Bibliographic record
Abstract
With the large demand for energy consumption, sodium-ion batteries have been considered as an alternative to lithium-ion batteries for energy storage1. Among various cathode materials, the Na-Mn-O system is promising with its high specific capacities and excellent battery life. Herein, we applied our high throughput (HTP) system2,3 to explore Na0.66Mn0.9M0.1O2+δ (M: dopant) chemical compositions to perform fast screening of the doping effect in these materials. Using a systematic approach, we establish the relationships between the doping species, crystal structure, electrochemical performance, and air/moisture stability. In detail, 52 different dopants were utilized and prepared as precursor solutions to achieve the HTP sol-gel synthesis. HTP-XRD is performed after sintering to understand the structural changes occurring in doping. HTP-cyclic voltammetry (CV) is then performed to analyze the electrochemical properties. Additionally, to comprehend the relationship between dopants and air stability, an accelerated ageing process is carried out at various humidity levels for several weeks. In this study, we found a broad range of dopants with 20 distinct elements that can be integrated into the layered Na-Mn-O structures, including several previously unstudied dopants (Si, Sc, Ga, Rb, Rh, Cs, Re, and Tl), confirmed by XRD refinements. It's interesting to note that at 10% composition, the dopant species can significantly alter the crystal structure. For the undoped sample, a P2/P’2 biphasic material is obtained by slow cooling. However, with the presence of Li, Mg, Fe, Co, Ni, Zn, and Ga, pure P2 structures are obtained with smooth CV curves, suggesting fewer phase transitions via doping. Meanwhile, Si, B, and Mo-doped samples can introduce more distorted P’2 structures, giving more high-voltage plateaus to increase the voltage of the batteries. Nevertheless, the Rh-doped sample achieves a pure P3 structure, exhibiting smooth CV and higher voltage, but fair cycling. Tunnel structure is also found by Ti-doped samples, with an increased average discharged voltage of 2.74V (undoped:2.47V) and excellent rate performance. A series of novel cathodes yields excellent battery performance, including a high specific capacity of 200 mAh g-1 for the Rb-doped sample, and good capacity retention of 100% after 10 cycles for the Nb-doped sample (undoped showed 92% retention). Significantly, a number of doped samples exhibit high reversible anion redox which was shown to be strongly correlated with the bond valence mismatch of the M-O bonds, suggesting by tuning the polarity of M-O bonds triggered by dopants can stimulate the anionic redox behaviour. Moreover, air/moisture stability analysis is conducted for co-doped samples and found that as little as 5% Li in co-doped samples can stabilize the layered structure dramatically. Na0.66Mn0.9Li0.05Co0.05O2, for instance, substantially improves the air stability to over 99 % structure retention, compared to 0 % in the undoped P2 NMO after a one-month accelerated aging protocol. Therefore, these new insights into the impacts of doping via high throughput screening can serve as new guidelines for rational battery material design and development. Figure 1: Heat map of specific capacities in the periodic table with various dopants. The inset image demonstrates the workflow of the high throughput system. References: Hwang, Jang-Yeon, Seung-Taek Myung, and Yang-Kook Sun. "Sodium-ion batteries: present and future." Chemical Society Reviews46, no. 12 (2017): 3529-3614. Jia, Shipeng, Jonathan Counsell, Michel Adamič, Antranik Jonderian, and Eric McCalla. "High-throughput design of Na–Fe–Mn–O cathodes for Na-ion batteries." Journal of Materials Chemistry A10, no. 1 (2022): 251-265. Jonderian, Antranik, Shipeng Jia, Gabin Yoon, Victor Teodor Cozea, Nooshin Zeinali Galabi, Sang Bok Ma, and Eric McCalla. "Accelerated Development of High Voltage Li‐Ion Cathodes." Advanced Energy Materials12, no. 40 (2022): 2201704. 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 | 0.000 | 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".