications for particle growth in the atmosphere

2018 ◽  
Vol 20 (11) ◽  
pp. 1593-1610 ◽  
Author(s):  
A. C. Vander Wall ◽  
P. S. J. Lakey ◽  
E. Rossich Molina ◽  
V. Perraud ◽  
L. M. Wingen ◽  
...  
Keyword(s):  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Particle Growth ◽  
Organic Films ◽  
Organic Nitrates ◽  
Kinetics Modeling ◽  
Equilibrium Partition ◽  
Initial Uptake

Experiments, kinetics modeling and quantum chemical calculations are combined to probe both initial uptake and equilibrium partition coefficients for organic nitrates into various organic films.

scholarly journals Adsorption of amyloglucosidase from Aspergillus niger NRRL 3122 using ion exchange resin

2008 ◽  
Vol 51 (5) ◽  
pp. 1015-1024 ◽  
Author(s):  
Ana Paula Manera ◽  
Eliana Setsuko Kamimura ◽  
Luciana Machado Brites ◽  
Susana Juliano Kalil
Keyword(s):  
Aspergillus Niger ◽  
Partition Coefficient ◽  
Partition Coefficients ◽  
Deae Cellulose ◽  
Ion Exchange Resin ◽  
Least Square ◽  
Optimum Conditions ◽  
Equilibrium Partition ◽  
Equilibrium Partition Coefficient ◽  
Least Square Procedure

Amyloglucosidase enzyme was produced by Aspergillus niger NRRL 3122 from solid-state fermentation, using deffated rice bran as substrate. The effects of process parameters (pH, temperature) in the equilibrium partition coefficient for the system amyloglucosidase - resin DEAE-cellulose were investigated, aiming at obtaining the optimum conditions for a subsequent purification process. The highest partition coefficients were obtained using 0.025M Tris-HCl buffer, pH 8.0 and 25ºC. The conditions that supplied the highest partition coefficient were specified, the isotherm that better described the amyloglucosidase process of adsorption obtained. It was observed that the adsorption could be well described by Langmuir equation and the values of Qm and Kd estimated at 133.0 U mL-1 and 15.4 U mL-1, respectively. From the adjustment of the kinetic curves using the fourth-order Runge-Kutta algorithm, the adsorption (k1) and desorption (k2) constants were obtained through optimization by the least square procedure, and the values calculated were 2.4x10-3 mL U-1 min-1 for k1 and 0.037 min-1 for k2 .

Chevkinite-group minerals from salic volcanic rocks of the East African Rift

2002 ◽  
Vol 66 (2) ◽  
pp. 287-299 ◽  
Author(s):  
R. Macdonald ◽  
A. S. Marshall ◽  
J. B. Dawson ◽  
R. W. Hinton ◽  
P. G. Hill
Keyword(s):  
Partition Coefficients ◽  
Volcanic Rocks ◽  
East African Rift ◽  
Degree Of Polymerization ◽  
Volcanic Complex ◽  
East African ◽  
African Rift ◽  
Image Position ◽  
Charge Balancing ◽  
Peralkaline Rock

AbstractElectron microprobe analyses are presented of chevkinite-group minerals occurring as microphenocrysts in peralkaline rhyolites of the Greater Olkaria Volcanic Complex (Kenya) and as a groundmass phase in a peralkaline quartz trachyte lava from the Tarosero volcano (Tanzania), both in the East African Rift Valley. Their compositions conform closely to the formula: (REE, Ca, Th)4 Fe2+ (Fe2+, Al, Ti, Zr, Nb)2 Ti2 (Si4O22). Compared to published analyses of chevkinite-group minerals, the Olkaria phases are relatively enriched in Nb and the LREE; the Tarosero phase is more calcic and relatively Zr- and Nb-rich. The main substitution in the A site at Olkaria is Ca ⇌ Ce. The overall charge-balancing substitution seems to be (McDowell, 1979):Phenocryst/glass ratios are presented for Nb, REE, Sr, Th, U and Y in two, and Ba, Zr and Hf in one, Kenyan samples. Partition coefficients are lower in the more peralkaline rock, with the exception of Sr, which is higher. The lower values are consistent with a lower degree of polymerization of more peralkaline melts. The higher Sr value may be a function of Sr partitioning into phenocryst phases coexisting with chevkinite.

scholarly journals The road to nowhere: equilibrium partition coefficients for nanoparticles

2014 ◽  
Vol 1 (4) ◽  
pp. 317-323 ◽  
Author(s):  
Antonia Praetorius ◽  
Nathalie Tufenkji ◽  
Kai-Uwe Goss ◽  
Martin Scheringer ◽  
Frank von der Kammer ◽  
...  
Keyword(s):  
Partition Coefficients ◽  
The Road ◽  
Equilibrium Partition

This perspective demonstrates the inapplicability of equilibrium partition coefficients as fate descriptors for nanoparticles.

Equilibrium partition coefficients in iron-based alloys

1989 ◽  
Vol 20 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Thomas P. Battle ◽  
Robert D. Pehlke
Keyword(s):  
Partition Coefficients ◽  
Equilibrium Partition ◽  
Iron Based

scholarly journals Applicability Domain of Polyparameter Linear Free Energy Relationship Models for Predicting Equilibrium Partition Coefficients

2022 ◽  
Author(s):  
Satoshi Endo
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Training Data ◽  
Applicability Domain ◽  
Linear Free Energy Relationship ◽  
Prediction Errors ◽  
Calibration Data ◽  
Data Set ◽  
Linear Free Energy ◽  
Equilibrium Partition

Polyparameter linear free energy relationships (PP-LFERs) are accurate and robust models to predict equilibrium partition coefficients (K) of organic chemicals. The accuracy of predictions by a PP-LEFR depends on the composition of the respective calibration data set. It is generally expected that extrapolation outside the model calibration domain is less accurate than interpolation. In this study, the applicability domain (AD) of PP-LFERs is systematically evaluated by calculation of the leverage (h), a measure of distance from the calibration set in the descriptor space. Repeated simulations with experimental data show that the root mean squared error of predictions increases with h, and that large prediction errors (>3 SDtraining, the standard deviation of training data) occur more frequently when h exceeds the common threshold of 3 hmean, where hmean is the mean h of all training compounds. Nevetheless, analysis also shows that well-calibrated PP-LFERs with many (e.g., 100), diverse, and accurate training data are highly robust against extrapolation; extreme prediction errors (> 5 SDtraining) are rare. For such PP-LFERs, 3 hmean may be too strict as the cutoff for AD. Evaluation of published PP-LFERs in terms of their AD using 25 chemically diverse, environmentally relevant chemicals as AD probes indicated that many reported PP-LFERs do not cover organosiloxanes, per- and polyfluorinated alkylsubstances, highly polar chemicals, and/or highly hydrophobic chemicals in their AD. It is concluded that calculation of h is useful to identify model extrapolations as well as the strengths and weaknesses of the trained PP-LFERs.

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