Femtosecond laser micromachining of polyvinylidene fluoride (PVDF) based piezo films
Bibliographic record
Abstract
Piezoelectric polymers have been known to exist for more than 40 years, but in recent years they have been recognized as smart materials for the fabrication of microsensors, microactuators and other micro-electro-mechanical systems (MEMS). In this work, femtosecond laser micromachining of a polyvinylidene fluoride (PVDF) film, coated with NiCu on both sides, has been studied to understand selective patterning mechanisms of NiCu layers and ablation characteristics of PVDF films. A detailed characterization of morphological changes of the laser-irradiated areas has been investigated using scanning electron microscopy. Through morphological analysis, the multiple shot damage thresholds of a 28 µm thick PVDF film and 40 nm thick NiCu layer have been determined. Surface morphology examination indicates that NiCu layers are removed from the PVDF film through a sequence of cracking–peeling off-curling. In addition, the NiCu layer on the rear side was also removed by the partially transmitted laser energy. The PVDF film was removed in forms of bundles of filaments and solid fragments by a combination of pure ablation and explosive removal of material by bursting of bubbles; the role of the explosive removal becomes more dominant with the increase of laser fluence. Optimal process conditions for cutting of the PVDF film and patterning of the NiCu coating without damaging the PVDF polymer have been established and applied to fabricate a vibration microsensor prototype that shows significant potential in using PVDF-based functional microdevices for telecommunications, transportation and biomedical applications.
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) | 0.002 | 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".