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774766

Sigma-Aldrich

Lithium diisopropylamide solution

1.0 M in THF/hexanes

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Synonym(s):
LDA
Linear Formula:
[(CH3)2CH]2NLi
CAS Number:
4111-54-0
Molecular Weight:
107.12
Beilstein:
3655042
MDL number:
MFCD00064449
PubChem Substance ID:
329767712
NACRES:
NA.22

form

liquid

Quality Level

200

concentration

1.0 M in THF/hexanes

density

0.719 g/mL at 25 °C

storage temp.

2-8°C

SMILES string

[Li]N(C(C)C)C(C)C

InChI

1S/C6H14N.Li/c1-5(2)7-6(3)4;/h5-6H,1-4H3;/q-1;+1

InChI key

ZCSHNCUQKCANBX-UHFFFAOYSA-N

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General description

This material is in a solution of THF/Hexanes (ca. 1:7 ratio, respectively)

Application

Titanium-ate enolates prepared from ketones by Ti(OiPr)4 and LDA underwent efficient radical trifluoromethylation providing trifluoromethyl ketones in good yield. Used in directed ortho-lithiation of arylsulfonyloxazolidinones to prepare N-substituted saccharin analogs.

Packaging

The 25 mL Sure/Seal™ bottle is recommended as a single-use bottle. Repeated punctures will likely result in decreased performance of product.

Legal Information

Sure/Seal is a trademark of Sigma-Aldrich Co. LLC

Signal Word

Danger

Hazard Classifications

Acute Tox. 4 Oral - Aquatic Chronic 2 - Asp. Tox. 1 - Carc. 2 - Eye Dam. 1 - Flam. Liq. 2 - Pyr. Liq. 1 - Repr. 2 - Skin Corr. 1B - STOT RE 2 Inhalation - Water-react 1

Target Organs

Nervous system

Supplementary Hazards

EUH019

Storage Class Code

4.2 - Pyrophoric and self-heating hazardous materials

WGK

WGK 2

Flash Point(F)

-7.6 °F

Flash Point(C)

-22 °C

Regulatory Information

危险化学品

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Tetrahedron Letters, 47, 6405-6405 (2006)
Martin E Gosnell et al.
Biochimica et biophysica acta, 1863(1), 56-63 (2015-10-04)
Hyperspectral imaging uses spectral and spatial image information for target detection and classification. In this work hyperspectral autofluorescence imaging was applied to patient olfactory neurosphere-derived cells, a cell model of a human metabolic disease MELAS (mitochondrial myopathy, encephalomyopathy, lactic acidosis
Juan Eugenio Iglesias et al.
NeuroImage, 115, 117-137 (2015-05-06)
Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the
Joshua D Schaefferkoetter et al.
Physics in medicine and biology, 60(14), 5543-5556 (2015-07-03)
In the context of investigating the potential of low-dose PET imaging for screening applications, we developed methods to assess small lesion detectability as a function of the number of counts in the scan. We present here our methods and preliminary
Putri W Novianti et al.
BMC bioinformatics, 16, 199-199 (2015-06-22)
Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends
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