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- Amit K Naskar
- Jaswinder Sharma
- Logan Kearney
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- Nihal Kanbargi
- Arit Das
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- Debangshu Mukherjee
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- Santanu Roy
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- Uvinduni Premadasa
- Vera Bocharova
- Xiaobing Liu

Efficient thermal management in polymers is essential for developing lightweight, high-strength materials with multifunctional capabilities.

The disclosure is directed to optimized fiber geometries for use in carbon fiber reinforced polymers with increased compressive strength per unit cost. The disclosed fiber geometries reduce the material processing costs as well as increase the compressive strength.

A novel and cost-effective process for the activation of carbon fibers was established.
Contact
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

ORNL contributes to developing the concept of passive CO2 DAC by designing and testing a hybrid sorption system. This design aims to leverage the advantages of CO2 solubility and selectivity offered by materials with selective sorption of adsorbents.

Moisture management accounts for over 40% of the energy used by buildings. As such development of energy efficient and resilient dehumidification technologies are critical to decarbonize the building energy sector.

The invention addresses the long-standing challenge of inorganic phase change materials use in buildings envelope and other applications by encapsulating them in a secondary sheath.

The technologies described herein provides for the High Temperature Carbonization (HTC) in the manufacturing of carbon fibers (CF). The conventional method for HTC is based in thermal radiation and this technology uses in a liquid medium.

The widespread use of inexpensive salt hydrate-based phase change materials, or PCMs, has been prevented by a key technical challenge: phase separation, also known as incongruency, which results in the significant degradation of the materials' ability to store thermal energy o

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.