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M4187

Sigma-Aldrich

Greiner Sensoplate™ glass bottom multiwell plates

96 well, sterile

Synonym(s):

96 multiwell plates, 96 well microplates, 96 well microtiter plates, 96 well plates

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About This Item

UNSPSC Code:
41122107
NACRES:
NB.15

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
  • 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

Certificates of Analysis (COA)

Search for Certificates of Analysis (COA) by entering the products Lot/Batch Number. Lot and Batch Numbers can be found on a product’s label following the words ‘Lot’ or ‘Batch’.

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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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