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Chemical Structure| 34671-83-5 Chemical Structure| 34671-83-5

Structure of 2,2′-Bipyrimidine
CAS No.: 34671-83-5

Chemical Structure| 34671-83-5

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Dunlap, John H ; Feng, Haosheng ; Pioch, Thomas ; Volk, Amanda A ; Giordano, Andrea N ; Reidell, Alexander , et al.

Abstract: We report the preparation of poly(ionic) polymer-wrapped single-walled carbon nanotube dispersions for chemiresistive methane (CH4) sensors with improved humidity tolerance. Single-walled CNTs (SWCNTs) were noncovalently functionalized by poly(4-vinylpyridine) (P4VP) with varied amounts of a poly(ethylene glycol) (PEG) moiety bearing a Br and terminal azide group (Br-R1). The quaternization of P4VP with Br-R1 was performed using continuous flow chemistry and Bayesian optimization-guided reaction selection. Polymers (PyBrR1) with different degrees of functionalization were used to disperse SWCNTs and subsequently incorporated into sensors containing a platinum complex as an aerobic oxidative catalyst with a polyoxometalate (POM) redox mediator to facilitate room-temperature CH4 sensing. As the degree of quaternization in the PyBrR1-CNT composites increased, improvements in response magnitude were observed, with nominally 10% quaternized PyBrR1 giving the largest response. Incorporation of PEG improved sensor stability at relative humidities between 57−90% versus sensors fabricated from CNT dispersions with unfunctionalized P4VP. Devices fabricated with these dispersions outperformed those prepared in situ under dry conditions, and exhibited greater stability at elevated humidities. The influence of Keggin-type POM character was also evaluated to identify alternative POMs for enhanced sensor performance at high humidity. In an effort to identify areas for further improvement in algorithm performance for polymer functionalization, a kinetically informed machine learning model was explored as a route to predict reactivity of pyridine units and alkyl bromides under flow conditions.

Keywords: Bayesian optimization ; flow chemistry ; polymer wrapped carbon nanotube ; sensors ; chemiresistor ; methane

Purchased from AmBeed: ;

Tobias W. Morris ; David L. Wisman ; Nassem U. Din ; Duy Le ; Talat S. Rahman ; Steven L. Tait

Abstract: The creation of single-site metal centers (SSMCs) through the formation of metal-organic coordination networks is an area of interest due to the proven ability of SSMCs to improve selectivity for heterogeneous catalysts. In order to better understand the reactivity potential for the SSMCs it is necessary to study the ligand-metal interaction in the metal-organic coordination networks. In the work reported here, we demonstrate the ability to tune the oxidation state of vanadium from II to IV through the tailoring of redox-active ligands. Using the N-heterocyclic ligands of bipyrimidine (BP), bispyrimidinyltetrazine (BMTZ), and biimidazole (BIM) complexed with metallic V, we have shown that the oxidation state of the V metal centers can be tuned to V(II) for BP, V(III) for BMTZ, and V(IV) for BIM. These redox-active ligands provide similar coordination environments when complexed into one dimensional chains but result in different oxidation states for the single-site metal center.

Keywords: Metal-organic coordination ; On-surface redox assembly ; Scanning tunneling microscopy ; Density functional calculations ; X-ray photoelectron spectroscopy ; Redox-active ligands ; Charge transfer ; Metals

Purchased from AmBeed: ;

Wisman II, David L ;

Abstract: Surface-assisted self-assembly of architectures is showing promise for the development of more efficient technologies and processes related to the fields of organic electronics, photovoltaics and heterogeneous catalysis. The detailed understanding of the intermolecular and molecule-substrate interactions that are responsible for resulting surface architectures is necessary for the rational design molecular systems. The experiments detailed within this thesis show that through careful tuning of organic molecules significant changes are observed in the 2-D self-assembly, 3-D stacking, or metal-organic coordination properties. These ideas are first demonstrated by tuning the intermolecular interactions of organic molecules on metal surfaces. It is shown that the 2-D self-assembly can be controlled through the functionalization of the parent molecule. In one study the addition of bulky methoxy groups was shown to modify 2-D surface structure, which also led to a change in the 3-D stacking of the molecules due to the loss of overlap of the π systems of the molecules in the layers below. Modification of the self-assembly is also investigated through changing of the intermolecular hydrogen bonding potential in a series of molecules with similar properties. Here it was found that the stability and long-range order can be tuned through the introduction of functional groups with a higher electro-negativities.

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Alternative Products

Product Details of [ 34671-83-5 ]

CAS No. :34671-83-5
Formula : C8H6N4
M.W : 158.16
SMILES Code : C1=CC=NC(=N1)C2=NC=CC=N2
MDL No. :MFCD00014600
InChI Key :HKOAFLAGUQUJQG-UHFFFAOYSA-N
Pubchem ID :123444

Safety of [ 34671-83-5 ]

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

Computational Chemistry of [ 34671-83-5 ] Show Less

Physicochemical Properties

Num. heavy atoms 12
Num. arom. heavy atoms 12
Fraction Csp3 0.0
Num. rotatable bonds 1
Num. H-bond acceptors 4.0
Num. H-bond donors 0.0
Molar Refractivity 43.06
TPSA ?

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

51.56 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

1.46
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

0.18
Log Po/w (WLOGP)?

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

0.93
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.

-0.36
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

1.51
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

0.75

Water Solubility

Log S (ESOL):?

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

-1.61
Solubility 3.9 mg/ml ; 0.0247 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.

-0.82
Solubility 23.9 mg/ml ; 0.151 mol/l
Class?

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

Very 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

-3.41
Solubility 0.0614 mg/ml ; 0.000388 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

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

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

Yes
Log Kp (skin permeation)?

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

-7.14 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.

0.55

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

0.0 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)

1.42
 

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