(Invited) Polyoxometalate Modified Biomass Carbon for Supercapacitor Electrodes
Bibliographic record
Abstract
Biomass waste carbon materials have been emerging as high performance and low cost electrodes for energy storage such as supercapacitors. Addition of thin layers of redox active pseudocapacitive materials onto biomass activated carbon can leverage the strength of the electrochemical activity of the former and the high surface area as well as the low cost of the latter. In our study, several biomass carbon materials, including corncobs and pine cones, have been investigated as substrates for adsorption of electrochemically active and highly reversible polyoxometalates (POM) clusters. In this talk we will compare these two carbons to show the influential factors on the adsorption of POM clusters. Among various chemical and structural properties of biomass activated carbons, we have found that POM adsorption is highly favored within a carbon matrix possessing pore diameters in the 1-2 nm range. These large micropores are big enough to accommodate the large POM cluster, while still being small enough to effectively trap and hold the molecule. Pine cone activated carbon with this optimal pore arrangement demonstrated ultra-high loading of the PMo12O40 3- (PMo12) molecule resulting in carbon-POM hybrid materials consisting of over 55 wt. % PMo12. This large POM loading imparted tremendous redox activity to the already high double layer capacity of the carbon substrate, leading to a high areal capacitance of 1.19 F cm-2 for the hybrid material, close to 2.5 times larger than for unmodified carbon. We have also demonstrated that a mixed molecular coating combining multiple POM chemistries can be adsorbed onto the activated carbon substrate to create a more ideally capacitive charge storage profile. These results demonstrate a promising method for the design of high performance yet cost effective hybrid energy storage electrodes.
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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) | 0.003 | 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".