Oxygen-Based Anion Redox for Lithium Batteries
Bibliographic record
Abstract
ConspectusThe importance of current Li-ion batteries (LIBs) in modern society cannot be overstated. While the energy demands of devices increase, the corresponding enhancements in energy density of battery technologies are highly sought after. Currently, many different battery concepts, such as Li–S and metal–air among many others, have been investigated. However, their practical implementation has mostly been restricted to the prototyping stage. In fact, most of these technologies require rework of existing Li-ion battery manufacturing facilities and will naturally incur resistance to change from industry. For this reason, one specifically attractive technology, anionic redox in transition metal oxides, has gained much attention in the recent years. Its ability to be directly used in already established processes and higher energy density with similar electrolyte formulation make it a key materials research direction for next generation Li-ion batteries. In regular LIBs, the redox active centers are the transition metal cation. In anion redox, both the anion (typically O) and the transition metal cation are utilized as redox centers with enormous implications for increasing energy density. This new material can be highly competitive for replacing the current LIB technologies. However, much is still unknown about its cycling mechanism. Upon activating the O redox couples, most cationic and anionic redox active materials will either evolve O2 or undergo irreversible structural degradation with associated severe decreases in electrochemical performance. By understanding the transition from full anion redox to partial cationic and anionic redox, we hope readers can gain a deeper understanding of the topic.This Account will focus mainly on the work that was conducted by our group at Argonne National Laboratory. The phenomenon of cationic and anionic redox in a lithium-ion battery cathode will first be discussed. Our work in resonant inelastic X-ray scattering to investigate the spectroscopic features of O after delithiation has found potential “fingerprint” signals that could likely be used to identify and confirm reversible O redox if corroborated with other techniques. To follow, we will examine our work on Li–O2 batteries. While our group and the research community have had many significant contributions and improvements to the field of Li–O2 (such as decreasing overpotential and achieving cyclability in air environment), its practical application is still far from realization. Perhaps our most important contribution to this area is the discovery that Ir deposited on reduced graphene oxide can be used to halt the reduction of O2 at the LiO2 oxidation state. This not only significantly decreases the charge overpotential but also presents the important concept of oxidation-state controlled discharge. Subsequently, we will focus on our oxidation state-controlled redox-based charging of oxygen in a pure oxygen redox Li-ion battery. Future implications of this technology will be emphasized.
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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 | 0.000 | 0.001 |
| 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".