High-throughput Characterization Methods Research Articles

High-throughput exploration of the Na2O-ZrO2-SiO2-P2O5 composition space using thin film material libraries

AbstractThe composition space Na2O-ZrO2-SiO2-P2O5 was explored using thin film material libraries and high-throughput characterization methods. The combinatorial synthesis comprised non-reactive magnetron co-sputtering at room temperature from two elemental targets Zr and Si, and a compound Na3PO4 target followed by a two-step annealing process. The NASICON phase formed for all compositions on the material library produced at the optimized deposition parameters, also displaying a phase-pure Na1+xZr2SixP3−xO12 (x = 0.4—2.9) region. Analytical TEM analysis of the NASICON phase at a measurement area with stoichiometric composition revealed a fine-grained microstructure and homogeneous Si distribution, in contrast to Na-P excess conditions, where large Si-free NASICON grains have formed.

High Throughput Electrochemical Screening of Phosphate-Rich Nonflammable Electrolytes in Lithium-Ion Batteries

Frequent fires and explosions in lithium-ion batteries (LIBs) used in grid energy storage systems (ESS) highlight the necessity of revisiting nonflammable phosphate electrolytes as alternatives to the currently used flammable carbonates. However, previous studies have shown the difficulty of integrating phosphate solvents into LIB electrolytes due to compatibility issues with graphite. In this work, we developed a high-throughput (HTP) electrochemical characterization method, akin to pH test paper, to rapidly screen potential phosphate electrolytes and graphite materials. Through HTP screening, we identified 101 promising combinations out of 1,740. This number was reduced to 26 after testing in Li/Graphite half cells. The optimized phosphate-rich electrolyte (60 v% phosphate) with cosolvents demonstrated 300 stable cycles at 0.1 C in Graphite/LiFePO4 (LFP) full cells with thick electrodes (∼3.0 mAh cm−2), surpassing prior research findings. This unique HTP method provides a powerful tool to expedite the development of safe LIBs for ESS applications.

Virus-like particles as robust tools for functional assessment: Deciphering the pathogenicity of ABCA4 genetic variants of uncertain significance

The retina-specific ABCA transporter, ABCA4, is essential for vision, and its genetic variants are associated with a wide range of inherited retinal degenerative diseases (IRDs) leading to blindness. Of the 1,630 identified missense variants in ABCA4, ∼50% are of unknown pathogenicity (variants of unknown significance, VUS). This genetic uncertainty presents three main challenges: (i) inability to predict disease-causing variants in relatives of IRD patients with multiple ABCA4 mutations; (ii) limitations in developing variant-specific treatments; and (iii) difficulty in using these variants for future disease prediction, affecting patients' life-planning and clinical trial participation. To unravel the clinical significance of ABCA4 genetic variants at the level of protein function, we have developed a virus-like particle (VLP)-based system that expresses the ABCA4 protein and its variants. We validated the efficacy of this system in the enzymatic characterization (ATPase activity) of VLPs harboring ABCA4 and two variants of established pathogenicity: p.N965S and p.C1488R. Our results were consistent with previous reports and clinical phenotypes. We also applied this platform to characterize the VUS p.Y1779F and observed a functional impairment, suggesting a potential pathogenic impact. This approach offers an efficient, high-throughput method for ABCA4 VUS characterization. Our research points to the significant promise of the VLP-based system in the functional analysis of membrane proteins, offering important perspectives on the disease-causing potential of genetic variants and shedding light on genetic conditions involving such proteins.

High-throughput preparation and quick characterization of oxidation behaviors of complex Al–Cr compositional gradient coatings on a novel Co–Al–W–based superalloy prepared using multi-arc ion plating technology

The early oxidation behaviors of complex Al–Cr compositional gradient coatings on a novel Co–Al–W substrate prepared by multi-arc ion plating technology (MIPT) at 1000 °C for 20–60 min were rapidly characterized. The as-deposited coatings with compositions ranging from (55%–90%Al, 45%–10%Cr) exhibited the laminated structure α-Al + Al80Cr20+Al8Cr5+α-Cr. After annealing at 640 °C for 40 h, a two-layered coating composed of (Co(Al, Cr) + Al8Co18Cr4) and IRL formed, and the thickness of the coating increased from the 14–18 μm of the as-deposited state to 25–35 μm. During oxidation at 1000 °C for 20–60 min, a three-layer structure consisting of an outer α-Al2O3+α-Cr2O3+CoCr2O4 layer, a central unoxidized Al–Cr layer, and an inner μ-Co7(W0.55Cr0.45) 6+B2–CoAl layer formed on the substrate. The Al–Cr coatings with higher Cr content had a looser oxide layer due to the CoCr2O4 phase, which might degrade the intended protective effect of the Al–Cr coatings. Thus, the Al–Cr coatings with higher Al content were suitable for preventing oxidation behavior. This work proposes a high-throughput screening method for rapid characterization of the early oxidation mechanism of the complex Al–Cr compositional gradient coatings.

Simple Scattering: Lipid nanoparticle structural data repository.

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

Correlation of microstructure and hardness distribution of high-speed train wheels under original and service statuses

High-speed train wheels have complex service environments during wheel–rail contact, and a comprehensive understanding of the microstructural inhomogeneity at the rim is critical for service safety assessment. The local quenching process in the vicinity of the rim will cause a difference in microstructure, directly affecting the service performance of the material. In this study, high-throughput characterization method based on material genome engineering was used, the methods for characterising and identifying the microstructure of wheel steel were developed using high-throughput scanning electron microscopy, and the accuracy and reproducibility of the methods were evaluated. A point-to-point research scheme for microstructure image and hardness data was designed based on the concept of statistical mapping characterization. The area fraction of the proeutectoid ferrite and micro-Vickers hardness distribution in a large size range of the key area of the rim and their internal correlation was obtained, these helped avoid statistical and representativeness limitations of traditional local selection analysis of microstructure and hardness. The results indicates that the area fraction of proeutectoid ferrite and micro-Vickers hardness showed a clear gradient distribution, and opposite distribution trend, with a high negative correlation. In addition, by observing the correlation between microstructure and hardness, and studying the evolution of microstructure, it was found that the microstructure and hardness of the rim near the tread demonstrated a significant synergistic evolution law under the influence of service, this was observed both statistically and microscopically. These findings enhanced the application prospects of the microstructure-performance statistical mapping method, and were beneficial in efficiently evaluating the service life and safety of key components.

Mechanical Metamaterials Fabricated From Self-Assembly: A Perspective

Abstract Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials.

An improved method for large scale generation and high-throughput functional characterization of human iPSC-derived microglia

Neuroscience drug discovery has faced significant challenges due to restricted access to relevant human cell models and limited translatability of existing preclinical findings to human pathophysiology. Induced pluripotent stem cells (iPSCs) have emerged as a promising solution, offering the potential to generate patient-specific cell types, including in the recent years, iPSC-derived microglia (iMGL). Current methods rely on complex and time-consuming differentiation procedures, leading to considerable batch-to-batch variability consequently hindering the establishment of standardized and reproducible high-throughput functional screening approaches. Addressing these challenges is critical in ensuring the generation of homogenous iMGL populations with consistent functional properties. In this study we describe an improved high-yield protocol for generating iMGL, which allows for increased reproducibility and flexibility in the execution of high-throughput functional screens. We introduce a two-step process in embryoid bodie (EB) maintenance and a stop point allowing for cryopreservation at the hematopoietic progenitor cell (iHPC) stages. Furthermore, we demonstrate inter-operator robustness of this modified protocol in a range of high-throughput functional assays including phagocytosis, lysosomal acidification, chemotaxis, and cytokine release. Our study underscores the importance of quality control checks at various stages of iPSC-differentiation and functional assay set up, highlighting novel workarounds to the existing challenges such as limited yield, flexibility, and reproducibility, all critical in drug discovery.

Biofilm development of Candida boidinii and the effect of tyrosol on biofilm formation

ObjectivesThe applicability of a simple and high-throughput method for quantitative characterization of biofilm formation by Candida boidinii was tested in order to evaluate the effects of exogenous tyrosol on yeast growth and biofilm formation capacity.ResultsSignificant concentration-, temperature and time-dependent effect of tyrosol (2-(4-hydroxyphenyl)ethanol) was demonstrated, but it differentially affected the growth and biofilm formation (characterized by crystal violet staining and XTT-reduction assay) of Candida boidinii. Testing biofilm based on metabolic activity displayed sensitively the differences in the intensity of biofilm in terms of temperature, tyrosol concentration, and exposure time. At 22 °C after 24 h none of the tyrosol concentrations had significant effect, while at 30 °C tyrosol-mediated inhibition was observed at 50 mM and 100 mM concentration. After 48 h and 72 h at 22 °C, biofilm formation was stimulated at 6.25–25 mM concentrations, meanwhile at 30 °C tyrosol decreased the biofilm metabolic activity proportionally with the concentration.ConclusionsThe research concludes that exogenous tyrosol exerts unusual effects on Candida boidinii growth and biofilm formation ability and predicts its potential application as a regulating factor of various fermentations by Candida boidinii.

A high-throughput phenotyping method for sugarcane rind penetrometer resistance and breaking force characterization by near-infrared spectroscopy

BackgroundSugarcane (Saccharum spp.) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Rind penetrometer resistance (RPR) and breaking force are two kinds of regular parameters for mechanical strength characterization. However, due to the lack of efficient methods for determining RPR and breaking force in sugarcane, genetic approaches for improving these traits are generally limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.ResultsBased on well-established laboratory measurements of sugarcane stalk internodes collected in the years 2019 and 2020, considerable variations in RPR and breaking force were observed in the stalk internodes. Following a standard NIRS calibration process, two online models were obtained with a high coefficient of determination (R2) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R2 and RPD values as high as 0.997 and 17.70, as well as showing relatively low root mean square error values at 0.44 N mm−2 during global modeling, demonstrating excellent predictive performance.ConclusionsThis study delivered a successful attempt for rapid and precise prediction of rind penetrometer resistance and breaking force in sugarcane stalk by NIRS assay. These established models can be used to improve phenotyping jobs for sugarcane germplasm on a large scale.

An assessment of polarized light microscopy as a characterization method for crystal plasticity simulations

Polarized light microscopy is a large-area, high-resolution, high-throughput microstructural characterization method for polycrystalline materials comprised of hexagonal close-packed crystals. Due to the fact that polarized light microscopy only determines the orientation of a crystal’s c axis, it is necessary to assess the applicability of this new characterization modality for use in integrated computational materials engineering workflows for simulating the deformation response of polycrystalline materials. We present a computational study in which the effect of this orientation ambiguity on the predictions of crystal plasticity finite element simulations is quantified. We focus on an idealized polycrystalline sample with random texture, from which a number of c axis-similar samples are generated, each with random rotations about each constituent crystal’s c axis. We scrutinize the differences in stress field predictions between the reference sample and the randomly altered samples during monotonic tensile tests, as well as the spatial differences in predictions. Our findings indicate that differences are lowest in the elastic regime, and increase dramatically during the macroscopic elastic–plastic transition. We further find that results do not exhibit a strong spatial dependence, indicating that orientation and neighborhood are the primary causes of differences in stress field predictions.

Candida Cell Heterogeneity Measured With a Microwave Flow Cytometer

Heterogeneity is an important property of a cell population. It is an intrinsic driving force behind biological evolution and a main challenge for effective disease treatment, such as precision solid cancer therapy. Various “omics” methods, mass spectroscopy-based techniques, and flow cytometry have been developed for cell heterogeneity characterization. But current methods are either low throughput or invasive. In this work, we develop a simple microwave flow cytometer to measure single <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. tropicalis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. albicans</i> , which are two of the most important <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Candida</i> yeasts that cause candidemia disease. The obtained scatter plots of cell permittivity for both the species show a colinear relationship for cells of different volumes, which indicates a largely conserved cell molecular composition profile despite heterogeneous cell sizes. We also apply microwave flow cytometer to measure antifungal drug effect on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. tropicalis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. albicans</i> cell population and observe that drugs induce significant heterogeneity increase. The results show that microwave flow cytometry is a promising new method for high throughput and noninvasive characterization of cell heterogeneity. More work is needed, including the determination of biological significance of the observed microwave heterogeneity.

Amplicon-Based High-Throughput Sequencing Method for Genotypic Characterization of Norovirus in Oysters.

Norovirus is a highly diverse RNA virus often implicated in foodborne outbreaks, particularly those associated with shellfish. Shellfish are filter feeders, and when harvested in bays exposed to wastewater overflow or storm overflows, they can harbor various pathogens, including human-pathogenic viruses. The application of Sanger or amplicon-based high-throughput sequencing (HTS) technologies to identify human pathogens in shellfish faces two main challenges: (i) distinguishing multiple genotypes/variants in a single sample and (ii) low concentrations of norovirus RNA. Here, we assessed the performance of a novel norovirus capsid amplicon HTS method. We generated a panel of spiked oysters containing various norovirus concentrations with different genotypic compositions. Several DNA polymerases and reverse transcriptases (RTs) were compared, and performance was evaluated based on (i) the number of reads passing quality filters per sample, (ii) the number of correct genotypes identified, and (iii) the sequence identity of outputs compared to Sanger-derived sequences. A combination of the reverse transcriptase LunaScript and the DNA polymerase AmpliTaq Gold provided the best results. The method was then employed, and compared with Sanger sequencing, to characterize norovirus populations in naturally contaminated oysters. IMPORTANCE While foodborne outbreaks account for approximately 14% of norovirus cases (L. Verhoef, J. Hewitt, L. Barclay, S. Ahmed, R. Lake, A. J. Hall, B. Lopman, A. Kroneman, H. Vennema, J. Vinjé, and M. Koopmans, Emerg Infect Dis 21:592-599, 2015), we do not have standardized high-throughput sequencing methods for genotypic characterization in foodstuffs. Here, we present an optimized amplicon high-throughput sequencing method for the genotypic characterization of norovirus in oysters. This method can accurately detect and characterize norovirus at concentrations found in oysters grown in production areas impacted by human wastewater discharges. It will permit the investigation of norovirus genetic diversity in complex matrices and contribute to ongoing surveillance of norovirus in the environment.

Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning

High-throughput characterization (HTC) of composition-process-structure-property relations is essential for accelerating molecular and material discovery and manufacturing paradigms. Here, we present a rapid, autonomous method for HTC of hydrogel rheological properties in well plate formats via automated sensing and physics-guided supervised machine learning. The novel HTC method facilitates rapid, autonomous characterization of hydrogel rheological properties and percolation processes associated with gelation and network interpenetration in 96-well plate formats at a rate of 24 s/sample (70 times faster than the state-of-the-art). Viscoelastic properties and phase behavior obtained by the method were benchmarked against traditional rheology studies. The speed and utility of the method were demonstrated by high-resolution characterization of the gel point of Pluronic F127, collagen, and alginate-PNIPAM hydrogels in 96-well plate formats at resolutions of 0.31 wt% (Pluronic F127), 0.031 mg/ml (collagen), and 0.069 wt% (NIPAM), respectively. Experimental composition-property relation data generated from sensor multivariate time-series data, calibration data, and fluid-structure interaction models enabled accurate classification of sample phase using supervised machine learning. Feature augmentation using sensor physics, here, a fluid-structure interaction model, improved material (i.e., sample) phase classification accuracy relative to that obtained in the absence of physics-based feature augmentation. Ultimately, creating rapid, autonomous HTC methods that synergize with common high-throughput experimentation formats, such as well plates, can accelerate the pace of research across several disciplines as well as generate new tools for quality assurance and control across emerging industries.

High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning

The strengthening phases characteristics in the alloy determine the mechanical properties of the alloy, but it is a hard task to predict the precipitation of complex alloys. In this work, we quickly detected 33,484 groups of Ni-based superalloys composition information and microstructure image by integrating high-throughput experiment and a nested UNet 3+ architecture for image recognition, and established a database of γ′ precipitation. Based on the database, a high-confidence prediction model was established, which could accurately predict the volume fraction, average size and size distribution of γ′ prediction in different alloys. Compared with the traditional methods, the proposed approach has a remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multi-component alloys.

Correlations of Composition, Structure, and Hardness in the High-Entropy Alloy System Nb–Mo–Ta–W

Refractory high-entropy alloys are of interest due to the potential of compositionally complex alloys to achieve combinations of mechanical properties such as room-temperature ductility and high-temperature strength rarely found in simpler alloys. To study a large compositional range of the system Nb–Mo–Ta–W, thin-film materials libraries were fabricated by combinatorial sputtering. High-throughput characterization methods were used to systematically determine composition-dependent properties: (I) the extent and stability of the complex solid solution range and (II) the mechanical properties (Young’s modulus, hardness). The whole investigated composition range of Nb20–59Mo9–31Ta10–42W12–32 crystallized in a bcc phase, independent of annealing temperatures ranging from 300 to 900 °C. Mechanical strength values of the Nb–Mo–Ta–W compositions were calculated using the Maresca–Curtin analytical model parameterized with experimental data. A strong positive correlation with measured hardness was observed that allows using this analytical model for optimization of the mechanical strength. We predict that compositions with high Mo contents provide the highest hardness values.

High-throughput characterization of mechanical parameters of thin-film array

The thin-film array is a typical multi-film structure in material genetic engineering. With the requirements for rapid and efficient characterization of new film materials, a high-throughput characterization method based on digital image correlation for mapping the film elastic modulus and Poisson’s ratio of thin-film arrays is developed. First, a high-throughput experimental structure and testing system for the film array are designed, from which we can load 10 × 10 circular film arrays in the range of 0–25 kPa and obtain the full-field bulging displacement. Then, the experimental results are combined with the surface wave velocity equation to decouple the elastic modulus and Poisson’s ratio of the film array. The results are consistent with the material manual parameters, which verifies the feasibility of the high-throughput test method proposed in this article. When the method in this paper is applied to gradient materials, high-throughput testing of the mechanical parameters of a variety of film materials can be achieved. The results show that this method is valuable for quick, reliable and high-throughput determination of the film elastic modulus and Poisson’s ratio in material genetic engineering.

Reactive phosphine combinatorial co-sputtering of cation disordered ZnGeP2 films

High-throughput synthesis and characterization methods were used to investigate ZnGeP2 thin films.

High-throughput experiment for the rapid screening of organic phase change materials

An emerging dynamic calorimetry method based on infrared thermography (IRT method) has recently proven to be of great interest for the fast establishment of the phase diagrams of binary systems of organic materials (sugar alcohols, fatty acids, fatty alcohols). The methods allowed for their determination in a single 2-h experiment when standard techniques require weeks of measurements hence enabling a significant improvement of the efficiency of material screening processes. As adding a component to the system greatly increases the number of compositions to be studied (21 for binary, 231 for ternary assuming a 5 mol% increment between compositions), the IRT method may be of even greater use with increasing system complexity. This work then aims at studying the applicability of IRT for the fast establishment of phase diagrams of ternary systems with the lauric (LA), myristic (MA), and palmitic (PA) fatty acids ternary system as an illustrative case. A phase diagram in agreement with the results of the literature and with DSC measurements is established using IRT in a few hours-experiments instead of weeks to months with standard methods. The obtained results (e.g.67 mol%LA + 23 mol%MA + 10 mol%PA for the eutectic composition, occurrence of a ternary eutectic reaction at 32 °C) are promising. This technique also successfully allows for rapid exploration of the phase diagram: reconstruction of the liquidus surface, identification of the eutectic transition, 4-phase equilibrium. IRT is a particularly interesting high-throughput characterization method for the establishment of the phase diagrams of ternary (or higher-order) systems.

High-Throughput FRAP Analysis of Solute Diffusion in Hydrogels.

Increasingly accurate mathematical models have been developed to relate solute and hydrogel properties to solute diffusion coefficients in hydrogels, primarily by comparing solute sizes and hydrogel mesh sizes. Here, we use a standardized, high-throughput method for fluorescence recovery after photobleaching (FRAP) experiments and analysis to characterize the diffusion coefficients of fluorescein, three sizes of FITC-dextran, and three sizes of FITC-conjugated poly(ethylene glycol) (PEG) through 18 structurally varied poly(vinyl alcohol) (PVA) hydrogel formulations. Increasing the hydrogel mesh radii increased the diffusivities of all the tested solutes within the hydrogels. While the diffusivity of FITC-dextrans in hydrogels decreased with increasing solute size, the diffusivity of FITC-PEGs increased with increasing solute size, suggesting that a generalized hydrodynamic radius-based model is not universally applicable for solute diffusion in hydrogels. The high-throughput characterization method for solute diffusion in hydrogels described here facilitates precise hydrogel design for biomedical applications.