Intake of different chemical species of dietary arsenic by the japanese, and their blood and urinary arsenic levels

1992 ◽  
Vol 6 (4) ◽  
pp. 383-388 ◽  
Author(s):  
Hiroshi Yamauchi ◽  
Keiko Takahashi ◽  
Mari Mashiko ◽  
Juichiro Saitoh ◽  
Yukio Yamamura
Keyword(s):  
Chemical Species ◽  
Urinary Arsenic ◽  
Dietary Arsenic

Determination of urinary arsenic and impact of dietary arsenic intake

Talanta ◽  
1993 ◽  
Vol 40 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Xiao-Chun Le ◽  
William R. Cullen ◽  
Kenneth J. Reimer
Keyword(s):  
Urinary Arsenic ◽  
Dietary Arsenic

scholarly journals Measured versus modeled dietary arsenic and relation to urinary arsenic excretion and total exposure

2013 ◽  
Vol 23 (4) ◽  
pp. 442-449 ◽  
Author(s):  
Margaret Kurzius-Spencer ◽  
Mary K O'Rourke ◽  
Chiu-Hsieh Hsu ◽  
Vern Hartz ◽  
Robin B Harris ◽  
...  
Keyword(s):  
Total Exposure ◽  
Urinary Arsenic ◽  
Dietary Arsenic ◽  
Arsenic Excretion

Chemical Species Identification in an SEM by Changes in Emitted X-Ray Energies

1974 ◽  
Vol 32 ◽  
pp. 570-571
Author(s):  
R. H. Duff
Keyword(s):  
Species Identification ◽  
Valence Electron ◽  
Chemical Species ◽  
X Rays ◽  
Chemical Bonds ◽  
Small Shift ◽  
X Ray ◽  
Electron Transitions ◽  
Peak Energy ◽  
Chemical Combination

A material irradiated with electrons emits x-rays having energies characteristic of the elements present. Chemical combination between elements results in a small shift of the peak energies of these characteristic x-rays because chemical bonds between different elements have different energies. The energy differences of the characteristic x-rays resulting from valence electron transitions can be used to identify the chemical species present and to obtain information about the chemical bond itself. Although these peak-energy shifts have been well known for a number of years, their use for chemical-species identification in small volumes of material was not realized until the development of the electron microprobe.

Confocal Raman mapping

1992 ◽  
Vol 50 (2) ◽  
pp. 1514-1515
Author(s):  
J. Barbillat ◽  
M. Delhaye ◽  
P. Dhamelincourt
Keyword(s):  
Chemical Species ◽  
Diffraction Limit ◽  
Microscopic Level ◽  
Raman Mapping ◽  
Complete Spectrum ◽  
Laser Illumination ◽  
Ccd Detector ◽  
Raman Microprobe ◽  
Photodiode Array ◽  
Raman Image

Raman mapping, with a spatial resolution close to the diffraction limit, can help to reveal the distribution of chemical species at the surface of an heterogeneous sample.As early as 1975,three methods of sample laser illumination and detector configuration have been proposed to perform Raman mapping at the microscopic level (Fig. 1),:- Point illumination:The basic design of the instrument is a classical Raman microprobe equipped with a PM tube or either a linear photodiode array or a two-dimensional CCD detector. A laser beam is focused on a very small area ,close to the diffraction limit.In order to explore the whole surface of the sample,the specimen is moved sequentially beneath the microscope by means of a motorized XY stage. For each point analyzed, a complete spectrum is obtained from which spectral information of interest is extracted for Raman image reconstruction.- Line illuminationA narrow laser line is focused onto the sample either by a cylindrical lens or by a scanning device and is optically conjugated with the entrance slit of the stigmatic spectrograph.

Supersonic jet spectrometry of chemical species resulting from thermal decomposition of polystyrene and polycarbonate

1992 ◽  
Vol 64 (19) ◽  
pp. 931A-940A ◽  
Author(s):  
Totaro Imasaka ◽  
Masami Hozumi ◽  
Nobuhiko Ishibashi
Keyword(s):  
Thermal Decomposition ◽  
Supersonic Jet ◽  
Chemical Species

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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