Advancing Tomographic Volumetric Printing Via Oxygen Inhibition Control: Improved Accuracy and Large‐Volume Capability
Bibliographic record
Abstract
Tomographic volumetric additive manufacturing (TVAM) is an emerging 3D printing technology capable of producing complex structures in seconds. However, achieving reliable prints using TVAM requires sufficient light penetration throughout the print volume, which often limits the photoinitiator (PI) concentration that can be used. In (meth)acrylate-based photoresins, this constraint severely restricts achievable print size and quality due to oxygen inhibition. To address this challenge, a chemical strategy is demonstrated to control the oxygen inhibition period without compromising light penetration, using an amine, a thiol, and a phosphine additive as representative examples. Among these, N-methyldiethanolamine (MDEA) emerged as the most promising candidate, effectively reacting with non-reactive peroxy radicals to regenerate propagating radicals and sustain polymerization. Incorporating MDEA into a low-PI photoresin enabled high-resolution and large-volume printing in a custom-built TVAM system, achieving a root-mean-square surface deviation of 0.175 mm (≈2 pixels) and printable structure sizes up to 60 mm. These advances represent a 16-fold increase in print volume relative to the previous TVAM demonstrations and enable high-throughput fabrication of multiple complex parts without sacrificing print quality. This work establishes a scalable approach to overcoming oxygen inhibition in (meth)acrylate TVAM systems, unlocking new possibilities for large-volume, high-resolution additive manufacturing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".