Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/49806
Title: Phase and Chemical Segregation in Cobalt-Free Lithium-Rich Cathodes Doped With Magnesium
Authors: Biendicho, Jordi Jacas
Prakasha, Kunkanadu Rajappa
DE SLOOVERE, Dries 
MYLAVARAPU, Satish Kumar 
HARDY, An 
Josang, Leif Olav
Ramos, Sergio
Merlo, Maximiliano
Chang, Xingqi
Grins, Jekabs
Svensson, Gunnar
Issue Date: 2026
Publisher: WILEY-V C H VERLAG GMBH
Source: Batteries & Supercaps, 9 (8) (Art N° e70424)
Abstract: Cobalt-free, lithium-rich composites are interesting cathodes for generation 3b Li-ion batteries due to their high discharge capacity and improved sustainability with respect to state-of-the-art NMC oxides, which are commonly doped with magnesium to stabilize their layered structure. In this article, the role of the magnesium dopant, including its solubility, chemical distribution, and phase evolution, has been characterized and optimized in the context of nanoscale composites. By combining XRD, XPS, TEM-EDX, and coin cell testing, a physically and chemically segregated model for Li1.21-2xMgxNi0.16Mn0.62O2 (x = 0, 0.01, 0.03, 0.05, and 0.1) as a function of Mg dopant level is presented, assuming an ionic doping compensation mechanism. The best-performing cathode, i.e., Li1.20Mg0.01Ni0.16Mn0.62O2, displays a homogeneous distribution of magnesium at the bulk particle level with an incipient phase-segregated Ni-rich structure at the particle edge, resulting in a discharge capacity of 187 mAhg-1 at 0.2C and an enhanced performance compared to undoped and x >= 0.03 samples. The structural and chemical results presented herein highlight the complex engineering required to achieve a specific phase structure and chemical (dopant) distribution of Li-rich nanoscale composites.
Notes: Biendicho, JJ (corresponding author), Catalonia Inst Energy Res IREC, Barcelona, Spain.
jjacas@irec.cat
Keywords: cathode;generation 3b;Li-ion;Li-rich;long-cyclability;Mg doping;phase composition
Document URI: http://hdl.handle.net/1942/49806
e-ISSN: 2566-6223
DOI: 10.1002/batt.70424
ISI #: 001835586500001
Rights: 2026 Wiley‐VCH GmbH. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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