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Investigating Oxygen Content in Poro...

O'Brien, Maeve.

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Investigating Oxygen Content in Porous Transport Layers of PEM Electrolyzers with Operando X-Ray Computed Tomography and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigating Oxygen Content in Porous Transport Layers of PEM Electrolyzers with Operando X-Ray Computed Tomography and Machine Learning./
作者:	O'Brien, Maeve.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
標題:	Chemical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997132
ISBN:	9798662412330

Investigating Oxygen Content in Porous Transport Layers of PEM Electrolyzers with Operando X-Ray Computed Tomography and Machine Learning.
O'Brien, Maeve.

Investigating Oxygen Content in Porous Transport Layers of PEM Electrolyzers with Operando X-Ray Computed Tomography and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 59 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--University of California, Irvine, 2020.

This item must not be sold to any third party vendors.

Hydrogen gas is a good candidate to replace traditional, carbon-based energy carriers; it is easily transported, and many vehicles already utilize hydrogen-based fuel cells to power locomotion. To produce hydrogen gas, water electrolysis is the go-to electrochemical process. Although water electrolysis is a well-understood process, the morphological and transport processes inherent to a proton exchange membrane (PEM) electrolyzer and how these affect the efficiency of water electrolysis in a PEM electrolyzer are still relatively unexplored. In order to probe these processes in-situ, x-ray computed tomography and radiography were performed on catalyst coated membranes(CCM) of a model electrolyzer at different flowrates and current densities to determine how these different operating conditions alter the oxygen production, slug or bubble formation, and dwell time in the porous transport layer(PTL). X-ray computed tomography was utilized to create 3D images, and radiography was utilized to visualize transient oxygen production and dwell time in-situ. Machine learning was used to quantify the oxygen content in the model electrolyzer, as well as any pathways or patterns the oxygen took as it exited the electrolyzer through the PTL. From these results there was a direct observation of oxygen taking preferential pathways through the PTL regardless of the flow rate or current density, and at higher current densities more oxygen became trapped beneath the land. This is the first time this phenomenon has been directly observed with x-ray computed tomography and is in good agreement with recent neutron radiography experiments using Ti PTLs.

ISBN: 9798662412330Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Electrolyzer

Investigating Oxygen Content in Porous Transport Layers of PEM Electrolyzers with Operando X-Ray Computed Tomography and Machine Learning.
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520 $a Hydrogen gas is a good candidate to replace traditional, carbon-based energy carriers; it is easily transported, and many vehicles already utilize hydrogen-based fuel cells to power locomotion. To produce hydrogen gas, water electrolysis is the go-to electrochemical process. Although water electrolysis is a well-understood process, the morphological and transport processes inherent to a proton exchange membrane (PEM) electrolyzer and how these affect the efficiency of water electrolysis in a PEM electrolyzer are still relatively unexplored. In order to probe these processes in-situ, x-ray computed tomography and radiography were performed on catalyst coated membranes(CCM) of a model electrolyzer at different flowrates and current densities to determine how these different operating conditions alter the oxygen production, slug or bubble formation, and dwell time in the porous transport layer(PTL). X-ray computed tomography was utilized to create 3D images, and radiography was utilized to visualize transient oxygen production and dwell time in-situ. Machine learning was used to quantify the oxygen content in the model electrolyzer, as well as any pathways or patterns the oxygen took as it exited the electrolyzer through the PTL. From these results there was a direct observation of oxygen taking preferential pathways through the PTL regardless of the flow rate or current density, and at higher current densities more oxygen became trapped beneath the land. This is the first time this phenomenon has been directly observed with x-ray computed tomography and is in good agreement with recent neutron radiography experiments using Ti PTLs.
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