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Chemical Structure| 19675-63-9

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Karabi Nath ; Keenan R. Wright ; Alauddin Ahmed ; Donald J. Siegel ; Adam J. Matzger ;

Abstract: Evaluation of metal–organic frameworks (MOFs) for adsorbed natural gas (ANG) technology employs pure methane as a surrogate for natural gas (NG). This approximation is problematic, as it ignores the impact of other heavier hydrocarbons present in NG, such as ethane and propane, which generally have more favorable adsorption interactions with compared to methane. Herein, using quantitative Raman spectroscopic analysis and Monte Carlo calculations, we demonstrate the adsorption selectivity of high-performing , such as , , and SNU-70, for a methane and ethane mixture (95:5) that mimics the composition of NG. The impact of selectivity on the storage and deliverable capacities of these adsorbents during successive cycles of adsorption and desorption, simulating the filling and emptying of an ANG tank, is also demonstrated. The study reveals a gradual in the storage performance of , particularly with smaller pore volumes, due to ethane accumulation over long-term cycling, until a steady state is reached with substantially degraded storage performance.

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Karabi Nath ; Alauddin Ahmed ; Donald J. Siegel ; Adam J. Matzger ;

Abstract: The experimental determination of mixed gas isotherms is challenging and thus rarely performed. Nevertheless, characterizing the performance of adsorbents toward mixtures of gases is critical in most adsorptive separations. Here, the utility of Raman spectroscopy in determining binary gas adsorption isotherms on the microscale with metal–organic framework (MOF) single crystals is demonstrated for quantifying C2H6/CH4 selectivity. The influence of pore size on sorption selectivity is determined experimentally. The technique also allows determination of kinetics of methane adsorption in MOFs, which is critical for refueling times in adsorbed natural gas storage.

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Alternative Products

Product Details of [ 19675-63-9 ]

CAS No. :19675-63-9
Formula : C10H8O4
M.W : 192.17
SMILES Code : O=C(O)C1=CC=C(/C=C/C(O)=O)C=C1
MDL No. :MFCD00016543
Boiling Point : No data available
InChI Key :HAEJSGLKJYIYTB-ZZXKWVIFSA-N
Pubchem ID :697959

Safety of [ 19675-63-9 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H302-H315-H319-H335
Precautionary Statements:P261-P305+P351+P338

Computational Chemistry of [ 19675-63-9 ] Show Less

Physicochemical Properties

Num. heavy atoms 14
Num. arom. heavy atoms 6
Fraction Csp3 0.0
Num. rotatable bonds 3
Num. H-bond acceptors 4.0
Num. H-bond donors 2.0
Molar Refractivity 50.07
TPSA ?

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

74.6 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

1.14
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

1.34
Log Po/w (WLOGP)?

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

1.37
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.

1.47
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

1.14
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

1.29

Water Solubility

Log S (ESOL):?

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

-1.99
Solubility 1.94 mg/ml ; 0.0101 mol/l
Class?

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

Very soluble
Log S (Ali)?

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

-2.51
Solubility 0.596 mg/ml ; 0.0031 mol/l
Class?

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

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

-1.24
Solubility 11.1 mg/ml ; 0.0578 mol/l
Class?

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

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

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.

-6.52 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

0.0
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.

0.56

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

1.0 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<1.0
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)

1.85
 

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