From Single to Multi‐Material 3D Printing of Glass‐Ceramics for Micro‐Optics
Bibliographic record
Abstract
Feynman's statement, "There is plenty of room at the bottom", underscores vast potential at the atomic scale, envisioning microscopic machines. Today, this vision extends into 3D space, where thousands of atoms and molecules are volumetrically patterned to create light-driven technologies. To fully harness their potential, 3D designs must incorporate high-refractive-index elements with exceptional mechanical and chemical resilience. The frontier, however, lies in creating spatially patterned micro-optical architectures in glass and ceramic materials of dissimilar compositions. This multi-material capability enables novel ways of shaping light, leveraging the interaction between diverse interfaced chemical compositions to push optical boundaries. Specifically, it encompasses both multi-material integration within the same architectures and the use of different materials for distinct architectural features in an optical system. Integrating fluid handling systems with two-photon lithography (TPL) provides a promising approach for rapidly prototyping such complex components. This review examines single and multi-material TPL processes, discussing photoresin customization, essential physico-chemical conditions, and the need for cross-scale characterization to assess optical quality. It reflects on challenges in characterizing multi-scale architectures and outlines advancements in TPL for both single and spatially patterned multi-material structures. The roadmap provides a bridge between research and industry, emphasizing collaboration and contributions to advancing micro-optics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".