Solid-state nanopores for ion and small molecule analysis

2019 ◽  
Vol 30 (9) ◽  
pp. 1607-1617 ◽  
Author(s):  
Qi Zhang ◽  
Yue Cheng ◽  
Peisheng Cao ◽  
Zhiyuan Gu
2017 ◽  
Vol 53 (66) ◽  
pp. 9269-9272 ◽  
Author(s):  
Ning-Ning Zhang ◽  
Cai Sun ◽  
Xiao-Ming Jiang ◽  
Xiu-Shuang Xing ◽  
Yong Yan ◽  
...  

A family of two small and easily synthesizable 1,2,3-triazole molecules with intrinsic white-light-emission in the solid state has been reported. The white light is assigned to the supramolecular aggregate emission (SAE) that is unusual for single-component white light phosphors.


2016 ◽  
Vol 128 (19) ◽  
pp. 5807-5811 ◽  
Author(s):  
Dong-Kyu Kwak ◽  
Hongsik Chae ◽  
Mi-Kyung Lee ◽  
Ji-Hyang Ha ◽  
Gaurav Goyal ◽  
...  

2020 ◽  
Vol 53 (22) ◽  
pp. 10078-10085
Author(s):  
Xiaobin Fu ◽  
Yiyang Liu ◽  
Wei Wang ◽  
Ling Han ◽  
Jing Yang ◽  
...  

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Domingo-Almenara ◽  
Carlos Guijas ◽  
Elizabeth Billings ◽  
J. Rafael Montenegro-Burke ◽  
Winnie Uritboonthai ◽  
...  

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.


2017 ◽  
Vol 28 (3) ◽  
pp. 525-538 ◽  
Author(s):  
Randy W. Purves ◽  
Satendra Prasad ◽  
Michael Belford ◽  
Albert Vandenberg ◽  
Jean-Jacques Dunyach

2014 ◽  
Vol 70 (a1) ◽  
pp. C631-C631
Author(s):  
Elena Boldyreva

Supramolecular interactions in the solid state attract much attention. Different experimental and computational approaches are used, to predict and to design crystal structures, to predict the properties based on molecular and crystal structures, to range different types of intermolecular interactions. Analysis of the crystal structures at fixed (e.g. ambient) temperature and pressure conditions is most common for experiments, whereas most DFT calculations are limited to 0 K, to minimize computational costs. At the same time, evolution of a crystal structure as a function of experimental conditions can contribute significantly to understanding the structure-forming role and relative energies of different types of intermolecular interactions in the same crystal structure and of similar interactions in a series of different but structurally or chemically related compounds. In the present invited contribution I attempt to illustrate this using several selected examples from my own practice and from the papers published by other research groups. I consider, in particular, the results of variable-temperature and variable-pressure studies of continuous lattice strain and phase transitions in small-molecule organic compounds, the results of variable-temperature and variable-pressure crystallization, the results of comparing the dissolution profiles of mono- and multi-component small-molecule organic crystals. I shall also discuss how variable-temperature and variable-pressure experimental diffraction data can assist in optimizing the calculations aimed at comparing the relative stability of polymorphs and predicting polymorph transitions. The study was supported by Russian Ministry of Science and Education and Russian Academy of Sciences.


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