While injectable in situ cross-linking hydrogels have attracted increasing attention as minimally invasive tissue scaffolds and controlled delivery systems, their inherently disorganized and isotropic network structure limits their utility in engineering oriented biological tissues. Traditional methods to prepare anisotropic hydrogels are not easily translatable to injectable systems given the need for external equipment to direct anisotropic gel fabrication and/or the required use of temperatures or solvents incompatible with biological systems. Herein, we report a new class of injectable nanocomposite hydrogels based on hydrazone cross-linked poly(oligoethylene glycol methacrylate) and magnetically aligned cellulose nanocrystals (CNCs) capable of encapsulating skeletal muscle myoblasts and promoting their differentiation into highly oriented myotubes in situ. CNC alignment occurs on the same time scale as network gelation and remains fixed after the removal of the magnetic field, enabling concurrent CNC orientation and hydrogel injection. The aligned hydrogels show mechanical and swelling profiles that can be rationally modulated by the degree of CNC alignment and can direct myotube alignment both in two- and three-dimensions following coinjection of the myoblasts with the gel precursor components. As such, these hydrogels represent a critical advancement in anisotropic biomimetic scaffolds that can be generated noninvasively in vivo following simple injection.
In this work, we present a novel, data-driven, quality modeling, and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting linear time-invariant (LTI), dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate polymerization reactor. Results for both disturbance rejection and set-point changes (i.e., new quality grades) are demonstrated.
The problem of driving a batch process to a specified product quality using data-driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this "missing data" problem by integrating a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of a nylon-6,6 batch polymerization process with limited measurements.
Batch process reactors are often
used for products where quality
is of paramount importance. To this end, this work addresses the problem
of direct, data-driven, quality control for batch processes. Specifically,
previous results using subspace identification for modeling dynamic
evolution and making quality predictions are extended with two key
novel contributions: first, a method is proposed to account for midbatch
ingredient additions in both the modeling and control stages. Second,
a novel model predictive control scheme is proposed that includes
batch duration as a decision variable. The efficacy of the proposed
modeling and control approaches are demonstrated using a simulation
study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation
results show that the proposed controller is able to reject disturbances
in feed stock and drive the number-average molecular weight, weight-average
molecular weight, and conversion to their respective set-points. Specifically,
mean absolute percentage errors (MAPE) in these variables are reduced
from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%,
1.90%, and 1.67%, respectively.
The development of in situ-gelling hydrogels that can enable prolonged protein release is increasingly important due to the emergence of a growing number of protein-based therapeutics. Herein, we describe a highthroughput strategy to fabricate, characterize, and subsequently optimize hydrazone-cross-linked in situ-gelling hydrogels for protein delivery. Hydrogels are fabricated using an automated high-throughput robot to mix a variety of thermoresponsive, nonthermoresponsive, charged, neutral, naturally sourced, and synthetic polymers functionalized with hydrazide or aldehyde groups, generating in situ-gelling hydrogels with well-defined compositions within a 96-well plate. High-throughput characterization strategies are subsequently developed to enable on-plate analysis of hydrogel swelling, mechanics, degradation, transparency, and protein (ovalbumin) release kinetics that yield results consistent with those collected using traditional bulk hydrogel analysis techniques. Dynamic regression and latent variable modeling are then applied to fit performance statistics to the collected data set; subsequently, numerical optimization is used to identify mixtures of precursor polymers that exhibit targeted combinations of minimal burst release, maximum total protein release, minimum release rate, and maximum transparency (the latter of particular relevance for ophthalmic protein delivery applications). Given the rapid throughput of the protocols developed (i.e., 126 hydrogels can be synthesized and screened in quadruplicate within hours), this approach offers particular promise for accelerating the identification of injectable hydrogel compositions relevant for both protein delivery as well as other biomedical applications for which clearly predefined materials properties are required.
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