3D Co‐Printing of 3D Electronics with a Dual Light Source Technology
Bibliographic record
Abstract
Abstract This study reports a new strategy—3D co‐printing technology by collaboratively employing a dual‐light curing process—to fabricate 3D electronics selectively either deposited on a free‐form surface or embedded within a bulk structure. The 3D co‐printing technology only involves one printing material, which works for both the construction of polymeric structures and the metallization process of conductive circuits resulted from the metal precursor loaded in photosensitive resin. The photoreduction of metal nanoparticles (NPs) can be efficiently activated with a second laser scanning. 3D co‐printing technology addresses the challenge of conventional methods requiring multiple materials deposition processes. The resultant conductive trace composed of NPs exhibits a superior resistivity as low as ≈6.12 µΩ m. The resistivity is further controllable ranging from 10−6 to 10 Ω m through adjusting the material formulation and processing parameters. This study has demonstrated that the proposed 3D co‐printing is a high‐efficiency and low‐cost co‐printing approach which opens a new avenue of making 3D electronics.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".