Fabrication of a selective 4-amino phenol sensor based on H-ZSM-5 zeolites deposited silver electrodes

RSC Advances ◽  
2016 ◽  
Vol 6 (54) ◽  
pp. 48435-48444 ◽  
Author(s):  
Mohammed M. Rahman ◽  
Bahaa M. Abu-Zied ◽  
Mohammad M. Hasan ◽  
Abdullah M. Asiri ◽  
Mohammad A. Hasnat
Keyword(s):  
Chemical Sensor ◽  
Amino Phenol ◽  
Silver Electrodes

A promising sensitive phenolic chemical sensor was developed using a porous pentasil type framework of protonated H-ZSM-5 zeolite by the reliable I–V method.

scholarly journals Studies on the Modification of Commercial Bisphenol-A-Based Epoxy Resin Using Different Multifunctional Epoxy Systems

2021 ◽  
Vol 2 (2) ◽  
pp. 419-430
Author(s):  
Ankur Bajpai ◽  
James R. Davidson ◽  
Colin Robert
Keyword(s):  
Mechanical Properties ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Bisphenol A ◽  
Epoxy Resin ◽  
Tensile Fracture ◽  
Chemical Structures ◽  
Amino Phenol ◽  
Epoxy Systems

The tensile fracture mechanics and thermo-mechanical properties of mixtures composed of two kinds of epoxy resins of different chemical structures and functional groups were studied. The base resin was a bi-functional epoxy resin based on diglycidyl ether of bisphenol-A (DGEBA) and the other resins were (a) distilled triglycidylether of meta-amino phenol (b) 1, 6–naphthalene di epoxy and (c) fluorene di epoxy. This research shows that a small number of multifunctional epoxy systems, both di- and tri-functional, can significantly increase tensile strength (14%) over neat DGEBA while having no negative impact on other mechanical properties including glass transition temperature and elastic modulus. In fact, when compared to unmodified DGEBA, the tri-functional epoxy shows a slight increase (5%) in glass transition temperature at 10 wt.% concentration. The enhanced crosslinking of DGEBA (90 wt.%)/distilled triglycidylether of meta-amino phenol (10 wt.%) blends may be the possible reason for the improved glass transition. Finally, the influence of strain rate, temperature and moisture were investigated for both the neat DGEBA and the best performing modified system. The neat DGEBA was steadily outperformed by its modified counterpart in every condition.

scholarly journals Highly-Efficient Sulfonated UiO-66(Zr) Optical Fiber for Rapid Detection of Trace Levels of Pb2+

2021 ◽  
Vol 22 (11) ◽  
pp. 6053
Author(s):  
Marziyeh Nazari ◽  
Abbas Amini ◽  
Nathan T. Eden ◽  
Mikel C. Duke ◽  
Chun Cheng ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Rapid Detection ◽  
Chemical Sensor ◽  
Metal Organic Framework ◽  
Aqueous System ◽  
Icp Oes ◽  
Efficient Detection ◽  
Low Concentrations ◽  
Fabry Perot ◽  
Metal Organic

Lead detection for biological environments, aqueous resources, and medicinal compounds, rely mainly on either utilizing bulky lab equipment such as ICP-OES or ready-made sensors, which are based on colorimetry with some limitations including selectivity and low interference. Remote, rapid and efficient detection of heavy metals in aqueous solutions at ppm and sub-ppm levels have faced significant challenges that requires novel compounds with such ability. Here, a UiO-66(Zr) metal-organic framework (MOF) functionalized with SO3H group (SO3H-UiO-66(Zr)) is deposited on the end-face of an optical fiber to detect lead cations (Pb2+) in water at 25.2, 43.5 and 64.0 ppm levels. The SO3H-UiO-66(Zr) system provides a Fabry–Perot sensor by which the lead ions are detected rapidly (milliseconds) at 25.2 ppm aqueous solution reflecting in the wavelength shifts in interference spectrum. The proposed removal mechanism is based on the adsorption of [Pb(OH2)6]2+ in water on SO3H-UiO-66(Zr) due to a strong affinity between functionalized MOF and lead. This is the first work that advances a multi-purpose optical fiber-coated functional MOF as an on-site remote chemical sensor for rapid detection of lead cations at extremely low concentrations in an aqueous system.

scholarly journals Crystal structure of dichlorido-bis((E)-2-((pyridin-4-ylmethylene)amino)phenol)zinc(II), C24H20Cl2N4O2Zn

2020 ◽  
Vol 235 (3) ◽  
pp. 625-628
Author(s):  
Adesola A. Adeleke ◽  
Sizwe J. Zamisa ◽  
Bernard Omondi
Keyword(s):  
Crystal Structure ◽  
Amino Phenol

AbstractC24H20Cl2N4O2Zn, triclinic, P1̄ (no. 2), a = 9.9265(6) Å, b = 15.368(1) Å, c = 16.636(1) Å, α = 102.760(1)°, β = 90.385(2)°, γ = 94.112(3)°, V = 2311.4(3) Å3, Z = 4, Rgt(F) = 0.0260, wRref(F2) = 0.0653, T = 100(2) K.

scholarly journals Identification and Quantification of Olive Oil Quality Parameters Using an Electronic Nose

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 674
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Olive Oil ◽  
Electronic Nose ◽  
Chemical Sensor ◽  
Chemical Constituents ◽  
Oil Quality ◽  
Quality Parameters ◽  
Signal Acquisition ◽  
Good Precision ◽  
Olive Oil Quality ◽  
Quality Categories

An electronic nose (EN), which is a kind of chemical sensor, was employed to check olive oil quality parameters. Fifty samples of olive oil, covering the four quality categories extra virgin, virgin, ordinary virgin and lampante, were gathered from different Palestinian cities. The samples were analysed chemically using routine tests and signals for each chemical were obtained using EN. Each signal acquisition represents the concentration of certain chemical constituents. Partial least squares (PLS) models were used to analyse both chemical and EN data. The results demonstrate that the EN was capable of modelling the acidity parameter with a good performance. The correlation coefficients of the PLS-1 model for acidity were 0.87 and 0.88 for calibration and validation sets, respectively. Furthermore, the values of the standard error of performance to standard deviation (RPD) for acidity were 2.61 and 2.68 for the calibration and the validation sets, respectively. It was found that two principal components (PCs) in the PLS-1 scores plot model explained 86% and 5% of EN and acidity variance, respectively. PLS-1 scores plot showed a high performance in classifying olive oil samples according to quality categories. The results demonstrated that EN can predict/model acidity with good precision. Additionally, EN was able to discriminate between diverse olive oil quality categories.

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