Capacitance Improvement of Carbon Aerogels by the Immobilization of Polyoxometalates Nanoparticles
Bibliographic record
Abstract
A hybrid material based on the immobilization of H3PMo12O40 polyoxometalate nanoparticles (POM) was obtained by using an activated carbon aerogel (ACP-100Q2) matrix, and was tested as a potential electrode material for a supercapacitor cell. A chemical activation with KOH was carried out in the carbon aerogel matrix (ACP-100), obtaining a greater BET surface area (334g/cm2) and different electrochemical behavior (ACP-100Q2). Both matrices (ACP-100 and ACP-100Q2) were immersed in a 1.15mM POM solution in order to determine the role of the chemical activation procedure in the immobilization of POM nanoparticles. All materials were characterized by Attenuated Total Reflection (ATR) and nitrogen isotherms. For the electrochemical characterization, the synthesized materials were mixed with 10% of Teflon and 20% conducting carbon in weight ratio. Then, a film was made and a portion was pressed onto a stainless steel grid as current collector. Cyclic voltammetry in 3-electrode cells using a 0.5 M H2SO4 electrolyte was used to determine the electrochemical performance. The chemical activation of the aerogel matrix with KOH was the key factor to immobilize and disperse the POM nanoparticles, which improved the capacitance behavior making this material suitable for its application as supercapacitor electrode material.
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 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".