M4187
Greiner Sensoplate™ glass bottom multiwell plates
96 well, sterile
别名:
96 multiwell plates, 96 well microplates, 96 well microtiter plates, 96 well plates, Greiner 655892
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关于此项目
NACRES:
NB.15
UNSPSC Code:
41122107
Material:
black polystyrene plate, colorless wells, flat clear borosilicate glass wells (175um thick), polystyrene
Size:
96 wells
Sterility:
sterile
Binding type:
non-treated surface
Feature:
lid, skirt (F-bottom)
material
black polystyrene plate, colorless wells, flat clear borosilicate glass wells (175um thick), polystyrene
description
glass bottom microplates
sterility
sterile
feature
lid, skirt (F-bottom)
packaging
case of 16 plates
manufacturer/tradename
Greiner 655892
L × W
127.76 mm × 85.48 mm
size
96 wells
well working volume
25- 340 μL
color
black plate, clear wells
binding type
non-treated surface
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General description
Greiner Bio-One and Aventis Pharma have collaborated to develop a range of unique glass bottom microplates (24, 96, 384, 1536 well). Each microplate incorporates high quality optical glass, with a thickness of 175 μm, bonded to the parent plate. All plates comply to the standardized microplate footprint and offer high quality performance in applications where low autofluorescence and optical clarity are required. Available in opaque black, the plates are ideally suited for high-resolution imaging, sensitive fluorescence and confocal microscopy applications, like single molecule detection (SMD) or fluorescence correlation spectroscopy (FCS).
Features and Benefits
- Dimensions: Length: 127.76mm;
- Width: 85.48mm
- Borosilicate glass (175um thick)
- High Optical Clarity
- Low autofluorescence
- Bottom flatness better than 100um
- Class VI biocompatible adhesive
Legal Information
SensoPlate is a trademark of Greiner Bio-One GmbH
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Samuel Berryman et al.
Communications biology, 3(1), 674-674 (2020-11-15)
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used
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