High-Throughput Electrochemical Testing of Positive Electrode for Li-Ion Batteries
Bibliographic record
Abstract
In the search for high-energy Li-ion positive electrodes, the layered lithium NMC oxides have been extensively studied. Despite wide interest in this system, the phase diagram remained poorly understood until recently, which resulted in many conflicting reports in the literature. Combinatorial synthesis, coupled with X-ray diffraction was used to determine the phase diagrams under various synthetic conditions [1-2]. The unexpected complexity seen in this system included multiple 3-phase regions that transform during cooling along with boundaries to single phase regions which also shift during cooling. Even with a deepened understanding of the structural phase diagram, there is still minimal knowledge of how the electrochemical properties evolve across these complex phase spaces. Herein, we adapt a high-throughput electrochemical testing system wherein 64 samples are cycled simultaneously in order to measure the cycling of mg-scale powder combinatorial samples. The methodology involves using a solution-dispensing robot to make the samples by co-precipitation synthesis, followed by high temperature annealing. The samples are subsequently mounted in the combinatorial electrochemical cell and cycled simultaneously. Figure 1 shows cyclic voltammograms obtained for identical LiCoO2 samples (weighing at most 2.5 mg) in the combinatorial cell. A remarkable level of consistency is achieved in comparison to published cyclic voltammograms for LiCoO2 [3]. The described methodology allows for the determination of specific capacity, with an RSD approaching 10%. Given that these combinatorial samples are powders, synthesized using methods comparable to those used commercially, the results scale-up very well. The proof-of-concept of this novel high-throughput electrochemical technique will be presented, exploring the level of precision that can be achieved for important electrochemical metrics (redox potential, specific capacity, irreversible capacity, voltage during storage experiments, etc.). Preliminary results from this combinatorial electrochemistry methodology using the Li-Mn-Ni-O system will be displayed, and the ramifications of electrochemically probing this critical composition space will be examined. [1] McCalla, E., Rowe, A. W., Shunmugasundaram, R., & Dahn, J. R. (2013). Structural study of the Li–Mn–Ni oxide Pseudoternary system of interest for positive electrodes of Li-ion batteries. Chemistry of Materials, 25(6), 989-999. [2] Brown, C. R., McCalla, E., Watson, C., & Dahn, J. R. (2015). Combinatorial study of the Li–Ni–Mn–Co oxide pseudoquaternary system for use in Li–Ion battery materials research. ACS combinatorial science, 17(6), 381-391. [3] Cho, J., Kim, Y.J., Park, P. (2001). LiCoO2 cathode material that does not show a phase transition from hexagonal to monoclinic phase. Journal of the Electrochemical Society, 148(10), A1110-A1115 Figure 1: Cyclic voltammetry of LiCoO2 performed in the high-throughput electrochemical testing system Figure 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".