All chemicals were purchased from Merck and stored under Argon atmosphere in a glovebox. Electrolytes and organic solvents were used without further purification. LTO nanopowder (<200nm) and lithium salts were stored for at least one week in a glovebox before use to remove potential water impurities. For each experiment a Young-type NMR tube 150mg of LTO was filled and 1mL of electrolyte was added. The electrolyte consists of EC/DMC (1:1 weight ratio) mixture, having a 0M, 1M or 2M concentration of LiPF6, LiTfl, LiTpb or NaTfl. The tube was shaken until a homogeneous slurry was obtained. Measurements were performed after 24h of settlement. Static NMR experiments were performed on a Bruker Avance III HD 600 wide-bore spectrometer equipped with a gradient probe (Bruker PA BBO 600W2/S4 BB-H&F-D-05 DIFF). Saturation recovery experiments were used to determine Li T1 relaxation time. The number of accumulations was 8, using pi/2 pulses of 15µs at 50W. A series of up to 38 delays were used from 0.00001s to 1500s determine the buildup of the Li signal. Relaxation data was inverted using uniform penalty regularization, which is a Tikhonov regularization algorithm in generalized form, in combination with a zero-crossing penalty to prevent sign changes in the calculated distribution that are not supported by the data. Analyses were done with GNU Octave (version 4.0.3) using a home-written ILT function. For inversion, the 2D relaxation vs. spectral data was used. Thereby, only the relaxation dimension was inverted, but both dimensions were regularized. The resulting sensitivity gain from regularization along the non-inverted spectral dimension facilitated an improved resolution in the inverted relaxation dimension. The regularization parameters have been chosen as detailed in. The T1 distribution was calculated at logarithmically spaced time constant values between 10 -5 s and 10 8 s, with 10 points per decade. The upper limit was chosen to fit the bias, which occurs in saturation recovery experiments, into the distribution rather than as a single value with nominally infinite time constant. It was found empirically that this procedure produces more robust results in the case of multidimensional data sets.