Micro/nanoscale electrohydrodynamic printing: from 2D to 3D
Bibliographic record
Abstract
Electrohydrodynamic printing (EHDP), based on the electrohydrodynamically induced flow of materials, enables the production of micro/nanoscale fibers or droplets and has recently attracted extensive interest to fabricate user-specific patterns in a controlled and high-efficiency manner. However, most of the existing EHDP techniques can only print two-dimensional (2D) micropatterns which cannot meet the increasing demands for the direct fabrication of three-dimensional (3D) microdevices. The integration of EHDP techniques with the layer-by-layer stacking principle of additive manufacturing has emerged as a promising solution to this limitation. Here we present a state-of-the-art review on the translation of 2D EHDP technique into a viable micro/nanoscale 3D printing strategy. The working principle, essential components as well as critical process parameters for EHDP are discussed. We highlight recent explorations on both solution-based and melt-based 3D EHDP techniques in cone-jet and microdripping modes for the fabrication of multimaterial structures, microelectronics and biological constructs. Finally, we discuss the major challenges as well as possible solutions with regard to translating the 3D EHDP process into a real micro/nanoscale additive manufacturing strategy for the freeform fabrication of 3D structures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".