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安全信息

M4187

Sigma-Aldrich

Greiner Sensoplate™ glass bottom multiwell plates

96 well, sterile

别名:

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

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About This Item

UNSPSC代码:
41122107
NACRES:
NB.15
物料 :
black polystyrene plate
colorless wells
flat clear borosilicate glass wells (175um thick)
polystyrene
长度 × 宽度:
127.76 mm × 85.48 mm
尺寸:
96 wells
孔工作体积:
25- 340 μL
无菌性:
sterile
吸附类型:
non-treated surface
特点 :
lid
skirt (F-bottom)

物料

black polystyrene plate
colorless wells
flat clear borosilicate glass wells (175um thick)
polystyrene

描述

glass bottom microplates

无菌性

sterile

特点

lid
skirt (F-bottom)

包装

case of 16 plates

制造商/商品名称

Greiner 655892

长度 × 宽度

127.76 mm × 85.48 mm

尺寸

96 wells

孔工作体积

25- 340 μL

颜色

black plate
 clear wells

吸附类型

non-treated surface

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相关类别

一般描述

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).

特点和优势

  • 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

法律信息

SensoPlate is a trademark of Greiner Bio-One GmbH

法规信息

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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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