High performance aqueous asymmetric supercapacitors developed by interfacial engineering wood-derived nanostructured electrodes
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
Supercapacitors (SCs) are becoming new candidates in the field of sustainable energy supply. Nevertheless, the unsatisfactory energy density due to the narrow voltage window of the supercapacitor is the main barrier to its practical application. Developing appropriate electrode materials to improve the electrochemical performance of SCs and broaden the operating voltage of supercapacitors are imperative. Herein, compatible interfacial engineering wood-derived nanostructured electrodes for aqueous asymmetric supercapacitors (AASCs) are demonstrated. The 3D carbonized wood matrix serves as a high specific surface area current collector and a porous host for the high capacitive active substance, which is composed of manganese dioxide (MnO2) nanowires and polyaniline (PANI) nanoparticles grown uniformly on the porous wall of aligned microchannels ([email protected]2 and [email protected]). The resulting electrochemical exchange reactive sites enable the positive electrode to deliver a high areal capacitance of 729 mF cm−2 at 1 mA cm−2 (369 F g−1 at 0.5 A g−1). The negative electrode delivers a high areal capacitance of 1721 mF cm−2 at 1 mA cm−2 (848 F g−1 at 0.5 A g−1). The areal capacitance of two electrodes is superior to most reported supercapacitor electrodes. Additionally, the assembled [email protected]2//[email protected] AASC achieves a desirable energy density (170.84 μWh cm−2 at a power density of 0.5 mW cm−2) because a considerable work function difference between electrodes achieved a 2 V wide voltage window. The results of this study provide a critical benchmark and optimistic incentives to adopt natural hierarchical structures to enhance the performance of new-generation energy-related systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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