Gradient-Layered MXene/Hollow Lignin Nanospheres Architecture Design for Flexible and Stretchable Supercapacitors
Bibliographic record
Abstract
Abstract With the rapid development of flexible wearable electronics, the demand for stretchable energy storage devices has surged. In this work, a novel gradient-layered architecture was design based on single-pore hollow lignin nanospheres (HLNPs)-intercalated two-dimensional transition metal carbide (Ti 3 C 2 T x MXene) for fabricating highly stretchable and durable supercapacitors. By depositing and inserting HLNPs in the MXene layers with a bottom-up decreasing gradient, a multilayered porous MXene structure with smooth ion channels was constructed by reducing the overstacking of MXene lamella. Moreover, the micro-chamber architecture of thin-walled lignin nanospheres effectively extended the contact area between lignin and MXene to improve ion and electron accessibility, thus better utilizing the pseudocapacitive property of lignin. All these strategies effectively enhanced the capacitive performance of the electrodes. In addition, HLNPs, which acted as a protective phase for MXene layer, enhanced mechanical properties of the wrinkled stretchable electrodes by releasing stress through slip and deformation during the stretch-release cycling and greatly improved the structural integrity and capacitive stability of the electrodes. Flexible electrodes and symmetric flexible all-solid-state supercapacitors capable of enduring 600% uniaxial tensile strain were developed with high specific capacitances of 1273 mF cm −2 (241 F g −1 ) and 514 mF cm −2 (95 F g −1 ), respectively. Moreover, their capacitances were well preserved after 1000 times of 600% stretch-release cycling. This study showcased new possibilities of incorporating biobased lignin nanospheres in energy storage devices to fabricate stretchable devices leveraging synergies among various two-dimensional nanomaterials.
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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".