A New Generation of Rechargeable Aluminum Ion Battery Technology
Bibliographic record
Abstract
Among available energy storage technologies, rechargeable batteries rank at the top, as they offer the required energy and power density and on-demand response. Although there are several battery technologies available in the market – including lead-acid and Li-ion batteries – the future of these technologies, is of concern, because in addition to cost and safety related challenges, we may face material deficits due to the increasingly high demand and geopolitically restricted abundance. Therefore, it is imperative to develop efficient and economically viable battery technologies that rely on more earth abundant elements. Among potential candidates, aluminum ranks high because of low cost, high abundance, high volumetric capacity, and ability to exchange three electrons. Despite positive attributes, some previous studies on aluminum ion batteries have faced several challenges, including anode passivation, cathode degradation, low voltage, and lack of a stable electrolyte. Here we present a new generation of aluminum ion batteries made of an alloy of aluminum as the anode and a nanotextured activated graphitic sheet as the cathode. The electrolyte is composed of an organic solvent containing aluminum ions. Charge-discharge cycling indicates that cells are stable with minimal capacity decay and they show high discharge voltage and specific capacity. To elucidate the mechanism of electrochemical reactions within the cell, we utilized various microscopy and spectroscopy techniques including X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), scanning electron microscopy energy dispersive spectroscopy (SEM-EDS), transmission electron microscopy (TEM) before and after charge-discharge cycles. This research is ongoing to further develop and refine this innovative battery technology, which could enable sustainable generation and efficient utilization of electric energy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".