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Merck
모든 사진(1)

주요 문서

296961

Sigma-Aldrich

Lithium diisopropylamide

10 wt. % suspension in hexanes

동의어(들):

LDA

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About This Item

Linear Formula:
[(CH3)2CH]2NLi
CAS Number:
Molecular Weight:
107.12
Beilstein:
3655042
MDL number:
UNSPSC 코드:
12352001
PubChem Substance ID:
NACRES:
NA.22

vapor density

>1 (vs air)

농도

10 wt. % suspension in hexanes

density

0.864 g/mL at 25 °C (lit.)

작용기

amine

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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신호어

Danger

유해 및 위험 성명서

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

표적 기관

Central nervous system, Nervous system

Storage Class Code

3 - Flammable liquids

WGK

WGK 3

Flash Point (°F)

-14.8 °F - closed cup

Flash Point (°C)

-26 °C - closed cup


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시험 성적서(COA)

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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
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
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
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
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

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