Fabrication and Characterization of Piezoelectric PVDF-TrFE Sensor Fabricated Using Spin Coating Method for Biomedical Device Applications
Bibliographic record
Abstract
Minimally Invasive Surgery (MIS) has been considered and received more favor since the last two decades by the physicians and researchers rather than traditional open surgery due to better healing conditions after the surgery, less physical pain, and faster recovery process. There are various types of force/pressure sensor technologies in the market. However, the fittest one is the piezoelectric transduction principle for the MIS’s devices like endoscopic grasper or biomedical insertion catheter. A common design and fabrication technique for these kinds of sensors is using PVDF with its copolymer, TrFE, as a piezoelectric material due to its biocompatibility and ease of implantation. In this study, PVDF-TrFE polymer is deposited between two aluminum electrodes via spin-coating method and gone under different post-fabrication processes in order to examine its piezoelectricity and amount of electroactive $\beta$-phase. These processes investigate the effect of various post thermal annealing temperatures, different number of layers of polymer deposition, and the effect of spin-coating speed. This research clearly presents an effective way to fabricate a PVDF-TrFE based tactile sensor and some enhancement techniques according to the author’s novel experimental design to obtain higher $\beta$-phase and, consequently, piezoelectric constant to get a better sense of touch at the end effector of biomedical devices.
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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 | 0.001 | 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".