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Chemical Structure| 82200-53-1

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Emeršič, Tadej ; Bagchi, Kushal ; Fitz, Sullivan ; Jensen, Aiden ; Nealey, Paul F ; de Pablo, Juan J

Abstract: Cholesteric liquid crystals (CLCs) are compelling responsive materials with applications in next-generation sensing, imaging, and display technologies. While electric fields and surface treatments have been used to manipulate the molecular organization and, subsequently, the optical properties of CLCs, their response to controlled fluid flow has remained largely unexplored. Here, we investigate the influence of microfluidic flow on the structure of thermotropic CLCs that can exhibit structural coloration. We demonstrate that the shear forces that arise from microfluidic flow align the helical axis of CLCs; alignment is a prerequisite for harnessing the promising photonic properties of CLCs. Moreover, we show that microfluidic flow can generate non_x005f_x0002_equilibrium structures exhibiting photonic band gaps that are inaccessible in the stationary cholesteric phase. Our findings have implications for the use of CLCs in applications involving flow processing such as additive manufacturing.

Purchased from AmBeed: ;

Gabinet, Uri Roei ;

Abstract: Anisotropic nanomaterials have propelled new technologies and materials in diverse fields ranging from electronics and photonics to catalysis and biomedicine. While initially nanomaterials’ utilization in application focused on their unique properties which are a direct result of their confinement to the nanoscale, such as size-dependent fluorescence or bandgap, more recently, additional properties and enhanced functionality are sought after by controlling the spatial organization and orientation of nanomaterials. Such advanced functionality can be enabled by controlling the juxtaposition of nanomaterials, eliciting an array-geometry-dependent effect from multiple individual nanostructures collectively interacting with one another, as is evident in plasmonic metamaterials. Another possibility for complex functionality can be achieved by altering the orientation of anisotropic nanomaterials, achieving direction-selective properties, such as polarized emission or direction-selective conductivity in 1D nanorods or 2D nanosheets. In order to fully realize the potential in nanomaterials, as presented above, reliable methodologies are needed to achieve both spatial and orientational control of anisotropic nanomaterials. A possible handle to do so is their embedment in a soft-matter matrix. Soft materials are inexpensive, easy to modify and can be made compatible with multiple inorganic nanomaterials. Some, such as block-copolymers (BCPs), create arrays or ordered features on multiple length-scales, from just a few- to hundreds- of nanometers. Others, such as liquid crystals (LCs), are stimuli responsive and can drive the alignment and reorientation of embedded 1D nanorods or 2D sheets. This dissertation explores two main themes to achieve positional and orientational control over anisotropic nanomaterials, exemplified by two model systems: 1D ZnO nanorods and 2D MoS2 nanosheets. First, we explore BCP templated Au covered ZnO nanorod arrays, and their emerging optical properties dictated by the BCP template, and realized as a platform for surface enhanced Raman scattering (SERS) or direct plasmonic sensing. In addition, other optical effects elicited by such a platform are explored, including it being an ‘epsilon-near-zero’ (ENZ) material, or ones resulting from the BCP template being a disordered hyperuniform (DH) material. The second part of this dissertation switches gears and discusses orientation control of 2D MoS2 nanosheets in LC matrices. 2D-MoS2 was dispersed for the first time in thermotropic LCs and subsequently magnetically aligned, revealing anisotropic optical effects. This result opens up a pathway for the incorporation of 2D-MoS2 into LC-based systems and the study of MoS2’s anisotropic properties. Finally, we explore 2D-MoS2 dispersed into a lyotropic LC phase, and examines the transport properties of planarly stacked MoS2 in membrane applications. Overall, this dissertation introduces new techniques to enable positional and orientational control over anisotropic nanomaterials by embedding them in soft-matter matrices, and by doing so enables new functional properties which can be utilized in advanced applications.

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Alternative Products

Product Details of [ 82200-53-1 ]

CAS No. :82200-53-1
Formula : C23H26O6
M.W : 398.45
SMILES Code : O=C(OC1=CC=C(OC)C=C1)C2=CC=C(OCCCCCCOC(C=C)=O)C=C2
MDL No. :MFCD27979221
Boiling Point : No data available
InChI Key :JWXYMBDCGNXXRO-UHFFFAOYSA-N
Pubchem ID :14150346

Safety of [ 82200-53-1 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H317-H413
Precautionary Statements:P280

Computational Chemistry of [ 82200-53-1 ] Show Less

Physicochemical Properties

Num. heavy atoms 29
Num. arom. heavy atoms 12
Fraction Csp3 0.3
Num. rotatable bonds 14
Num. H-bond acceptors 6.0
Num. H-bond donors 0.0
Molar Refractivity 110.09
TPSA ?

Topological Polar Surface Area: Calculated from
Ertl P. et al. 2000 J. Med. Chem.

71.06 Ų

Lipophilicity

Log Po/w (iLOGP)?

iLOGP: in-house physics-based method implemented from
Daina A et al. 2014 J. Chem. Inf. Model.

4.6
Log Po/w (XLOGP3)?

XLOGP3: Atomistic and knowledge-based method calculated by
XLOGP program, version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry

5.18
Log Po/w (WLOGP)?

WLOGP: Atomistic method implemented from
Wildman SA and Crippen GM. 1999 J. Chem. Inf. Model.

4.58
Log Po/w (MLOGP)?

MLOGP: Topological method implemented from
Moriguchi I. et al. 1992 Chem. Pharm. Bull.
Moriguchi I. et al. 1994 Chem. Pharm. Bull.
Lipinski PA. et al. 2001 Adv. Drug. Deliv. Rev.

3.61
Log Po/w (SILICOS-IT)?

SILICOS-IT: Hybrid fragmental/topological method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

5.24
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

4.64

Water Solubility

Log S (ESOL):?

ESOL: Topological method implemented from
Delaney JS. 2004 J. Chem. Inf. Model.

-4.96
Solubility 0.00441 mg/ml ; 0.0000111 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Moderately soluble
Log S (Ali)?

Ali: Topological method implemented from
Ali J. et al. 2012 J. Chem. Inf. Model.

-6.42
Solubility 0.000152 mg/ml ; 0.000000381 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Poorly soluble
Log S (SILICOS-IT)?

SILICOS-IT: Fragmental method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

-6.96
Solubility 0.0000438 mg/ml ; 0.00000011 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Poorly soluble

Pharmacokinetics

GI absorption?

Gatrointestinal absorption: according to the white of the BOILED-Egg

High
BBB permeant?

BBB permeation: according to the yolk of the BOILED-Egg

Yes
P-gp substrate?

P-glycoprotein substrate: SVM model built on 1033 molecules (training set)
and tested on 415 molecules (test set)
10-fold CV: ACC=0.72 / AUC=0.77
External: ACC=0.88 / AUC=0.94

No
CYP1A2 inhibitor?

Cytochrome P450 1A2 inhibitor: SVM model built on 9145 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.83 / AUC=0.90
External: ACC=0.84 / AUC=0.91

Yes
CYP2C19 inhibitor?

Cytochrome P450 2C19 inhibitor: SVM model built on 9272 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.80 / AUC=0.86
External: ACC=0.80 / AUC=0.87

No
CYP2C9 inhibitor?

Cytochrome P450 2C9 inhibitor: SVM model built on 5940 molecules (training set)
and tested on 2075 molecules (test set)
10-fold CV: ACC=0.78 / AUC=0.85
External: ACC=0.71 / AUC=0.81

Yes
CYP2D6 inhibitor?

Cytochrome P450 2D6 inhibitor: SVM model built on 3664 molecules (training set)
and tested on 1068 molecules (test set)
10-fold CV: ACC=0.79 / AUC=0.85
External: ACC=0.81 / AUC=0.87

Yes
CYP3A4 inhibitor?

Cytochrome P450 3A4 inhibitor: SVM model built on 7518 molecules (training set)
and tested on 2579 molecules (test set)
10-fold CV: ACC=0.77 / AUC=0.85
External: ACC=0.78 / AUC=0.86

No
Log Kp (skin permeation)?

Skin permeation: QSPR model implemented from
Potts RO and Guy RH. 1992 Pharm. Res.

-5.05 cm/s

Druglikeness

Lipinski?

Lipinski (Pfizer) filter: implemented from
Lipinski CA. et al. 2001 Adv. Drug Deliv. Rev.
MW ≤ 500
MLOGP ≤ 4.15
N or O ≤ 10
NH or OH ≤ 5

0.0
Ghose?

Ghose filter: implemented from
Ghose AK. et al. 1999 J. Comb. Chem.
160 ≤ MW ≤ 480
-0.4 ≤ WLOGP ≤ 5.6
40 ≤ MR ≤ 130
20 ≤ atoms ≤ 70

None
Veber?

Veber (GSK) filter: implemented from
Veber DF. et al. 2002 J. Med. Chem.
Rotatable bonds ≤ 10
TPSA ≤ 140

1.0
Egan?

Egan (Pharmacia) filter: implemented from
Egan WJ. et al. 2000 J. Med. Chem.
WLOGP ≤ 5.88
TPSA ≤ 131.6

0.0
Muegge?

Muegge (Bayer) filter: implemented from
Muegge I. et al. 2001 J. Med. Chem.
200 ≤ MW ≤ 600
-2 ≤ XLOGP ≤ 5
TPSA ≤ 150
Num. rings ≤ 7
Num. carbon > 4
Num. heteroatoms > 1
Num. rotatable bonds ≤ 15
H-bond acc. ≤ 10
H-bond don. ≤ 5

1.0
Bioavailability Score?

Abbott Bioavailability Score: Probability of F > 10% in rat
implemented from
Martin YC. 2005 J. Med. Chem.

0.55

Medicinal Chemistry

PAINS?

Pan Assay Interference Structures: implemented from
Baell JB. & Holloway GA. 2010 J. Med. Chem.

0.0 alert
Brenk?

Structural Alert: implemented from
Brenk R. et al. 2008 ChemMedChem

2.0 alert: heavy_metal
Leadlikeness?

Leadlikeness: implemented from
Teague SJ. 1999 Angew. Chem. Int. Ed.
250 ≤ MW ≤ 350
XLOGP ≤ 3.5
Num. rotatable bonds ≤ 7

No; 1 violation:MW<3.0
Synthetic accessibility?

Synthetic accessibility score: from 1 (very easy) to 10 (very difficult)
based on 1024 fragmental contributions (FP2) modulated by size and complexity penaties,
trained on 12'782'590 molecules and tested on 40 external molecules (r2 = 0.94)

3.08
 

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