Tailoring Li₄Ti₅O₁₂ Thin Film Carrier Kinetics Through Solid Solution Doping for Battery and Memristor Applications

A Lithium titanate, Li₄Ti₅O₁₂ (LTO4), due to its zero-strain behavior during cycling, excellent chemical stability and cyclability, is a promising anode material for solid-state batteries (SSB) applications. As a thin film, its applications expand to integrated circuits, sensors, flexible batteries, IoT devices, and memristors. Across these, precise control of mixed Li⁺ ionic–electronic transport is vital. While dopants have been shown to improve electron conduction and Li⁺ diffusion in LTO4 powders, thin-film studies remain limited. To bridge this gap, we investigate solid solution dopants (Nb⁵⁺, V⁵⁺, Mg²⁺, Cu²⁺) and their effects on LTO4 thin-film kinetics and performance in batteries and memristors. Films doped with Mg, Cu, Nb, and V with a 0.2M dopant concentration were deposited on Nbdoped SrTiO₂ substrates. Cyclic voltammetry and impedance spectroscopy show that Mg, Nb, and V improve kinetic metrics, while Cu reduces diffusivity but boosts electronic conductivity. Through galvanostatic cycling-based capacity, rate capability, and stability measurements, we found that while all dopants displayed enhanced rate performance, the capacity improved only with Mg, Nb, and V. Furthermore, the Mg-doped film was found to have an unstable capacity leading to Nb- and V-doped thin-films as the best overall performing battery anodes. For memristors, current–voltage cycling measurements revealed that low concentrations (0.05 M) of Cu and Nb doped devices presented the largest improvements in cycle-to-cycle stability, switching ON-voltages, ON-OFF current ratios, and lower loss in peak current with increasing scan rate. With increasing dopant concentrations however, devices would see relative drops in performance. In summary, the inclusion of dopants in LTO4 at the right concentration level leads to improvements in both battery and memristor performance allowing for one material multi-functional systems.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Buzzell, Drew E.

Advisors dc:contributor.advisor

• del Alamo, Jesús A.
• Rupp, Jennifer L.M.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164575

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164575