Optical method for monitoring the concentration of general anesthetics and other small organic molecules. An example of phase transition sensing

1990 ◽  
Vol 62 (24) ◽  
pp. 2728-2735 ◽  
Author(s):  
Sabina. Merlo ◽  
Paul. Yager
Keyword(s):  
Phase Transition ◽  
Optical Method ◽  
Organic Molecules ◽  
General Anesthetics ◽  
Small Organic Molecules

scholarly journals Pressure-driven phase transition mechanisms revealed by quantum chemistry: l-serine polymorphs

2017 ◽  
Vol 19 (9) ◽  
pp. 6671-6676 ◽  
Author(s):  
Denis A. Rychkov ◽  
Jernej Stare ◽  
Elena V. Boldyreva
Keyword(s):  
Phase Transition ◽  
Phase Transitions ◽  
Quantum Chemistry ◽  
Organic Molecules ◽  
Computational Approach ◽  
Small Organic Molecules ◽  
Pressure Driven

The present study delivers a computational approach for the understanding of the mechanism of phase transitions between polymorphs of small organic molecules.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

scholarly journals Interaction Mechanisms between Lithium Polysulfides/Sulfide and Small Organic Molecules

ACS Omega ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 4995-5000 ◽  
Author(s):  
Jiaxiang Zhang ◽  
Junwen Yang ◽  
Ziyue Liu ◽  
Bin Zheng
Keyword(s):  
Organic Molecules ◽  
Small Organic Molecules ◽  
Interaction Mechanisms

Dual functioning porous catalysts: Robust electro-oxidation of small organic molecules and water electrolysis at bimetallic Ni/Cu foam

2021 ◽  
Author(s):  
Mohamed R. Rizk ◽  
Muhammad G. Gamal ◽  
Amina Mazhar ◽  
Mohamed El-Deab ◽  
Bahgat El-Anadouli
Keyword(s):  
Thin Layer ◽  
Organic Molecules ◽  
Water Electrolysis ◽  
Single Step ◽  
Hydrogen Bubble ◽  
Small Organic Molecules ◽  
Porous Catalysts ◽  
Electro Oxidation ◽  
Bubble Template ◽  
Cu Foam

In this work, we report a single-step preparation of porous Ni-based foams thin layer atop Cu substrate via a facile dynamic hydrogen bubble template technique (DHBT). The prepared porous Ni-based...

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