Nuclear Cogeneration of Methanol and Acetaldehyde from Ethylene Glycol Using Ionizing Radiation.
Arran George PlantBor KosAnže JazbecLuka SnojMalcolm John JoyceVesna Najdanovic-VisakPublished in: Industrial & engineering chemistry research (2023)
Despite offering low-carbon and reliable energy, the utilization of nuclear energy is declining globally due to high upfront capital costs and longer returns on investments. Nuclear cogeneration of valuable chemicals from waste biomass-derived feedstocks could have beneficial impacts while harnessing the underutilized resource of ionizing energy. Here, we demonstrate selective methanol or acetaldehyde production from ethylene glycol, a feedstock derived from glycerol, a byproduct of biodiesel, using irradiations from a nuclear fission reactor. The influence of radiation quality, dose rate, and the absorbed dose of irradiations on radiochemical yields ( G -value) has been studied. Under low-dose-rate, γ-only radiolysis during reactor shutdown rate (<0.018 kGy min -1 ), acetaldehyde is produced at a maximum G -value of 8.28 ± 1.05 μmol J -1 and a mass productivity of 0.73 ± 0.06% from the 20 kGy irradiation of neat ethylene glycol. When exposed to a high-dose-rate (6.5 kGy min -1 ), 100 kGy mixed-field of neutron + γ-ray radiations, the radiolytic selectivity is adjusted from acetaldehyde to generate methanol at a G -value of 2.91 ± 0.78 μmol J -1 and a mass productivity of 0.93 ± 0.23%. Notably, utilizing 422 theoretical systems could contribute to 4.96% of worldwide acetaldehyde production using a spent fuel pool γ-ray scheme. This research reports G -values and production capacities for acetaldehyde for high-dose scenarios and shows the potential selectivity of a nuclear cogeneration process to synthesize chemicals based on their irradiation conditions from the same reagent.
Keyphrases
- high dose
- low dose
- climate change
- wastewater treatment
- stem cell transplantation
- radiation induced
- anaerobic digestion
- emergency department
- carbon dioxide
- heavy metals
- machine learning
- radiation therapy
- quality improvement
- artificial intelligence
- human health
- deep learning
- structural basis
- municipal solid waste
- life cycle