A Combinatorial Approach to Materials Discovery

Synthesis at the Interface of Chemistry and Biology

ConspectusChemical synthesis as a tool to control the structure and properties of matter is at the heart of chemistry─from the synthesis of fine chemicals and polymers to drugs and solid-state materials. But as the field evolves to tackle larger and larger molecules and molecular complexes, the traditional tools of synthetic chemistry become limiting. In contrast, Mother Nature has developed very different strategies to create the macromolecules and molecular systems that make up the living cell. Our focus has been to ask whether we can use the synthetic strategies and machinery of Mother Nature, together with modern chemical tools, to create new macromolecules, and even whole organisms with properties not existing in nature. One such example involves reprogramming the complex, multicomponent machinery of ribosomal protein synthesis to add new building blocks to the genetic code, overcoming a billion-year constraint on the chemical nature of proteins. This methodology exploits the concept of bioorthogonality to add unique codons, tRNAs and aminoacyl-tRNA synthetases to cells to encode amino acids with physical, chemical and biological properties not found in nature. As a result, we can make precise changes to the structures of proteins, much like those made by chemists to small molecules and beyond those possible by biological approaches alone. This technology has made it possible to probe protein structure and function in vitro and in vivo in ways heretofore not possible, and to make therapeutic proteins with enhanced pharmacology. A second example involves exploiting the molecular diversity of the humoral immune system together with synthetic transition state analogues to make catalytic antibodies, and then expanding this diversity-based strategy (new to chemists at the time) to drug discovery and materials science. This work ushered in a new nature-inspired synthetic strategy in which large libraries of natural or synthetic molecules are designed and then rationally selected or screened for new function, increasing the efficiency by which we can explore chemical space for new physical, chemical and biological properties. A final example is the use of large chemical libraries, robotics and high throughput phenotypic cellular screens to identify small synthetic molecules that can be used to probe and manipulate the complex biology of the cell, exemplified by druglike molecules that control cell fate. This approach provides new insights into complex biology that complements genomic approaches and can lead to new drugs that act by novel mechanisms of action, for example to selectively regenerate tissues. These and other advances have been made possible by using our knowledge of molecular structure and reactivity hand in hand with our understanding of and ability to manipulate the complex machinery of living cells, opening a new frontier in synthesis. This Account overviews the work in my lab and with our collaborators, from our early days to the present, that revolves around this central theme.

Harnessing Compositional Gradients to Elucidate Phase Behaviors toward High Performance Polymer Semiconductor Blends

Polymer semiconductor/insulator blends offer a promising avenue to achieve desired mechanical properties, environmental stability, and high device performance in organic field-effect transistors. A comprehensive understanding of process-structure-property relationships necessitates a thorough exploration of the composition space to identify transitions in performance, morphology, and phase behavior. Hence, this study employs a high-throughput gradient thin film library, enabling rapid and continuous screening of composition-morphology-device performance relationships in conjugated polymer blends. Applied to a donor-acceptor copolymer blend, this technique efficiently surveys a broad composition range, capturing trends in device performance across the gradient. Furthermore, characterizing the gradient library using microscopy and depth profiling techniques pinpointed composition-dependent transitions in morphology. To validate the results and gain deeper insights, uniform-composition experiments were conducted on select compositions within and outside the gradient range. Depth profiling experiments on the constant composition films unveil the presence of the semiconducting polymer at the air interface, with apparent enrichment of the semiconductor at the substrate interface at low ratios of the semiconducting component, transitioning to a more even distribution within the bulk of the film at higher ratios. The generalizability of the gradient approach was further confirmed by its application to a homopolymer under different solution processing conditions.

Automation and machine learning augmented by large language models in a catalysis study

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

Long-Range SECCM Enables High-Throughput Electrochemical Screening of High Entropy Alloy Electrocatalysts at Up-To-Industrial Current Densities

High-entropy alloys (HEAs), especially in the form of compositional complex solid solutions (CCSS), have gained attention in the field of electrocatalysis. However, exploring their vast composition space concerning their electrocatalytic properties imposes significant challenges. Scanning electrochemical cell microscopy (SECCM) offers high-speed electrochemical analysis on surface areas with a lateral resolution down to tens of nm. However, high-precision piezo positioners often used for the motion of the tip limit the area of SECCM scans to the motion range of the piezo positioners which is typically a few tens of microns. To bridge this experimental gap, the study proposes a long-range SECCM system with a rapid gas-exchange environmental cell for high-throughput electrochemical characterization of 100 mm diameter HEA thin-film material libraries (ML) obtained by combinatorial co-sputtering. Due to the gas-liquid interface at the positioned SECCM droplet on the sample, high-throughput evaluation under industrial current density conditions becomes feasible. This allows the direct correlation between electrocatalytic activity and material composition with high statistical reliability. The multidimensional data obtained accelerates materials discovery, development, and optimization.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Unleashing the Power of PET-RAFT Polymerization: Journey from Porphyrin-Based Photocatalysts to Combinatorial Technologies and Advanced Bioapplications

The emergence of photoinduced energy/electron transfer-reversible addition-fragmentation chain transfer polymerization (PET-RAFT) not only revolutionized the field of photopolymerization but also accelerated the development of porphyrin-based photocatalysts and their analogues. The continual expansion of the monomer family compatible with PET-RAFT polymerization enhances the range of light radiation that can be harnessed, providing increased flexibility in polymerization processes. Furthermore, the versatility of PET-RAFT polymerization extends beyond its inherent capabilities, enabling its integration with various technologies in diverse fields. This integration holds considerable promise for the advancement of biomaterials with satisfactory bioapplications. As researchers delve deeper into the possibilities afforded by PET-RAFT polymerization, the collaborative efforts of individuals from diverse disciplines will prove invaluable in unleashing its full potential. This Review presents a concise introduction to the fundamental principles of PET-RAFT, outlines the progress in photocatalyst development, highlights its primary applications, and offers insights for future advancements in this technique, paving the way for exciting innovations and applications.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Chronological development of functional fluorophores for bio-imaging

Functional fluorophores represent an emerging research field, distinguished by their diverse applications, especially in sensing and cellular imaging. After the discovery of quinine sulfate and subsequent elucidation of the fluorescence mechanism by Sir George Stokes, research in the field of fluorescence gained momentum. Over the past few decades, advancements in sophisticated instruments, including super-resolution microscopy, have further promoted cellular imaging using traditional fluorophores. These advancements include deciphering sensing mechanisms via photochemical reactions and scrutinizing the applications of fluorescent probes that specifically target organelles. This approach elucidates molecular interactions with biomolecules. Despite the abundance of literature illustrating different classes of probe development, a concise summary of newly developed fluorophores remains inadequate. In this review, we systematically summarize the chronological discovery of traditional fluorophores along with new fluorophores. We briefly discuss traditional fluorophores ranging from visible to near-infrared (NIR) in the context of cellular imaging and in vivo imaging. Furthermore, we explore ten new core fluorophores developed between 2007 and 2022, which exhibit advanced optical properties, providing new insights into bioimaging. We illustrate the utilization of new fluorophores in cellular imaging of biomolecules, such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and proteins and microenvironments, especially pH and viscosity. Few of the fluorescent probes provided new insights into disease progression. Furthermore, we speculate on the potential prospects and significant challenges of existing fluorophores and their potential biomedical research applications. By addressing these aspects, we intend to illuminate the compelling advancements in fluorescent probe development and their potential influence across various fields.

High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high-throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high-throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low-discrepancy sampling, including on-the-fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov-Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points.

Combinatorial mixtures of organic solutes for improved liquid/liquid extraction of ions

Biological systems routinely extract and organize ions in complex yet highly ordered and active systems. Much of this function is attributed to proteins, although recent evidence indicates aggregates of lipids are also capable of molecular recognition. Here we tested the hypothesis that combinatorial mixtures of organic solutes might lead to enhanced liquid/liquid extraction. We started with liquid oleic acid as an organic phase extracting copper ions from water and added a library of additives. By using Bayesian optimization to autonomously direct the combinatorial formulation, we discovered mixtures that enhanced the extraction performance. The main additive that improved the system was octylphosphonic acid. Interestingly, the optimal mixture has a significant improvement compared to this additive alone. This suggests that the combinations of organic solutes are better than using pure components in liquid/liquid extraction. Furthermore, we found that precipitation occurs in the samples showing better extraction efficiency, which has interesting material properties and potential for new types of supramolecular biosensors.

Towards scanning nanostructure X-ray microscopy

This article demonstrates spatial mapping of the local and nanoscale structure of thin film objects using spatially resolved pair distribution function (PDF) analysis of synchrotron X-ray diffraction data. This is exemplified in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid-handling system. A software implementation is presented of the whole protocol, including an approach for automated data acquisition and analysis using the atomic PDF method. The protocol software can handle semi-automated data reduction, normalization and modeling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions, it is possible to build images of different contrast features chosen by the user, giving insights into different aspects of the local structure.

Artificial Intelligence and Advanced Materials

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

High-throughput printing of combinatorial materials from aerosols

The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time consuming and resource inefficient, particularly when contrasted with vast materials design spaces1. Whereas traditional combinatorial deposition methods can generate material libraries2,3, these suffer from limited material options and inability to leverage major breakthroughs in nanomaterial synthesis. Here we report a high-throughput combinatorial printing method capable of fabricating materials with compositional gradients at microscale spatial resolution. In situ mixing and printing in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multimaterials printing using feedstocks in liquid-liquid or solid-solid phases4-6. We demonstrate a variety of high-throughput printing strategies and applications in combinatorial doping, functional grading and chemical reaction, enabling materials exploration of doped chalcogenides and compositionally graded materials with gradient properties. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over local material compositions promises the development of compositionally complex materials inaccessible via conventional manufacturing approaches.

Review on High-Throughput Micro-Combinatorial Characterization of Binary and Ternary Layers towards Databases

The novel, single-sample concept combinatorial method, the so-called micro-combinatory technique, has been shown to be suitable for the high-throughput and complex characterization of multicomponent thin films over an entire composition range. This review focuses on recent results regarding the characteristics of different binary and ternary films prepared by direct current (DC) and radiofrequency (RF) sputtering using the micro-combinatorial technique. In addition to the 3 mm diameter TEM grid used for microstructural analysis, by scaling up the substrate size to 10 × 25 mm, this novel approach has allowed for a comprehensive study of the properties of the materials as a function of their composition, which has been determined via transmission electron microscopy (TEM), scanning electron microscopy (SEM), Rutherford backscattering spectrometry (RBS), X-ray diffraction analysis (XRD), atomic force microscopy (AFM), spectroscopic ellipsometry, and nanoindentation studies. Thanks to the micro-combinatory technique, the characterization of multicomponent layers can be studied in greater detail and efficiency than before, which is beneficial for both research and practical applications. In addition to new scientific advances, we will briefly explore the potential for innovation with respect to this new high-throughput concept, including the creation of two- and three-component thin film databases.

Microscale Combinatorial Libraries for the Discovery of High-Entropy Materials

Polyelemental material systems, specifically high-entropy alloys, promise unprecedented properties. Due to almost unlimited combinatorial possibilities, their exploration and exploitation is hard. This challenge is addressed by co-sputtering combined with shadow masking to produce a multitude of microscale combinatorial libraries in one deposition process. These thin-film composition spreads on the microscale cover unprecedented compositional ranges of high-entropy alloy systems and enable high-throughput characterization of thousands of compositions for electrocatalytic energy conversion reactions using nanoscale scanning electrochemical cell microscopy. The exemplary exploration of the composition space of two high-entropy alloy systems provides electrocatalytic activity maps for hydrogen evolution and oxygen evolution as well as oxygen reduction reactions. Activity optima in the system Ru-Rh-Pd-Ir-Pt are identified, and active noble-metal lean compositions in the system Co-Ni-Mo-Pd-Pt are discovered. This illustrates that the proposed microlibraries are a holistic discovery platform to master the multidimensionality challenge of polyelemental systems.

High-Throughput Preparation and High-Throughput Detection of Polymer-Dispersed Liquid Crystals Based on Ink-Jet Printing and Grayscale Value Analysis

In this paper, based on high-throughput technology, polymer dispersed liquid crystals (PDLC) composed of pentaerythritol tetra (2-mercaptoacetic acid) (PETMP), trimethylolpropane triacrylate (TMPTA), and polyethylene glycol diacrylate (PEGD 600) were investigated in detail. A total of 125 PDLC samples with different ratios were quickly prepared using ink-jet printing. Based on the method of machine vision to identify the grayscale level of samples, as far as we know, it is the first time to realize high-throughput detection of the electro-optical performance of PDLC samples, which can quickly screen out the lowest saturation voltage of batch samples. Additionally, we compared the electro-optical test results of manual and high-throughput preparation PDLC samples and discovered that they had very similar electro-optical characteristics and morphologies. This demonstrated the viability of PDLC sample high-throughput preparation and detection, as well as promising application prospects, and significantly increased the efficiency of PDLC sample preparation and detection. The results of this study will contribute to the research and application of PDLC composites in the future.

Next-generation intelligent laboratories for materials design and manufacturing

The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.

Thermal Plasma Synthesis of Different Alloys and Intermetallics from Ball Milled Al-Mo and Al-Ni Powder Systems

Lightweight alloys have great importance for car manufacturers that aim to produce safer, lighter, and more environmentally friendly vehicles. As a result, it is essential to develop new lightweight alloys, with superior properties to conventional ones, respecting the demands of the market. Al and its alloys are good candidates for reducing the overall weight of vehicles. The objective of this research was to understand the possibility to synthesize different Al alloys and intermetallics by implementing the plasma system and using two different Al-Ni and Al-Mo powder systems. This was done by separately injecting non-reacted raw Al-Ni and Al-Mo composite powder systems into the plasma reactor. In the first step, the milling parameters were optimized to generate Al-Ni and Al-Mo composite powders, with sizes over about 30 µm, having, respectively, a homogeneous mixture of elemental Al and Ni, and Al and Mo in their particles. Each of the composite powders was then injected separately into the plasma system to provide conditions for the reaction of their elements together. The obtained Al-Ni and Al-Mo powders were then studied using different methods such as scanning electron microscopy, X-ray diffractometry, and energy dispersive X-ray analysis. Regardless of the initially used powder system, the obtained powders were consisting of large spherical particles surrounded by a cloud of fine porous particles. Different phases such as Al, AlNi3, Al3Ni2, and AlNi were detected in the particles of the Al-Ni powder system and Al, Mo, AlMo3, MoO3, and MoO2 in the Al-Mo powder system.

High-Throughput Preparation and Machine Learning Screening of a Blue-Phase Liquid Crystal Based on Inkjet Printing

Blue-phase liquid crystal (BPLC) is considered as the next-generation liquid crystal display material, but its practical application is seriously affected by a narrow temperature range and a long research period. In this paper, we used inkjet printing technology to prepare BPLC materials with high throughput, and try to use machine vision technology to test BPLC with high throughput. The "standard curve method" for establishing each printing channel and the "vector matching method" for searching the chromaticity value of the minimum distance were proposed to improve the accuracy of inkjet printing BPLC materials. For a large number of sample-phase images, we propose a machine learning method to identify the liquid crystal phase. In this paper, for the first time, the high-throughput preparation and high-throughput detection of 1080 BPLC samples with five common components by a comprehensive experimental method has been successfully realized. The results are helpful to improve the research efficiency of blue-phase materials and provide a theoretical basis and practical guidance for rapid screening of multi-component BPLC materials.

High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective

Thin films are an integral part of many electronic and optoelectronic devices. They also provide an excellent platform for material characterization. Therefore, strategies for the fabrication of thin films are constantly developed and have significantly benefited from the advent of high-throughput synthesis (HTS) platforms. This perspective summarizes recent advances in HTS of thin films from experimentalists’ point of view. The work analyzes general strategies of HTS and then discusses their use in developing new energy materials for applications that rely on thin films, such as solar cells, light-emitting diodes, batteries, superconductors, and thermoelectrics. The perspective also summarizes some key challenges and opportunities in the HTS of thin films.

High-Throughput Preparation of Supramolecular Nanostructures on Metal Surfaces

One of the contemporary challenges in materials science lies in the rapid materials screening and discovery. Experimental sample libraries can be generated by high-throughput parallel synthesis to map the composition space for rapid material discoveries. Molecular self-assembly on surfaces has proved a useful way to construct nanostructures with interesting topologies or properties. Despite the strong dependence of molecular stoichiometry on the structures, high-throughput preparations of supramolecular surface nanostructures have been far less explored. Here, by integrating a physical mask into the standard ultra-high-vacuum (UHV) molecular preparation system we show a high-throughput approach for preparing supramolecular nanostructures of continuous composition spreads on metal surfaces. The spatially addressable sample libraries of supramolecular self-assemblies are characterized by high-resolution scanning probe microscopy. We could explore different binary nanostructures of varying molecular ratios on one single substrate. Moreover, we use the minimum spanning tree approach to qualitatively and quantitatively study the structural properties of the formed nanostructures. This high-throughput approach may accelerate the screening and exploration of surface-supported, low-dimensional nanostructures not limited to supramolecular interactions.

Thin-Film Ferroelectrics

Over the last 30 years, the study of ferroelectric oxides has been revolutionized by the implementation of epitaxial-thin-film-based studies, which have driven many advances in the understanding of ferroelectric physics and the realization of novel polar structures and functionalities. New questions have motivated the development of advanced synthesis, characterization, and simulations of epitaxial thin films and, in turn, have provided new insights and applications across the micro-, meso-, and macroscopic length scales. This review traces the evolution of ferroelectric thin-film research through the early days developing understanding of the roles of size and strain on ferroelectrics to the present day, where such understanding is used to create complex hierarchical domain structures, novel polar topologies, and controlled chemical and defect profiles. The extension of epitaxial techniques, coupled with advances in high-throughput simulations, now stands to accelerate the discovery and study of new ferroelectric materials. Coming hand-in-hand with these new materials is new understanding and control of ferroelectric functionalities. Today, researchers are actively working to apply these lessons in a number of applications, including novel memory and logic architectures, as well as a host of energy conversion devices.

Materials Data toward Machine Learning: Advances and Challenges

Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.

Fabrication of Ni–Cu–W Graded Coatings by Plasma Spray Deposition and Laser Remelting

In this study, Ni–Cu–W graded coatings are produced by atmospheric plasma spraying and subsequently remelted by laser. The surface morphology, hardness, compositional fluctuations and corrosion resistance of the Ni–Cu–W coating are investigated. The coatings after laser remelting are densified and become more homogenous with an excellent corrosion resistance and high hardness, which can be used to explore the new materials.

Large-Scale Dried Reagent Reconstitution and Diffusion Control Using Microfluidic Self-Coalescence Modules

The positioning and manipulation of large numbers of reagents in small aliquots are paramount to many fields in chemistry and the life sciences, such as combinatorial screening, enzyme activity assays, and point-of-care testing. Here, a capillary microfluidic architecture based on self-coalescence modules capable of storing thousands of dried reagent spots per square centimeter is reported, which can all be reconstituted independently without dispersion using a single pipetting step and ≤5 μL of a solution. A simple diffusion-based mathematical model is also provided to guide the spotting of reagents in this microfluidic architecture at the experimental design stage to enable either compartmentalization, mixing, or the generation of complex multi-reagent chemical patterns. Results demonstrate the formation of chemical patterns with high accuracy and versatility, and simple methods for integrating reagents and imaging the resulting chemical patterns.

Combinatorial synthesis of heteroepitaxial, multi-cation, thin-films via pulsed laser deposition coupled with in-situ, chemical and structural characterization

Combinatorial synthesis via a continuous composition spread is an excellent route to develop thin-film libraries as it is both time- and cost-efficient. Creating libraries of functional, multicomponent, complex oxide films requires excellent control over the synthesis parameters combined with high-throughput analytical feedback. A reliable, high-throughput, in-situ characterization analysis method is required to meet the crucial need to rapidly screen materials libraries. Here, we report on the combination of two in-situ techniques—(a) Reflection high-energy electron diffraction (RHEED) for heteroepitaxial characterization and a newly developed compositional analysis technique, low-angle x-ray spectroscopy (LAXS), to map the chemical composition profile of combinatorial heteroepitaxial complex oxide films deposited using a continuous composition spread method via pulsed laser deposition. This is accomplished using a unique state-of-the-art combinatorial growth system with a fully synchronized four-axis mechanical substrate stage without shadow masks, alternating acquisition of chemical compositional data using LAXS at various different positions on the $$\sim$$ ∼ 41 mm $$\times$$ × 41 mm range and sequential deposition of multilayers of SrTiO$$_3$$ 3 and $$\hbox {SrTi}_{0.8}\hbox {Ru}_{0.2}\hbox {O}_3$$ SrTi 0.8 Ru 0.2 O 3 on a 2-inch (50.8 mm) $$\hbox {LaAlO}_3$$ LaAlO 3 wafer in a single growth run. Rutherford backscattering spectrometry (RBS) is used to calibrate and validate the compositions determined by LAXS. This study shows the feasibility of combinatorial synthesis of heteroepitaxial, functional complex oxide films at wafer-scale via two essential in-situ characterization tools—RHEED for structural analysis or heteroepitaxy and LAXS for compositional characterization. This is a powerful technique for development of new films with optimized heteroepitaxy and composition.

Magnetization-structure-composition phase diagram mapping in Co-Fe-Ni alloys using diffusion multiples and scanning Hall probe microscopy

Transition metal alloys are essential for magnetic recording, memory, and new materials-by-design applications. Saturation magnetization in these alloys have previously been measured by conventional techniques, for a limited number of samples with discrete compositions, a laborious and time-consuming effort. Here, we propose a method to construct complete saturation magnetization diagrams for Co-Fe-Ni alloys using scanning Hall probe microscopy (SHPM). A composition gradient was created by the diffusion multiple technique, generating a full combinatorial materials library with an identical thermal history. The composition and crystallographic phases of the alloys were identified by integrated energy dispersive X-ray spectroscopy and electron backscatter diffraction. "Pixel-by-pixel" perpendicular components of the magnetic field were converted into maps of saturation magnetization using the inversion matrix technique. The saturation magnetization dependence for the binary alloys was consistent with the Slater-Pauling behavior. By using a significantly denser data point distribution than previously available, the maximum of the Slater-Pauling curve for the Co-Fe alloys was identified at ~ 32 at% of Co. By mapping the entire ternary diagram of Co-Fe-Ni alloys recorded in a single experiment, we have demonstrated that SHPM-in concert with the combinatorial approach-is a powerful high-throughput characterization tool, providing an effective metrology platform to advance the search for new magnetic materials.

Machine learning-accelerated design and synthesis of polyelemental heterostructures

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

Evaluation of Optical and Acoustical Properties of Ba1-xSrxTiO3 Thin Film Material Library via Conjugation of Picosecond Laser Ultrasonics with X-ray Diffraction, Energy Dispersive Spectroscopy, Electron Probe Micro Analysis, Scanning Electron and Atomic Force Microscopies

Wide-range continuous spatial variation of the film composition in lateral compositionally graded epitaxial films requires the development of high throughput measurement techniques for their local and non-destructive characterization with the highest possible spatial resolution. Here we report on the first application of the picosecond laser ultrasonics (PLU) technique for the evaluation of acoustical and optical parameters of lateral compositionally graded film, the Ba_1−xSr_xTiO₃ (0 ≤ x ≤ 1) material library. The film was not dedicatedly prepared for its opto-acousto-optic evaluation by PLU, exhibiting significant lateral variations in thickness and surface roughness. Therefore, the achieved measurements of the sound velocity and of the optical refractive index, and characterization of the surface roughness confirm the robustness of the PLU technique for thin film evaluation. We hope that the first measurements of the acoustical and optical properties of epitaxial grown Ba_1−xSr_xTiO₃ (0 ≤ x ≤ 1) by PLU technique accomplished here provide the parameters required for more extended predictive design of the phononic, photonic and phoxonic mirrors and cavities with superior properties/functionalities for novel multifunctional nanodevices.

A time-space conversion method for material synthesis research

Research on material synthesis is mostly performed through batch by batch testing with each corresponding to a set of parameters and a reaction time. Concurrent experiments that allow for multiple loadings throughout an inhomogeneous reaction zone provide a way to obtain high-throughput results. Here, a time-space conversion method is proposed. By sequentially passing a number of identical objects through a reaction zone, a significant diversity of reactions in one batch can be achieved depending on the spatial distribution and changes with time of the reaction zone. In particular, when the reaction zone is steady, the evolution of a reaction can be associated with the objects at their corresponding reaction stage. This greatly improves the efficiency and accuracy of research on material synthesis kinetics. This method may initiate a new wave of material synthesis research and accelerate the development of material science.

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High-throughput time-space conversion method by adding a moving rate

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Improving the efficiency and accuracy of research on material synthesis kinetics

A microfluidic-enabled combinatorial formulation and integrated inkjet printing platform for evaluating functionally graded material blends

Sample library preparation is a central step in the process of evaluating materials with the general aim of efficient library formulation while minimizing resource consumption. We demonstrate here the first implementation of a microfluidic-enabled thin film sample library formulation platform with integrated inkjet printing capability for directly patterning these libraries with reduced material wastage. System development and general performance screening protocol for these patterned thin films are described. We study the combinatorial formulation capabilities of this system by focusing on some practical case studies for probing the electrical conductivity in organic, biocompatible and electroactive polymer/additive (PEDOT:PSS/DMSO and PEDOT:PSS/EG) blends. Functionally-graded thin film libraries are prepared by mixing ink components and directly dispensing the processed blends into programmed geometries using the integrated platform. Electrical and morphological characterization of these printed thin film libraries is conducted to validate the formulation efficacy of the platform. Interrogating these printed libraries, we were able to iteratively identify the location of conductivity maxima for the studied blends and corroborate the morphological basis of this enhancement with established theories.

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High-Throughput Engineering Platform

An important step of the great achievement of organic solar cells in power conversion efficiency is the development of low-band gap polymer donors, PBDB-T derivatives, which present interesting aggregation effects dominating the device performance. The aggregation of polymers can be manipulated by a series of variables from a materials design and processing conditions perspective; however, optimization of film quality is a time- and energy-consuming work. Here, we introduce a robot-based high-throughput platform (HTP) that is offering automated film preparation and optical spectroscopy thin-film characterization in combination with an analysis algorithm. PM6 films are prepared by the so-called spontaneous film spreading (SFS) process, where a polymer solution is coated on a water surface. Automated acquisition of UV/Vis and photoluminescence (PL) spectra and automated extraction of morphological features is coupled to Gaussian Process Regression to exploit available experimental evidence for morphology optimization but also for hypothesis formulation and testing with respect to the underlying physical principles. The integrated spectral modeling workflow yields quantitative microstructure information by distinguishing amorphous from ordered phases and assesses the extension of amorphous versus the ordered domains. This research provides an easy to use methodology to analyze the exciton coherence length in conjugated semiconductors and will allow to optimize exciton splitting in thin film organic semiconductor layers as a function of processing.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Electrochemical Polymer Pen Lithography

The development of a massively parallel lithographic technique called electrochemical polymer pen lithography is reported. Pyramidal pen arrays, consisting of more than 10 000 hydrogel pens loaded with metal salts, are integrated into a three-electrode cell and used to locally reduce ions at each pen tip. This system enables high-throughput patterning of a variety of metallic inks (e.g., Ni²⁺ , Pt²⁺ , Ag⁺ ) on the nanometer to micrometer length scale. By incorporating a z-direction piezo actuator, the extension length and dwell time can be used to precisely define feature dimensions (210 to 10 µm in width, and up to 900 nm in height, thus far). Furthermore, by controlling the potential and precursor concentrations, more than one element can be simultaneously deposited, creating a new tool for the synthesis of alloy features, such as NiCo, which are relevant for catalysis. Importantly, this methodology enables fine control over feature size and composition in a single pattern, which may make it ultimately useful for rapid, high-throughput combinatorial screening of metallic features.

Accelerating organic solar cell material's discovery: high-throughput screening and big data

The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhance big data readiness. Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure-activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics.

Screening RAFT agents and photocatalysts to mediate PET-RAFT polymerization using a high throughput approach

We report a high throughput approach for the screening of RAFT agents and photocatalysts to mediate photoinduced electron/energy transfer-reversible addition–fragmentation chain transfer (PET-RAFT) polymerization.

Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an "Electronic Olfaction" Unit

Information about the surrounding atmosphere at a real timescale significantly relies on available gas sensors to be efficiently combined into multisensor arrays as electronic olfaction units. However, the array's performance is challenged by the ability to provide orthogonal responses from the employed sensors at a reasonable cost. This issue becomes more demanded when the arrays are designed under an on-chip paradigm to meet a number of emerging calls either in the internet-of-things industry or in situ noninvasive diagnostics of human breath, to name a few, for small-sized low-powered detectors. The recent advances in additive manufacturing provide a solid top-down background to develop such chip-based gas-analytical systems under low-cost technology protocols. Here, we employ hydrolytically active heteroligand complexes of metals as ink components for microplotter patterning a multioxide combinatorial library of chemiresistive type at a single chip equipped with multiple electrodes. To primarily test the performance of such a multisensor array, various semiconducting oxides of the p- and n-conductance origins based on pristine and mixed nanocrystalline MnO_x, TiO₂, ZrO₂, CeO₂, ZnO, Cr₂O₃, Co₃O₄, and SnO₂ thin films, of up to 70 nm thick, have been printed over hundred μm areas and their micronanostructure and fabrication conditions are thoroughly assessed. The developed multioxide library is shown to deliver at a range of operating temperatures, up to 400 °C, highly sensitive and highly selective vector signals to different, but chemically akin, alcohol vapors (methanol, ethanol, isopropanol, and n-butanol) as examples at low ppm concentrations when mixed with air. The suggested approach provides us a promising way to achieve cost-effective and well-performed electronic olfaction devices matured from the diverse chemiresistive responses of the printed nanocrystalline oxides.

Microfluidic High-Throughput Platforms for Discovery of Novel Materials

High-throughput screening is a potent technique to accelerate the discovery and development of new materials. By performing massive synthesis and characterization processes in parallel, it can rapidly discover materials with desired components, structures and functions. Among the various approaches for high-throughput screening, microfluidic platforms have attracted increasing attention. Compared with many current strategies that are generally based on robotic dispensers and automatic microplates, microfluidic platforms can significantly increase the throughput and reduce the consumption of reagents by several orders of magnitude. In this review, we first introduce current advances of the two types of microfluidic high-throughput platforms based on microarrays and microdroplets, respectively. Then the utilization of these platforms for screening different types of materials, including inorganic metals, metal alloys and organic polymers are described in detail. Finally, the challenges and opportunities in this promising field are critically discussed.

Bi Alloying into Rare Earth Double Perovskites Enhances Synthesizability and Visible Light Absorption

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm₂MnNiO₆ (double perovskite structure), of the form (Bi,Sm)₂MnNiO₆. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

High-Throughput Exploration of Metal Vanadate Thin-Film Systems (M-V-O, M = Cu, Ag, W, Cr, Co, Fe) for Solar Water Splitting: Composition, Structure, Stability, and Photoelectrochemical Properties

Combinatorial synthesis and high-throughput characterization of thin-film materials libraries enable to efficiently identify both photoelectrochemically active and inactive, as well as stable and instable systems for solar water splitting. This is shown on six ternary metal vanadate (M-V-O, M = Cu, Ag, W, Cr, Co, Fe) thin-film materials libraries, fabricated using combinatorial reactive magnetron cosputtering with subsequent annealing in air. By means of high-throughput characterization of these libraries correlations between composition, crystal structure, photocurrent density, and stability of the M-V-O systems in different electrolytes such as acidic, neutral and alkaline media were identified. The systems Cu-V-O and Ag-V-O are stable in alkaline electrolyte and exhibited photocurrents of 170 and 554 μA/cm², respectively, whereas the systems W-V-O, Cr-V-O, and Co-V-O are not stable in alkaline electrolyte. However, the Cr-V-O and Co-V-O systems showed an enlarged photoactive region in acidic electrolyte, albeit with very low photocurrents (<10 μA/cm²). Complete data sets obtained from these different screening sets, including information on nonpromising systems, lays groundwork for their use to predict new systems for solar water splitting, for example, by machine learning.

Exploring the First High-Entropy Thin Film Libraries: Composition Spread-Controlled Crystalline Structure

Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)Ox, all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)Ox, the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. The resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)Ox and Mn and Ni in (CrFeMnCoNi)Ox. The (MgZnMnCoNi)Ox sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)Ox over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)Ox was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

Multi-channel fiber optical spectrometer for high-throughput characterization of photoluminescence properties

High-throughput experiment can significantly accelerate the materials research efficiency. Thanks to national efforts, the Materials Genome Initiative further promotes the development of high-throughput experimental technology. A multi-channel fiber optical spectrometer has been designed and developed by us for high-throughput characterization of photoluminescence (PL) properties. It can quickly and automatically detect the PL spectrum, Commission International de l'Eclairage chromaticity, and PL intensity over time for luminescent materials under a given condition. The multi-channel fiber optical spectrometer synergistically combines a sample library holder, multiple modular excitation sources, multiple spectrometers, and Coral software, so it can measure and analyze multiple samples simultaneously. The number of channels in the multi-channel fiber optical spectrometer can be added or subtracted as required. Various modular light-emitting diode or laser diode excitation sources with the wavelength from 370 nm to 980 nm and corresponding filters can be provided according to the measurement need of different luminescent materials. The monitoring wavelength of the currently used fiber optical spectrometer is from 300 nm to 1000 nm. For example, the PL spectral measurement of 54 samples in a {6 × 9} array is completed in only about 30 min by using a representative triple-channel fiber optical spectrometer. The designed multi-channel fiber optical spectrometer facility not only makes PL measurements faster and more intuitive but is also easy to popularize for wide users.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.