Nanoscale PDA disassembly in ionic liquids: structure–property relationships underpinning redox tuning

2019 ◽  
Vol 21 (23) ◽  
pp. 12380-12388 ◽  
Author(s):  
Marianna Ambrico ◽  
Paola Manini ◽  
Paolo F. Ambrico ◽  
Teresa Ligonzo ◽  
Giuseppe Casamassima ◽  
...  

An integrated EPR and electrical impedance spectroscopy approach to predict ionic liquid-mediated tuning of the redox properties of polydopamine nanoparticles.

2006 ◽  
Vol 270 (1-2) ◽  
pp. 42-49 ◽  
Author(s):  
Raquel Fortunato ◽  
Luís C. Branco ◽  
Carlos A.M. Afonso ◽  
Juana Benavente ◽  
João G. Crespo

2021 ◽  
Vol 232 (2) ◽  
Author(s):  
Rakibul Islam Chowdhury ◽  
Rinku Basak ◽  
Khan Arif Wahid ◽  
Katy Nugent ◽  
Helen Baulch

2020 ◽  
Vol 28 ◽  
pp. 1679-1685
Author(s):  
Angeliki-Eirini Dimou ◽  
Ioanna Sakellariou ◽  
George M. Maistros ◽  
Nikolaos D. Alexopoulos

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1001
Author(s):  
Sooin Huh ◽  
Hye-Jin Kim ◽  
Seungah Lee ◽  
Jinwoo Cho ◽  
Aera Jang ◽  
...  

This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.


Allergy ◽  
2021 ◽  
Author(s):  
Arturo O. Rinaldi ◽  
Angelica Korsfeldt ◽  
Siobhan Ward ◽  
Daniel Burla ◽  
Anita Dreher ◽  
...  

Polymer ◽  
2018 ◽  
Vol 148 ◽  
pp. 14-26 ◽  
Author(s):  
A. Vashchuk ◽  
A. Rios de Anda ◽  
O. Starostenko ◽  
O. Grigoryeva ◽  
P. Sotta ◽  
...  

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