Chemical Space Mapping for Multicomponent Gas Mixtures

In our manuscript, we present our protocol for data processing to mitigate the effects of interfering analytes on the identification of the chemical species detected by sensors. Considering NO2 and CO2, we designed electrochemical sensors whose response yielded the cyclic voltammetry data that we analyzed to classify single-species components and their mixtures using a data-driven approach to generate a chemical space where their mixtures can be deconvoluted.

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Miniature Multi-electrode Electrochemical Cell in LTCC

Journal of Microelectronics and Electronic Packaging ◽

10.4071/1551-4897-4.1.31 ◽

2007 ◽

Vol 4 (1) ◽

pp. 31-36

Author(s):

John Youngsman ◽

Brian Marx ◽

Scott Wolter ◽

Jeff Glass ◽

Amy Moll

Keyword(s):

Cyclic Voltammetry ◽

Electrochemical Sensors ◽

Electrochemical Cell ◽

Sea Water ◽

Electrochemical Impedance ◽

Chemical Species ◽

Analytical Instruments ◽

Toxic Gases ◽

Significant Research ◽

Eis Analysis

The miniaturization of analytical instruments and packaging of novel sensors is an area that has attracted significant research interest and offers many opportunities for product commercialization. Electrochemical sensors have been used to detect a wide variety of compounds including toxic gases. A miniature electrochemical cell has been designed, constructed, and tested for functionality. The cell will be used in identifying and selecting chemical species in solutions. The cell was constructed of low temperature co-fired ceramic (LTCC) material using gold for the electrodes. Tests performed in sulfuric acid and sea water solutions show that the cell is functioning based on cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) analysis. Miniaturization allows the cell to be deployed as a sensor in many different environments.

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‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

10.26434/chemrxiv.9938969.v3 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Nidhal Selmi ◽

Jean-Louis Reymond ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Network ◽

Chemical Space ◽

Ring System ◽

Data Driven ◽

Ring Systems ◽

The Neural Network ◽

Synthetic Accessibility ◽

Synthesis Planning ◽

Data Driven Approach ◽

Synthetic Routes

Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

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‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

10.26434/chemrxiv.9938969 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Nidhal Selmi ◽

Jean-Louis Reymond ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Network ◽

Chemical Space ◽

Ring System ◽

Data Driven ◽

Ring Systems ◽

The Neural Network ◽

Synthetic Accessibility ◽

Synthesis Planning ◽

Data Driven Approach ◽

Synthetic Routes

Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

Download Full-text