Chemical Reaction-Driven Spreading of an Organic Extractant on the Gas–Water Interface: Insight into the Controllable Formation of a Gas Bubble-Supported Organic Extractant Liquid Membrane

Langmuir ◽  
2019 ◽  
Vol 35 (10) ◽  
pp. 3859-3868 ◽  
Author(s):  
Jie Liu ◽  
Kun Huang ◽  
Wenqian Liu ◽  
Huizhou Liu
2020 ◽  
Author(s):  
Suman Samantray ◽  
David Cheung

Using MD simulation the conformation of the fibril forming protein amyloid beta at the air-water interface. It is found that adsorption at the air-water interface induces the formation of aggregation prone alpha-helical conformations, consistent with experimental studies of amyloid beta. Adsorption on the air-water interface also reduces the number of distinct conformations that the protein exhibits. This provides insight into the role of protein conformational change into the enhancement of protein fibrillation at interfaces.


2020 ◽  
Author(s):  
Suman Samantray ◽  
David Cheung

Using MD simulation the conformation of the fibril forming protein amyloid beta at the air-water interface. It is found that adsorption at the air-water interface induces the formation of aggregation prone alpha-helical conformations, consistent with experimental studies of amyloid beta. Adsorption on the air-water interface also reduces the number of distinct conformations that the protein exhibits. This provides insight into the role of protein conformational change into the enhancement of protein fibrillation at interfaces.


Author(s):  
Philippe Schwaller ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
Teodoro Laino

<div><div><div><p>Organic reactions are usually clustered in classes that collect entities undergoing similar structural rearrangement. The classification process is a tedious task, requiring first an accurate mapping of the rearrangement (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present a transformer-based model that infers reaction classes from the SMILES representation of chemical reactions. The model reaches an accuracy of 93.8 % for a multi-class classification task involving several hundred different classes. The attention weights provided by the model give an insight into what parts of the SMILES strings are taken into account for classification, based solely on data. We study the incorrect predictions of our model and show that it uncovers different biases and mistakes in the underlying data set.</p></div></div></div>


2018 ◽  
Vol 122 (51) ◽  
pp. 29386-29397 ◽  
Author(s):  
Hussein Kaddour ◽  
Selim Gerislioglu ◽  
Punam Dalai ◽  
Toshikazu Miyoshi ◽  
Chrys Wesdemiotis ◽  
...  

2019 ◽  
Vol 177 ◽  
pp. 786-797 ◽  
Author(s):  
Hosein Rezvani ◽  
Yousef Kazemzadeh ◽  
Mohammad Sharifi ◽  
Masoud Riazi ◽  
Sanaz Shojaei

Sign in / Sign up

Export Citation Format

Share Document