Fifty-one chemical elements in till from the Oppdal region, Mid-Norway: relation to mineralization, Quaternary and bedrock geology

2018 ◽  
Vol 18 (3) ◽  
pp. 229-240
Author(s):  
Malin Andersson ◽  
Ola A. Eggen ◽  
Deta Gasser ◽  
Clemens Reimann ◽  
Fredrik Høgaas ◽  
...  
Keyword(s):  
Chemical Elements ◽  
Bedrock Geology

What Are Vineyards Made Of ?

2018 ◽  
Author(s):  
Alex Maltman
Keyword(s):  
Chemical Elements ◽  
Bedrock Geology ◽  
Living Organisms ◽  
The Military

What strikes you first when looking at a vineyard? Perhaps the vines themselves? Your eye may be caught by random scatterings of gnarly old bushes or by the military neatness of rows of trained vines, luxuriant in foliage in summer and little more than gaunt woody skeletons in winter. But possibly more striking might be the land itself—the geology, or at least manifestations of it. The vines may extend across a vast, flat plain, or they may be perched on a vertiginous slope, or anywhere in between—it depends on the bedrock geology. How well the vines grow will be influenced by how that bedrock weathers into soil and how the vine roots respond. The soil may have an eye-catching color or may be astonishingly stony, consisting of little more than rock debris. This quality, too, depends on the geology. But what exactly is this vineyard ground? What are such things as bedrock, soil, and stone made of? Where do they come from? How did they get this way? The answers form the basis of understanding vineyard geology, so let’s begin here, with a few fundamentals. We can think about what the ground in a vineyard is made of in three ways. The first way is that, like all matter, it consists of atoms of chemical elements. And remarkably, although there are nearly a hundred different chemical elements in nature, the ground is dominated by just eight of them (Figure 1.1a). You could even say that it’s pretty much made up of only four of these elements, as the first four on the list account for nearly 88% of the composition. Preponderant among them are oxygen, at no less than 46%, and silicon, at 28%. So there’s a lot of these two elements in most vineyards! As an aside, it’s the same kind of story with living organisms: about 95% of their composition consists of just three elements—carbon, oxygen, and hydrogen—and that includes grapevines and grapes (Figure 1.1b).

Nuclear Processes Related to the Ap Stars and the Heaviest Chemical Elements

1976 ◽  
Vol 32 ◽  
pp. 169-182
Author(s):  
B. Kuchowicz
Keyword(s):  
Nuclear Physics ◽  
Nuclear Reactions ◽  
Chemical Elements ◽  
Fission Products ◽  
Abundance Pattern ◽  
Isotopic Shifts ◽  
Processed Material ◽  
R Process ◽  
Ap Stars ◽  
Nuclear Processes

SummaryIsotopic shifts in the lines of the heavy elements in Ap stars, and the characteristic abundance pattern of these elements point to the fact that we are observing mainly the products of rapid neutron capture. The peculiar A stars may be treated as the show windows for the products of a recent r-process in their neighbourhood. This process can be located either in Supernovae exploding in a binary system in which the present Ap stars were secondaries, or in Supernovae exploding in young clusters. Secondary processes, e.g. spontaneous fission or nuclear reactions with highly abundant fission products, may occur further with the r-processed material in the surface of the Ap stars. The role of these stars to the theory of nucleosynthesis and to nuclear physics is emphasized.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

How SIMS microscopy can be used in medicine

1992 ◽  
Vol 50 (2) ◽  
pp. 1602-1603
Author(s):  
Philippe Fragu
Keyword(s):  
Mass Spectrometry ◽  
Biomedical Applications ◽  
Secondary Ion Mass Spectrometry ◽  
Chemical Elements ◽  
Biological Applications ◽  
The Past ◽  
Potential Source ◽  
Secondary Ion ◽  
Chemical Integrity ◽  
Scientific Endeavour

The identification, localization and quantification of intracellular chemical elements is an area of scientific endeavour which has not ceased to develop over the past 30 years. Secondary Ion Mass Spectrometry (SIMS) microscopy is widely used for elemental localization problems in geochemistry, metallurgy and electronics. Although the first commercial instruments were available in 1968, biological applications have been gradual as investigators have systematically examined the potential source of artefacts inherent in the method and sought to develop strategies for the analysis of soft biological material with a lateral resolution equivalent to that of the light microscope. In 1992, the prospects offered by this technique are even more encouraging as prototypes of new ion probes appear capable of achieving the ultimate goal, namely the quantitative analysis of micron and submicron regions. The purpose of this review is to underline the requirements for biomedical applications of SIMS microscopy.Sample preparation methodology should preserve both the structural and the chemical integrity of the tissue.

X-ray mapping without STEM

1994 ◽  
Vol 52 ◽  
pp. 508-509
Author(s):  
Judith M. Brock ◽  
Max T. Otten
Keyword(s):  
Life Science ◽  
Materials Science ◽  
Chemical Elements ◽  
Full Description ◽  
Scanning System ◽  
Elemental Distribution ◽  
X Ray ◽  
Transmission Electron ◽  
Distribution Of Elements ◽  
Made In

A knowledge of the distribution of chemical elements in a specimen is often highly useful. In materials science specimens features such as grain boundaries and precipitates generally force a certain order on mental distribution, so that a single profile away from the boundary or precipitate gives a full description of all relevant data. No such simplicity can be assumed in life science specimens, where elements can occur various combinations and in different concentrations in tissue. In the latter case a two-dimensional elemental-distribution image is required to describe the material adequately. X-ray mapping provides such of the distribution of elements.The big disadvantage of x-ray mapping hitherto has been one requirement: the transmission electron microscope must have the scanning function. In cases where the STEM functionality – to record scanning images using a variety of STEM detectors – is not used, but only x-ray mapping is intended, a significant investment must still be made in the scanning system: electronics that drive the beam, detectors for generating the scanning images, and monitors for displaying and recording the images.

The Essential Chemical Elements of Plants

1886 ◽  
Vol 21 (524supp) ◽  
pp. 8371-8373
Author(s):  
Thomas Jamieson
Keyword(s):  
Chemical Elements ◽  
Essential Chemical

Becoming the Nuclear-Front-End

2017 ◽  
Vol 47 (189) ◽  
Author(s):  
Patrick Schukalla
Keyword(s):  
Nuclear Reactor ◽  
Chemical Elements ◽  
High Technology ◽  
Power Production ◽  
Uranium Mining ◽  
Nuclear Industry ◽  
Current State ◽  
Front End ◽  
Production And Consumption ◽  
Uranium Exploration

Uranium mining often escapes the attention of debates around the nuclear industries. The chemical elements’ representations are focused on the nuclear reactor. The article explores what I refer to as becoming the nuclear front – the uranium mining frontier’s expansion to Tanzania, its historical entanglements and current state. The geographies of the nuclear industries parallel dominant patterns and the unevenness of the global divisions of labour, resource production and consumption. Clearly related to the developments and expectations in the field of atomic power production, uranium exploration and the gathering of geological knowledge on resource potentiality remains a peripheral realm of the technopolitical perceptions of the nuclear fuel chain. Seen as less spectacular and less associated with high-technology than the better-known elements of the nuclear industry the article thus aims to shine light on the processes that pre-figure uranium mining by looking at the example of Tanzania.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

2020 ◽  
Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  
Keyword(s):  
Drug Development ◽  
Autonomous Vehicles ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Molecular Potential ◽  
Wide Range ◽  
Energy Surfaces ◽  
New Applications ◽  
Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

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