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Ovalbumin (OVA), one of the major allergens in hen egg, exhibits extensive structural heterogeneity due to a range of post-translational modifications (PTMs). However, analyzing the structural heterogeneity of native OVA is challenging, and the relationship between heterogeneity and IgG/IgE-binding of OVA remains unclear. In this work, ion exchange chromatography (IXC) with salt gradient elution and on-line detection by native electrospray ionization mass spectrometry (ESI MS) was used to assess the structural heterogeneity of OVA, while inhibition-ELISA was used to assess the IgG/IgE binding characteristics of OVA. Over 130 different OVA proteoforms (including glycan-free species and 32 pairs of isobaric species) were identified. Proteoforms with acetylation, phosphorylation, oxidation and succinimide modifications had reduced IgG/IgE binding capacities, whereas those with few structural modifications had higher IgG/IgE binding capacities. OVA isoforms with a sialic acid-containing glycan modification had the highest IgG/IgE binding capacity. Our results demonstrate that on-line native IXC/MS with salt gradient elution can be used for rapid assessment of the structural heterogeneity of proteins. An improved understanding of the relationship between IgG/IgE binding capacity and OVA structure provides a basis for developing biotechnology or food processing methods for reducing protein allergenicity reduction. 

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

 

Abstract An additive manufacturing-enabled bi-continuous piezocomposite architecture is presented to achieve mechanical flexibility and piezoelectricity simultaneously in piezoelectric materials. This architecture comprises an active ferroelectric ceramic phase and a passive flexible polymer phase, which are separated by a tailorable phase interface. Triply periodic minimal surfaces were used to define the phase interface, due to their excellent elastic properties and load transfer efficiency. A suspension-enclosing projection-stereolithography process was used to additively manufacture this material. Postprocesses including polymer infiltration, electroding, and poling are introduced. Piezoelectric properties of the piezocomposites are numerically and experimentally studied. The results highlight the role of tailorable triply periodic phase interfaces in promoting mechanical flexibility and piezoelectricity of bi-continuous piezocomposites.