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Converting Methane to Methanol

August 31, 2020
Emily Wong
Mechanical Engineering, Class of 2022

Component pieces laid out on a bench

Methane is a common byproduct of fracking for natural gas, livestock, coal, and burning forests. As a gas, it is one of the worst of the greenhouse gases, and can trap radiation much more efficiently than carbon dioxide. As a result, when methane gas is unable to be transported, which is often the case in remote oil rigs, it is burned in a process called flaring, producing carbon dioxide. 140 billion cubic meters of methane are burned each year, accounting for ~1% of global carbon dioxide emissions.

In comparison, liquid methanol is energy-dense and is useful in products such as plastics, paints, and building materials. Its liquid state allows it to be easily transportable, solving the need for flaring for remote or small-scale locations. However, the current steam-reforming process to turn methane gas into liquid methanol is energy intensive and not economical on the small scale.

My lab seeks to find a way to directly convert methane to methanol through photocatalysis, in a process deemed the “holy grail” of catalysis. With a vast array of catalysts and light intensities to test, finding the material that produces the best methanol yield is a daunting task.

To solve this problem, my job for this summer was to build a high-pressure multiplexed photoreactor to quickly screen different catalysts. When built, it should be able to complete 96 tests at one time, and simultaneously measure the output of OH radical, an indicator for methanol output.

However, my plans for this summer were somewhat derailed with the closure of the Stanford labs. Since I was initially unable to obtain any of the materials to work on the photoreactor, I had the opportunity to really delve into the chemical aspect. I read research articles and textbooks on photocatalysis, and solved “homework” problems demonstrating how our reaction worked. This was a topic that was completely new for me, and which I probably would not have been able to study if it weren’t for quarantine.

I also worked to use machine learning to discover trends within our data and discover new catalysts on a platform called Citrine. While this tool did help narrow down focus areas for possible catalysts, our small dataset resulted in data with an error too large to be considered useful. I plan on continuing to experiment with Citrine as we are able to test more catalysts and dopant combinations.


Once PhD students were allowed back into the lab, I was finally able to get some materials to work with! My dining room became my mini lab, and with guidance from my advisors I was able to create an LED control system that could change the intensity of our light array as well as turn it on and off. The lights were controlled and the voltage was regulated through a Teensy LC microcontroller and an LED driver.

My next steps are to either purchase an LED PCB or to create an LED light panel from scratch. I am currently working on the CAD model for the light panel, which we will try first to test for uniformity. Once these parts are in, I will finally be able to put the entire thing together!