Shaping the next-generation of fused deposition modeling three-dimensional-printing-based electrochemical (bio)sensing: Drawing a realistic horizon
Bibliographic record
Abstract
The emergence of 3D-printing as a fabrication tool has revolutionized the development of customized electrochemical (bio)sensors offering exceptional design flexibility, cost-effective and rapid prototyping. Among the additive manufacturing technologies, Fused Deposition Modeling (FDM) stands out for its affordability, ease of use, and the growing availability of conductive filaments, providing a new approach to produce tailored electrodes with an enormous analytical potential and capabilities. This perspective presents a critical overview of the current opportunities and limitations of FDM-3D-printing as a technology for the design and development of electrochemical (bio)sensors, addressing material formulation, electrode architecture, surface modification strategies, analytical performance and emerging applications. Current challenges and directions to overcome them are identified and discussed, drawing a realistic horizon for the next generation of FDM-3D-printed electrochemical (bio)sensors.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".