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Spent lithium-ion batteries contain valuable metals that are difficult to separate from each other for recycling purposes. Used batteries present a sustainable source of these metals, especially cobalt and nickel, but the current methods used for their separation have environmental and efficiency drawbacks. A new technology uses electrochemistry to efficiently separate and recover the metals, making spent batteries a highly sustainable secondary source of cobalt and nickel—the reserves of which are currently dwindling.

A new study, led by University of Illinois Urbana-Champaign chemical and biomolecular engineering professor Xiao Su, uses selective electrodeposition to recover valuable metals from commercially sourced lithium nickel manganese cobalt oxide—or NMC—battery electrodes. The method, published in the journal Nature Communications, produces final product purities of approximately 96.4% and 94.1% for cobalt and nickel, respectively, from spent NMC electrode wastes.

Su said cobalt and nickel have similar electrochemical properties—or standard reduction potentials—making it challenging for chemists to recover pure forms of each metal from battery electrodes.

“A combination of grassy notes with a tang of acids and a hint of vanilla over an underlying mustiness” is how an international team of chemists describes the unique odor of old books in a study. Poetic, sure, but what causes it?

Books are made up almost entirely of organic materials: paper, ink, glue, fibers. All these materials react to light, heat, moisture, and even each other over the years, and release a number of volatile organic compounds (VOCs). While the blend of compounds released by any one book is dependent on the exact things that went into making it, there’s only so much variation in materials.

The researchers tested 72 books and found some 15 compounds that came up again and again. They were reliable markers for degradation. These include acetic acid, benzaldehyde, butanol, furfural, octanal, methoxyphenyloxime, and other chemicals with funny-sounding names. A book’s smell is also influenced by its environment and materials it encounters over the course of its life (which is why some books have hints of cigarette smoke, others smell a little like coffee, and still others, cat dander).

While traditional computers use magnetic bits to represent a one or a zero for computation, quantum computers use quantum bits or qubits to represent a one or a zero or simultaneously any number in between.

Today’s quantum computers use several different technologies for qubits. But regardless of the technology, a common requirement for all quantum computing qubits is that it must be scalable, high quality, and capable of fast quantum interaction with each other.

IBM uses superconducting qubits on its huge fleet of about twenty quantum computers. Although Amazon doesn’t yet have a quantum computer, it plans to build one using superconducting hardware. Honeywell and IonQ both use trapped-ion qubits made from a rare earth metal called ytterbium. In contrast, Psi Quantum and Xanadu use photons of light.

Continue reading “Atom Computing: A Quantum Computing Startup That Believes It Can Ultimately Win The Qubit Race” »

Geological evidence suggests that Mars was temporarily habitable three billion years ago when liquid water existed on the surface of the planet. Because life had little time to develop and flourish, possible microfossils found in the Martian rocks will likely resemble simple organisms. On Earth, life persisted for over three billion years in the form of single-celled bacteria and algae.

In a new open-access study published in the Journal of the Geological Society, the two authors, astrobiologists Sean McMahon and Julie Cosmidis from the Universities of Edinburgh and Oxford, note that the origins of any fossil-like specimens found on Mars are likely to be very ambiguous.

Rocks on Mars may contain numerous types of pseudofossils, structures formed by chemical processes or minerals resembling organic structures, that look similar to the kinds of fossils likely to be found if the planet ever supported life, a press release provided by University of Edinburgh explains.

The new type of catalyst, known as a biohybrid photocatalyst, contains a light-harvesting protein that absorbs light and transfers the energy to a metal-containing catalyst. This catalyst then uses the energy to perform reactions that could be useful for synthesizing pharmaceuticals or converting waste products into biofuels or other useful compounds.

“By replacing harmful conditions and reagents with light, photocatalysis can make pharmaceutical, agrochemical, and fuel synthesis more efficient and environmentally compatible,” says Gabriela Schlau-Cohen, an associate professor of chemistry at MIT and the senior author of the new study.

How are chemical elements produced in our Universe? Where do heavy elements like gold and uranium come from? Using computer simulations, a research team from the GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, together with colleagues from Belgium and Japan, shows that the synthesis of heavy elements is typical for certain black holes with orbiting matter accumulations, so-called accretion disks. The predicted abundance of the formed elements provides insight into which heavy elements need to be studied in future laboratories — such as the Facility for Antiproton and Ion Research (FAIR), which is currently under construction — to unravel the origin of heavy elements. The results are published in the journal Monthly Notices of the Royal Astronomical Society.

All heavy elements on Earth today were formed under extreme conditions in astrophysical environments: inside stars, in stellar explosions, and during the collision of neutron stars. Researchers are intrigued with the question in which of these astrophysical events the appropriate conditions for the formation of the heaviest elements, such as gold or uranium, exist. The spectacular first observation of gravitational waves and electromagnetic radiation originating from a neutron star merger in 2017 suggested that many heavy elements can be produced and released in these cosmic collisions. However, the question remains open as to when and why the material is ejected and whether there may be other scenarios in which heavy elements can be produced.

Promising candidates for heavy element production are black holes orbited by an accretion disk of dense and hot matter. Such a system is formed both after the merger of two massive neutron stars and during a so-called collapsar, the collapse and subsequent explosion of a rotating star. The internal composition of such accretion disks has so far not been well understood, particularly with respect to the conditions under which an excess of neutrons forms. A high number of neutrons is a basic requirement for the synthesis of heavy elements, as it enables the rapid neutron-capture process or r-process. Nearly massless neutrinos play a key role in this process, as they enable conversion between protons and neutrons.

Machine learning (ML) models are powerful tools to study multivariate correlations that exist within large datasets but are hard for humans to identify^16,23. Our aim is to build a model that captures the chemical interactions between the element combinations that afford reported crystalline inorganic materials, noting that the aim of such models is efficacy rather than interpretability, and that as such they can be complementary guides to human experts. The model should assist expert prioritization between the promising element combinations by ranking them quantitatively. Researchers have practically understood how to identify new chemistries based on element combinations for phase-field exploration, but not at significant scale. However, the prioritization of these attractive knowledge-based choices for experimental and computational investigation is critical as it determines substantial resource commitment. The collaborative ML workflow^24,25 developed here includes a ML tool trained across all available data at a scale beyond that, which humans can assimilate simultaneously to provide numerical ranking of the likelihood of identifying new phases in the selected chemistries. We illustrate the predictive power of ML in this workflow in the discovery of a new solid-state Li-ion conductor from unexplored quaternary phase fields with two anions. To train a model to assist prioritization of these candidate phase fields, we extracted 2021 M_xM ′_yA_zA ′_t phases reported in ICSD (Fig. 1, Step 1), and associated each phase with the phase fields M-M ′-A-A′ where M, M ′ span all cations, A, A ′ are anions {N³⁻, P³⁻, As³⁻, O²⁻, S²⁻, Se²⁻, Te²⁻, F⁻, Cl⁻, Br⁻, and I⁻} and x, y, z, t denote concentrations (Fig. 1, Step 2). Data were augmented by 24-fold elemental permutations to enhance learning and prevent overfitting (Supplementary Fig. 2).

ML models rely on using appropriate features (often called descriptors)²⁶ to describe the data presented, so feature selection is critical to the quality of the model. The challenge of selecting the best set of features among the multitude available for the chemical elements (e.g., atomic weight, valence, ionic radius, etc.)²⁶ lies in balancing competing considerations: a small number of features usually makes learning more robust, while limiting the predictive power of resulting models, large numbers of features tend to make models more descriptive and discriminating while increasing the risk of overfitting. We evaluated 40 individual features^26,27 (Supplementary Fig. 4, 5) that have reported values for all elements and identify a set of 37 elemental features that best balance these considerations. We thus describe each phase field of four elements as a vector in a 148-dimensional feature space (37 features × 4 elements = 148 dimensions).

To infer relationships between entries in such a high-dimensional feature space in which the training data are necessarily sparsely distributed²⁸, we employ the variational autoencoder (VAE), an unsupervised neural network-based dimensionality reduction method (Fig. 1, Step 3), which quantifies nonlinear similarities in high-dimensional unlabelled data²⁹ and, in addition to the conventional autoencoder, pays close attention to the distribution of the data features in multidimensional space. A VAE is a two-part neural network, where one part is used to compress (encode) the input vectors into a lower-dimensional (latent) space, and the other to decode vectors in latent space back into the original high-dimensional space. Here we choose to encode the 148-dimensional input feature space into a four-dimensional latent feature space (Supplementary Methods).

A group of scientists at the U.S. Department of Energy’s Ames Laboratory has developed computational quantum algorithms that are capable of efficient and highly accurate simulations of static and dynamic properties of quantum systems. The algorithms are valuable tools to gain greater insight into the physics and chemistry of complex materials, and they are specifically designed to work on existing and near-future quantum computers.

Scientist Yong-Xin Yao and his research partners at Ames Lab use the power of advanced computers to speed discovery in condensed matter physics, modeling incredibly complex quantum mechanics and how they change over ultra-fast timescales. Current high performance computers can model the properties of very simple, small quantum systems, but larger or more complex systems rapidly expand the number of calculations a computer must perform to arrive at an accurate model, slowing the pace not only of computation, but also discovery.

“This is a real challenge given the current early-stage of existing quantum computing capabilities,” said Yao, “but it is also a very promising opportunity, since these calculations overwhelm classical computer systems, or take far too long to provide timely answers.”

Scientists found an easier way to produce a chemical reaction important in making biofuels, possibly lowering the cost of biofuel production.