High Throughput Doping Study of Na-Mn-O Cathode Materials
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».