Microwave Environmental Chemistry

2019 ◽  
Vol 03 (01) ◽  
pp. 7-13
Author(s):  
芳芳 邹
Keyword(s):  
Environmental Chemistry

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

Recent Trends in Development of Nanomaterials Based Green Analytical Methods for Environmental Remediation

2020 ◽  
Vol 16 ◽  
Author(s):  
Sidra Amin ◽  
Amber R. Solangi ◽  
Dilawar Hassan ◽  
Nadir Hussain ◽  
Jamil Ahmed ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Environmental Remediation ◽  
Environmental Chemistry ◽  
Limit Of Detection ◽  
Environmental Pollutants ◽  
Toxic Metal ◽  
Electrochemical Analysis ◽  
Good Choice ◽  
Nano Materials ◽  
Limit Of Quantification

Background: In recent years, the occurrence and fate of environmental pollutants has been recognized as one of the emerging issues in environmental chemistry. A survey documented about a wide variety of these pollutants, which are often detected in our environment and these are major cause of shortened life spans and the global warming. These pollutants include toxic metal, pesticides, fertilizers, drugs and dyes released into soil and major water bodies. The presence of these contaminants causes major disturbance in eco-system’s balance. To tackle these issues many technological improvements are made to detect minute contaminations. The latest issue being answered by the scientists is the use of green nano materials as sensors which are economical, instant and give much better results at low concentrations and can be used for the field measurements resulting in no dangerous by-product that could lead to more environmental contamination. Nano materials are known for their wide band gap, enhanced physical and optical properties with option of tuneablity as per need, by optimizing certain parameters. They are proved to be good choice for analytical/optical sensors with high sensitivity. Objective: This review holds information about multiple methods that use green nanomaterials for the analytical assessment of environmental pollutants. UV-Vis spectrophotometry and electrochemical analysis using green and reproducible nanomaterials are the major focus of this review article. To date, there are number of spectrophotometric and electro chemical methods available that have been used for the detection of environmental pollutants such as toxic metals, pesticides and dyes. Conclusion: The use of nanomaterials can drastically change the detection limits due to having large surface area, strong catalytic properties, and tunable possibility. With the use of nano materials, lower than the marked limit of detection and limit of quantification were seen when compared with previously reported work. The used nano-materials could be washed, dried, and reused, which makes the methods more proficient, cost effective and environmentally friendly.

Demonstration of environmental chemistry with microscale chemistry and a low-cost four-led based photom

2018 ◽  
Vol 16 (4) ◽  
pp. 510
Author(s):  
Wai Kin Kee ◽  
Wing Hong Chan
Keyword(s):  
Phenolic Compounds ◽  
Environmental Chemistry ◽  
Low Cost ◽  
Light Sources ◽  
Electrochemical Degradation

<span>In this article, a four-LED based photometer, in which four LEDs are used as light sources, are demonstrated to be a useful instrument for the study of pollution problems caused by phenols and of their remediation by electrochemical degradation method and the iron (II) catalyzed homogeneous Fenton’s reaction. The fate of phenols can be monitored by the photometer via the 4-aminoantipyrine method. The results revealed that the latter method was a superior method to treat the phenolic compounds.</span>

scholarly journals Understanding (coupled) large amplitude motions: the interplay of microwave spectroscopy, spectral modeling, and quantum chemistry

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ha Vinh Lam Nguyen ◽  
Isabelle Kleiner
Keyword(s):  
Fourier Transform ◽  
Quantum Chemistry ◽  
Large Amplitude ◽  
Environmental Chemistry ◽  
Broad Band ◽  
Microwave Spectroscopy ◽  
Rotational Spectrum ◽  
Spectral Modeling ◽  
Rotational Spectra ◽  
Large Amplitude Motions

AbstractA large variety of molecules contain large amplitude motions (LAMs), inter alia internal rotation and inversion tunneling, resulting in tunneling splittings in their rotational spectrum. We will present the modern strategy to study LAMs using a combination of molecular jet Fourier transform microwave spectroscopy, spectral modeling, and quantum chemical calculations to characterize such systems by the analysis of their rotational spectra. This interplay is particularly successful in decoding complex spectra revealing LAMs and providing reference data for fundamental physics, astrochemistry, atmospheric/environmental chemistry and analytics, or fundamental researches in physical chemistry. Addressing experimental key aspects, a brief presentation on the two most popular types of state-of-the-art Fourier transform microwave spectrometer technology, i.e., pulsed supersonic jet expansion–based spectrometers employing narrow-band pulse or broad-band chirp excitation, will be given first. Secondly, the use of quantum chemistry as a supporting tool for rotational spectroscopy will be discussed with emphasis on conformational analysis. Several computer codes for fitting rotational spectra exhibiting fine structure arising from LAMs are discussed with their advantages and drawbacks. Furthermore, a number of examples will provide an overview on the wealth of information that can be drawn from the rotational spectra, leading to new insights into the molecular structure and dynamics. The focus will be on the interpretation of potential barriers and how LAMs can act as sensors within molecules to help us understand the molecular behavior in the laboratory and nature.

scholarly journals Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nadin Ulrich ◽  
Kai-Uwe Goss ◽  
Andrea Ebert
Keyword(s):  
Partition Coefficient ◽  
Data Augmentation ◽  
Environmental Chemistry ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Chemical Structures ◽  
Tautomeric Forms ◽  
Learning Techniques ◽  
Water Partition Coefficient ◽  
Using Data

AbstractToday more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

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