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Nanocellulose, as a promising natural material, has recently received much attention because of its remarkable features including recyclability, biocompatibility, low risk of toxicity, and tunable surface properties. This review article first introduces three types of nanocellulose (nanocrystalline, nanofibrillar, and bacterial) and evaluates their production processes. In addition, contemporary research is discussed in the formulations of nanoparticles, tablets, hydrogels, and aerogels. As reported in the literature, the release time of nanocellulose-based systems varies from a few minutes to several days as they provide a controlled and sustained release. Thus, such systems have shown considerable potential for developing a novel generation of controlled drug delivery for different routes of administration (oral, transdermal, etc.). This review facilitates the selection of proper source and processing techniques for nanocellulose production while addressing opportunities and challenges ahead ending up with identifying sustainable ongoing research directions into its applications in drug delivery.

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The solubilities of amiodarone HCl (AMDH) in binary mixtures of ethanol + water and N-methyl pyrrolidone (NMP) + water at (293.2, 298.2, 303.2, 308.2, and 313.2) K are reported. In the ethanol + water system the maximum solubility of AMDH was observed at 313.2 K and an ethanol mass fraction of 0.76. The combination of the Jouyban−Acree model and van't Hoff equation was used to predict the solubility of AMDH in the mixed solvents at different temperatures. A mean relative deviation of 12.2 % was obtained for the correlated data. The standard enthalpy, entropy ,and Gibbs energy changes of solutions for AMDH in ethanol + water and NMP + water mixtures were calculated from the measured solubility data using the modified version of the van't Hoff equation.

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Nonlinear time series analysis techniques were applied to predict pressure fluctuation data in fluidized beds in two different hydrodynamic states. The method of delays was used to reconstruct the state space attractor to carry out analysis in the reconstructed state space. The state space reconstruction parameters, i.e., time delay and embedding dimension, were determined and the results shown that their values were different for various types of methods introduced in the literature. Chaotic behavior and predictability of fluidized system were determined by introducing two nonlinear dynamic invariants, correlation dimension and entropy, in different ways. The traditional linear autoregression method and state space based prediction methods (SSBPMs), i.e., nearest neighbors and locally linear, and global linear methods, were applied to predict the pressure fluctuation signals. The quality of prediction was assessed by comparison of the predicted data with its original benchmark. In addition, the dynamic invariants of measured and predicted attractor of the pressure signals were compared. The results showed that SSBPMs are preferred to the traditional linear methods. Finally, a continuous uncertainty band of pressure signals of single and multiple bubble regimes for the prediction methods was presented.

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