Comprehensive Characterization of Solution-Cast Pristine and Reduced Graphene Oxide Composite Polyvinylidene Fluoride Films for Sensory Applications
Bibliographic record
Abstract
Pristine and doped polyvinylidene fluoride (PVDF) are actively investigated for a broad range of applications in pressure sensing, energy harvesting, transducers, porous membranes, etc. There have been numerous reports on the improved piezoelectric and electric performance of PVDF-doped reduced graphene oxide (rGO) structures. However, the common in situ doping methods have proven to be expensive and less desirable. Furthermore, there is a lack of explicit extraction of the compression mode piezoelectric coefficient (d33) in ex situ rGO doped PVDF composite films prepared using low-cost, solution-cast processes. In this work, we describe an optimal procedure for preparing high-quality pristine and nano-composite PVDF films using solution-casting and thermal poling. We then verify their electromechanical properties by rigorously characterizing β-phase concentration, crystallinity, piezoelectric coefficient, dielectric permittivity, and loss tangent. We also demonstrate a novel stationary atomic force microscope (AFM) technique designed to reduce non-piezoelectric influences on the extraction of d33 in PVDF films. We then discuss the benefits of our d33 measurements technique over commercially sourced piezometers and conventional piezoforce microscopy (PFM). Characterization outcomes from our in-house synthesized films demonstrate that the introduction of 0.3%w.t. rGO nanoparticles in a solution-cast only marginally changes the β-phase concentration from 83.7% to 81.7% and decreases the crystallinity from 42.4% to 37.3%, whereas doping increases the piezoelectric coefficient by 28% from d33 = 45 pm/V to d33 = 58 pm/V, while also improving the dielectric by 28%. The piezoelectric coefficients of our films were generally higher but comparable to other in situ prepared PVDF/rGO composite films, while the dielectric permittivity and β-phase concentrations were found to be lower.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".