Polyoxometalate Modified Biomass Carbon Composites for Supercapacitor Electrodes
Bibliographic record
Abstract
Polyoxometalates (POMs), nanoscale transition metal-oxide clusters, have emerged as promising building blocks for the fabrication of pseudocapacitive electrodes.[i] These POM clusters undergo fast reversible electron transfer reactions and have multiple stable redox states, ideal characteristics for energy storage applications.[ii] In order to leverage these properties for use in electrochemical capacitor (EC) electrodes, these POM clusters must be immobilized on a stable carbon substrate to create a composite electrode. There is synergy in this combination as the POMs contribute faradaic redox processes, while the carbon provides stability, high conductivity, and electrochemical double layer capacitive (EDLC) contributions. Porous carbon derived from biomass is an excellent potential substrate for these POM-carbon composites owing to its low cost, simple synthesis procedure, and high specific surface area. Here, we will report on our synthesis of porous carbon substrates derived from pine cone biomass via a two-stage carbonization and chemical activation procedure. These pine cone carbon samples are highly porous with specific surface areas over 2500 m 2 g -1 . In a sulfuric acid electrolyte, the high surface area of these samples results in a double layer capacitance of 265 F g -1 . In order to further enhance this already large capacitance, the biomass carbons were modified with Keggin POM clusters, PMo 12 O 40 3- and PW 12 O 40 3- via a single step chemisorption. We have discovered that the intrinsic porosity and surface conditions of the pine cone biomass carbon are particularly well suited to the immobilization of Keggin POMs. Additionally, by adjusting the activation conditions, the carbon porosity can be tuned to further enhance POM chemisorption. The result is biomass carbon hybrids with exceptionally high loading of POM clusters. The redox activity of these immobilized POMs leads to significantly enhanced area and volume specific capacitance compared to the bare carbon substrate (Figure 1). In this talk, we will present the full characterization of the POM-biomass carbon hybrids as well as discuss the activation conditions and resulting substrate porosity necessary for superior POM adsorption. The POM chemistry that results in the optimal pseudocapacitive performance will also be presented and discussed. [i] M. Genovese, K. Lian. Current Opinion in Solid State and Materials Science. 19 (2015) 126-127 [ii] B.B Xu, L. Xu, G.G. Gao, W.H. Guo, S.P. Liu. J. Colloid Interface Sci . 330 (2009) 408 Figure 1
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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) | 0.000 | 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) | 0.000 | 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".