Ni, Co bimetallic MOF of dual‐controlled by micro‐morphology and unit cell structure for biomass‐based self‐supporting energy storage device
Bibliographic record
Abstract
Abstract Increasing the interlayer spacing of metal–organic frameworks (MOFs) through multi‐metal ion doping has emerged as an effective strategy to enhance electrolyte‐ion transport within the MOF unit cell, enabling the design of nickel‐based MOF materials with high capacity and energy density. In this work, a series of NiCo‐MOF‐ x ( x = 1–5) were synthesized by incorporating Co 2+ ions into Ni‐MOF. The introduction of Co 2+ modulated the unit cell structure and governed the stacking configuration of MOF nanosheets. At an optimal Ni/Co molar ratio of 4:1, the NiCo‐MOF‐2 sample demonstrates superior electrochemical performance, delivering a specific capacitance of 1238.6 F g −1 at 0.2 A g −1 . Subsequently, NiCo‐MOF‐2 was grown in situ on carbonized wood (CW) to fabricate a NiCo‐MOF@CW composite, which exhibits an areal capacitance of 4960 mF cm −2 at 0.6 mA cm −2 . An asymmetric supercapacitor (NiCo‐MOF@CW//AC) was assembled using NiCo‐MOF@CW as the positive electrode and activated carbon (AC) as the negative electrode. The device achieves an areal energy density of 1.88 mWh cm −2 at a power density of 2.88 mW cm −2 (1 mA cm −2 ), with 83.6% capacitance retention after 2000 charge–discharge cycles. Notably, two serially connected NiCo‐MOF@CW//AC devices successfully illuminate a red LED (operating voltage: 1.6–1.75 V) for 20 min. The multi‐metal ion doping strategy combined with binder‐free, self‐supporting electrode architecture presents a novel approach for synthesizing high‐performance energy storage materials.
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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) | 0.001 | 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".