Recent Advances and Prospects of FeOOH-Based Electrode Materials for Supercapacitors
Bibliographic record
Abstract
Supercapacitors (SCs) offer a potential replacement for traditional lithium-based batteries in energy-storage devices thanks to the increased power density and stable charge–discharge cycles, as well as negligible environmental impact. Given this, a vast array of materials has been explored for SCs devices. Among the materials, iron oxyhydroxide (FeOOH) has gained significant attention in SC devices, owing to its superior specific capacitance, stability, eco-friendliness, abundance, and affordability. However, FeOOH has certain limitations that impact its energy storage capabilities and thus implicate the need for optimizing its structural, crystal, electrical, and chemical properties. This review delves into the latest advancements in FeOOH-based materials for SCs, exploring factors that impact their electrochemical performance. To address the limitations of FeOOH’s materials, several strategies have been developed, which enhance the surface area and facilitate rapid electron transfer and ion diffusion. In this review, composite materials are also examined for their synergistic effects on supercapacitive performance. It investigates binary, ternary, and quaternary Fe-based hydroxides, as well as layered double hydroxides (LDHs). Promising results have been achieved with binder-free Fe-based binary LDH composites featuring unique architectures. Furthermore, the analysis of the asymmetric cell performance of FeOOH-based materials is discussed, demonstrating their potential exploitation for high energy-density SCs that could potentially provide an effective pathway in fabricating efficient, cost-effective, and practical energy storage systems for future exploitations in devices. This review provides up-to-date progress studies of novel FeOOH’s based electrodes for SCs applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".