High-Performance Supercapacitors from Niobium Nanowire Yarns
Bibliographic record
Abstract
The large-ion-accessible surface area of carbon nanotubes (CNTs) and graphene sheets formed as yarns, forests, and films enables miniature high-performance supercapacitors with power densities exceeding those of electrolytics while achieving energy densities equaling those of batteries. Capacitance and energy density can be enhanced by depositing highly pseudocapacitive materials such as conductive polymers on them. Yarns formed from carbon nanotubes are proposed for use in wearable supercapacitors. In this work, we show that high power, energy density, and capacitance in yarn form are not unique to carbon materials, and we introduce niobium nanowires as an alternative. These yarns show higher capacitance and energy per volume and are stronger and 100 times more conductive than similarly spun carbon multiwalled nanotube (MWNT) and graphene yarns. The long niobium nanowires, formed by repeated extrusion and drawing, achieve device volumetric peak power and energy densities of 55 MW·m(-3) (55 W·cm(-3)) and 25 MJ·m(-3) (7 mWh·cm(-3)), 2 and 5 times higher than that for state-of-the-art CNT yarns, respectively. The capacitance per volume of Nb nanowire yarn is lower than the 158 MF·m(-3) (158 F·cm(-3)) reported for carbon-based materials such as reduced graphene oxide (RGO) and CNT wet-spun yarns, but the peak power and energy densities are 200 and 2 times higher, respectively. Achieving high power in long yarns is made possible by the high conductivity of the metal, and achievement of high energy density is possible thanks to the high internal surface area. No additional metal backing is needed, unlike for CNT yarns and supercapacitors in general, saving substantial space. As the yarn is infiltrated with pseudocapacitive materials such as poly(3,4-ethylenedioxythiophene) (PEDOT), the energy density is further increased to 10 MJ·m(-3) (2.8 mWh·cm(-3)). Similar to CNT yarns, niobium nanowire yarns are highly flexible and show potential for weaving into textiles and use in wearable devices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".