Method enables CO2 recycling and drug development simultaneously

Researchers from the Institute for Chemical Reaction Design and Discovery (ICReDD) at Hokkaido University have developed a technique that could help recycle waste carbon dioxide (CO2) while creating beneficial molecules for drug development.

Artistic representation of electricity allowing the addition of CO2 to heteroaromatic compounds. Image credit: Tsuyoshi Mita.

Besides the ever-crucial requirement of carbon neutrality, chemists are increasingly turning to the use of CO2 in syntheses because it is abundant, economical, renewable and not very toxic. But its responsiveness is relatively low.

To overcome this, the team led by Professor Tsuyoshi Mita applied an electrochemical technique in which an electron is incorporated either into CO2 molecule or to the other molecule in the solution, which allows them to react much more easily with each other.

This study marks a particularly important step forward since the CO2 is used to conduct a traditionally difficult type of transformation with unparalleled efficiency. When specific conditions are met, electrons can be shared among a number of atoms in a molecule by what is called an aromatic system.

These systems are particularly stable and difficult to break, but the new technique formulated at ICReDD can dearomatize, or break, these stable aromatic systems by incorporating CO2 into the molecule using electricity.

This method has the potential to recycle CO2 as well as producing high added value dicarboxylic acids from basic raw materials, simultaneously solving two problems.

Prior to the actual experiments, ICReDD researchers screened many heteroaromatic compounds by evaluating their reduction potential, which is a measure of how a compound reacts when exposed to an electrical environment.

The results allowed scientists to detect potentially reactive compounds and perform targeted electrochemical experiments. They show that a wide range of substrates that display very negative reduction potentials can very effectively experience this extraordinary disaromatic incorporation of two CO2 molecules.

Acquired dicarboxylic acids can be cost-effectively modified without difficulty into important intermediates for biologically active compounds, which could pave the way for more efficient and cost-effective drug development.

The scientists involved in the study attribute the rapid development of this new method to their strategy of performing computational analyzes first which aided their experimental selections in the laboratory.

I started learning computational chemistry when I joined ICReDD. Within a year, I was able to use advanced computational techniques, which was very helpful in guiding my decisions in the lab.

Dr. Yong You, Study First Author, Institute for Chemical Reaction Design and Discovery, Hokkaido University

“It took only eight months to complete the research and publish the paper, which is much faster than a conventional project involving experiments. Considerable research time is saved because a computer can reliably predict the feasibility of reactant structures and possible reaction pathways” observed Tsuyoshi Mita, who led this study.

Funding

This research has received financial support from the Exploratory Research for Advanced Technologies of the Japan Science and Technology Agency (JST-ERATO; JPMJER1903); the Japan Society for the Promotion of Science’s World Premier International Research Center Initiative (JSPS-WPI) and Challenging (Exploratory) Research Grant (21K1894501); the Fugaku Trust for Medical Research; the Uehara Memorial Foundation; and the Naito Foundation.

Journal reference:

You, Y. et al. (2022) Electrochemical dearomatic dicarboxylation of heterocycles with highly negative reduction potentials. Journal of the American Chemical Society. doi.org/10.1021/jacs.1c13032.

Source: https://www.global.hokudai.ac.jp

Source link

About Donald P. Hooten

Check Also

Distributed deep learning method without sharing sensitive data

Data sharing is one of the major challenges of machine learning models. The advent of …