跳转至内容
Merck
所有图片(1)

主要文件

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

登录查看公司和协议定价


About This Item

分類程式碼代碼:
41122107
NACRES:
NB.15

材料

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

描述

glass bottom microplates

無菌

sterile

特點

lid
skirt (F-bottom)

包裝

case of 16 plates

製造商/商標名

Greiner 655892

長度 × 寬度

127.76 mm × 85.48 mm

尺寸

96 wells

孔有效容積

25- 340 μL

顏色

black plate
clear wells

結合類型

non-treated surface

正在寻找类似产品? 访问 产品对比指南

一般說明

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).

特點和優勢

  • 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

法律資訊

SensoPlate is a trademark of Greiner Bio-One GmbH

历史批次信息供参考:

分析证书(COA)

Lot/Batch Number

It looks like we've run into a problem, but you can still download Certificates of Analysis from our 文件 section.

如需帮助,请联系 客户支持

已有该产品?

在文件库中查找您最近购买产品的文档。

访问文档库

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

我们的科学家团队拥有各种研究领域经验,包括生命科学、材料科学、化学合成、色谱、分析及许多其他领域.

联系技术服务部门