Secondly, such understanding can allow for process predictions and optimization, both in terms of energy efficiency (for example, avoiding overreactions such as in the parameter paths shown in Figures ) and in terms of the content or purity of a specific phase (in the present case, δ-Al2O3). In particular, this overcomes purely empirical process decisions, usually originating from qualitative insight. For example, magnetron power is a crucial parameter for the plasma temperature, therefore, by its increasing, the amount of underreacted material would be decreased. Another example, is the precursor solvent, where water injected into the plasma acts as a heat sink and can delay material reactions, whereas ethanol has the opposite effect.