Combinatorial Materials Science Research Articles

Dielectrophoretic bead-droplet reactor for solid-phase synthesis

Solid-phase synthesis underpins many advances in synthetic and combinatorial chemistry, biology, and material science. The immobilization of a reacting species on the solid support makes interfacing of reagents an important challenge in this approach. In traditional synthesis columns, this leads to reaction errors that limit the product yield and necessitates excess consumption of the mobile reagent phase. Although droplet microfluidics can mitigate these problems, its adoption is fundamentally limited by the inability to controllably interface microbeads and reagent droplets. Here, we introduce Dielectrophoretic Bead-Droplet Reactor as a physical method to implement solid-phase synthesis on individual functionalized microbeads by encapsulating and ejecting them from microdroplets by tuning the supply voltage. Proof-of-concept demonstration of the enzymatic coupling of fluorescently labeled nucleotides onto the bead using this reactor yielded a 3.2-fold higher fidelity over columns through precise interfacing of individual microreactors and beads. Our work combines microparticle manipulation and droplet microfluidics to address a long-standing problem in solid-phase synthesis with potentially wide-ranging implications.

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Cutting Edge High‐Throughput Synthesis and Characterization Techniques in Combinatorial Materials Science

AbstractThe Materials Genome Initiative is expected to accelerate the materials discovery and design by fundamentally changing the trial‐and‐error research paradigm. However, mass data from high‐throughput experiments is still essential for the revelation of rules and verification of theories. In fact, the development of combinatorial materials science is always on the strength of the upgrade and evolution of high‐throughput techniques in each stage, especially high‐throughput materials synthesis and characterization. Herein, this review summarizes the high‐throughput synthesis methods for combinatorial materials libraries, especially the co‐deposition and masking techniques of thin‐film fabrication; and details the high‐throughput characterization methods for specific material properties and typical material categories, which comes down to the spectroscopy and microscopy techniques. It is considered that high‐throughput concepts will be the predominant lab experimentation in the future, along with advanced experimental techniques and convenient data processing procedures. Before that, more cooperation between multiple researchers from different fields should be conducted to complete the combinatorial materials research, since the high‐throughput technology covers multiple disciplines with a huge span.

A Sustainable synthesis, eco-safe approach efficiency and DFT study of novel 5,6,7,8-Tetrahyroquinazolin-2(1H)-one derivatives as antioxidant reagents

5,6,7,8-Tetrahydroquinazolin-2-(thio)-ones (THQ) fits the class of N-heterocycles as a structural core in numerous bioactive compounds. They promptly extended previous decades. They were significantly recognized in combinatorial chemistry and materials science to determine the drug discovery, antioxidants, and pharmaceuticals fields. In the present work, one-pot multicomponent sustainable synthesis of THQ with easily accessible starting materials, i.e., cyclohexanone, different aromatic aldehydes and (thio)urea, has been performed to determine the proposed Biginelli mechanism that is supported by DFT. It is found that the THQs are synthesized by a mechano-chemical (grinding) tool to achieve a yield of 85.2% within 3.5 min, i.e., YE (% yield/time) 24.34 differs from the conventional method in which lower % yield (YE = 0.72) of THQ was achieved. This confirmed that in the green chemistry principle, the determination of % yield according to saving reaction time must be considered. Moreover, DFT-based antioxidant properties of the THQ were also studied in which the most potent antioxidant compounds were 7b > 6d > 2f. Softness (σ, eV−1) and hardness (η, eV mol−1) can approve the soft molecule that stays more reactive as a result of decreasing the energy gap along heterocyclic with values 0.1491 > 0.1300 > 0.1168 eV−1 one-to-one with the efficiency of antioxidant.

Sm-Co-based amorphous alloy films for zero-field operation of transverse thermoelectric generation

ABSTRACT Transverse thermoelectric generation using magnetic materials is essential to develop active thermal engineering technologies, for which the improvements of not only the thermoelectric output but also applicability and versatility are required. In this study, using combinatorial material science and lock-in thermography technique, we have systematically investigated the transverse thermoelectric performance of Sm-Co-based alloy films. The high-throughput material investigation revealed the best Sm-Co-based alloys with the large anomalous Nernst effect (ANE) as well as the anomalous Ettingshausen effect (AEE). In addition to ANE/AEE, we discovered unique and superior material properties in these alloys: the amorphous structure, low thermal conductivity, and large in-plane coercivity and remanent magnetization. These properties make it advantageous over conventional materials to realize heat flux sensing applications based on ANE, as our Sm-Co-based films can generate thermoelectric output without an external magnetic field. Importantly, the amorphous nature enables the fabrication of these films on various substrates including flexible sheets, making the large-scale and low-cost manufacturing easier. Our demonstration will provide a pathway to develop flexible transverse thermoelectric devices for smart thermal management.

Thioester deprotection using a biomimetic NCL approach.

The reversibility of the thiol-thioester linkage has been broadly employed in many fields of biochemistry (lipid synthesis) and chemistry (dynamic combinatorial chemistry and material science). When the transthioesterification is followed by a S-to-N acyl transfer to give an amide bond, it is called Native Chemical Ligation (NCL), a high-yield chemoselective process used for peptide synthesis. Recently, we described thioglycolic acid (TGA) as a useful reagent for thioester deprotection both in solution and anchored to a solid-support under mild conditions. Inspired by NCL, in this work, we extended this approach and explored the use of 2-aminothiols for the deprotection of thiols bearing an acyl group. The best results were obtained using cysteamine or L-cysteine in an aqueous buffer pH 8 at room temperature for 30 min. The described approach was useful for S-acetyl, S-butyryl, and S-benzoyl heterocycles deprotection with yields up to 84%. Employing this methodology, we prepared six new analogs 2 of mercaptomethyl bisthiazolidine 1, a useful inhibitor of a wide-range of Metallo-β-Lactamases (MBLs). Compared with the previous methodologies (TGA polymer supported and TGA in solution), the biomimetic deprotection herein described presents better performance with higher yields, shorter reaction times, less time-consuming operations, easier setup, and lower costs.

Combinatorial growth of multinary nanostructured thin functional films

The rapid generation of material libraries with multidimensional gradients is important for the discovery of new functional materials. Here we report an integrated fabrication scheme, based on glancing angle physical vapor deposition, to form a thin-film materials library with controlled variations in nanoshape, multinary composition, and oxidation state on a single large area substrate. We demonstrate the versatility of the method by growing an octonary materials system, which we characterize with high-throughput methods, and reveal variations in several physico-chemical properties. Among others, we examine the materials library in the frame of the oxygen evolution reaction and show that nanostructuring leads to NiO clusters that are active towards such a reaction. Our scheme can be readily extended to include more starting elements, and can be transferred to other deposition methods, making this an adaptable and versatile platform for combinatorial materials science.

Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni–Ti–Cu–V Quaternary Thin-Film Library

Abstract Ni–Ti–based shape memory alloys (SMAs) have found widespread use in the last 70 years, but improving their functional stability remains a key quest for more robust and advanced applications. Named for their ability to retain their processed shape as a result of a reversible martensitic transformation, SMAs are highly sensitive to compositional variations. Alloying with ternary and quaternary elements to fine-tune the lattice parameters and the thermal hysteresis of an SMA, therefore, becomes a challenge in materials exploration. Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques. In this study, a composition spread of Ni–Ti–Cu–V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer. Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy, synchrotron X-ray diffraction, and temperature-dependent resistance measurements. Of the 177 compositions in the materials library, 32 were observed to have shape memory effect, of which five had zero or near-zero thermal hysteresis. These compositions provide flexibility in the operating temperature regimes that they can be used in. A phase map for the quaternary system and correlations of functional properties are discussed with respect to the local microstructure and composition of the thin-film library.

Early Years of High-Throughput Experimentation and Combinatorial Approaches in Catalysis and Materials Science

This is a report on the early years of combinatorial materials science and technology. High-throughput technologies (HTTs) are found in life- and materials-science laboratories. Although HTTs have long been the standard in life sciences in academia as well as in industry, HTTs in materials science have become the standard in industry but not in academia. In life science, successful drugs developed with HTTs have been reported, but there is no information on successful materials developed with HTTs that have made it to the market. Some initial development of HTTs in materials science is summarized, especially early applications of artificial intelligence. This outlook attempts to summarize the development of combinatorial materials sciences from the early years to today.

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

Structural and Functional Properties of the Thin Film System Ti-Ni-Si.

The thin film system Ti-Ni-Si was investigated using methods of combinatorial materials science. A thin film composition spread library of the system was fabricated using combinatorial magnetron sputtering. The functional properties Seebeck coefficient, electrical resistivity, and luminance were determined using high-throughput characterization techniques. A thin-film phase diagram was established by the assessment of high-throughput X-ray diffraction results. Correlations between composition, phase constitution, and functional properties with focus on the binary composition space are discussed.

Electrical and Structural Properties of the Partial Ternary Thin-Film System Ni-Si-B.

High-throughput and combinatorial materials science methods were used to investigate the dependence of the work function in the Ni-Si system on the B content (0-30 at. %). Alloying of NiSi is used to adapt its properties to suit the needs as a gate electrode material. Thin-film materials libraries were fabricated and investigated with respect to their structural and electrical properties. Further the work function values of selected samples in the region of interest were analyzed. The results show that the work function can be adjusted between 4.86 eV (B = 4.2 at. %) and 5.16 eV (B = 29.2 at. %) for (NiSi)B x.

Combinatorial investigation of spin-orbit materials using spin Peltier effect

Conversion between spin and charge currents is essential in spintronics, since it enables spin-orbit-torque magnetization switching, spin-current-driven thermoelectric generation, and nano-scale thermal energy control. To realize efficient spin-charge conversion, a variety of mechanisms, including spin Hall effects, Rashba-Edelstein effects, and spin-momentum locking in topological insulators, have been investigated and more comprehensive material exploration is necessary. Here we demonstrate high-throughput screening of spin-charge conversion materials by means of the spin Peltier effect (SPE). This is enabled by combining recently-developed SPE-imaging techniques with combinatorial materials science; using a composition-spread alloy film formed on a magnetic insulator, we observe the SPE-induced temperature change due to the spin Hall effect and obtain a continuous mapping of its composition dependence from the single sample. The distribution of the SPE signals reflects local spin-charge conversion capability in the alloy owing to unique heat-generation nature of the SPE. This combinatorial approach will accelerate materials research towards high-performance spintronic devices.

Combinatorial Material Science and Strain Engineering Enabled by Pulsed Laser Deposition Using Radially Segmented Targets.

Vertical composition gradients of ternary alloy thin films find applications in numerous device structures. Up to now such gradients along the growth direction have not been realized by standard pulsed laser deposition (PLD) systems. In this study, we propose an approach based on a single elliptically segmented PLD target suited for the epitaxial growth of vertically graded layers. The composition of the thin films can be varied by a simple adjustment of the position of the PLD laser spot on the target surface. We demonstrate this principle for the Mg xZn1- xO alloy system. Such vertically composition-graded Mg xZn1- xO thin films exhibit high optical quality and a well-defined Mg-content for each layer. No signs of interdiffusion of Mg-atoms between the layers have been found. Further, this method is capable to deposit homogeneous thin films with any desired, well-defined cation composition having the same high optical and structural quality as films grown by conventional PLD.

Charge Carrier Lifetimes in Cr-Fe-Al-O Thin Films.

The effect of compositional variation on charge carrier lifetimes of Cr1Fe0.84Al0.16O3, a promising material for solar water splitting recently identified using combinatorial materials science, is explored using ultrafast time-resolved optical reflectance. The transient signal can be described by a biexponential decay, where the shorter time constant varies over 1 order of magnitude with changing Cr content while the longer one stays constant. Intrinsic performance limitations such as a low charge carrier mobility on the order of 10-3 cm2/(Vs) are identified. Charge carrier lifetime and mobility are discussed as screening criteria for solar water splitting materials.

Combinatorial Synthesis and High-Throughput Characterization of Fe-V-O Thin-Film Materials Libraries for Solar Water Splitting.

The search for suitable materials for solar water splitting is addressed with combinatorial material science methods. Thin film Fe-V-O materials libraries were synthesized using combinatorial reactive magnetron cosputtering and subsequent annealing in air. The design of the libraries comprises a combination of large compositional gradients (from Fe10V90O x to Fe79V21O x) and thickness gradients (from 140 to 425 nm). These material libraries were investigated by high-throughput characterization techniques in terms of composition, structure, optical, and photoelectrochemical properties to establish correlations between composition, thickness, crystallinity, microstructure, and photocurrent density. Results show the presence of the Fe2V4O13 phase from ∼11 to 42 at. % Fe (toward low-Fe region) and the FeVO4 phase from ∼37 to 79 at. % Fe (toward Fe-rich region). However, as a third phase, Fe2O3 is present throughout the compositional gradients (from low-Fe to Fe-rich region). Material compositions with increasing crystallinity of the FeVO4 phase show enhanced photocurrent densities (∼160 to 190 μA/cm2) throughout the thickness gradients whereas compositions with the Fe2V4O13 phase show comparatively low photocurrent densities (∼28 μA/cm2). The band gap energies of Fe-V-O films were inferred from Tauc plots. The highest photocurrent density of ∼190 μA/cm2 was obtained for films with ∼54 to 66 at. % Fe for the FeVO4 phase with ∼2.04 eV for the indirect and ∼2.80 eV for the direct band gap energies.

How Transparent Oxides Gain Some Color: Discovery of a CeNiO3 Reduced Bandgap Phase As an Absorber for Photovoltaics.

In this work, we describe the formation of a reduced bandgap CeNiO3 phase, which, to our knowledge, has not been previously reported, and we show how it is utilized as an absorber layer in a photovoltaic cell. The CeNiO3 phase is prepared by a combinatorial materials science approach, where a library containing a continuous compositional spread of Ce xNi1- xO y is formed by pulsed laser deposition (PLD); a method that has not been used in the past to form Ce-Ni-O materials. The library displays a reduced bandgap throughout, calculated to be 1.48-1.77 eV, compared to the starting materials, CeO2 and NiO, which each have a bandgap of ∼3.3 eV. The materials library is further analyzed by X-ray diffraction to determine a new crystalline phase. By searching and comparing to the Materials Project database, the reduced bandgap CeNiO3 phase is realized. The CeNiO3 reduced bandgap phase is implemented as the absorber layer in a solar cell and photovoltages up to 550 mV are achieved. The solar cells are also measured by surface photovoltage spectroscopy, which shows that the source of the photovoltaic activity is the reduced bandgap CeNiO3 phase, making it a viable material for solar energy.

Oxygen concentration as a combinatorial parameter: The effect of continuous oxygen vacancy variation on SnO2 layer conductivity

Combinatorial materials science is a powerful approach to discover new materials, especially by using the continuous compositional spread (CCS) method, which forms spatially varying stoichiometry across a sample. Though the chemical composition of the candidate materials is typically the primary parameter studied, in the case of metal oxides CCS the oxygen concentration is usually either neglected or studied in a discrete and non-combinatorial manner. The present work reports the use of oxygen concentration as a combinatorial parameter that varies continuously across a sample, using a pulsed laser deposited (PLD) SnO2 film as a model system. As the oxygen concentration decreases, the SnO2 crystal lattice expands, the number of defects is increased, and the electrical conductivity rises exponentially. A relatively low electrical resistivity of 8.16∙10−4 Ω cm is achieved. The sample also showed superior infrared transparency, 67% at 2000 nm, compared to commercial F:SnO2 (FTO) which is only 12% transparent at this wavelength. The improved transparency and conductivity were achieved within a single experiment, without any additional optimization steps, and with further improvement may allow reconsideration of SnO2 as a transparent conductive oxide. Our findings serve as a demonstration for the importance of oxygen concentration as a combinatorial parameter.

(Invited) A New Model for Materials Genome Initiative - Driven Research: The High Throughput Experimental Materials Science Virtual Laboratory

The main goal of the Materials Genome Initiative (MGI) is the discovery, optimization, and commercial deployment of novel materials twice as fast as today’s practice, and at reduced cost. Since its inception in 2011, the MGI has resulted in significant progress in computational simulation and modeling to enable predictions of materials properties. More recently, an experimental component, based on high-throughput experimental (also known as combinatorial) materials science has been added. However, there remain serious challenges that the materials community must overcome to enable widespread deployment of an MGI-type approach to novel materials development. For example, data, both experimental and simulated, must be made discoverable, accessible, and interoperable. Further, even one “brick and mortar” high-throughput experimental facility would be very costly, whereas multiple facilities dedicated to different materials classes (e.g., catalysts, photovoltaics, lightweight structural materials) would be needed. Therefore, we propose a “High Throughput Experimental Materials Science Virtual Laboratory” (HTEMSVL) to facilitate MGI-driven research. The virtual laboratory would consist of an integrated, delocalized network of high-throughput synthesis and characterization tools, as well as a best-in-class, configurable data curation system, consisting of NIST (and other) materials resource registries and materials data repositories. Thus, data generated in the HTEMSVL could readily be made discoverable and accessible, at the discretion of the owner. Ultimately, users of the HTEMSVL, such as researchers at national labs, universities, and industry could leverage it to facilitate the rapid development and commercialization of novel materials and products.

Expediting Combinatorial Data Set Analysis by Combining Human and Algorithmic Analysis.

A challenge in combinatorial materials science remains the efficient analysis of X-ray diffraction (XRD) data and its correlation to functional properties. Rapid identification of phase-regions and proper assignment of corresponding crystal structures is necessary to keep pace with the improved methods for synthesizing and characterizing materials libraries. Therefore, a new modular software called htAx (high-throughput analysis of X-ray and functional properties data) is presented that couples human intelligence tasks used for "ground-truth" phase-region identification with subsequent unbiased verification by an algorithm to efficiently analyze which phases are present in a materials library. Identified phases and phase-regions may then be correlated to functional properties in an expedited manner. For the functionality of htAx to be proven, two previously published XRD benchmark data sets of the materials systems Al-Cr-Fe-O and Ni-Ti-Cu are analyzed by htAx. The analysis of ∼1000 XRD patterns takes less than 1 day with htAx. The proposed method reliably identifies phase-region boundaries and robustly identifies multiphase structures. The method also addresses the problem of identifying regions with previously unpublished crystal structures using a special daisy ternary plot.