Recycling of waste aluminum/magnesium metal scrap into useful Al‐ZrO<sub>2</sub> alloy composite for eco‐friendly structural applications
Bibliographic record
Abstract
Abstract Fabrication industries are emerging to reduce and recycle waste scrap materials into useful products for various engineering applications such as domestic, structural, marine, and construction doors and windows. Most waste scrap materials are affected by soil and water pollution, resulting in unsuitable environmental living. This study is to fabricate the low‐cost and eco‐friendly Al‐ZrO2 alloy composite made with waste Al/Mg metal scrap microparticles through stir casting technique and the developed composite with 0 wt%, 5 wt%, 10 wt%, and 15 wt% of ZrO2 were studied its mechanical properties (ASTM). The synthesized composites' mechanical tensile strength, impact, and hardness were evaluated by ASTM test standard. The revealed experimental results were compared and an optimum sample was addressed. The optical micrograph studies revealed the metal particle presence. The sample 4 composite contained 15 wt% of ZrO2 particles and showed superior mechanical properties like 7.7%, 57.7%, and 13.66% improvement in tensile strength, impact toughness, and hardness compared to sample 1 without ZrO2 particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".