Negative Thermal Expansion Metamaterials: A Review of Design, Fabrication, and Applications
Bibliographic record
Abstract
Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".