Passive Droplet Control in Two-Dimensional Microfluidic Networks

2019 ◽  
Vol 5 (3) ◽  
pp. 189-206
Author(s):  
Gerold Fink ◽  
Medina Hamidovic ◽  
Robert Wille ◽  
Werner Haselmayr
Keyword(s):  
Two Dimensional ◽  
Microfluidic Networks ◽  
Droplet Control

Passive droplet control in microfluidic networks: A survey and new perspectives on their practical realization

2019 ◽  
Vol 19 ◽  
pp. 33-46 ◽  
Author(s):  
Medina Hamidović ◽  
Werner Haselmayr ◽  
Andreas Grimmer ◽  
Robert Wille ◽  
Andreas Springer
Keyword(s):  
Practical Realization ◽  
Microfluidic Networks ◽  
Droplet Control

Two-Dimensional Ordered Arrays of Aligned Lipid Tubules on Substrates with Microfluidic Networks

Langmuir ◽  
2005 ◽  
Vol 21 (7) ◽  
pp. 3153-3157 ◽  
Author(s):  
Nidhi Mahajan ◽  
Jiyu Fang
Keyword(s):  
Two Dimensional ◽  
Ordered Arrays ◽  
Microfluidic Networks ◽  
Lipid Tubules

Spectrophotometric measurements of objective-prism spectra

1966 ◽  
Vol 24 ◽  
pp. 118-119
Author(s):  
Th. Schmidt-Kaler
Keyword(s):  
Progress Report ◽  
Two Dimensional ◽  
Objective Prism ◽  
Made In

I should like to give you a very condensed progress report on some spectrophotometric measurements of objective-prism spectra made in collaboration with H. Leicher at Bonn. The procedure used is almost completely automatic. The measurements are made with the help of a semi-automatic fully digitized registering microphotometer constructed by Hög-Hamburg. The reductions are carried out with the aid of a number of interconnected programmes written for the computer IBM 7090, beginning with the output of the photometer in the form of punched cards and ending with the printing-out of the final two-dimensional classifications.

Introductory paper

1966 ◽  
Vol 24 ◽  
pp. 3-5
Author(s):  
W. W. Morgan
Keyword(s):  
Line Intensity ◽  
Classification Scheme ◽  
Two Dimensional ◽  
Spectral Classification ◽  
Dimensional Array ◽  
Rr Lyrae ◽  
Metallic Line ◽  
Introductory Paper ◽  
Parameter Varying ◽  
Definition Of

1. The definition of “normal” stars in spectral classification changes with time; at the time of the publication of theYerkes Spectral Atlasthe term “normal” was applied to stars whose spectra could be fitted smoothly into a two-dimensional array. Thus, at that time, weak-lined spectra (RR Lyrae and HD 140283) would have been considered peculiar. At the present time we would tend to classify such spectra as “normal”—in a more complicated classification scheme which would have a parameter varying with metallic-line intensity within a specific spectral subdivision.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

High Resolution Microscopy of Multi-Layered Biological Crystals

1982 ◽  
Vol 40 ◽  
pp. 80-83
Author(s):  
H.A. Cohen ◽  
T.W. Jeng ◽  
W. Chiu
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Three Dimensional ◽  
Data Interpretation ◽  
Two Dimensional ◽  
Biological Objects ◽  
Density Maps ◽  
Density Map ◽  
Complex Crystal ◽  
High Resolution Microscopy

This tutorial will discuss the methodology of low dose electron diffraction and imaging of crystalline biological objects, the problems of data interpretation for two-dimensional projected density maps of glucose embedded protein crystals, the factors to be considered in combining tilt data from three-dimensional crystals, and finally, the prospects of achieving a high resolution three-dimensional density map of a biological crystal. This methodology will be illustrated using two proteins under investigation in our laboratory, the T4 DNA helix destabilizing protein gp32*I and the crotoxin complex crystal.

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

Comparison of Recovery and Recrystallization in Stainless Steel Following Plane-Wave Shock Loading and Explosive Forming

1970 ◽  
Vol 28 ◽  
pp. 428-429 ◽  
Author(s):  
J. A. Korbonski ◽  
L. E. Murr
Keyword(s):  
Stainless Steel ◽  
Shock Loading ◽  
Pressure Pulse ◽  
Shock Pressure ◽  
304 Stainless Steel ◽  
Strain Rates ◽  
Two Dimensional ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304 ◽  
Explosive Forming

Comparison of recovery rates in materials deformed by a unidimensional and two dimensional strains at strain rates in excess of 104 sec.−1 was performed on AISI 304 Stainless Steel. A number of unidirectionally strained foil samples were deformed by shock waves at graduated pressure levels as described by Murr and Grace. The two dimensionally strained foil samples were obtained from radially expanded cylinders by a constant shock pressure pulse and graduated strain as described by Foitz, et al.

Reconstruction of Two-Dimensional Projections of GP 32*I

1981 ◽  
Vol 39 ◽  
pp. 38-39
Author(s):  
H.A. Cohen ◽  
W. Chiu ◽  
J. Hosoda
Keyword(s):  
Electron Microscopy ◽  
Molecular Weight ◽  
Uranyl Acetate ◽  
Distilled Water ◽  
Acetate Solution ◽  
Two Dimensional ◽  
Parent Molecule ◽  
T4 Bacteriophage ◽  
Electron Imaging ◽  
Better Than

GP 32 (molecular weight 35000) is a T4 bacteriophage protein that destabilizes the DNA helix. The fragment GP32*I (77% of the total weight), which destabilizes helices better than does the parent molecule, crystallizes as platelets thin enough for electron diffraction and electron imaging. In this paper we discuss the structure of this protein as revealed in images reconstructed from stained and unstained crystals.Crystals were prepared as previously described. Crystals for electron microscopy were pelleted from the buffer suspension, washed in distilled water, and resuspended in 1% glucose. Two lambda droplets were placed on grids over freshly evaporated carbon, allowed to sit for five minutes, and then were drained. Stained crystals were prepared the same way, except that prior to draining the droplet, two lambda of aqueous 1% uranyl acetate solution were applied for 20 seconds. Micrographs were produced using less than 2 e/Å2 for unstained crystals or less than 8 e/Å2 for stained crystals.

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