Shockwave consolidation of nano silver powder into bulk nano structured silver
Bibliographic record
Abstract
Bulk nanostructured silver components were fabricated from nano-sized powder using a shockwave consolidation technique. The grain size evolution during compaction, the mechanical properties of the bulk components, and the effect of surface finish on the mechanical behavior were studied. X-Ray diffraction, transmission electron microscopy (TEM), atomic force microscopy (AFM), microhardness, compression testing and shear punch testing at room temperature were used to characterize the materials. Upon consolidation, the average grain size calculated from image analysis of the TEM micrographs was 49+/-22 nm, showing the feasibility of maintaining a nanostructure upon dynamic consolidation. The hardness of the bulk nanostructured components was constant across the diameter with an average of 83+/-1 HV. Compression results showed strength about 390+/-10 MPa and ductility of 23+/-2%, which is well above strength level obtainable from strain hardened Ag components. The AFM results show that samples possessing a surface roughness of 267 nm exhibited a brittle behavior and a reduction in strength of 35% when compared to the smoother surfaces. Dimples were observed for the samples exhibiting plasticity, while an intergranular pattern was identified for the brittle materials. Fracture toughness of 0.2 MPa m was calculated, which confirms the strong relationship between fracture toughness and defects observed in nanomaterials.
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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".