Climate warming affects the depth distribution of marine ectotherms

2020 ◽  
Author(s):  
S Thatje
Keyword(s):  
Climate Warming ◽  
Depth Distribution

Applications of SIMS to electronic materials and devices

1992 ◽  
Vol 50 (2) ◽  
pp. 1682-1683
Author(s):  
S.F. Corcoran
Keyword(s):  
Depth Distribution ◽  
Secondary Ion Mass Spectrometry ◽  
Semiconductor Device ◽  
Electronic Materials ◽  
Profile Analysis ◽  
Semiconductor Industry ◽  
Small Diameter ◽  
Depth Resolution ◽  
Detection Sensitivity

Over the past decade secondary ion mass spectrometry (SIMS) has played an increasingly important role in the characterization of electronic materials and devices. The ability of SIMS to provide part per million detection sensitivity for most elements while maintaining excellent depth resolution has made this technique indispensable in the semiconductor industry. Today SIMS is used extensively in the characterization of dopant profiles, thin film analysis, and trace analysis in bulk materials. The SIMS technique also lends itself to 2-D and 3-D imaging via either the use of stigmatic ion optics or small diameter primary beams.By far the most common application of SIMS is the determination of the depth distribution of dopants (B, As, P) intentionally introduced into semiconductor materials via ion implantation or epitaxial growth. Such measurements are critical since the dopant concentration and depth distribution can seriously affect the performance of a semiconductor device. In a typical depth profile analysis, keV ion sputtering is used to remove successive layers the sample.

Fluorescence effects in quantitative microprobe analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 188-189
Author(s):  
S.J.B. Reed
Keyword(s):  
Microprobe Analysis ◽  
Depth Distribution ◽  
Exciting Radiation ◽  
Primary Radiation ◽  
List Type ◽  
Type Number ◽  
Computing Power ◽  
X Ray ◽  
Fluorescent Radiation

Characteristic fluorescenceThe theory of characteristic fluorescence corrections was first developed by Castaing. The same approach, with an improved expression for the relative primary x-ray intensities of the exciting and excited elements, was used by Reed, who also introduced some simplifications, which may be summarized as follows (with reference to K-K fluorescence, i.e. K radiation of element ‘B’ exciting K radiation of ‘A’):1.The exciting radiation is assumed to be monochromatic, consisting of the Kα line only (neglecting the Kβ line).2.Various parameters are lumped together in a single tabulated function J(A), which is assumed to be independent of B.3.For calculating the absorption of the emerging fluorescent radiation, the depth distribution of the primary radiation B is represented by a simple exponential.These approximations may no longer be justifiable given the much greater computing power now available. For example, the contribution of the Kβ line can easily be calculated separately.

A New Numerical Model for Electron-Probe Analysis at High Depth Resolution

1996 ◽  
Vol 54 ◽  
pp. 490-491
Author(s):  
P.-F. Staub ◽  
C. Bonnelle ◽  
F. Vergand ◽  
P. Jonnard
Keyword(s):  
Electron Probe ◽  
Surface Segregation ◽  
Depth Distribution ◽  
Computing Time ◽  
Depth Resolution ◽  
Physical Parameters ◽  
Electron Probe Analysis ◽  
Interface Phases ◽  
Excitation Conditions ◽  
High Depth

Characterizing dimensionally and chemically nanometric structures such as surface segregation or interface phases can be performed efficiently using electron probe (EP) techniques at very low excitation conditions, i.e. using small incident energies (0.5<E0<5 keV) and low incident overvoltages (1<U0<1.7). In such extreme conditions, classical analytical EP models are generally pushed to their validity limits in terms of accuracy and physical consistency, and Monte-Carlo simulations are not convenient solutions as routine tools, because of their cost in computing time. In this context, we have developed an intermediate procedure, called IntriX, in which the ionization depth distributions Φ(ρz) are numerically reconstructed by integration of basic macroscopic physical parameters describing the electron beam/matter interaction, all of them being available under pre-established analytical forms. IntriX’s procedure consists in dividing the ionization depth distribution into three separate contributions:

Possible vegetation change in Mountain Altai under climate warming and compiling the prognosis maps

2000 ◽  
pp. 26-31
Author(s):  
E. I. Parfenova ◽  
N. M. Chebakova
Keyword(s):  
Climate Warming ◽  
Vegetation Change ◽  
Climate Change Scenario ◽  
Change Scenario ◽  
Forest Steppe ◽  
Vegetation Map ◽  
Climatic Indices ◽  
Bioclimatic Model ◽  
Open Woodland ◽  
Mountain Taiga

Global climate warming is expected to be a new factor influencing vegetation redistribution and productivity in the XXI century. In this paper possible vegetation change in Mountain Altai under global warming is evaluated. The attention is focused on forest vegetation being one of the most important natural resources for the regional economy. A bioclimatic model of correlation between vegetation and climate is used to predict vegetation change (Parfenova, Tchebakova 1998). In the model, a vegetation class — an altitudinal vegetation belt (mountain tundra, dark- coniferous subalpine open woodland, light-coniferous subgolets open woodland, dark-coniferous mountain taiga, light-coniferous mountain taiga, chern taiga, subtaiga and forest-steppe, mountain steppe) is predicted from a combination of July Temperature (JT) and Complex Moisture Index (CMI). Borders between vegetation classes are determined by certain values of these two climatic indices. Some bioclimatic regularities of vegetation distribution in Mountain Altai have been found: 1. Tundra is separated from taiga by the JT value of 8.5°C; 2. Dark- coniferous taiga is separated from light-coniferous taiga by the CMI value of 2.25; 3. Mountain steppe is separated from the forests by the CMI value of 4.0. 4. Within both dark-coniferous and light-coniferous taiga, vegetation classes are separated by the temperature factor. For the spatially model of vegetation distribution in Mountain Altai within the window 84 E — 90 E and 48 N — 52 N, the DEM (Digital Elevation Model) was used with a pixel of 1 km resolution. In a GIS Package IDRISI for Windows 2.0, climatic layers were developed based on DEM and multiple regressions relating climatic indices to physiography (elevation and latitude). Coupling the map of climatic indices with the authors' bioclimatic model resulted into a vegetation map for the region of interest. Visual comparison of the modelled vegetation map with the observed geobotanical map (Kuminova, 1960; Ogureeva, 1980) showed a good similarity between them. The new climatic indices map was developed under the climate change scenario with summer temperature increase 2°C and annual precipitation increase 20% (Menzhulin, 1998). For most mountains under such climate change scenario vegetation belts would rise 300—400 m on average. Under current climate, the dark-coniferous and light-coniferous mountain taiga forests dominate throughout Mountain Altai. The chern forests are the most productive and floristically rich and are also widely distributed. Under climate warming, light-coniferous mountain taiga may be expected to transform into subtaiga and forest-steppe and dark-coniferous taiga may be expected to transform partly into chern taiga. Other consequences of warming may happen such as the increase of forest productivity within the territories with sufficient rainfall and the increase of forest fire occurrence over territories with insufficient rainfall.

Amount and timing of permafrost carbon release in response to climate warming

Tellus B ◽  
2011 ◽  
Vol 63 (2) ◽  
Author(s):  
Kevin Chaefer ◽  
Tingjun Zhang ◽  
Lori Bruhwiler ◽  
Andrew P. Barrett
Keyword(s):  
Climate Warming ◽  
Carbon Release
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