Chemical consequences of strong hydrogen bonding in the reactions of organic ions in the gas phase. Interaction of remote functional groups

1972 ◽  
Vol 94 (10) ◽  
pp. 3671-3672 ◽  
Author(s):  
Thomas H. Morton ◽  
J. L. Beauchamp
Keyword(s):  
Hydrogen Bonding ◽  
Gas Phase ◽  
Functional Groups ◽  
Phase Interaction ◽  
Organic Ions

Carbohydrate based hyper-crosslinked organic polymers with –OH functional groups for CO2 separation

2015 ◽  
Vol 3 (42) ◽  
pp. 20913-20918 ◽  
Author(s):  
Haiying Li ◽  
Bo Meng ◽  
Shannon M. Mahurin ◽  
Song-Hai Chai ◽  
Kimberly M. Nelson ◽  
...  
Keyword(s):  
Hydrogen Bonding ◽  
Functional Groups ◽  
Carbon Capture ◽  
Carbon Capture And Storage ◽  
Co2 Separation ◽  
Organic Polymers ◽  
Microporous Polymers ◽  
Quadrupole Interactions ◽  
And Storage

A class of novel hyper-crosslinked microporous polymers, based on green and renewable carbohydrates, was synthesized for carbon capture and storage with high CO2/N2 selectivity by hydrogen bonding and dipole–quadrupole interactions.

scholarly journals Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

2021 ◽  
Author(s):  
Abigail Enders ◽  
Nicole North ◽  
Chase Fensore ◽  
Juan Velez-Alvarez ◽  
Heather Allen
Keyword(s):  
Machine Learning ◽  
Gas Phase ◽  
Functional Groups ◽  
Functional Group ◽  
Spectroscopic Method ◽  
Group Identification ◽  
Spectroscopic Technique ◽  
Ftir Spectra ◽  
Spectral Interpretation ◽  
Time Required

<p>Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.</p>

ChemInform Abstract: THE STRUCTURE AND REACTIVITY OF ORGANIC IONS IN GAS-PHASE RADIOLYSIS PART 8, THE C7H7(+) ION FROM TOLUENE AND ETHYLBENZENE

1974 ◽  
Vol 5 (36) ◽  
Author(s):  
NOBUO SAGI ◽  
YUKIO YAMAMOTO ◽  
KENJI NAGAOKA ◽  
SETSUO TAKAMUKU ◽  
HIROSHI SAKURAI
Keyword(s):  
Gas Phase ◽  
Organic Ions
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