Electric field-modulated evaporative thin film deposition of bio-particles for piezoelectric applications
Bibliographic record
Abstract
Bio-based functional materials can be used to replace or limit the use of synthetic materials sourced from unsustainable sources. However, the potential of such materials remains largely unexplored. In this study, we demonstrate the use of weak AC electric fields to deposit ultra-thin piezoelectric films from cellulose nanocrystals (CNC). This is the first time electric fields are used to realize <50 nm thick uniform bio-based piezoelectric films wherein the bioparticles exhibit unidirectional arrangement. Interestingly, we found that the use of weak AC electric fields of suitable frequencies completely mitigates the coffee ring effect (CRE), which results in defect-free uniform ultra-thin films. Additionally, the electric fields appear to help in realizing unidirectional alignment of particles in the films, which enhances their piezoelectric properties. The method was also tested for chitin nanocrystals (ChNC), which have a similar aspect ratio but bear opposite polarity surface charges, and the influence of the field on coffee ring formation and particle orientation in CNC thin film deposition was validated. The phenomena can be attributed to the constant spatio-temporal curvature of the evaporating liquid film, the transient state between the three-phase contact (TPC) line, the electric field-dependent contact angle, and the permanent and field-induced dipole moments. These factors lead to particle polarization and alignment. The films have an optimum electrical frequency of deposition at which they are continuous and uniformly thin, have unidirectional alignment of particles, and function as a single dipole.
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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 | 0.000 | 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".