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This article developes a pediatric membrane oxygenator that is compact, high performance, and highly safe. This novel experimental approach, which imaging the inside of a membrane oxygenator during fluid perfusion using high-power X-ray CT, identifies air and blood retention in the local part of a membrane oxygenator. The cause of excessive pressure drop in a membrane oxygenator, which has been the most serious dysfunction in cardiovascular surgery and extracorporeal membrane oxygenation (ECMO), is the local retention of blood and air inside the oxygenator. Our designed blood flow channel for a membrane oxygenator has a circular channel and minimizes the boundary between laminated parts. The pressure drop in the blood flow channel is reduced, and the maximum gas transfer rates are increased by using this pediatric membrane oxygenator, as compared with the conventional oxygenator. Furthermore, it would be possible to reduce the incidents, which have occurred clinically, due to excessive pressure drop in the blood flow channel of the membrane oxygenator. The membrane oxygenator is said to be the “last stronghold” for patients with COVID-19 receiving ECMO treatment. Accordingly, the specification of our prototype is promising for low weight and pediatric patients.

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Due to the increase in material databases in recent years, there has been a lot of research regarding deep learning models which use large sizes of datasets and are aimed at the prediction of the material properties of inorganic compounds. Particularly, prediction models with Self-Attention structures, such as Roost and CrabNet, have garnered attention because of two reasons: (1) input variables are confined to the chemical composition of each formula and (2) Self-Attention enables models to learn individual element representations based on their chemical environment. However, the existing Self-Attention model yields low prediction accuracy when predicting structure-dependent material properties, such as the magnetic moment, for lack of structural information of compounds as input. In this research, based on the existing Self-Attention model, we set both elemental and structural information, especially the space group number and lattice constant, as input information and successfully construct a prediction model that is more versatile than existing methods. Furthermore, we visualized lists of promising materials by adopting Bayesian optimization. As a result, we have developed a system to propose desired materials for materials researchers.

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