Thermoresponsive behavior of poly(N-isopropylacrylamide)s with dodecyl and carboxyl terminal groups in aqueous solution: pH-dependent cloud point temperature

2017 ◽  
Vol 295 (8) ◽  
pp. 1343-1349 ◽  
Author(s):  
Juraj Škvarla ◽  
Rahul K. Raya ◽  
Mariusz Uchman ◽  
Jiří Zedník ◽  
Karel Procházka ◽  
...  
Keyword(s):  
Aqueous Solution ◽  
Cloud Point ◽  
Solution Ph ◽  
Point Temperature ◽  
Cloud Point Temperature ◽  
Ph Dependent ◽  
Carboxyl Terminal

Measurement of cloud point temperature in polymer solutions

2013 ◽  
Vol 84 (7) ◽  
pp. 075118 ◽  
Author(s):  
G. A. Mannella ◽  
V. La Carrubba ◽  
V. Brucato
Keyword(s):  
Cloud Point ◽  
Polymer Solutions ◽  
Point Temperature ◽  
Cloud Point Temperature

Solubility of fluorinated compounds in a range of ionic liquids. Cloud-point temperature dependence on composition and pressure

2008 ◽  
Vol 10 (9) ◽  
pp. 918 ◽  
Author(s):  
Rui Ferreira ◽  
Marijana Blesic ◽  
Joana Trindade ◽  
Isabel Marrucho ◽  
José N. Canongia Lopes ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Temperature Dependence ◽  
Cloud Point ◽  
Point Temperature ◽  
Cloud Point Temperature

scholarly journals Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination

2020 ◽  
Author(s):  
Marieke E. Klijn ◽  
Jürgen Hubbuch
Keyword(s):  
Image Analysis ◽  
Cooling Rate ◽  
Cloud Point ◽  
Detection Method ◽  
Sample Volume ◽  
Point Temperature ◽  
Analysis Strategies ◽  
Experimental Parameters ◽  
Analysis Strategy ◽  
Cloud Point Temperature

AbstractThe protein cloud-point temperature (TCloud) is a known representative of protein–protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume TCloud detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent TCloud (TCloud,app). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on TCloud,app detection of lysozyme. Different image analysis strategies showed that TCloud,app is detectable by means of total pixel intensity difference and the total number of white pixels, but the latter is also able to extract the ice nucleation temperature. Experimental parameter variation showed a TCloud,app depression for increasing cooling rates (0.1–0.5 °C/min), and larger sample volumes (5–24 μL). Exploratory thermographic data indicated this resulted from a temperature discrepancy between the measured temperature by the cryogenic device and the actual sample temperature. Literature validation confirmed that the discrepancy does not affect the relative inter-study comparability of the samples, regardless of the image analysis strategy or experimental parameters. Additionally, high measurement precision was demonstrated, as TCloud,app changes were detectable down to a sample volume of only 5 μL and for 0.1 °C/min cooling rate increments. This work explains the apparent nature of the TCloud detection method, showcases its detection precision, and broadens the applicability of the experimental setup.

Sign in / Sign up

Export Citation Format

Share Document