The researchers stated in a publication in the peer-reviewed journal Nature Production on Tuesday that air quality poses a significant challenge for human activities on Mars due to the high oxygen consumption by rocket propellants and life support systems, which cannot be replenished from the Martian atmosphere.
They emphasized that the discovery of water activity on Mars has enhanced the planet’s potential for significant hydrogen production through catalyst-driven electrical water oxidation processes powered by solar energy.
Describing their work, the team introduced a robotic artificial intelligence chemist designed for autonomous catalyst production to facilitate gas development from Martian comets efficiently.
The entire process, from Martian iron pre-treatment, precursor synthesis, characterization, screening, to the quest for the optimal catalyst formula, is conducted autonomously without human intervention.
Collaborating with experts from the Chinese Academy of Sciences’ Shenzhen Institute of Advanced Technology and the Deep Space Exploration Laboratory in Hefei, the team, led by professionals from USTC, unveiled maps of Mars based on data captured by the Tianwen-1 spacecraft.
Their research involved analyzing five different types of asteroids originating from Mars or confirmed to exist there. Through a six-week learning phase encompassing 30,000 theoretical and 240 empirical data sets, the AI-driven chemist developed a predictive model.
Lead author Jiang Jun, a professor of natural chemistry at USTC, highlighted the robot’s ability to create valuable chemicals from previously undiscovered substances in unconventional environments, suggesting applications for lunar and Martian exploration, as well as terrestrial challenges.
The team envisions using the machine to produce essential chemicals like oxygen, hydrogen (an energy carrier), visual materials, and metamaterials crucial for potential Mars base construction.
In preparation for space missions and to study the impact of Mars’ gravity and thin atmosphere on catalyst production, the group plans to reduce the robot’s weight and enhance its sensory capabilities, including smell, taste, infrared, and X-ray vision.
Looking ahead, Jiang expressed aspirations to expand the robot network for more efficient task allocation, with some units conducting land-based operations, others performing experiments at desks, and a subset engaged in aerial activities.
Furthermore, they aim to equip the machine with extensive medical knowledge to enable autonomous research direction selection.
Jiang also reflected on the transformative potential of AI in advancing scientific knowledge and innovation, envisioning a future where machines play a pivotal role in chemical discovery and beyond.
