Emulsion Electrospinning of Polytetrafluoroethylene (PTFE) Nanofibrous Membranes for High-Performance Triboelectric Nanogenerators
Bibliographic record
Abstract
Electrospinning is a simple, versatile technique for fabricating fibrous nanomaterials with the desirable features of extremely high porosities and large surface areas. Using emulsion electrospinning, polytetrafluoroethylene/polyethene oxide (PTFE/PEO) membranes were fabricated, followed by a sintering process to obtain pure PTFE fibrous membranes, which were further utilized against a polyamide 6 (PA6) membrane for vertical contact-mode triboelectric nanogenerators (TENGs). Electrostatic force microscopy (EFM) measurements of the sintered electrospun PTFE membranes revealed the presence of both positive and negative surface charges owing to the transfer of positive charge from PEO which was further corroborated by FTIR measurements. To enhance the ensuing triboelectric surface charge, a facile negative charge-injection process was carried out onto the electrospun (ES) PTFE subsequently. The fabricated TENG gave a stabilized peak-to-peak open-circuit voltage ( V oc ) of up to ∼900 V, a short-circuit current density ( J sc ) of ∼20 mA m –2, and a corresponding charge density of ∼149 μC m –2, which are ∼12, 14, and 11 times higher than the corresponding values prior to the ion-injection treatment. This increase in the surface charge density is caused by the inversion of positive surface charges with the simultaneous increase in the negative surface charge on the PTFE surface, which was confirmed by using EFM measurements. The negative charge injection led to an enhanced power output density of ∼9 W m –2 with high stability as confirmed from the continuous operation of the ion-injected PTFE/PA6 TENG for 30 000 operation cycles, without any significant reduction in the output. The work thus introduces a relatively simple, cost-effective, and environmentally friendly technique for fabricating fibrous fluoropolymer polymer membranes with high thermal/chemical resistance in TENG field and a direct ion-injection method which is able to dramatically improve the surface negative charge density of the PTFE fibrous membranes.
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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".