Additive manufacturing of smart <scp>3D</scp> ‐printed radiation shielding materials—An innovation in recent times
Bibliographic record
Abstract
Abstract Advancements in 3D‐printed radiation shielding materials have ushered in a new era of radiation protection, characterized by enhanced efficiency, accuracy, and personalization. The use of additive manufacturing technology in creating shielding materials against electromagnetic interference (EMI), gamma rays, neutrons, and X‐rays is well covered in this article. An overview of additive manufacturing and the basic ideas of radiation and shielding are covered first. This article also highlights the various types of 3D printing materials and technologies used to create radiation shielding components, including metal composites, polymers, and hybrid materials. The benefits of 3D printing are highlighted, including the ability to create intricate designs that enhance shielding effectiveness while using less weight and material. This paper also highlights the development of intelligent, multipurpose shielding structures tailored for specific applications, summarizing significant scientific advancements in the field. The conclusion highlights the potential of additive manufacturing to transform radiation shielding in the electronic, nuclear, aerospace, and medical sectors while outlining present issues and anticipated future developments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".