Laser-etched flexible microsupercapacitors based on nanocellulose and conductive metal–organic frameworks
Bibliographic record
Abstract
Freestanding, conductive, and flexible nanopapers composed of cellulose nanofibers (CNF) and conductive metal–organic frameworks (c-MOF) was prepared. A simple laser etching technique was employed to process the CNF@c-MOF nanopapers into interdigitated electrodes, enabling the assembly of ultrathin microsupercapacitors (MSCs). The MSCs exhibit a high volumetric capacitance of 36.7 F cm −3 , rapid charge–discharge rates, excellent flexibility, and outstanding cycle stability. • Freestanding, flexible cellulose@conductive MOF nanopaper electrodes were prepared. • Laser etching enabled the design of nanopaper-based microsupercapacitors (MSCs). • MSCs are ultrathin (45 µm) and exhibit excellent flexibility. • Ultrathin MSCs achieved a high volumetric capacitance of up to 36.7 F cm −3 . Flexible supercapacitors hold promise for applications in wearable electronic devices. However, the challenges of achieving flexibility, miniaturization, and high volumetric capacitance persist. In this work, precise laser etching of cellulose composites, prepared via in-situ growth of conductive metal–organic frameworks (c-MOFs) on cellulose nanofibers (CNF), was employed to fabricate flexible, binder-free, and integrated microsupercapacitors (MSCs). The interfacial synthesis of Ni 3 (HITP) 2 (a type of c-MOF) on the surface of CNF yields a continuous and uniform conductive shell, enabling efficient electron transfer along the CNF@c-MOF nanofibers. The interwoven structure of the nanofibers creates a hierarchical porous network with enhanced surface area featuring interconnected porous channels, enabling rapid ion transport. The laser etching technique facilitates one-step production of integrated MSCs with a precisely interdigitated configurations and micron-scale accuracy. The fabricated MSCs demonstrate excellent mechanical stability, with a tensile strength of up to 81.9 MPa, and remarkable flexibility, maintaining consistent electrochemical performance under bending stress. The flexible device, with a thickness of only 45 µm, achieves a high volumetric specific capacitance of 36.7 F cm −3 at a current density of 0.17 mA cm −2 and a specific energy density of 2,497.5 µWh cm −3 at a power density of 53.3 mW cm −3 . This study provides a new strategy for designing flexible, binder-free, integrated MSCs with high capacitances and long cyclic stability, demonstrating significant potential for applications in wearable 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".