12 research outputs found
The face behind the Covid-19 mask ??? A comprehensive review
The threat of epidemic outbreaks like SARS-CoV-2 is growing owing to the exponential growth of the global population and the continual increase in human mobility. Personal protection against viral infections was enforced using ambient air filters, face masks, and other respiratory protective equipment. Available facemasks feature considerable variation in efficacy, materials usage and characteristic properties. Despite their widespread use and importance, face masks pose major potential threats due to the uncontrolled manufacture and disposal techniques. Improper solid waste management enables viral propagation and increases the volume of associated biomedical waste at an alarming rate. Polymers used in single-use face masks include a spectrum of chemical constituents: plasticisers and flame retardants leading to health-related issues over time. Despite ample research in this field, the efficacy of personal protective equipment and its impact post-disposal is yet to be explored satisfactorily. The following review assimilates information on the different forms of personal protective equipment currently in use. Proper waste management techniques pertaining to such special wastes have also been discussed. The study features a holistic overview of innovations made in face masks and their corresponding impact on human health and environment. Strategies with SDG3 and SDG12, outlining safe and proper disposal of solid waste, have also been discussed. Furthermore, employing the CFD paradigm, a 3D model of a face mask was created based on fluid flow during breathing techniques. Lastly, the review concludes with possible future advancements and promising research avenues in personal protective equipment
PULSED DOUBLE-QUANTUM COHERENCE ELECTRON PARAMAGNETIC RESONANCE IN PROTEIN STRUCTURE DETERMINATION
161 pagesElectron paramagnetic resonance (EPR) or more specifically, pulsed dipolar EPR spectroscopy (PDS), combined with the site-directed spin labelling (SDSL) technique has emerged as a key technique in protein structure determination. The core concept is to filter out the weak dipolar interaction between a pair of spin labels by applying an appropriate pulse sequence and retrieve the inter-spin distance from the dipolar EPR signal. Double-quantum coherence (DQC) and double electron-electron resonance (DEER) are two such methods primarily used in studying the structure of proteins and other biomacromolecules. There are two main classes of spin labels used in PDS studies, (i) triarylmethyl (TAM) and (ii) nitroxides. DQC signal expression of nitroxide spin labels is extremely complex and without knowing the analytic form of the signal, the resulting spectra, especially in 2D, cannot be analyzed both accurately and efficiently. In the first part of the thesis, we derive analytic expressions of DQC signals for both TAM and nitroxide spin labels. These expressions are extremely useful in analyzing experimental signals using personal computers. Hence, we believe that this innovation is an important and necessary step in motivating the scientific community to use DQC more frequently in their studies. Another key challenge in PDS signal processing is the removal of intermolecular or background signal. An error in the process of background signal removal can translate into a critical error in obtaining the distance distribution. We have derived an analytic expression of the total DQC signal for spin, S=1/2, particles in frozen samples and this expression can be integrated over the spatial variables to derive the functional form of the signal. We have demonstrated the importance of the analytic expression in studying the spatial distribution of the spin-labeled proteins in frozen samples. In the last chapter, we present experimental studies that demonstrate the effect of the rate of freezing on the distance distributions derived from DEER experiments. In the same project, we have explored the effect of varying the amount of cryoprotectant and using different spin labels on the reconstructed distance distributions. We conclude that both slow freezing (>= 1 s) at 30% glycerol by weight and rapid freeze-quench (100 micro-s) at 10% glycerol result into reduced intermolecular spin-spin interactions and improved signal-to-noise ratio (snr). Additionally, we find that the effect of the conformational sub-states of the spin-labels on reconstructed distance distributions is averaged out in slow freezing, while the trapping of the conformational sub-states in rapid-quenched samples yields broadened distance distributions
Hyperfine Decoupling of ESR Spectra Using Wavelet Transform
The objective of spectral analysis is to resolve and extract relevant features from experimental data in an optimal fashion. In continuous-wave (cw) electron spin resonance (ESR) spectroscopy, both g values of a paramagnetic center and hyperfine splitting (A) caused by its interaction with neighboring magnetic nuclei in a molecule provide important structural and electronic information. However, in the presence of g- and/or A-anisotropy and/or large number of resonance lines, spectral analysis becomes highly challenging. Either high-resolution experimental techniques are employed to resolve the spectra in those cases or a range of suitable ESR frequencies are used in combination with simulations to identify the corresponding g and A values. In this work, we present a wavelet transform technique in resolving both simulated and experimental cw-ESR spectra by separating the hyperfine and super-hyperfine components. We exploit the multiresolution property of wavelet transforms that allow the separation of distinct features of a spectrum based on simultaneous analysis of spectrum and its varying frequency. We retain the wavelet components that stored the hyperfine and/or super-hyperfine features, while eliminating the wavelet components representing the remaining spectrum. We tested the method on simulated cases of metal–ligand adducts at L-, S-, and X-band frequencies, and showed that extracted g values, hyperfine and super-hyperfine coupling constants from simulated spectra, were in excellent agreement with the values of those parameters used in the simulations. For the experimental case of a copper(II) complex with distorted octahedral geometry, the method was able to extract g and hyperfine coupling constant values, and revealed features that were buried in the overlapped spectra
Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR
Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets
Analysis of small molecule mixtures by super-resolved 1H NMR spectroscopy
Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise
Analysis of Small-Molecule Mixtures by Super-Resolved <sup>1</sup>H NMR Spectroscopy
Analysis of small molecules is essential
to metabolomics, natural
products, drug discovery, food technology, and many other areas of
interest. Current barriers preclude from identifying the constituent
molecules in a mixture as overlapping clusters of NMR lines pose a
major challenge in resolving signature frequencies for individual
molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion
of typical NMR spectra to pure shift spectra by signal processing
without a priori knowledge about the coupling patterns
is essential for accurate analysis. We developed a super-resolved
wavelet packet transform based 1H NMR spectroscopy that
can be used in high-throughput studies to reliably decouple individual
constituents of small molecule mixtures. We demonstrate the efficacy
of the method on the model mixtures of saccharides and amino acids
in the presence of significant noise
Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 Ā°C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the processā self- sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The LevenbergāMarquardt (TRAINLM) and Bayesian regularization (TRAINBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.Lā1; a low temperature of 30 Ā°C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%
Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the process’ self- sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The Levenberg–Marquardt (TRAINLM) and Bayesian regularization (TRAINBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.L−1; a low temperature of 30 °C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%
Waste to energy: A review of biochar production with emphasis on mathematical modelling and its applications
United Nations charter to build a sustainable future has paved the way for the introduction of the Sustainability Development Goals (SDGs) at a global forum. In particular, SDG 11 is aligned with the idea of developing cities and communities that provide quality human life, by attaining net-zero discharge and self-sustainability. In line with the efforts of the global community, biochar has emerged as a viable solution due to its ability to convert waste into value. Finding applications in a spectrum of domains, biochar is being studied for use as an adsorbent, a co-catalyst to promote industrial-grade reactions and as a feed for fuel cells. Moreover, the inclusion of biochar as a soil enhancement material advocates the implementation of closed-loop nutrient cycles. Hence, it is imperative to have a proper understanding of the biomass characteristics, the hydrothermal treatment and the process parameters to be adopted for the production of char in order to identify biomass feedstock based on the application. The current work provides insight into the key factors and conditions employed for the production of biochar based on the plethora of applications. In order build a basic framework to aid in the production of char, the development of a statistical correlation was undertaken to determine the feed and optimum process parameters for the production of biochar based on its applications