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Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.
The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.
A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.
This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.
Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.
Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.
The first wall and blanket of a fusion energy reactor must maintain structural integrity and performance over long operational periods under neutron irradiation and minimize long-lived radioactive waste.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.