Surface Modification of Magnetite for Supercapacitors: Experiment and Theory
Bibliographic record
Abstract
Supercapacitors have emerged as a promising energy storage technology with unique characteristics that set them apart from conventional batteries and capacitors. Supercapacitors bridge the gap between these two technologies by combining the high power density of capacitors with the high energy storage capacity of batteries, offering a compelling solution for various applications. In the pursuit of enhancing supercapacitor performance, magnetite (Fe3O4) has been researched as a potential anode material. Fe3O4 offers several desirable properties, including high theoretical capacitance, low cost, and environmental friendliness. Compositing Fe3O4 with conductive additives has served to address the issue of limited conductivity in Fe3O4 anodes for practical uses, however, a focus must be shifted to enhancing the capacitive performance of such anodes to unlock their full potential. Achieving the full potential of Fe3O4 for supercapacitor applications requires addressing challenges in the colloidal fabrication of high-active mass electrodes. This is done by exploring the exceptional adsorption properties of two dispersing and capping agents: 3,4-dihydroxybenzoic acid and murexide. Exceptional adsorption properties of catecholate-type 3,4-dihydroxybenzoic acid molecules were explored for surface modification of Fe3O4 nanoparticles to enhance their colloidal dispersion as verified by sedimentation test results and Fourier-transform infrared spectroscopy measurements. Electrodes prepared in the presence of 3,4-dihydroxybenzoic acid exhibited nearly double the capacitance at slow charging rates as compared to the control samples without the dispersant or with benzoic acid as a non-catecholate dispersant. Density functional theory analysis of adsorption behavior of 3,4-dihydroxybenzoic acid and benzoic acid at the (001) surface of Fe3O4 corroborated these experimental results by providing an understanding of the basic mechanism of 3,4-dihydroxybenzoic acid adsorption on the surface of nanoparticles. Furthermore, murexide for surface modification of Fe3O4 nanoparticles effectively enhanced the performance of multi-walled carbon nanotube-Fe3O4 supercapacitor anodes. Our experimental results demonstrate significant improvements in electrode performance when murexide is used as a capping or dispersing agent compared to the case with no additives. From impedance measurements, we revealed a substantial decrease in the real part of impedance for samples prepared with murexide, indicating easier charge transfer at more negative electrode potentials, and reinforcing the role of murexide as a capping agent and charge transfer mediator. The theoretical investigation allowed us to identify the nature of chemical bonds between murexide and the surface, with significant charge transfer taking place between the Fe3O4 surface and murexide adsorbate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".