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Hassan, Kamrul ; Trong Tran, Anh Tuan ; Jalil, MA ; Tung, Tran Thanh ; Nine, Md Julker ; Losic, Dusan

Abstract: Methanol poisoning poses significant health risks, particularly in less developed countries, where adulterated alcoholic beverages often lead to severe morbidity and mortality. Current diagnostic methods, such as gas−liquid chromatography and blood gas analysis, are complex and prohibitively expensive, making them inaccessible in resource-constrained settings. To address this issue, we present a novel, simple, low-cost chemiresistive sensor for the rapid, selective, and ultrasensitive detection of methanol at ultralow concentrations in the presence of high ethanol concentrations. The sensor leverages an extrusion-printed hybrid composite of NU-1000 metal−organic frameworks (MOFs) and graphene, exploiting their unique structural and electronic synergies based on high porosity, functional metal sites of MOFs and graphene’s excellent conductivity that enhance sensitivity and selectivity. To further overcome challenges in selectivity, we integrated machine learning algorithms and principal component analysis (PCA), significantly improving the sensor’s ability to differentiate methanol from ethanol and other potential interferents. The extrusion printing technique ensures the fabrication of uniform, stable, and durable sensor layers on ceramic substrates, maintaining reproducible performance and stability. Our results demonstrate the sensor’s capability to detect methanol vapors at parts-per-billion (ppb) levels in the presence of higher concentration of ethanol (ppm), making it an effective tool for monitoring methanol intoxication through breath analysis. This innovative approach represents a notable advancement in gas sensing technologies, offering a scalable, cost-effective solution for applications in medical diagnostics, industrial monitoring, and consumer safety. This research highlights the potential of extrusion-printed hybrid materials in advancing gas sensing technologies to enhance public health and safety.

Keywords: chemiresistive sensor ; methanol detection ; ethanol adulteration ; NU-1000 MOFs ; graphene hybrid materials

Purchased from AmBeed:

Korhonen, Pekka ; Thangamuthu, Madasamy ; Castaldelli, Evandro ; Diego-Lopez, Ander ; Weilhard, Andreas ; Clowes, Rob , et al.

Abstract: CO2 adsorption and its subsequent utilization represent a promising avenue for mitigating climate change. The conversion of CO2 into valuable and useful products like carbon monoxide, methane, and methanol offers significant economic benefits. However, due to the low reactivity of CO2, the incorporation of CO2 adsorbents alongside catalytic materials has been pivotal in increasing the concentration of CO2 molecules around the catalytic sites. This strategy frequently relies on the precise deposition of the catalyst onto the adsorbent material. In this work, we explore NU-1000, a zirconium-based metal-organic framework originally designed as a CO2 adsorbent, to act as a selective photocatalyst for gas-phase CO2 reduction to CH4. NU-1000 contains UVA light absorbing chromophore linkers, endowing it with the dual functionality of CO2 adsorbent and photocatalyst, which is crucial for efficient CO2 reutilization. Our research showcases an easily reproducible, and greener synthesis method for NU-1000 using microwaves. We study the activity of NU-1000, including a functionalised variant, in the gas-phase photoreduction of CO2 to CH4 at room temperature and atmospheric pressure with electrons and protons derived from water. Remarkably, both the native and functionalised MOFs exhibit a rate of 170 and 800 µmol∙g-1∙h-1, respectively, alongside an exceptional selectivity of over 99%. These findings represent some of the highest reported values for gas phase CO2 photoreduction under atmospheric conditions. Our results provide a foundation for exploring materials that can serve as both catalysts and sorbents in the photocatalytic transformation of CO2 to value added products.

Keywords: CO2 reduction ; photocatalysis ; NU-1000 ; ; microwave synthesis

Purchased from AmBeed:

Kondo, Yoshifumi ; Hino, Kenta ; Kuwahara, Yasutaka ; Mori, Kohsuke ; Yamashita, Hiromi ;

Abstract: Photocatalytic production of hydrogen peroxide (H2O2) from dioxygen (O2) and water (H2O) has shown promise for the artificial photosynthesis of liquid fuel. We previously demonstrated that an Al-based metal-organic framework (MOF) functions as a suitable platform for photocatalytic H2O2 production owing to the efficient suppression of H2O2 decomposition caused by the photocatalysts themselves, which increases the yield of H2O2. However, the photocatalytic efficiency of Al-based MOFs is often limited by their short-lived charge separation The energy transfer process is a beneficial approach to promoting charge separation and thereby improving the photocatalytic activity; MOFs enable highly efficient energy transfer between organic linkers because they allow precise control of the arrangement of the building blocks. Herein, we demonstrate that an Al-based MOF composed of both porphyrin- and pyrene-based organic linkers (Al-TCPP(10-X)-TBAPyX) is a promising photocatalyst for producing H2O2 from O2 and H2O without additives under visible-light irradiation while simultaneously enabling efficient suppression of undesired H2O2 decomposition Efficient energy transfer from 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy) to tetrakis(4-carboxyphenyl)porphyrin (TCPP) was driven within Al-TCPP(10-X)-TBAPyX, resulting in dramatically enhanced photocatalytic H2O2 production through optimization of the linker mixture ratio in the MOF structure. The present work not only proposes a new reaction pathway for H2O2 generation via1O2 intermediates, which is quite different from well-accepted mechanisms involving O2 -, but also provides a promising strategy for designing catalysts to realize efficient photosynthetic H2O2 production

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Aahlen, Michelle ; Amombo Noa, Francoise M. ; Oehrstroem, Lars ; Hedbom, Daniel ; Stroemme, Maria ; Cheung, Ocean

Abstract: Anthropogenic greenhouse gas emission poses as serious threat to our environment and it is therefore of utmost importance that efficient systems are developed to mitigate these issues. SF6, in particular, has attracted more attention in recent years due to its global warming potential which severely exceeds that of CO2. In this study we present the SF6 sorption properties of four highly porous 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene-based (TBAPy4-) metal-organic frameworks containing either ytterbium(III), thulium(III), cerium(III), or hafnium(IV). These MOFs can be synthesized with high crystallinity in as little as 5 h and were found to have good SF6 uptakes due to their suitable pore size. The SF6 uptake of the Yb-TBAPy MOF reached 2.33 mmol g-1 with high Henry's law SF6-over-N2 selectivity of ∼80 at 1 bar and 293 K. The TBAPy-MOFs were also found to have good chem. stability and good cyclic SF6 sorption stability with fast SF6 uptake. These TBAPy-MOFs possesses many of the properties desired for an efficient SF6 sorbent and may be suitable sorbents for further development, including sorbent processing for industrial applications.

Keywords: Metal-organic frameworks ; Greenhouse gas capture ; Sulfur hexafluoride ; TBAPy

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Alternative Products

Product Details of [ 933047-52-0 ]

CAS No. :933047-52-0
Formula : C44H26O8
M.W : 682.67
SMILES Code : O=C(O)C1=CC=C(C2=C(C3=C45)C=CC5=C(C6=CC=C(C=C6)C(O)=O)C=C(C7=CC=C(C=C7)C(O)=O)C4=CC=C3C(C8=CC=C(C=C8)C(O)=O)=C2)C=C1
MDL No. :MFCD30478418
InChI Key :HVCDAMXLLUJLQZ-UHFFFAOYSA-N
Pubchem ID :90005588

Safety of [ 933047-52-0 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H315-H319-H335
Precautionary Statements:P261-P264-P271-P280-P302+P352-P305+P351+P338

Computational Chemistry of [ 933047-52-0 ] Show Less

Physicochemical Properties

Num. heavy atoms 52
Num. arom. heavy atoms 40
Fraction Csp3 0.0
Num. rotatable bonds 8
Num. H-bond acceptors 8.0
Num. H-bond donors 4.0
Molar Refractivity 199.73
TPSA ?

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

149.2 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

3.4
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

9.64
Log Po/w (WLOGP)?

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

10.04
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.

6.43
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

8.78
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

7.66

Water Solubility

Log S (ESOL):?

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

-10.19
Solubility 0.0000000444 mg/ml ; 0.0000000001 mol/l
Class?

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

Insoluble
Log S (Ali)?

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

-12.69
Solubility 0.0000000001 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

-13.73
Solubility 0.0 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.

-3.62 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

2.0
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.

0.56

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<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)

3.55
 

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