Fast Fourier Transform-Based Activation and Monitoring of Micro-Supercapacitors: Enabling Energy-Autonomous Actuators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)2 on Ni foam—stabilizing α-nanoflakes at 0.7 V·s−1 and β-platelets at 0.007 V·s−1—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm−2 at 5 mA·cm−2) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring.
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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 | 0.001 |
| 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 it