跳轉至內容
Merck
全部照片(1)

重要文件

M4187

Sigma-Aldrich

Greiner Sensoplate glass bottom multiwell plates

96 well, sterile

同義詞:

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

登入查看組織和合約定價


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

尋找類似的產品? 前往 產品比較指南

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

從最近期的版本中選擇一個:

分析證明 (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.

如果您需要協助,請聯絡 客戶支援

已經擁有該產品?

您可以在文件庫中找到最近購買的產品相關文件。

存取文件庫

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

我們的科學家團隊在所有研究領域都有豐富的經驗,包括生命科學、材料科學、化學合成、色譜、分析等.

聯絡技術服務