An improved capillary model for describing the microstructure characteristics, fluid hydrodynamics and breakthrough performance of proteins in cryogel beds

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10-12 m2, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

Characterization of a convection-based support microstructure through a flow resistance parameter

Modern convection-based supports differ substantially in pore size, porosity, and microstructure topology. Due to such variability, it is challenging to evaluate the contribution of a particular microstructure topology on flow resistance. We demonstrated that the flow resistance parameter (ϕ) introduced decades ago can be used as a criterion to evaluate the effect of microstructure topology on a pressure drop when the pore size is used as a characteristic support dimension. Furthermore, the ϕ value of simple cubic packing was calculated over the entire range of open porosity and compared to the ϕ values determined for pressure drop models derived for particular convection-based supports and experimental values of various convection-based supports from the literature. It was shown that different convection-based supports become clustered into distinct groups when plotted according to their ϕ and open porosity values, allowing their discrimination. This article is protected by copyright. All rights reserved.

Simultaneous depletion of immunoglobulin G and albumin from human plasma using novel monolithic cryogel columns

In this study, we aimed to develop an alternative matrix able to deplete the albumin (Alb) and immunoglobulin G (IgG) from blood plasma simultaneously to prepare plasma samples for large-scale applications of blood-related proteomics. As a first step, nano-protein A nanoparticles (nanoProA) were prepared and characterized. Subsequently, cibacron blue F3GA (CB) was immobilized onto the nanoProA's to enhance their specific affinity for Alb molecules. Finally, both nanoparticles, specifically, nanoProA and CB-nanoProA, were separately embedded into cryogel structures to combine advantages of the nanoparticles with those of the cryogels. The protein adsorption was optimized using aqueous Alb and IgG solutions separately. Subsequently, competitive protein adsorption was performed using a protein mixture prepared with Alb and IgG adhering to their plasma protein ratios. Because of the CB-immobilization, the Alb depletion performance of the cryogels increased whereas the IgG depleting performance decreased. Using the nanoProA, embedded cryogel removed 99.3% of the IgG, while using the CB-nanoProA embedded cryogel removed 97.5% of the Alb content. The simultaneous depletion performances of the cryogels for Alb and IgG were characterized using sodium dodecyl sulfate-polyacrylamide gel electrophoresis. In this study, the monolithic cryogel-based adsorbents were classified as an alternative matrix to prepare plasma samples for proteomics applications at the preparative scale.

Cryogels: morphological, structural and adsorption characterisation

Experimental results on polymer, protein, and composite cryogels and data treatment methods used for morphological, textural, structural, adsorption and diffusion characterisation of the materials are analysed and compared. Treatment of microscopic images with specific software gives quantitative structural information on both native cryogels and freeze-dried materials that is useful to analyse the drying effects on their structure. A combination of cryoporometry, relaxometry, thermoporometry, small angle X-ray scattering (SAXS), equilibrium and kinetic adsorption of low and high-molecular weight compounds, diffusion breakthrough of macromolecules within macroporous cryogel membranes, studying interactions of cells with cryogels provides a consistent and comprehensive picture of textural, structural and adsorption properties of a variety of cryogels. This analysis allows us to establish certain regularities in the cryogel properties related to narrow (diameter 0.4<d<2 nm), middle (2<d<50 nm) and broad (50<d<100 nm) nanopores, micropores (100 nm<d<100 μm) and macropores (d>100 μm) with boundary sizes within modified life science pore classification. Particular attention is paid to water bound in cryogels in native superhydrated or freeze-dried states. At least, five states of water - free unbound, weakly bound (changes in the Gibbs free energy-ΔG<0.5-0.8 kJ/mol) and strongly bound (-ΔG>0.8 kJ/mol), and weakly associated (chemical shift of the proton resonance δ(H)=1-2 ppm) and strongly associated (δ(H)=3-6 ppm) waters can be distinguished in hydrated cryogels using (1)H NMR, DSC, TSDC, TG and other methods. Different software for image treatment or developed to analyse the data obtained with the adsorption, diffusion, SAXS, cryoporometry and thermoporometry methods and based on regularisation algorithms is analysed and used for the quantitative morphological, structural and adsorption characterisation of individual and composite cryogels, including polymers filled with solid nano- or microparticles.