A review of high throughput and combinatorial electrochemistry

In this paper, the slow evolution of combinatorial chemistry from its dawn in 1980’s to today’s oil field product development is discussed. Combinatorial chemistry comprises chemical synthetic methods that make it possible to prepare a vast number of compounds in a single process. These compound libraries can be made as mixtures, sets of individual compounds or chemical structures generated by computer software. This phenomenon was first invented by Arpad Furka (Lorand University, Budapest) in 1982. He described the principle of it, the combinatorial synthesis and a deconvolution procedure. The methodology was first used in drug discovery using a wide range of linear or wide range of macrocyclic chemical molecules: peptides, non-peptide oligomers, peptidomimetics, small-molecules, and natural product-like organic molecules. However, handling vast amounts of data and extremely small chemical recovery were a very difficult endeavor. To avoid this problem and help to refine the size of the chemical libraries, various software programs were utilized. This was achieved by utilizing a tool known as Design of Experiment (DoE). In this paper, the high throughput product screening to identify corrosion inhibitors was performed by utilizing critical micelle concentration (CMC). CMC was used to differentiate performance of libraries of chemical blends. Combinatorial synthesis (or blends) and combinatorial screening were performed by utilizing robotics methodologies. The corrosion inhibitor formulations predicted by DoE were built out by using combinatorial chemical methods and the arrays of chemical formulations were screened by utilizing high throughput robotics, using CMC as the selection guide. To validate the concept, several known corrosion inhibitor formulas were selected to optimize their efficacy. Each formula contained several active ingredients and a solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. After formulation of a vast array of formulation by using Design Expert solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. After formulation of a vast array of formulation by using Design Expert (DE) software, the products were screened for by CMC using automated surface tension workstation. Several formulations with lower CMC than the reference products were selected. The selected corrosion inhibitor formulations were identified and blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. These tests were performed against the original reference corrosion inhibitor. The testing indicated that several corrosion inhibitor formulations outperform the original blend thus validating the proof of concept.

The materials genome initiative (MGI) aims to accelerate the process of materials discovery and reduce the time to commercialization of advanced materials. Thus far, the MGI has resulted in significant progress in computational simulation, modeling, and predictions of catalytic materials. However, prodigious amounts of experimental data are necessary to inform and validate these computational models. High-throughput (HT) methodologies, in which hundreds of materials are rapidly synthesized, processed, and characterized for their figure of merit, represent the key experimental enabler of the theoretical aspects of the MGI. HT methodologies have been used since the early 1980s for identifying novel catalyst formulations and optimizing existing catalysts. Many sophisticated screening and data mining techniques have been developed and since the mid-1990s, this approach has become a widely accepted industrial practice. This article will provide a short history of major developments in HT and will discuss screening approaches combining rapid, qualitative primary screens via thin-film techniques with a series of quantitative screens using plug flow reactors. An illustrative example will be provided of one such study in which novel fuel-flexible sulfur tolerant cracking catalysts were developed. We will then illustrate a path forward that leverages existing HT expertise to validate, provide empirical data to and help guide future theoretical studies.

Quaternary Pt-based electrocatalyst for methanol oxidation by combinatorial electrochemistry

A high-throughput DNA analysis method based on isothermal amplification on a suspension microarray for detecting mpox virus and viruses with comparable symptoms

Stainless steel 304 substrates were coated with different materials in order to find a suitable coating material for corrosion protection of separator plates in molten-carbonate fuel cells (MCFCs). Five titanium carbonitride coatings differing in composition and morphology and a titanium monoxide coating were deposited with chemical vapour deposition techniques. Also double-layer coatings of TiN/Au and TiN/Ni were prepared. The coatings were tested on their corrosion protection of separator plates in four different environments: under MCFC-cathode or anode gas, at load or at open circuit conditions. The corrosion behaviour was characterized using cyclic voltammetry. Corrosion rates were determined with electrochemical methods and cross-section analyses of corrosion layers. Titanium nitride coatings showed the best corrosion protection. The titanium carbide and titanium monoxide coating showed respectively less and no protection. The thin gold and Ni-coatings were unstable. Under cathode gas, the most important corrosion protection is given by keeping the cell at load, and then a titanium nitride coating might provide lifetime protection. Under anode gas, corrosion is most severe at load conditions. A titanium nitride coating also gives corrosion protection, but not enough for lifetime protection.

The development of biomedical nanoparticulate materials for use in diagnostics is a delicate balance between performance, particle size, shape, and stability. To identify materials that satisfy all of the criteria it is useful to employ automated high-throughput (HT) techniques for the study of these materials. The structure and performance of surfactant templated mesoporous silica is very sensitive to a wide number of variables. Variables, such as the concentration of the structure-directing agent, the cosolvent and dopant ions and also the temperature and concentration of quenching all have an influence on the structure, surface chemistry, and therefore, the performance of the mesoporous silica nanoparticles generated. Using an automated robotic synthetic platform, a technique has been developed for the high-throughput preparation of mesoporous silica and gadolinium-doped silicate (gadoliniosilicate) nanoparticulate MRI contrast agents. Twelve identical repeats of both the mesoporous silica and gadolinosilicate were synthesized to investigate the reproducibility of the HT technique. Very good reproducibility in the production of the mesoporous silica and the gadolinosilcate materials was obtained using the developed method. The performance of the gadolinosilicate materials was comparable as a T(1) agent to the commercial MRI contrast agents. This HT methodology is highly reproducible and an effective tool that can be translated to the discovery of any sol-gel derived nanomaterial.

Luminous Glass: A Study on the Optics Governing Luminescent Solar Concentrators and Optimization of Luminescent Materials through Combinatorial Gradient Sputter Deposition

High throughput experimental methods are known to accelerate the rate of research, development, and deployment of electronic materials. For example, thin films with lateral gradients in composition, thickness, or other parameters have been used alongside spatially-resolved characterization to assess how various physical factors affect the material properties under varying measurement conditions. Similarly, multi-layer electronic devices that contain such graded thin films as one or more of their layers can also be characterized spatially in order to optimize the performance. In this work, we apply these high throughput experimental methods to thin film transistors (TFTs), demonstrating combinatorial channel layer growth, device fabrication, and semi-automated characterization using sputtered oxide TFTs as a case study. We show that both extrinsic and intrinsic types of device gradients can be generated in a TFT library, such as channel thickness and length, channel cation compositions, and oxygen atmosphere during deposition. We also present a semi-automated method to measure the 44 devices fabricated on a 50 mm×50 mm substrate that can help to identify properly functioning TFTs in the library and finish the measurement in a short time. Finally, we propose a fully automated characterization system for similar TFT libraries, which can be coupled with high throughput data analysis. These results demonstrate that high throughput methods can accelerate the investigation of TFTs and other electronic devices.

A combinatorial materials science method for screening potential elemental additions to mitigate tin whisker formation on Pb-free electronics is presented. This combinatorial method is demonstrated using compositional gradient libraries deposited on glass substrates. Fabrication and heat treatment of combinatorial libraries is discussed. The effectiveness of this high-throughput screening method becomes evident from the analysis of two ternary systems (W-Cr-Ni and Mo-Al-Co) deposited over a tin matrix on glass substrates. Scanning electron microscopy revealed that tin whiskers tend to nucleate and grow on samples with no compressive stresses associated with the formation of intermetallic layers. Additionally, a nickel layer between the glass and tin was found to hinder the formation of whiskers; however, the traditional argument of Ni as a diffusion barrier between Sn-Cu not longer explains the observed behavior. This combinatorial method provides a rapid assessment tool to identify potential elemental mixtures to mitigate tin whiskers while using a simple, cost effective experimental approach.

High-throughput characterization methods for lithium batteries

Magnetic materials are of great importance for energy harvesting and conversion and information technologies, as exemplified by permanent magnets and spintronic applications, respectively. There is a strong impetus to develop new high performance magnets to satisfy the increasing requirements of such technologies. On the other hand, high-throughput (HTP) screening based on density functional theory calculations provides an efficiency way to predict novel materials with target properties. Therefore, it is meaningful to accelerate the development of functional magnetic materials by using the high-throughput screening method. In this thesis, we endeavored to perform HTP engineering of both 3D and 2D magnetic materials, focusing on spintronic and permanent magnet applications. For the spintronic applications, based on HTP density functional theory calculations, we performed a systematic screening by examining peculiar electronic structure. For instance, we have done HTP screening for spin-gapless semiconductors (SGSs) in quaternary Heusler alloys XX'YZ. Following an empirical rule, we focused on compounds with 21, 26, or 28 valence electrons, resulting in 12000 possible chemical compositions. After systematically evaluating the thermodynamic, mechanical, and dynamical stabilities, we have identified 70 so far unreported SGSs, confirmed by explicit electronic structure calculations with proper magnetic ground states, of which 17 candidates have a distance to the convex hull smaller than 0.10 eV/atom. It is demonstrated that all four types of SGSs can be realized, defined based on the spin characters of the touching bands around the Fermi energy. Particularly, it is found that the type-II SGSs exhibit promising transport properties for spintronic applications, such as large anisotropic magnetoresistance and anomalous Nernst effects driven by spin-orbit coupling. On the other hand, as to 2D materials, we carried out a systematic HTP screening for in-plane ordered MXene (i-MXene), which can be obtained by etching the main group element (A) away from the recently synthesized in-plane ordered MAX (i-MAX) phase. Such 2D i-MXenes provide us a new playground for 2D magnetic materials. It is observed that the spin configurations, hence the magnetic anisotropy, can be tuned by strain. We found that five i-MXenes have a significantly large out-of-plane magnetic anisotropy energy (>0.5 meV/f.u.), and the four ferromagnetic candidates have high Curie temperature based on the 2D Ising model. Additionally, we found i-MXene can realize large Seebeck effect, antiferromagnetic topological insulator, and spin-gapless semiconductors, making them interesting for future studies. In terms of designing permanent magnets, we carried out a systematic HTP screening for rare-earth free permanent magnets by incorporating light interstitials (H, B, C, N) into magnetic full Heusler alloys. We successfully identified 32 candidates with an out-of-plane magnetic anisotropy larger than 0.4 MJ/m$^3$ as well as 10 cases with large in-plane anisotropy. Detailed analysis reveals that the interstitials are very efficient in inducing global tetragonal distortions, whereas the local chemical bonding and changes in the crystalline environment result in significant enhancement of MAE. We strongly believe this provides an efficient way to tailor MAE, and such newly predicted permanent magnet candidates are promising gap magnets between rare earth based Sm-Co and Nd-Fe-B and the transition metal based AlNiCo and ferrite. To summarise, in addition to screening over various types of functional magnetic materials, my work provides valuable solutions to the pending challenges of HTP design of magnetic materials. We have established automated workflows, which can be easily applied on the other magnetic materials. This paves the way for further design of advanced magnetic materials with optimal performance

Combinatorial and high throughput methods have recently been applied to chemical reaction development as it greatly increases the reactivity "space" available to the chemist. Recent focus has been the development of detection methods, particularly those directed towards enantioselectivity. Progress in this area has been reviewed with particular emphasis placed on recent enantioselective methods. Keywords: high throughput screening, combinatorial chemistry, parallel synthesis, reaction screening, asymmetric reaction screening, enantioselective reaction screening

[RETRACTED] Coronavirus disease (COVID-19) is an infectious disease caused by the SARS coronavirus 2 (SARS-CoV-2) virus. Direct assessment, detection, and quantitative analysis using high throughput methods like single-cell RNA sequencing (scRNAseq) is imperative to understanding the host response to SARS-CoV-2. One barrier to studying SARS-CoV-2 in the laboratory setting is the requirement to process virus-infected cell cultures, and potentially infectious materials derived therefrom, under Biosafety Level 3 (BSL-3) containment. However, BSL3 laboratory facilities are rare and only a subset of these facilities are outfitted with the equipment needed to perform high-throughput molecular assays. Here, we describe a method for preparing non-hazardous RNA samples from SARS-CoV-2 infected cells, that enables scRNAseq analyses to be conducted safely in a BSL2 facility – thereby making molecular assays of SARS-CoV-2 cells accessible to a much larger community of researchers. (The last step in this version contains a supplemental video with extra context and tips, as part of the protocols.io Spotlight series, featuring conversations with protocol authors.)

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Webster’s New Collegiate Dictionary defines promise as ‘... a right to expect or to claim the performance or forbearance of a specified act; ground for hope, expectation or assurance of eventual success ...’. Combinatorial chemistry has certainly generated very high expectations in the drug discovery community, but has the technique of combinatorial chemistry, and the practitioners of the technique, really delivered on that promise? The real promise of combinatorial chemistry should be defined in terms of a drug discovery and not a preclinical or, for that matter, a clinical candidate. Quite clearly, combinatorial methodology has yet to deliver on that promise, since no drugs have yet to emerge from clinical trials. However, for the purposes of this chapter, the editors of this series hope to document the continued advance of drug candidates generated by combinatorial methods through the clinic and, ultimately, to the market place. That is, the promise fulfilled. At this stage in the development of combinatorial methods, this chapter represents more of a place holder than a chapter of substance. In a sense, this chapter is as much an unfulfilled promise as the technique it reports upon, since there is really very little accessible clinical data to report. Therefore, we will outline the seemingly ever-expanding list of practicing combinatorial chemistry companies, report anecdotal information concerning pre-clinical discoveries and, most significantly, summarize the progress of those candidates actually in clinical development. In some respects the pre-clinical information would be very useful, but, not too surprisingly, this is very difficult information to document and to verify. Hopefully, in the not too distant future, there will be a detailed drug discovery story, with combinatorial chemistry as the centerpiece, which will appear in this chapter.KeywordsDrug DiscoveryStructural ClassCombinatorial MethodCombinatorial ChemistryLead StructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.