Laser Powder Bed Fusion Additive Manufacturing of Mo and TZM Exoskeleton with Cu Infiltration for New Heat Sinks Configuration
Bibliographic record
Abstract
This study reports the fabrication and characterization of molybdenum (Mo) metal and titanium‐zirconium‐molybdenum (TZM) alloy exoskeletons with honeycomb cavity structures (HCS) that are infiltrated with oxygen‐free high conductivity (OFHC) Cu under an inert atmosphere as a potential replacement for Cu‐Mo‐Cu laminate in heat sink applications for power electronics semiconductors like GaAs. The thermal expansion behavior and the thermal loading of the starting Mo and TZM structures, and of the Cu‐infiltrated parts are evaluated. The fabricated Mo and TZM structures with density >99% and Mo‐based heat sinks with improved CTE (6.6 × 10−6 K−1) when compared to conventional Cu‐Mo‐Cu laminated heat sinks (CTE = 7.6 × 10−6 K−1). The new Mo and TZM structures promise superior performance due to their closer CTE to that of GaAs and similar semiconductors (CTE = 5.7 × 10−6 K−1). Exposure to temperatures up to 1073 K did not affect the Mo microstructure due to the inherent resistance to recrystallization, while exposure to 1373 K did reduce hardness. In contrast, TZM exoskeletons showed resistance to recrystallization even at 1373 K. The fabricated composite heat sinks showed thermal diffusivity (≈61 × 106 m2 s−1) that is within the upper limits of those reported for commercial laminated heat sinks (45 to 65 × 106 m2 s−1).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".