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Chemical Structure| 125248-71-7

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Michael J. Ford ; Dominique H. Porcincula ; Rodrigo Telles ; Julie A. Mancini ; Yuchen Wang ; Mehedi H. Rizvi , et al.

Abstract: Soft machines will require soft materials that exhibit a rich diversity of functionality, including shape morphing and photoresponsivity. The combination of these functionalities enables useful behaviors in soft machines that can be further developed by synthesizing materials that exhibit localized responsivity. Localized responsivity of liquid crystal elastomers (LCEs), which are soft materials that exhibit shape morphing, can be enabled by formulating composite inks for direct ink writing (DIW). nanorods (AuNRs) can be added to LCEs to enable photothermal shape change upon absorption of light through a localized surface plasmon resonance. We compared LCE formulations, focusing on their amenability for printing by DIW and the photoresponsivity of AuNRs. The local responsivity of different three-dimensional architectures enabled soft machines that could oscillate, crawl, roll, transport mass, and display other unique modes of actuation and motion in response to light, making these promising functional materials for advanced applications

Keywords: soft machines ; soft matter ; liquid crystal elastomers ; nanorods ; shape morphing ; photothermal ; additive manufacturing ; 3D printing ; intelligent materials ; actuation

Purchased from AmBeed:

Uri R. Gabinet ; Changyeon Lee ; Ryan Poling-Skutvik ; Daniel Keane ; Na Kyung Kim ; Ruiqi Dong , et al.

Abstract: Atomically thin MoS2 nanosheets are of interest due to unique electronic, optical, and catalytic properties that are absent in the bulk material. Methods to prepare nanosheets from bulk material that facilitate studies of 2D-MoS2 and the fabrication of useful devices have consequently assumed considerable importance. Here, we report the simultaneous exfoliation and stable dispersion of MoS2 nanosheets in a liquid crystal. Exfoliation of bulk MoS2 in mesogen-containing solutions produced stable dispersions of 2D-MoS2 that retained suspension stability for several weeks. Solvent removal in cast films yielded nanocomposites of 2D-MoS2. Preservation of single- and few-sheet MoS2 was confirmed utilizing UV–vis and Raman spectroscopy in the nematic and isotropic fluid states of the system and, remarkably, in the solid crystal as well. Importantly, the MoS2 nanosheets remained well-dispersed upon polymerization of the reactive mesogen to form a liquid crystal polymer. The ability to stably disperse 2D-MoS2 in a structured fluid opens up new possibilities for studying anisotropic properties of MoS2 and for exploiting such properties in responsive materials.

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 [ 125248-71-7 ]

CAS No. :125248-71-7
Formula : C39H44O10
M.W : 672.76
SMILES Code : CC1=CC(OC(C2=CC=C(OCCCCCCOC(C=C)=O)C=C2)=O)=CC=C1OC(C3=CC=C(OCCCCCCOC(C=C)=O)C=C3)=O
MDL No. :MFCD11225140
InChI Key :FQCKIWWAEIOPSD-UHFFFAOYSA-N
Pubchem ID :21183256

Safety of [ 125248-71-7 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H315-H319-H335
Precautionary Statements:P261-P305+P351+P338

Computational Chemistry of [ 125248-71-7 ] Show Less

Physicochemical Properties

Num. heavy atoms 49
Num. arom. heavy atoms 18
Fraction Csp3 0.33
Num. rotatable bonds 26
Num. H-bond acceptors 10.0
Num. H-bond donors 0.0
Molar Refractivity 185.73
TPSA ?

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

123.66 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

7.25
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

8.87
Log Po/w (WLOGP)?

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

7.77
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.

4.98
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

9.47
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

7.67

Water Solubility

Log S (ESOL):?

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

-8.16
Solubility 0.00000471 mg/ml ; 0.000000007 mol/l
Class?

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

Poorly soluble
Log S (Ali)?

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

-11.35
Solubility 0.000000003 mg/ml ; 0.0 mol/l
Class?

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

Insoluble
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

-11.4
Solubility 0.0000000027 mg/ml ; 0.0 mol/l
Class?

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

Insoluble

Pharmacokinetics

GI absorption?

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

Low
BBB permeant?

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

No
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

No
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

No
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

No
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.

-4.11 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

2.0
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

1.0
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

3.0
Bioavailability Score?

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

0.17

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

3.0 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)

4.69

Application In Synthesis of [ 125248-71-7 ]

* All experimental methods are cited from the reference, please refer to the original source for details. We do not guarantee the accuracy of the content in the reference.

  • Downstream synthetic route of [ 125248-71-7 ]
 

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