Material Measurement Laboratory

The propensity of metals to form irregular and nonplanar electrodeposits at liquid-solid interfaces has emerged as a fundamental barrier to high-energy, rechargeable batteries that use metal anodes. We report an epitaxial mechanism to regulate nucleation, growth, and reversibility of metal anodes. The crystallographic, surface texturing, and electrochemical criteria for reversible epitaxial electrodeposition of metals are defined and their effectiveness demonstrated by using zinc (Zn), a safe, low-cost, and energy-dense battery anode material. Graphene, with a low lattice mismatch for Zn, is shown to be effective in driving deposition of Zn with a locked crystallographic orientation relation. The resultant epitaxial Zn anodes achieve exceptional reversibility over thousands of cycles at moderate and high rates. Reversible electrochemical epitaxy of metals provides a general pathway toward energy-dense batteries with high reversibility.

The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.

Abstract Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals 1 . These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.

Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

Current interest in melanin films derived from the autoxidation of dopamine stems from their use as a universal adhesion layer. Here we report chemical and physical characterization of polydopamine films deposited on gold surfaces from stirred basic solutions at times ranging from 2 to 60 min, with a focus on times ≤10 min. Data from Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and electrochemical methods suggest the presence of starting (dopamine) and intermediate (C=N-containing tautomers of quinone and indole) species in the polydopamine films at all deposition times. A uniform overlayer analysis of the XPS data indicates that film thickness increased linearly at short deposition times of ≤10 min. At deposition times ≥10 min, the films appeared largely continuous with surface roughness ≈ ≤ 2 nm, as determined by atomic force microscopy (AFM). Pinhole-free films, as determined by anionic redox probe measurements, required deposition times of 60 min or greater.

Zirconias, the strongest of the dental ceramics, are increasingly being fabricated in monolithic form for a range of clinical applications. Y-TZP (yttria-stabilized tetragonal zirconia polycrystal) is the most widely used variant. However, current Y-TZP ceramics on the market lack the aesthetics of competitive glass-ceramics and are therefore somewhat restricted in the anterior region. This article reviews the progressive development of currently available and next-generation zirconias, representing a concerted drive toward greater translucency while preserving adequate strength and toughness. Limitations of efforts directed toward this end are examined, such as reducing the content of light-scattering alumina sintering aid or incorporating a component of optically isotropic cubic phase into the tetragonal structure. The latest fabrication routes based on refined starting powders and dopants, with innovative sintering protocols and associated surface treatments, are described. The need to understand the several, often complex, mechanisms of long-term failure in relation to routine laboratory test data is presented as a vital step in bridging the gaps among material scientist, dental manufacturer, and clinical provider.

This paper documents the design, layout and algorithms of the IUPAC International Chemical Identifier, InChI.

Abstract Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks with better or comparable model training speed.

A technique is described for the measurement of fluid temperatures in microfluidic systems based on temperature-dependent fluorescence. The technique is easy to implement with a standard fluorescence microscope and CCD camera. In addition, the method can be used to measure fluid temperatures with micrometer spatial resolution and millisecond time resolution. The efficacy of the method is demonstrated by measuring temperature distributions resulting from Joule heating in a variety of microfluidic circuits that are electrokinetically pumped. With the equipment used for these measurements, fluid temperatures ranging from room temperature to 90 degrees C were measured with a precision ranging from 0.03 to 3.5 degrees C-dependent on the amount of signal averaging done. The spatial and temporal resolutions achieved were 1 microm and 33 ms, respectively.

Tabulations are presented of relativistic Hartree-Fock atomic form factors F (x,Z), for values of x (=sin(ϑ/2)/λ from 0.01 to 109 Å−1, for all elements Z=1 to 100. For Z=1, F (x,Z) is given by the exact expression of Pirenne. For Z=2 to 98, x=0.01 to 2.0 Å−1, the tabulated values are those of Cromer and Waber given in the International Tables for X−Ray Crystallography (Vol. IV, 1974), based in part on the work of Doyle and Turner. For Z=21 to 92, x=2.2 to 6.0 Å−1, the present tables are based on the values of Doyle and Turner and additional values (Z=44,60,68, and 74) as given by O/verbo/. For Z=3 to 20.x=2.2 to 45 Å−1, Z=21 to 92,x=62 to 45 Å−1 the tables are interpolated from values given for 36 elements by O/verbo/, extended to x=109 Å−1 using O/verbo/’s corrections to the Bethe-Levinger K-shell expression. The remainder of the table is filled in by interpolation and extrapolation, guided for high x-values by the Bethe-Levinger result. Tables of relativistic coherent (Rayleigh) scattering cross sections, obtained by numerical integration of the Thomson formula weighted by F2(x,Z), are presented for all elements Z=1 to 100, for photon energies 100 eV (λ=124 Å=12.4 nm) to 100 MeV (λ=0.000 124 Å=12.4 fm). Departures from the nonrelativistic coherent scattering cross sections tabulated in J. Phys. Chem. Ref. Data 4, 471 (1975) are less than 1% for Z<20. However for a high-Z element such as lead, for example, the relativistic coherent scattering cross section is systematically higher by less than 0.4% below 1 keV, by 8% at 100 keV and by 13% above 1 MeV.

Deceleration and velocity bunching of Na atoms in an atomic beam have been observed. The deceleration, caused by absorption of counterpropagating resonant laser light, amounts to 40% of the initial thermal velocity, corresponding to about 15 000 absorptions. Atoms were kept in resonance with the laser by using a spatially varying magnetic field to provide a changing Zeeman shift to compensate for the changing Doppler shift as the atoms decelerated.

A preformed T-microchannel imprinted in polycarbonate was postmodified with a pulsed UV excimer laser (KrF, 248 nm) to create a series of slanted wells at the junction. The presence of the wells leads to a high degree of lateral transport within the channel and rapid mixing of two confluent streams undergoing electroosmotic flow. Several mixer designs were fabricated and investigated. All designs were relatively successful at low flow rates (0.06 cm/s, > or = 75% mixing), but had varying degrees of success at high flow rates (0.81 cm/s, 45-80% mixing). For example, one design operating at high flow rates was able to split an incoming fluorescent stream into two streams of varying concentrations depending on the number of slanted wells present. The final mixer design was able to overcome stream splitting at high flow rates, and it was shown that the two incoming streams were 80% mixed within 443 microm of the T-junction for a flow rate of 0.81 cm/s. Without the presence of the mixer and at the same high flow rate, a channel length of 2.3 cm would be required to achieve the same extent of mixing when relying upon molecular diffusion entirely, while 6.9 cm would be required for 99% mixing.

Abstract The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

Carbon nanotubes (CNTs) are currently incorporated into various consumer products, and numerous new applications and products containing CNTs are expected in the future. The potential for negative effects caused by CNT release into the environment is a prominent concern and numerous research projects have investigated possible environmental release pathways, fate, and toxicity. However, this expanding body of literature has not yet been systematically reviewed. Our objective is to critically review this literature to identify emerging trends as well as persistent knowledge gaps on these topics. Specifically, we examine the release of CNTs from polymeric products, removal in wastewater treatment systems, transport through surface and subsurface media, aggregation behaviors, interactions with soil and sediment particles, potential transformations and degradation, and their potential ecotoxicity in soil, sediment, and aquatic ecosystems. One major limitation in the current literature is quantifying CNT masses in relevant media (polymers, tissues, soils, and sediments). Important new directions include developing mechanistic models for CNT release from composites and understanding CNT transport in more complex and environmentally realistic systems such as heteroaggregation with natural colloids and transport of nanoparticles in a range of soils.

Engineered nanoparticles, due to their unique electrical, mechanical, and catalytic properties, are presently found in many commercial products and will be intentionally or inadvertently released at increasing concentrations into the natural environment. Metal- and metal oxide-based nanomaterials have been shown to act as mediators of DNA damage in mammalian cells, organisms, and even in bacteria, but the molecular mechanisms through which this occurs are poorly understood. For the first time, we report that copper oxide nanoparticles induce DNA damage in agricultural and grassland plants. Significant accumulation of oxidatively modified, mutagenic DNA lesions (7,8-dihydro-8-oxoguanine; 2,6-diamino-4-hydroxy-5-formamidopyrimidine; 4,6-diamino-5-formamidopyrimidine) and strong plant growth inhibition were observed for radish (Raphanus sativus), perennial ryegrass (Lolium perenne), and annual ryegrass (Lolium rigidum) under controlled laboratory conditions. Lesion accumulation levels mediated by copper ions and macroscale copper particles were measured in tandem to clarify the mechanisms of DNA damage. To our knowledge, this is the first evidence of multiple DNA lesion formation and accumulation in plants. These findings provide impetus for future investigations on nanoparticle-mediated DNA damage and repair mechanisms in plants.

Manipulating a crystalline material's configurational entropy through the introduction of unique atomic species can produce novel materials with desirable mechanical and electrical properties. From a thermal transport perspective, large differences between elemental properties such as mass and interatomic force can reduce the rate at which phonons carry heat and thus reduce the thermal conductivity. Recent advances in materials synthesis are enabling the fabrication of entropy-stabilized ceramics, opening the door for understanding the implications of extreme disorder on thermal transport. Measuring the structural, mechanical, and thermal properties of single-crystal entropy-stabilized oxides, it is shown that local ionic charge disorder can effectively reduce thermal conductivity without compromising mechanical stiffness. These materials demonstrate similar thermal conductivities to their amorphous counterparts, in agreement with the theoretical minimum limit, resulting in this class of material possessing the highest ratio of elastic modulus to thermal conductivity of any isotropic crystal.

The g-C3N4-based materials have excellent electronic band structures, electron-rich properties, basic surface functionalities, high physicochemical stabilities and are ''earth-abundant.'' Recent progress of g-C3N4-based nanostructures in catalyst, sensing, imaging and LEDs have been discussed in details.

Previously, catalytic cerium oxide nanoparticles (CNPs, nanoceria, CeO2-x NPs) have been widely utilized for chemical mechanical planarization in the semiconductor industry and for reducing harmful emissions and improving fuel combustion efficiency in the automobile industry. Researchers are now harnessing the catalytic repertoire of CNPs to develop potential new treatment modalities for both oxidative- and nitrosative-stress induced disorders and diseases. In order to reach the point where our experimental understanding of the antioxidant activity of CNPs can be translated into useful therapeutics in the clinic, it is necessary to evaluate the most current evidence that supports CNP antioxidant activity in biological systems. Accordingly, the aims of this review are three-fold: (1) To describe the putative reaction mechanisms and physicochemical surface properties that enable CNPs to both scavenge reactive oxygen species (ROS) and to act as antioxidant enzyme-like mimetics in solution; (2) To provide an overview, with commentary, regarding the most robust design and synthesis pathways for preparing CNPs with catalytic antioxidant activity; (3) To provide the reader with the most up-to-date in vitro and in vivo experimental evidence supporting the ROS-scavenging potential of CNPs in biology and medicine.

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .