Colloidal Processing of Metal Oxide-Carbon Nanotube Nanocomposite Electrodes for Supercapacitors
Bibliographic record
Abstract
There is considerable interest in ESs as they have huge potential in energy storage devices, play a key role in advanced power systems, and have the potential to revolutionize hybrid vehicles and electronics. SCs are known for their hybrid power and energy density, fast charge and discharge rates, and long-term cycling stability. The performance of SCs depend largely on the specific capacitance of their electrodes. Among various cathode materials, unitary transition metal oxides (TMOs), especially manganese oxide, are favored due to their multiple oxidation states, excellent redox properties, abundant availability, simple synthesis, and cost-effectiveness. The low intrinsic conductivity of manganese oxide can be significantly enhanced by adding conductive additives such as multi-walled carbon nanotubes (CNTs). We are developing a novel colloid processing technique to synthesize MnOx-CNT nanocomposites with enhanced electronic conductivity. Our research involves the use of advanced capping agents and co-dispersants to fabricate MnOx-CNTs nanocomposite electrodes that exhibit superior performance and bypass the lengthy activation process commonly cited in our previous results. Testing results indicate that functional catechol-based molecules, including quercetin (QC), rhamnolipid (RL), tetrahydroxy-1,4-quinone (TQ), catechin (CT) and gallocyanine (GA), have excellent dispersion properties for MnOx and CNTs. These compounds form uniform and stable suspensions that improve the nanostructure and electrochemical performance of the electrodes. They also serve as capping agents for Mn3O4 synthesis, reducing agglomeration and improving morphology. Additionally, murexide was tested as a co-dispersant and capping agent due to its chelating properties, forming a tridentate bond with Mn atoms and adsorbing onto the carbon rings of CNTs. As a capping agent, murexide can promote electrostatic dispersion by forming strong tridentate bonds with Mn3O4 particle surfaces, thereby reducing agglomeration and improving composite morphology. In addition, binary (MnFe2O4) or ternary (La0.65Sr0.35MnO3(LSM)) metal oxides can overcome the limitations of single metal oxides through the synergistic effect between metal ions, improve capacitive performance and expand the potential window. These compounds are promising candidates as ES electrode materials. High-energy ball milling (HEBM) helps reduce particle size, enhance electrolyte contact with active material surfaces, achieve high capacitance at high active mass loading, and produce high-performance supercapacitors (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) | 0.001 | 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".