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The renormalized bond lifetime model (RBLM) is a popular scaling theory for the effective lifetime of reversible bonds in transient networks. It recognizes that stickers connected by a reversible bond undergo many (J) cycles of dissociation and reassociation. After finally separating, one of these stickers finds a new open partner in time τ_openvia a subdiffusive process whose mean‐squared displacement is proportional tot^α, wheretis the time elapsed, and α is the subdiffusion exponent. The RBLM makes convenient mathematical approximations to obtain analytical expressions forJand τ_open. The consequences of relaxing these approximations is investigated by performing fractional Brownian motion (FBM) simulations. It is found that the scaling relations developed in the RBLM hold surprisingly well. However, RBLM overestimates both τ_openandJ, especially at lower values of α. For α = 0.5, corresponding to the Rouse limit, it is found that τ_openis overestimated by a factor of approximately 4x, while the approximation forJis nearly exact. The degree of overestimation worsens as α decreases, and increases to 1–2 orders of magnitude at α = 0.25, corresponding to the reptation limit. This has important ramifications for experimental studies that use RBLM to interpret rheology and dielectric spectroscopy observations.

 

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 model. Surprisingly, the solution to the inverse problem obtained using GP-SMs and TDD-DR was qualitatively similar. GP-SMs can be several orders of magnitude cheaper than expensive rheological models, which provides a proof-of-concept validation for using GP-SMs for inverse problems in polymer rheology. 

The linear viscoelasticity of polydisperse samples and their discretization into a blend ofn_fmonodisperse fractions is compared. The molecular weight distribution of the polydisperse sample is described using a lognormal distribution, which has two parameters that are related to the weight‐averaged molecular weight,Z_w, and the polydispersity index, ρ. Due to its success on similar systems, a variant of the double reptation model is used to describe the linear rheology. GivenZ_wand ρ, the optimal number of monodisperse fractions is obtained using the Bayesian information criterion. It quantifies and negotiates the tradeoff between accuracy (discrepancy with the response of the polydisperse sample) and complexity (number of fractions). The optimal number of fractions was found to be somewhat insensitive toZ_w; however, it increased with ρ from about 6 for ρ = 1.01 to about 20 for ρ = 2.0. Changing the underlying viscoelastic model had only a weak effect on the conclusions. Furthermore, using a blend of monodisperse fractions was found to be preferable to direct sampling even when an ensemble of chains was requested.