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Structure of 83411-71-6

Chemical Structure| 83411-71-6

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Rosenthal, Justin J ; Balogun, Mariam Y ; Davenport, Matthew N ; Cañada, Louise Marie C ; Brennecke, Joan F ; Freeman, Benny D

Abstract: Gas from shale reservoirs provides the U.S. with relatively clean-burning fuel and important precursors for petrochemicals. However, due to the similar thermophysical properties between shale gas components, energy-intensive cryogenic distillation is used to separate the heavier hydrocarbons from methane. Compared to distillation, membrane-based technology could yield an order of magnitude improvement in energy efficiency. Nevertheless, this requires membranes capable of operating at high transmembrane pressures. Here, we report supported ionic liquid membranes (SILMs) that operate within industrially relevant operating pressures (i.e., 7–30 bar) without experiencing blowout—a loss of ionic liquid (IL) and subsequent membrane defects at elevated transmembrane pressures. By considering the effect of the pore size distribution on capillary pressure, described by the Young-Laplace equation, we developed polyethersulfone-based SILMs that operate above 16 bar. To our knowledge, this is the highest reported blowout pressure for a SILM using a commercial membrane support. Pure-gas permeation experiments indicate promising C3H8/CH4 permselectivity values as high as 4 for SILMs with larger pores (30–100 nm). Yet, this reverse-selectivity is not observed for SILMs with smaller pores (4 nm), partly due to their lower surface porosity, which results in significantly higher mass transfer resistance from the polymeric support and, consequently, reduced permselectivity. Additionally, trends in glass transition temperature and melting point of the imbued ILs, as well as increased CO2/N2 and CO2/CH4 permselectivity suggest that nanoconfinement effects may also play a significant role in the separation performance of these membranes.

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Zhang, Ning ; Yang, Ruoxi ; Huang, Haonan Danny ; Meng, Jenny ; Zhang, Wencai ; Park, Ah-Hyung Alissa , et al.

Abstract: This study proposed a sustainable method for the concurrent recovery of metals from olivine minerals and carbon sequestration through carbon mineralization to address the challenges of climate change and critical mineral recovery for the renewable energy transition. It developed a comprehensive development in leaching processes, recovery of metals, and reagent recycling while assessing its economic benefits and environmental impact. Employing hydrometallurgical leaching, our approach facilitates the selective recovery of Ni2+ while converting Mg2+ into their carbonates. This approach is further refined through a stepwise technique that controls operating conditions to generate high-purity valuable products, enabling nearly 90% of Mg2+ and Ni2+ to be dissolved and converted to carbonates. This study evaluated various organic and inorganic acids for the leaching process, followed by Fe extraction and pH swing, to yield pure Fe salts and amorphous silica. Separately extracting iron from the solution significantly reduces the loss of valuable metals in subsequent stages by minimizing the coprecipitation of iron with silicon. A techno-economic assessment (TEA) was performed to evaluate the economic impact of removing iron before the solvent extraction of nickel. This analysis, based on mass balance flow comparisons, determined that the independent removal of iron is more profitable, resulting in the production of more and higher-value products. Ni2+ was selectively extracted from the leachate using Versatic 10, which forms a complex with nickel in the organic phase. The solution containing either a strong acid or a greener agent (i.e., gaseous CO2) was effectively used to strip Ni2+ from the organic phase. Different polymorphs of Mg carbonates were produced under ambient conditions. The proposed process flow results in high_x005f_x0002_purity products suitable for use in various industries, which enhances the economy, facilitating the rapid adoption of this technology.

Keywords: hydrometallurgy ; solvent extraction ; resource recovery ; critical mineral recovery ; carbon mineralization

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Alternative Products

Product Details of [ 83411-71-6 ]

CAS No. :83411-71-6
Formula : C16H35O2P
M.W : 290.42
SMILES Code : O=P(CC(C)CC(C)(C)C)(CC(C)CC(C)(C)C)O
MDL No. :MFCD28145690
InChI Key :QUXFOKCUIZCKGS-UHFFFAOYSA-N
Pubchem ID :157898

Safety of [ 83411-71-6 ]

GHS Pictogram:
Signal Word:Danger
Hazard Statements:H302-H319-H372-H410
Precautionary Statements:P501-P273-P260-P270-P264-P280-P391-P314-P337+P313-P305+P351+P338-P301+P312+P330
Class:9
UN#:3082
Packing Group:

Computational Chemistry of [ 83411-71-6 ] Show Less

Physicochemical Properties

Num. heavy atoms 19
Num. arom. heavy atoms 0
Fraction Csp3 1.0
Num. rotatable bonds 8
Num. H-bond acceptors 2.0
Num. H-bond donors 1.0
Molar Refractivity 88.33
TPSA ?

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

47.11 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

3.58
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

4.64
Log Po/w (WLOGP)?

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

5.4
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.

4.19
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

3.95
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

4.35

Water Solubility

Log S (ESOL):?

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

-4.04
Solubility 0.0267 mg/ml ; 0.0000921 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.

-5.36
Solubility 0.00128 mg/ml ; 0.00000441 mol/l
Class?

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

Moderately 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

-4.23
Solubility 0.0172 mg/ml ; 0.0000593 mol/l
Class?

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

Moderately 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

Yes
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

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.

-4.78 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

1.0
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

0.0
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

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<2.0
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)

5.59
 

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