5.3 Molecular understanding and control of the mineralization
process
While the basic processes of metal mineralization are qualitatively
understood, mechanistic details regarding the reaction mechanisms of
metal reduction and adsorption remain elusive. Current studies on metal
mineralization using TMV, BSMV and their corresponding VLPs as
biotemplates focused on characterization of synthesized nanostructures
using different techniques such as transmission electron microscopy
(TEM) [24],
Fourier transform infrared spectroscopy (FTIR), and X-ray scattering
analysis [19, 59]. These methods reveal the structure of the final
synthesized structure and inform hypotheses of how metal mineralization
has occurred. However, a more effective approach to understand how metal
mineralization takes place is to perform in-situ FTIR [90].
This allows direct observation of the reaction progress enabling
determination of the mechanism of mineralization. It is also possible to
observe how changes in reaction conditions such as pH, temperature, and
concentrations of precursors, reducing agents and biotemplate would
affect metal mineralization including particle size, particle size
homogeneity, and the type of metal nanostructures mineralized on the
surface. This would require an extensive design of experiments to
systematically evaluate the effect of each parameter and their
interactions. Machine learning algorithms such as artificial neural
networks have already been applied to these rich datasets to create
predictive models for nanoparticle synthesis as a function of processing
parameters [91]. Similarly, neural networks first used to predict
the binding of metallic ion cofactors to enzymes could be extended to
VLPs to predict and model metal-biotemplate interactions as a function
of engineered CP protein sequence [92]. Such computational tools
would greatly accelerate biotemplate engineering efforts and optimize
deposition processes for metallic nanomaterial synthesis.