In Situ Growth of NiCo Layered Double Hydroxide on Biomass Waste‐Based Substrate: A Novel Material with 3D Interconnected Structure as Electrodes for Supercapacitors
Bibliographic record
Abstract
Next‐generation energy storage systems require green and renewable electrodes with a high specific capacity, which combine biomass waste and bimetallic hydroxide synergically to satisfy environmental enhancements and economic benefits. Herein, a novel approach to improving the electrical conductivity of bimetallic materials by in situ growing NiCo layered double hydroxides (LDHs) with pseudocapacitance capability on KMnO4‐activated fungus bran‐derived carbons (FBCs) is reported, achieving improved electrochemical performance in supercapacitors (SCs). The hierarchical porous FBC substrate contributes to the homogeneous growth of the LDHs and high electronic and ionic conductivity. The optimal composite (FBC/NCL‐3) with a 3D interconnected structure provides a specific capacitance of 1938 F g−1 at a current density of 1 A g−1. Correspondingly, the hybrid battery‐SC device composed of FBC/NCL‐3 and FBC provides favorable stability (76% specific capacity retention at 5 A g−1 for 3000 cycles) with an operating voltage of 1.4 V and a high energy density of 37.3 Wh kg−1 at a power density of 695.6 W kg−1. This work demonstrates a promising strategy using FBC as a substrate for growing LDH materials aiming to achieve high‐performance SCs.
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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".