Investigation and Design of Soybean-Derived Carbon Anode Materials for Potassium-Ion Battery Applications
Bibliographic record
Abstract
Lithium-ion battery (LIBs) system is one the most widely used energy storage systems in today’s renewable energy field. However, with the dramatically increased demand over the past decades and limited core material storage, concerns arise regarding its sustainability and large-scale applications. While trying to make more efficient LIBs, the studies of alternative battery systems also appear to be more and more critical. Among many newly studied battery systems, Potassium-ion batteries (PIBs) have caught our attention. With the advantage of high abundance of Potassium (K) and low redox potential of K/K+ (2.93 V vs. standard hydrogen electrode), it appears to be a perfect candidate for substituting many of the current LIBs applications. However, the absence of a suitable carbon anode has hindered the development of PIBs. Herein, we used low-cost and abundant soybean as base material and developed a high-performance hard carbon anode for PIBs. Hard carbon, produced with 500 degrees process, exhibited the highest discharge capacity of 225 mA h g-1, long lifetime of 900 cycles, and good rate capability. Benefited from low activation temperature, the soybean-derived carbon has a medium surface area, large interplanar spacing graphene layers and low degree of graphitization, which are favored by the adsorption-dominated K-ion storage mechanism. To further improve the anode efficiency, a thin layer of Al2O3 coating (~ 2 nm) was applied on the hard carbon by Atomic Layer Deposition (ALD) to function as artificial solid electrolyte interphase and increased the Coulombic efficiency from 99.0% to 99.6%. After investigating the relationship among electrochemical performance, material interface, structural design, and mechanism of potassium-ion storage, we provided new insights for the design and synthesis of carbonaceous materials with improved storage capacity and efficiency for future developments of PIBs.
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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) | 0.001 | 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".