Development of ultralight, super-elastic, hierarchical metallic meta-structures with i3DP technology
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Bibliographic record
Abstract
Abstract Lightweight and mechanically robust materials show promising applications in thermal insulation, energy absorption, and battery catalyst supports. This study demonstrates an effective method for creation of ultralight metallic structures based on initiator-integrated 3D printing technology (i3DP), which provides a possible platform to design the materials with the best geometric parameters and desired mechanical performance. In this study, ultralight Ni foams with 3D interconnected hollow tubes were fabricated, consisting of hierarchical features spanning three scale orders ranging from submicron to centimeter. The resultant materials can achieve an ultralight density of as low as 5.1 mg cm −3 and nearly recover after significant compression up to 50%. Due to a high compression ratio, the hierarchical structure exhibits superior properties in terms of energy absorption and mechanical efficiency. The relationship of structural parameters and mechanical response was established. The ability of achieving ultralight density <10 mg cm −3 and the stable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mi>E</mml:mi> <mml:mo stretchy="true">¯</mml:mo> </mml:mover> <mml:mo>∼</mml:mo> <mml:msup> <mml:mrow> <mml:mover accent="true"> <mml:mi>ρ</mml:mi> <mml:mo stretchy="true">¯</mml:mo> </mml:mover> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> scaling through all range of relative density, indicates an advantage over the previous stochastic metal foams. Overall, this initiator-integrated 3D printing approach provides metallic structures with substantial benefits from the hierarchical design and fabrication flexibility to ultralight applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it