Liquid–Liquid Equilibria of Lactic Acid/Water Solutions in Tri-iso-octylamine/Dodecane/1-Dodecanol at 306.1, 310.1, and 316.1 K. Experimental Data and Prediction

2019 ◽  
Vol 64 (2) ◽  
pp. 603-610 ◽  
Author(s):  
Alan D. Pérez ◽  
Sneyder Rodríguez-Barona ◽  
Javier Fontalvo
Keyword(s):  
Experimental Data ◽  
Lactic Acid ◽  
Acid Water ◽  
Water Solutions

Dielectric Constants for Binary Amino Acid−Water Solutions from (278.15 to 313.15) K

2009 ◽  
Vol 54 (1) ◽  
pp. 137-141 ◽  
Author(s):  
Kelei Zhuo ◽  
Yujuan Chen ◽  
Lei Kang ◽  
Sijiao Xu ◽  
Jianji Wang
Keyword(s):  
Amino Acid ◽  
Dielectric Constants ◽  
Acid Water ◽  
Water Solutions

Stability of a Water-Stable Lithium Metal Anode for a Lithium–Air Battery with Acetic Acid–Water Solutions

2010 ◽  
Vol 157 (2) ◽  
pp. A214 ◽  
Author(s):  
Tao Zhang ◽  
Nobuyuki Imanishi ◽  
Yuta Shimonishi ◽  
Atsushi Hirano ◽  
Jian Xie ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Acid Water ◽  
Lithium Metal ◽  
Water Solutions ◽  
Metal Anode ◽  
Lithium Metal Anode ◽  
Acetic Acid Water ◽  
Air Battery

Experimental Data and Behavior of Starch/Water Solutions with an Ultrafiltration Module

1996 ◽  
Vol 31 (2) ◽  
pp. 173-188 ◽  
Author(s):  
A. Gomez-Gotor ◽  
P. Susial ◽  
A. Guerra ◽  
B. Ibarra
Keyword(s):  
Experimental Data ◽  
Water Solutions ◽  
And Behavior

ESTIMATION OF LACTIC ACID IN SEA WATER SOLUTIONS AND HOMOGENATES

1963 ◽  
Vol 8 (2) ◽  
pp. 292-294 ◽  
Author(s):  
H. BARNES ◽  
D. M. FINLAYSON
Keyword(s):  
Lactic Acid ◽  
Sea Water ◽  
Water Solutions

A pulse radiolysis study of solvated electrons in tert-butanol/water solutions

1986 ◽  
Vol 64 (8) ◽  
pp. 1548-1552
Author(s):  
Joanna Cygler ◽  
Norman V. Klassen ◽  
Carl K. Ross
Keyword(s):  
Experimental Data ◽  
Pulse Radiolysis ◽  
Oh Radicals ◽  
Solvated Electrons ◽  
Secondary Electrons ◽  
Water Solutions ◽  
Tert Butanol

Many solutes, added to water in amounts of a few mol%, cause an increase in the yield of solvated electrons (es−) measured by pulse radiolysis. A pulse radiolysis study of tert-butanol (tBuOH) in D2O has been carried out to investigate this phenomenon. Detailed measurements of the yield, measured as Gεmax(es−), and the deeay of solvated electrons were made at 6, 25, and 46 °C over the range 0–5mol% tBuOH. The maximum Gεmax(es−) occurs at about 1 mol% tBuOH, but the exact concentration depends on the temperature of the sample and the time after the pulse at which the measurement is made. Three factors are examined as contributing to the increased Gεmax(es−) in the presence of tBuOH and certain other solutes. They are (i) the change in viscosity produced by the added solute, (ii) the scavenging of OH radicals by the solute, thereby reducing the reaction of OH with es− and (iii) the possibility that the addition of the solute leads to an increase in the thermalization distance of the secondary electrons. It is concluded that effects (i) and (ii) are sufficient to explain the existing experimental data.

scholarly journals Growth Limits of Listeria monocytogenesas a Function of Temperature, pH, NaCl, and Lactic Acid

2000 ◽  
Vol 66 (11) ◽  
pp. 4979-4987 ◽  
Author(s):  
S. Tienungoon ◽  
D. A. Ratkowsky ◽  
T. A. McMeekin ◽  
T. Ross
Keyword(s):  
Experimental Data ◽  
Logistic Regression ◽  
Lactic Acid ◽  
Model Parameters ◽  
Food Borne ◽  
Food Borne Illness ◽  
Effects Of Temperature ◽  
Growth Limits ◽  
Food Safety Risk ◽  
Temperature Water

ABSTRACT Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains ofListeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into “growth is possible” or “growth is not possible” classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773–1779, 1998). We used “nonlinear logistic regression” to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenesreported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes.

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