Enhanced Piezoelectric Nanogenerator Based on Tridoped Graphene and Ti<sub>3</sub>CNT<sub><i>x</i></sub> MXene Quasi-3D Heterostructure
Bibliographic record
Abstract
The demand for self-powered wearables is surging, as consumers seek convenience and portability. Energy-harvesting technologies, especially piezoelectric nanogenerators (PENGs), which convert mechanical energy to electrical energy, hold promise for harvesting human motion energy. Hence, ongoing research aims to enhance the output power efficiency and integrate nanogenerators with flexible materials. This involves material innovation to boost PENG performance, optimizing structure for flexibility, and improving manufacturing for scalable and cost-effective production. In this study, heterostructure nanofiller based on interfacial interaction was formed by mixing nitrogen, sulfur, and phosphorus tridoped graphene (NSPG) and Ti 3 CNT x MXene in an appropriate ratio, which produces a synergistic enhancement effect in the PENG’s electrical output performance. According to X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, X-ray diffractometer (XRD), and Fourier transform infrared spectroscopy (FTIR) chemical characterization analysis, it is proposed that the excellent conductivity and rich surface functional groups of these two-dimensional materials can effectively provide heterointerfaces to form a quasi-three-dimensional heterostructure and improve the interaction between the fillers and polymer matrix, promoting the electroactive β-phase, and consequently enhancing the output power density of PENG. NSPG and Ti 3 CNT x, with their remarkable electronic and chemical properties, were prepared using an environmentally friendly electrochemical exfoliation method. The short-circuit current of PENG can be improved to 1.48 μA, and the open-circuit voltage can be increased to 14.6 V, 5-fold compared to pure PVDF, and the output power density, P A, reaches 2.2 μW/cm 2 . When attached to different parts of the human body, the PENG can practically produce electrical signals, which can be rectified using a full-wave bridge rectifier and used to charge a capacitor and light up LEDs. This study establishes a robust connection between multifaceted heterostructures and flexible wearable energy harvesters, offering promising prospects for advancing flexible, sensitive, and self-powered electronics.
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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) | 0.000 | 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 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".