Fungi such as Candida albicans exist in biofilm phenotypes, which present as viscoelastic materials; however, a method to measure linear viscoelastic moduli, yield stress, and yield strain is lacking. Characterization methods for fungal materials have been limited to techniques specific to particular industries. Here, we present a method to measure the shear stress, strain amplitude, and creep of C. albicans BWP17 biofilms. Our method includes features tailored to the analysis of fungi including an in vitro growth protocol attuned to the slow growth rates of C. albicans biofilms and a resultant cultured biofilm that has sufficient integrity to be transferred to the rheometer tooling without disrupting its structure. The method's performance is demonstrated by showing that results are insensitive to gap, evaporative sealant, length of experiment, and specimen radius. Multiscale imaging of the fungal biofilm showed complex entanglement networks at the hundred-micrometer scale. For a wild-type strain cultivated for 14 days, using small-amplitude oscillatory rheology, we found that the elastic (G′) and viscous (G″) moduli were nearly independent of frequency over the range 0.1–10 s −1 , with magnitudes of [Formula: see text] and [Formula: see text], respectively. The yield stress was approximately [Formula: see text]. We modeled the linear creepmore »
This content will become publicly available on February 14, 2023
Surrogate Modeling with Gaussian Processes for an Inverse Problem in Polymer Dynamics
When rheological models of polymer blends are used for inverse modeling, they can characterize polymer mixtures from rheological observations. This requires repeated evaluation of potentially expensive rheological models. We explored surrogate models based on Gaussian processes (GP-SM) as a cheaper alternative for describing the rheology of polydisperse binary blends. We used the time-dependent diffusion double reptation (TDD-DR) model as the true model; it takes a 5-dimensional input vector specifying the binary blend as input and yields a function called the relaxation spectrum as output. We used the TDD-DR model to generate training data of different sizes [Formula: see text], via Latin hypercube sampling. The optimal values of the GP-SM hyper-parameters, assuming a separable covariance kernel, were obtained by maximum likelihood estimation. The GP-SM interpolates the training data by design and offers reasonable predictions of relaxation spectra with uncertainty estimates. In general, the accuracy of GP-SMs improves as the size of the training data [Formula: see text] increases, as does the cost for training and prediction. The optimal hyper-parameters were found to be relatively insensitive to [Formula: see text]. Finally, we considered the inverse problem of inferring the structure of the polymer blend from a synthetic dataset generated using the true more »
- Award ID(s):
- 1727870
- Publication Date:
- NSF-PAR ID:
- 10332579
- Journal Name:
- International Journal of Computational Methods
- Page Range or eLocation-ID:
- 2143003
- ISSN:
- 0219-8762
- Sponsoring Org:
- National Science Foundation
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$$\lambda _{E}$$ $$\lambda _{E}$$ $$\lambda _{E}$$ $$m=0.62$$ $$\nu =0.54$$ -
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