The Global Exportation of the U.S. Bayh-Dole Act

Special issue: Intellectual Property and Technolog

Modeling the drug release from hydrogel-based matrices

In this work the behavior of hydrogel-based matrices, the most widespread systems for oral controlled release of pharmaceuticals, has been mathematically described. In addition, the calculations of the model have been validated against a rich set of experimental data obtained working with tablets made of hydroxypropyl methylcellulose (a hydrogel) and theophylline (a model drug). The model takes into account water uptake, hydrogel swelling, drug release, and polymer erosion. The model was obtained as an improvement of a previous code, describing the diffusion in concentrated systems, and obtaining the erosion front (which is a moving boundary) from the polymer mass balance (in this way, the number of fitting parameters was also reduced by one). The proposed model was found able to describe all the observed phenomena, and then it can be considered a tool with predictive capabilities, useful in design and testing of new dosage systems based on hydrogels

Monte Carlo Simulations of Water Adsorption in Aluminum Oxide Rod-Based Metal–Organic Frameworks

Atmospheric water harvesting utilizing nanoporous sorbent materials with suitable adsorption characteristics has recently emerged as a potential solution for the global water crisis. Here, we probe the adsorption behavior of two high-performing Al(μ2-OH) rod-based metal–organic frameworks (MOFs), MOF-303 and MOF-333, using Gibbs ensemble Monte Carlo simulations. We find that simulations using nonpolarizable force fields and rigid framework structures optimized using periodic electronic structure calculations can achieve good agreement with experimental data for adsorption isotherms and isosteric heats of adsorption; however, for MOF-303, it is important to utilize a structure that accounts for the distortion associated with water adsorbed at the primary adsorption site

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application