scholarly journals Computational Method for Simulating Thermoset Polymer Curing and Prediction of Thermophysical Properties

2020 ◽  
Author(s):  
Jeffrey Sanders ◽  
Carla E. Estridge ◽  
Matthew B Jackson ◽  
Thomas JL Mustard ◽  
Samuel J. Tucker ◽  
...  
Keyword(s):  
High Throughput ◽  
Thermophysical Properties ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Polymer Network ◽  
Thermomechanical Properties ◽  
Computational Method ◽  
Cross Linking ◽  
Automated Simulation

Thermoset polymers are an area of intense research due to their low cost, ease of processing, environmental resistance, and unique physical properties. The favorable properties of this class of polymers have many applications in aerospace, automotive, marine, and sports equipment industries. Molecular simulations of thermosets are frequently used to model formation of the polymer network, and to predict the thermomechanical properties. These simulations usually require custom algorithms that are not easily accessible to non-experts and not suited for high throughput screening. To address these issues, we have developed a robust cross-linking algorithm that can incorporate different types of chemistries and leverage GPU-enabled molecular dynamics simulations. Automated simulation analysis tools for cross-linking simulations are also presented. Using four well known epoxy/amine formulations as a foundational case study and benzoxazine as an example of how additional chemistries can be modeled, we demonstrate the power of the algorithm to accurately predict curing and thermophysical properties. These tools are able to streamline the thermoset simulation process, opening up avenues to in-silico high throughput screening for advanced material development.

scholarly journals Computational Method for Simulating Thermoset Polymer Curing and Prediction of Thermophysical Properties

2020 ◽  
Author(s):  
Jeffrey Sanders ◽  
Carla E. Estridge ◽  
Matthew B Jackson ◽  
Thomas JL Mustard ◽  
Samuel J. Tucker ◽  
...  
Keyword(s):  
High Throughput ◽  
Thermophysical Properties ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Polymer Network ◽  
Thermomechanical Properties ◽  
Computational Method ◽  
Cross Linking ◽  
Automated Simulation

Thermoset polymers are an area of intense research due to their low cost, ease of processing, environmental resistance, and unique physical properties. The favorable properties of this class of polymers have many applications in aerospace, automotive, marine, and sports equipment industries. Molecular simulations of thermosets are frequently used to model formation of the polymer network, and to predict the thermomechanical properties. These simulations usually require custom algorithms that are not easily accessible to non-experts and not suited for high throughput screening. To address these issues, we have developed a robust cross-linking algorithm that can incorporate different types of chemistries and leverage GPU-enabled molecular dynamics simulations. Automated simulation analysis tools for cross-linking simulations are also presented. Using four well known epoxy/amine formulations as a foundational case study and benzoxazine as an example of how additional chemistries can be modeled, we demonstrate the power of the algorithm to accurately predict curing and thermophysical properties. These tools are able to streamline the thermoset simulation process, opening up avenues to in-silico high throughput screening for advanced material development.

Computer simulation study about the dependence of amorphous silicon photonic waveguides efficiency on the material quality

2020 ◽  
Vol 90 (3) ◽  
pp. 30502
Author(s):  
Alessandro Fantoni ◽  
João Costa ◽  
Paulo Lourenço ◽  
Manuela Vieira
Keyword(s):  
Amorphous Silicon ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Deposition Process ◽  
Large Area ◽  
Sidewall Roughness ◽  
Sensing Applications ◽  
Fabrication Defects ◽  
The Impact

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.

scholarly journals MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

2017 ◽  
Vol 22 (10) ◽  
pp. 1246-1252 ◽  
Author(s):  
Kishore Kumar Jagadeesan ◽  
Simon Ekström
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
High Throughput Screening ◽  
Web Application ◽  
Low Cost ◽  
Maldi Ms ◽  
Ionization Mass ◽  
Label Free ◽  
Label Free Detection ◽  
R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

scholarly journals Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

2018 ◽  
Author(s):  
isabelle Heath-Apostolopoulos ◽  
Liam Wilbraham ◽  
Martijn Zwijnenburg
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Degrees Of Freedom ◽  
Chemical Space ◽  
Low Cost ◽  
Computational Screening ◽  
Computational Workflow ◽  
Conformational Degrees Of Freedom

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

Genomics and Systems Biology.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. sci-51-sci-51
Author(s):  
Todd R. Golub
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Biological Networks ◽  
High Throughput Screening ◽  
Expression Profiles ◽  
Low Cost ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Small Molecule Libraries ◽  
Approved Drugs

Genomics holds particular potential for the elucidation of biological networks that underlie disease. For example, gene expression profiles have been used to classify human cancers, and have more recently been used to predict graft rejection following organ transplantation. Such signatures thus hold promise both as diagnostic approaches and as tools with which to dissect biological mechanism. Such systems-based approaches are also beginning to impact the drug discovery process. For example, it is now feasible to measure gene expression signatures at low cost and high throughput, thereby allowing for the screening libraries of small molecule libraries in order to identify compounds capable of perturbing a signature of interest (even if the critical drivers of that signature are not yet known). This approach, known as Gene Expression-Based High Throughput Screening (GE-HTS), has been shown to identify candidate therapeutic approaches in AML, Ewing sarcoma, and neuroblastoma, and has identified tool compounds capable of inhibiting PDGF receptor signaling. A related approach, known as the Connectivity Map (www.broad.mit.edu/cmap) attempts to use gene expression profiles as a universal language with which to connect cellular states, gene product function, and drug action. In this manner, a gene expression signature of interest is used to computationally query a database of gene expression profiles of cells systematically treated with a large number of compounds (e.g., all off-patent FDA-approved drugs), thereby identifying potential new applications for existing drugs. Such systems level approaches thus seek chemical modulators of cellular states, even when the molecular basis of such altered states is unknown.

scholarly journals Parallel Compression Is a Fast Low-Cost Assay for the High-Throughput Screening of Mechanosensory Cytoskeletal Proteins in Cells

2017 ◽  
Vol 9 (34) ◽  
pp. 28168-28179 ◽  
Author(s):  
Chunguang Miao ◽  
Eric S. Schiffhauer ◽  
Evelyn I. Okeke ◽  
Douglas N. Robinson ◽  
Tianzhi Luo
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Low Cost ◽  
Cytoskeletal Proteins ◽  
In Cells

scholarly journals Multiphasic construct studied in an ectopic osteochondral defect model

2014 ◽  
Vol 11 (95) ◽  
pp. 20140184 ◽  
Author(s):  
June E. Jeon ◽  
Cédryck Vaquette ◽  
Christina Theodoropoulos ◽  
Travis J. Klein ◽  
Dietmar W. Hutmacher
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Osteochondral Defect ◽  
Low Cost ◽  
Current Model ◽  
Large Animal ◽  
Micro Computed Tomography ◽  
Defect Model ◽  
Osteochondral Repair

In vivo osteochondral defect models predominantly consist of small animals, such as rabbits. Although they have an advantage of low cost and manageability, their joints are smaller and more easily healed compared with larger animals or humans. We hypothesized that osteochondral cores from large animals can be implanted subcutaneously in rats to create an ectopic osteochondral defect model for routine and high-throughput screening of multiphasic scaffold designs and/or tissue-engineered constructs (TECs). Bovine osteochondral plugs with 4 mm diameter osteochondral defect were fitted with novel multiphasic osteochondral grafts composed of chondrocyte-seeded alginate gels and osteoblast-seeded polycaprolactone scaffolds, prior to being implanted in rats subcutaneously with bone morphogenic protein-7. After 12 weeks of in vivo implantation, histological and micro-computed tomography analyses demonstrated that TECs are susceptible to mineralization. Additionally, there was limited bone formation in the scaffold. These results suggest that the current model requires optimization to facilitate robust bone regeneration and vascular infiltration into the defect site. Taken together, this study provides a proof-of-concept for a high-throughput osteochondral defect model. With further optimization, the presented hybrid in vivo model may address the growing need for a cost-effective way to screen osteochondral repair strategies before moving to large animal preclinical trials.

scholarly journals A High-Throughput Ligand Competition Binding Assay for the Androgen Receptor and Other Nuclear Receptors

2008 ◽  
Vol 14 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Clémentine Féau ◽  
Leggy A. Arnold ◽  
Aaron Kosinski ◽  
R. Kiplin Guy
Keyword(s):  
Androgen Receptor ◽  
Nuclear Receptors ◽  
High Throughput ◽  
High Throughput Screening ◽  
Low Cost ◽  
Environmental Chemicals ◽  
Binding Assays ◽  
Receptor Ligands ◽  
Competition Binding ◽  
Nuclear Receptor Ligands

Standardized, automated ligand-binding assays facilitate evaluation of endocrine activities of environmental chemicals and identification of antagonists of nuclear receptor ligands. Many current assays rely on fluorescently labeled ligands that are significantly different from the native ligands. The authors describe a radiolabeled ligand competition scintillation proximity assay (SPA) for the androgen receptor (AR) using Ni-coated 384-well FlashPlates® and liganded AR-LBD protein. This highly reproducible, low-cost assay is well suited for automated high-throughput screening. In addition, the authors show that this assay can be adapted to measure ligand affinities for other nuclear receptors (peroxisome proliferation-activated receptor γ, thyroid receptors α and β). ( Journal of Biomolecular Screening 2009:43-48)

scholarly journals Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

2020 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Correlation Energy ◽  
Low Cost ◽  
Small Data ◽  
Data Sets ◽  
Strongly Correlated ◽  
Heavy Atoms ◽  
Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

scholarly journals Optimization of the Curing and Post-Curing Conditions for the Manufacturing of Partially Bio-Based Epoxy Resins with Improved Toughness

Polymers ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1354 ◽  
Author(s):  
Diego Lascano ◽  
Luis Quiles-Carrillo ◽  
Sergio Torres-Giner ◽  
Teodomiro Boronat ◽  
Nestor Montanes
Keyword(s):  
Epoxy Resin ◽  
Epoxy Resins ◽  
Polymer Network ◽  
Thermomechanical Properties ◽  
Diglycidyl Ether ◽  
Polymer Chains ◽  
Cross Linking ◽  
Reactive Diluent ◽  
Gel Time ◽  
Curing Conditions

This research deals with the influence of different curing and post-curing temperatures on the mechanical and thermomechanical properties as well as the gel time of an epoxy resin prepared by the reaction of diglycidyl ether of bisphenol A (DGEBA) with an amine hardener and a reactive diluent derived from plants at 31 wt %. The highest performance was obtained for the resins cured at moderate-to-high temperatures, that is, 80 ° C and 90 ° C , which additionally showed a significant reduction in the gel time. This effect was ascribed to the formation of a stronger polymer network by an extended cross-linking process of the polymer chains during the resin manufacturing. Furthermore, post-curing at either 125 ° C   or 150 ° C yielded thermosets with higher mechanical strength and, more interestingly, improved toughness, particularly for the samples previously cured at moderate temperatures. In particular, the partially bio-based epoxy resin cured at 80 ° C and post-cured at 150 ° C for 1 h and 30 min, respectively, showed the most balanced performance due to the formation of a more homogeneous cross-linked structure.

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