Mechanochemical regeneration of NaBH4

Sodium borohydride (NaBH₄) is an attractive solid hydrogen carrier because it combines high gravimetric hydrogen capacity (~10.9 wt%) with stability and safe handling under ambient conditions, yet its large-scale use is constrained by the energy-intensive regeneration of the spent hydrolysis product sodium metaborate (NaBO₂). This thesis develops an energy-efficient mechanochemical pathway to regenerate NaBH₄ via high-energy ball milling, while simultaneously proposing a transferable methodology to make mechanochemical synthesis more reproducible, comparable across equipment, and scalable.

First, a fractional design of experiments quantifies the main and interaction effects of key operating variables (milling time, molar ratio, ball-to-powder ratio, and rotational speed), revealing that yield variability reported in the literature can largely be attributed to underreported or poorly controlled milling parameters and machine-specific characteristics. Using these insights, high regeneration yields reported in the literature are reproduced while operating at lower rotational speed, reducing specific energy demand and wear; the optimized procedure also enables direct production of a ready-to-use aqueous NaBH₄ solution, avoiding hazardous post-processing steps.

To connect operating settings to the “hidden” internal dynamics of the mill, the thesis employs Discrete Element Method (DEM) simulations and identifies a set of scale-independent mechanical descriptors that uniquely characterize milling conditions. Expressing experiments through these dimensionless groups collapses diverse conditions onto transferable master curves, providing a mechanical fingerprint that supports comparison across mills and scales. Building on this framework, the role of shear-versus-compression stressing is isolated: low fill ratios that enhance shearing substantially improve yield and enable record conversions (up to 94%), whereas higher fill ratios shift stressing toward compressive impacts and markedly reduce yield, producing practical guidelines to favor productive shear while limiting wasted energy.

Finally, data-driven models integrate chemistry and mechanics to accelerate discovery. A two-stage Gaussian-process-regression ensemble predicts out-of-sample yields with R² ≈ 0.83, enabling computational screening of operating windows before experimentation. In parallel, a graph neural network surrogate reproduces DEM-like particle trajectories with low error (MSE ≈ 2×10⁻⁴ m²) using time steps over 100× larger than DEM, and can dynamically predict energy dissipation, pointing to fast, accessible tools for mill design and reporting standardization. Together, the thesis delivers a validated route toward circular NaBH₄-based hydrogen storage and a general blueprint for reproducible, scalable mechanochemistry.