Computational Modelling — Multiscale optimization of Li-ion diffusion in solid lithium metal batteries: Via ion conductive metal-organic frameworks

3 measurement groups · 6 results

Reported values remain attached to the sample, method, conditions, extraction quality and source location that produced them.

UiO-66 DFT model · Model

500 eV plane-wave cutoff; 3 x 3 x 3 k-mesh; force convergence 0.01 eV/A; Grimme D2 dispersion; Li+ diffusion minimum-energy path.

Property	Reported value	Normalised value	Uncertainty	Origin and quality	Source
Li+ diffusion barrier	1.30 eV	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1a
estimated hopping coefficient	4.90 x 10^-22 cm2 s^-1	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1a

UiO-66-2CO2H DFT model · Model

Same computational settings for UiO-66-2CO2H Li+ diffusion.

Property	Reported value	Normalised value	Uncertainty	Origin and quality	Source
Li+ diffusion barrierMarked as a best value within this paper	0.66 eV	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1c
estimated hopping coefficientMarked as a best value within this paper	1.37 x 10^-11 cm2 s^-1	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1c

UiO-66-CO2H DFT model · Model

Same computational settings for UiO-66-CO2H Li+ diffusion.

Property	Reported value	Normalised value	Uncertainty	Origin and quality	Source
Li+ diffusion barrier	1.24 eV	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1b
estimated hopping coefficient	5.09 x 10^-21 cm2 s^-1	—	—	Text Exact Reported	p003 / 6978 · Results and discussion · Fig. 1b