Polymer Concentration Maximizes Encapsulation Efficiency in Electrohydrodynamic Mixing Nanoprecipitation

Encapsulation in self-assembled block copolymer (BCP) based nanoparticles (NPs) is a common approach to enhance hydrophobic drug solubility, and nanoprecipitation processes in particular can yield high encapsulation efficiency (EE). However, guiding principles for optimizing polymer, drug, and solvent selection are critically needed to facilitate rapid design of drug nanocarriers. Here, we evaluated the relationship between drug-polymer compatibility and concentration ratios on EE and nanocarrier size. Our studies employed a panel of four drugs with differing molecular structures (i.e., coumarin 6, dexamethasone, vorinostat/SAHA, and lutein) and two BCPs [poly(caprolactone)-b-poly(ethylene oxide) (PCL-b-PEO) and poly(styrene)-b-poly(ethylene oxide) (PS-b-PEO)] synthesized using three nanoprecipitation processes [i.e., batch sonication, continuous flow flash nanoprecipitation (FNP), and electrohydrodynamic mixing-mediated nanoprecipitation (EM-NP)]. Continuous FNP and EM-NP processes demonstrated up to 50% higher EE than batch sonication methods, particularly for aliphatic compounds. Drug-polymer compatibilities were assessed using Hansen solubility parameters, Hansen interaction spheres, and Flory Huggins interaction parameters, but few correlations were EE observed. Although some Hansen solubility (i.e., hydrogen bonding and total) and Flory Huggins interaction parameters were predictive of drug-polymer preferences, no parameter was predictive of EE trends among drugs. Next, the relationship between polymer: drug molar ratio and EE was assessed using coumarin 6 as a model drug. As polymer:drug ratio increased from <1 to 3–6, EE approached a maximum (i.e., ∼51% for PCL BCPs vs. ∼44% PS BCPs) with Langmuir adsorption behavior. Langmuir behavior likely reflects a formation mechanism in which drug aggregate growth is controlled by BCP adsorption. These data suggest polymer:drug ratio is a better predictor of EE than solubility parameters and should serve as a first point of optimization.

Beyond the Scales: A physics-informed machine learning approach for more efficient modeling of SARS-CoV-2 spike glycoprotein

This paper presents a physics-informed machine learning approach to the derivation of a bottom-up coarse-grained model of the SARS-CoV-2 spike glycoprotein from all-atomic molecular dynamics simulations. The machine learning procedure employs a force-matching scheme in the optimization of interaction parameters, where the force-matching scheme is combined in methodology with the initialization of the interaction parameters by the traditional iterative Boltzmann inversion method. The force-matched machine learning procedure is constructed based on two physics-informed layers: one is the Harmonic layer consisting of bond, angle, and dihedral terms as bonded potentials; the other is the Lennard-Jones layer consisting of the non-bonded Lennard-Jones potential. Coarse-grained validation simulations are performed with the learned parameters to test the derived bottom-up coarse-grained model. The simulations are able to reach the microsecond time scale with stability. The physics-informed learning approach yields simulation speeds nearly 40,000 times faster than conventional all-atomic simulations while maintaining comparable simulation accuracy. Additionally, through examination of the non-bonded Lennard-Jones parameters and the radial distribution function analysis, the learning approach matches pairwise distances of the ground-truth data with greater accuracy than the conventional iterative approach method.

Contraction: A Unified Perspective of Correlation Decay and Zero-Freeness of 2-Spin Systems

We study the connection between the correlation decay property (more precisely, strong spatial mixing) and the zero-freeness of the partition function of 2-spin systems on graphs of bounded degree. We show that for 2-spin systems on an entire family of graphs of a given bounded degree, the contraction property that ensures correlation decay exists for certain real parameters implies the zero-freeness of the partition function and the existence of correlation decay for some corresponding complex neighborhoods. Based on this connection, we are able to extend any real parameter of which the 2-spin system on graphs of bounded degree exhibits correlation decay to its complex neighborhood where the partition function is zero-free and correlation decay still exists. We give new zero-free regions in which the edge interaction parameters and the uniform external field are all complex-valued, and we show the existence of correlation decay for such complex regions. As a consequence, we obtain approximation algorithms for computing the partition function of 2-spin systems on graphs of bounded degree for these complex parameter settings.

Evaluation of radiation interaction parameters of some shape memory alloys

In this study, effective atomic number (Zeff), atomic (σta) and electronic cross section (σte) values of some shape memory alloys (SMA) were calculated at energies 5.9, 6.1, 8, 11.2, 25, 59.543, 75, 112, 149 keV. It has been observed that the obtained values of the calculated parameters vary depending on the photon intensity, chemical constitution and density of the alloys. Calculations were made using the WinXCom program and the graph of the change according to the energy of the obtained results was drawn. The results of this study are thought to be beneficial in the application of various fields.