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Merck
CN

296961

Lithium diisopropylamide

10 wt. % suspension in hexanes

Synonym(s):

LDA

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About This Item

Linear Formula:
[(CH3)2CH]2NLi
CAS Number:
Molecular Weight:
107.12
UNSPSC Code:
12352001
NACRES:
NA.22
PubChem Substance ID:
MDL number:
Beilstein/REAXYS Number:
3655042
Concentration:
10 wt. % suspension in hexanes
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InChI

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

SMILES string

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

InChI key

ZCSHNCUQKCANBX-UHFFFAOYSA-N

vapor density

>1 (vs air)

concentration

10 wt. % suspension in hexanes

density

0.864 g/mL at 25 °C (lit.)

functional group

amine

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flash_point_c

-26 °C - closed cup

signalword

Danger

Hazard Classifications

Aquatic Chronic 2 - Asp. Tox. 1 - Eye Dam. 1 - Flam. Liq. 2 - Repr. 2 - Skin Corr. 1B - STOT RE 1 Inhalation - STOT SE 3

target_organs

Central nervous system, Nervous system

Storage Class

3 - Flammable liquids

wgk

WGK 3

flash_point_f

-14.8 °F - closed cup

Regulatory Information

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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
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
Paweł Mandera et al.
Quarterly journal of experimental psychology (2006), 68(8), 1623-1642 (2015-02-20)
Subjective ratings for age of acquisition, concreteness, affective valence, and many other variables are an important element of psycholinguistic research. However, even for well-studied languages, ratings usually cover just a small part of the vocabulary. A possible solution involves using
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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