Green Route To Nylon Precursor Beckons

Oct. 30, 2019
Electrochemical method to make adiponitrile promises environmental advantages
Electrosynthesis Researchers

Figure 1. Miguel Modestino and Daniela Blanco pose with electrosynthesis device of their own design. The pair developed a new, green electro- synthetic route to adiponitrile and used artificial intelligence to improve upon it.  Source: NYU Tandon School of Engineering.

Using artificial intelligence (AI), researchers have found a way to improve the efficiency of organic electrosynthesis. This could lead to a more innovative, environmentally friendly process to make adiponitrile (ADN), the main precursor to nylon 6, 6, say the team at the NYU Tandon School of Engineering, Brooklyn.

Traditional ADN production involves toxic, energy intensive thermal hydrocyanation of butadiene. By contrast, electrosynthesis of ADN uses water-based electrolytes and renewable electricity sources such as wind or sunlight.

The standard electrosynthetic process for ADN employs an “always on” direct electrical current delivered to the electrocatalytic site. It also generates unwanted byproduct, propionitrile (PN). The researchers analyzed via AI 16 different experimental cases of pulse times to engineer a system delivering an intermittent current to constantly replenish reagent concentration at the electrocatalytic site.

“By analyzing electrochemical pulse techniques with AI, we were able to visualize ADN conversion efficiency across a range of pulse times without having to do more than a few physical experiments,” explains Miguel Modestino, a professor of chemical and biomolecular engineering at NYU. “This innovative, integrated approach led to an unprecedented 30% improvement in ADN production and a 325% increase in the ratio of ADN to PN, mostly due a large decrease in production of the latter,” he adds.

An article in the Proceedings of the National Academy of Sciences (PNAS) details the researchers’ findings.

Modestino’s partner on the project, doctoral student Daniela Blanco, believes the technique could advance industry adoption of more sustainable processes. She and a former student in Modestino’s laboratory founded a green-chemistry startup company, Sunthetics, to commercialize a sustainable nylon production process based on their research.

“We wanted to show with this new research that we can make the ADN electrochemical process more competitive,” she says. “Currently only 30% of global ADN output employs electrosynthesis; the rest of production involves processing over an energy- and oil-intensive catalytic reactor,” she notes.

Sunthetics is talking with companies throughout the nylon manufacturing supply chain. “We have received mostly interest from nylon manufacturers assessing the possibility to back-integrate with technologies on the production of nylon intermediates. Several chemical companies also are interested in seeing where the scale up of the technology takes us and if we can implement the concept to other chemical processes,” reveal the researchers.

Scaling-up the technology for a pilot demonstration will begin soon. “We envision using the core of this technology to develop more efficient and electricity-driven chemical reactors which can be implemented on new plants or to demonstrate the advances in pre-existing ones,” adds Modestino.

On an industrial scale, pulsed electrosynthesis would require additional investment in power electronics to control the amplitude and frequency of potential pulses; however, the researchers anticipate the reactor infrastructure wouldn’t need significant modification. “Once the potential pulse control is achieved, the most important challenge will be identifying the optimal pulse sequence at scale. Given that many variables can affect the process, long experimental campaigns are necessary to test all the possible combinations of parameters, and these require time, money and energy. This is exactly where machine learning comes into play, as a tool to accelerate process optimization to identify the optimal operation conditions,” explains Modestino.

“There are many possibilities for further implementation of machine-learning optimization of electrochemical processes,” he adds. “A natural next step is to continue on the production of nylon intermediates by developing an electrochemical hydrogenation process for ADN to produce hexamethylene diamine. We are also starting to look into the application of similar principles to control the reduction or oxidation of selective functional groups in complex molecules. This could have large implication in pharmaceutical manufacturing, where selectivity is a key driver in the selection of synthesis paths, and our electrosynthesis approach could lead to highly selective scalable processes.”