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CCQM-K143 is a key comparison that assesses participants’ ability to prepare single element calibration solutions. Preparing calibration solutions properly is the cornerstone of establishing a traceability link to the International System of Units (SI), and therefore should be tested in order to confirm the validity of CCQM comparisons of more complex materials. CCQM-K143 consisted of participants each preparing a single copper calibration solution at 10 g/kg copper mass fraction and shipping 10 bottled aliquots of that solution to the coordinating laboratory, the National Institute of Standards and Technology (NIST). The masses and mass fraction for the prepared solutions were documented with the submitted samples.
The solutions prepared by all participants were measured at NIST by high performance inductively coupled plasma optical emission spectroscopy (HP-ICP-OES). The intensity measurements for copper were not mapped onto values of mass fraction via calibration. Instead, ratios were computed between the measurements for copper and simultaneous measurements for manganese, the internal standard, and all subsequent data reductions, including the computation of the KCRV and the degrees of equivalence, were based on these ratios. Other than for two participants whose measurement results appeared to suffer
from calculation or preparation errors, all unilateral degrees of equivalence showed that the measured values did not differ significantly from the KCRV. These results were confirmed by a second set of ICP-OES measurements performed by the Physikalisch-Technische Bundesanstalt (PTB). CCQM-K143 showed that participants are capable of preparing calibration solutions starting from high purity, assayed copper metal.
Similar steps are involved when preparing solutions for other elements, so it seems safe to infer that similar capabilities should prevail when preparing many different, single-element solutions.
Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain−machine interfaces have changed over time, along with the available technology.
Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain−machine interfaces and look forward in discussing perspectives and limitations based on the authors’ expertise across a range of complementary disciplines from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
