Wet spinning of silkworm silk has
the potential to overcome the
limitations of the natural spinning process, producing fibers with
exceptional mechanical properties. However, the complexity of the
extraction and spinning processes have meant that this potential has
so far not been realized. The choice of silk processing parameters,
including fiber degumming, dissolving, and concentration, are critical
in producing a sufficiently viscous dope, while avoiding silk’s
natural tendency to gel via self-assembly. This study utilized recently
developed rapid Bayesian optimization to explore the impact of these
variables on dope viscosity. By following the dope preparation conditions
recommended by the algorithm, a 13% (w/v) silk dope was produced with
a viscosity of 0.46 Pa·s, approximately five times higher than
the dope obtained using traditional experimental design. The tensile
strength, modulus, and toughness of fibers spun from this dope also
improved by a factor of 2.20×, 2.16×, and 2.75×, respectively.
These results represent the outcome of just five sets of experimental
trials focusing on just dope preparation. Given the number of parameters
in the spinning and post spinning processes, the use of Bayesian optimization
represents an exciting opportunity to explore the multivariate wet
spinning process to unlock the potential to produce wet spun fibers
with truly exceptional mechanical properties.