Characterization of 3D Printed Thermoplastic Polyurethane and Lamp Black Electrodes Towards Bioanalytical Electrochemical Sensing
Bibliographic record
Abstract
3D printing electrodes using fused deposition modelling has emerged as an attractive fabrication process for electrodes enabling the rapid manufacture of complex geometry in a repeatable and low-cost manner. In this work, we report 3D printed electrodes fabricated from a commercial conductive thermoplastic polyurethane and lamp black filament. 3D printed electrodes comprising of a thermoplastic polyurethane matrix with carbon black, carbon nanotubes or graphene have typically been used to develop piezoresistive sensors, flexible strain sensors and electrodes for lithium-ion batteries. To the best of our knowledge, thermoplastic polyurethane and carbon black electrodes for electrochemical analysis are yet to be explored towards biocatalytic and affinity-based biorecognition sensors. Here we report fabrication and surface treatment methods of 3D printed electrodes using a commercial lamp black filled thermoplastic polyurethane filament. Surface treatment was used to activate the electrodes, cyclic voltammetry for electrochemical activation and electrochemical impedance spectroscopy to deconvolute individual processes with varied time constants.
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 | 0.001 |
| 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 | 0.001 | 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".