Effect of High-Energy Ball Milling, Capping Agents and Alkalizer on Capacitance of Nanostructured FeOOH Anodes
Bibliographic record
Abstract
This investigation is motivated by interest in nanostructured FeOOH anodes for aqueous asymmetric supercapacitors operating in Na2SO4 electrolyte. The research goal is the fabrication of anodes with high active mass loading of 40 mg cm−2, high capacitance and low resistance. The influence of high-energy ball milling (HEBM), capping agents and alkalizer on the nanostructure and capacitive properties is investigated. HEBM promotes the crystallization of FeOOH, which results in capacitance reduction. Capping agents from the catechol family, such as tetrahydroxy-1,4-benzoquinone (THB) and gallocyanine (GC), facilitate the fabrication of FeOOH nanoparticles, eliminate the formation of micron size particles and allow the fabrication of anodes with enhanced capacitance. The analysis of testing results provided insight into the influence of the chemical structure of the capping agents on nanoparticle synthesis and dispersion. The feasibility of a conceptually new strategy for the synthesis of FeOOH nanoparticles is demonstrated, which is based on the use of polyethylenimine as an organic alkalizer-dispersant. The capacitances of materials prepared using different nanotechnology strategies are compared. The highest capacitance of 6.54 F cm−2 is obtained using GC as a capping agent. The obtained electrodes are promising for applications as anodes for asymmetric supercapacitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".