Direct ink writing of 3D conductive polyaniline structures and rheological modelling
Bibliographic record
Abstract
Abstract The intractable nature of conjugated polymers (CP) leads to practical limitations in the fabrication of CP-based transducers having complex three-dimensional geometries. Conventional CP device fabrication processes have focused primarily on thin-film deposition techniques; this study explores novel additive manufacturing processes specifically developed for CP with the ultimate goal of increasing the functionality of CP 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 was enabled through a modified fused filament fabrication delta robot equipped with an integrated polymer paste extruder to fabricate high-resolution 3D conductive PANI structures. The required processability of PANI was achieved by means of a counterion-induced thermal doping method. The effect of thermal doping on the PANI-DBSA paste by means of a constitutive relationship to describe the paste flow as a function of the thermal doping time is explored. This relationship is incorporated within a flow model to predict the extruded track width as a function of various process parameters including: print speed, gauge pressure, nozzle diameter, and pre-extrusion thermal doping time.
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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) | 0.000 | 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".