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Kaio Labs

Aionics develops an autonomous materials discovery platform that uses robotics and artificial intelligence to accelerate the development of electrocatalysts for carbon conversion. This platform enables rapid catalyst screening and performance optimization, helping industries operate efficiently even with limited data.

Newark, United States510+ followers
Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

The development of efficient electrocatalysts for carbon conversion is a slow and resource-intensive process, hindering the widespread adoption of CO₂-to-chemicals technology. Traditional methods lack the speed and precision needed to optimize catalyst performance effectively. This bottleneck limits the potential for converting carbon dioxide into valuable fuels and chemicals on a large scale.

Solution

kaıolabs offers an AI-powered autonomous platform that accelerates the discovery and optimization of electrocatalysts for CO₂ conversion. The platform integrates catalyst design, robotic synthesis, parallel experimentation, and data analysis into a closed-loop automated workflow. By combining artificial intelligence, robotics, and electrochemistry, kaıolabs significantly reduces the development cycle time for electrolyzers. This approach enables the creation of high-performance electrolyzers that transform CO₂ into valuable products like syngas and ethylene, key building blocks for sustainable fuels and chemicals. The platform's modular architecture ensures compatibility with various CO₂ sources and reaction types, facilitating the transition to a circular carbon economy.

Target Audience

The primary customers are companies in the sustainable fuels, green chemicals, and sustainable polymers industries seeking to efficiently convert CO₂ into valuable products.

Features

  • AI-driven catalyst design with multi-objective optimization
  • Automated robotic synthesis with precision control
  • Parallel experimentation under industry-relevant conditions
  • Data-driven insights feeding back into the AI system
  • 40x faster development cycles compared to traditional methods
  • Compatible with any source of CO₂ and reaction agnostic
  • Architecture miniaturizes industrial conditions
This profile is AI-generated and may contain inaccuracies.