Deuterium retention and erosion resistance of low-pressure plasma sprayed tungsten coatings
Source abstract
Abstract
Low-pressure plasma-sprayed (LPPS) tungsten coatings of 300 μm thickness were produced with optimized production parameters to demonstrate the feasibility of the process as a repair technique for eroded or damaged plasma-facing components. The samples were, in two separate experiments, exposed to Neon (Ne) and Deuterium (D) plasma to investigate the sputtering resistance and the deuterium retention, and compare the performance to similarly exposed reference bulk tungsten samples. After applying a total fluence of 2⋅10 24 ions/m 2 s Ne ions with an ion impact energy of 100 eV at a sample temperature of 60 °C, the LPPS coatings showed a 0% to 20% higher sputtering yield than the similarly exposed reference bulk tungsten, indicating a slightly decreased performance. The deuterium plasma was characterized by 73 eV ions, and the exposure was run at a sample temperature of 250 °C, until the plasma fluence was 3⋅10 25 ions/m 2 s. The deuterium retention, which was evaluated using nuclear reaction analysis as well as thermal desorption spectroscopy, demonstrated an enhanced performance of the LPPS coatings, indicated by a lower total D inventory potentially by outgassing. In addition, the spectra indicated a D desorption at lower temperatures than that for pure bulk tungsten for the LPPS coatings and a significant impact of the LPPS process parameters on the grain structure and, therefore, the retention performance. • Plasma experiments of optimized low-pressure plasma-sprayed tungsten coatings. • Slightly lower sputtering resistance compared to bulk tungsten. • Lower D retention compared to bulk tungsten. • Suitable overall performance of LPPS coatings as repaired PFM.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Fusion materials and technologies · Ion-surface interactions and analysis · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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