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Ethanone, 1-(4-nitrophenyl)-2-[[5-(3,4,5-trimethoxyphenyl)-1,3,4-oxadiazol-2-yl]thio] - is a chemical with a specific purpose. Lookchem provides you with multiple data and supplier information of this chemical.

110767-49-2

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110767-49-2 Usage

Check Digit Verification of cas no

The CAS Registry Mumber 110767-49-2 includes 9 digits separated into 3 groups by hyphens. The first part of the number,starting from the left, has 6 digits, 1,1,0,7,6 and 7 respectively; the second part has 2 digits, 4 and 9 respectively.
Calculate Digit Verification of CAS Registry Number 110767-49:
(8*1)+(7*1)+(6*0)+(5*7)+(4*6)+(3*7)+(2*4)+(1*9)=112
112 % 10 = 2
So 110767-49-2 is a valid CAS Registry Number.

110767-49-2SDS

SAFETY DATA SHEETS

According to Globally Harmonized System of Classification and Labelling of Chemicals (GHS) - Sixth revised edition

Version: 1.0

Creation Date: Aug 16, 2017

Revision Date: Aug 16, 2017

1.Identification

1.1 GHS Product identifier

Product name 1-(4-Nitro-phenyl)-2-[5-(3,4,5-trimethoxy-phenyl)-[1,3,4]oxadiazol-2-ylsulfanyl]-ethanone

1.2 Other means of identification

Product number -
Other names -

1.3 Recommended use of the chemical and restrictions on use

Identified uses For industry use only.
Uses advised against no data available

1.4 Supplier's details

1.5 Emergency phone number

Emergency phone number -
Service hours Monday to Friday, 9am-5pm (Standard time zone: UTC/GMT +8 hours).

More Details:110767-49-2 SDS

110767-49-2Relevant academic research and scientific papers

Synthesis of novel 5-aryl-2-thio-1,3,4-oxadiazoles and the study of their structure-anti-mycobacterial activities

Macaev, Fliur,Rusu, Ghenadie,Pogrebnoi, Serghei,Gudima, Alexandru,Stingaci, Eugenia,Vlad, Ludmila,Shvets, Nathaly,Kandemirli, Fatma,Dimoglo, Anatholy,Reynolds, Robert

, p. 4842 - 4850 (2007/10/03)

The preparation of novel 5-aryl-2-thio-1,3,4-oxadiazoles 4a-41 and the computer-aided study of their in vitro anti-tubercular activity against Mycobacterium tuberculosis H37Rv (ATCC 27294) are reported. The average accuracy of the electronic-topological method and neural network methods applied to the activity prediction in leave-one-out cross validation is 80%.

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