3D printing of metallic structures using dopamine-integrated photopolymer
Bibliographic record
Abstract
After 3D printing technology has been advancing for several years, it still faces a huge challenge to fabricate 3D metal structures with high-resolution. In this study, a facile strategy of making high-resolution metallic structures based on the bioinspired 3D printing method was proposed. A bioinspired initiator, dopamine, was mixed into a 3D printing photopolymer, which could be conducive to fabricating metallic structures after direct surface-initiated electroless plating (ELP). Because of the intrinsic high resolution of the structures printed with the digital light processing (DLP) technology, complex objects with a feature resolution on the order of 137 μm were fabricated. Moreover, Cu- and Ni-parts of complex structures with a high resolution were demonstrated. By combining the advantages of 3D printing in structure design with those of surface modification assisted by catechol groups, the proposed method was confirmed as a cost-effective method to significantly enhance the capability of 3D printing, and it could endow the 3D printing technology with more practical applications in electronics, acoustic absorption, catalyst supports and other fields.
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 | 0.001 |
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".