Robotic extrusion processes for direct ink writing of 3D conductive polyaniline structures
Bibliographic record
Abstract
The intractable nature of intrinsically conductive polymers (ICP) leads to practical limitations in the fabrication of ICP-based transducers having complex three-dimensional geometries. Conventional ICP device fabrication processes have focused primarily on thin-film deposition techniques; therefore this study explores novel additive manufacturing processes specifically developed for ICP with the ultimate goal of increasing the functionality of ICP sensors and actuators. Herein we employ automated polymer paste extrusion processes for the direct ink writing of 3D conductive polyaniline (PANI) structures. Realization of these structures is enabled through a modified fused filament fabrication delta robot equipped with an integrated polymer paste extruder. This unique robot-controlled additive manufacturing platform is capable of fabricating high-resolution 3D conductive PANI and has been utilized to produce structures with a minimum feature size of 1.5 mm. The required processability of PANI is achieved by means of a counter-ion induced thermal doping method. Using this method, a viscous paste is formulated as the extrudate and a thermo-chemical treatment is applied post extrusion to finalize the complexation.
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 | 0.000 |
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".