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81.
The efflorescent carbon allotropes: Fractality preserved blooming through alkali treatment and exfoliation
Mohanachandran Nair Sindhu Swapna, Sankararaman S, 2020, izvirni znanstveni članek

Opis: The work reported in the paper elucidates morphological modification induced nanoart and surface area enhancement of graphite, graphene, and soot containing carbon allotropes through ultrasonication and alkali-treatment. The field emission scanning electron microscopic (FESEM) analysis of the samples before and after exfoliation reveals the formation of brilliant flower-like structures from spindle-like basic units due to Ostwald ripening. The x-ray diffraction analysis of the samples gives information about structural composition. The fractal analysis of the FESEM images indicates a multifractal structure with the dimensions—box-counting dimension D0 (1.72), information dimension D1 (1.66), and correlation dimension D2 (1.63)—preserved upon exfoliation. The process of ultra-sonication assisted liquid phase exfoliation resembles blooming as if the carbon allotropes are efflorescent.
Ključne besede: carbon allotropes, fractal dimension, soot, fractality, alkali treatment, exfoliation
Objavljeno v RUNG: 04.07.2022; Ogledov: 1147; Prenosov: 0
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82.
Fractal Applications in Bio-Nanosystems
Mohanachandran Nair Sindhu Swapna, Sankararaman S, 2019, pregledni znanstveni članek

Opis: We live in a world of high complexity in all means. The present article is an attempt to elucidate the potential of fractal analysis in understanding and quantifying the complexity. Of several methods of fractal analysis, we have used only the box counting and power spectral methods for explaining the potential of the technique. The application of fractal analysis in bio-nanosystems, thin films, are forensic science are exemplified though our own work.
Ključne besede: fractal application, bionanosystem
Objavljeno v RUNG: 04.07.2022; Ogledov: 905; Prenosov: 0
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83.
Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek

Opis: The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.
Ključne besede: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Objavljeno v RUNG: 30.06.2022; Ogledov: 1051; Prenosov: 0
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84.
Development of Zinc Oxide-Multi-Walled Carbon Nanotube hybrid nanofluid for energy-efficient heat transfer application: A thermal lens study
Mohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek

Opis: This paper addresses the need for developing an energy-efficient hybrid nanofluid with zinc oxide–multi-walled carbon nanotube (ZnO-MWCNT) for overcoming the bottleneck of efficient heat transfer in thermal systems. The concentration-dependent thermal diffusivity modifications are analyzed using the highly sensitive mode mismatched thermal lens technique. The hybrid composite is prepared by the solid-state mixing and annealing of a pure multi-walled carbon nanotube (MWCNT) and zinc oxide (ZnO), synthesized by the solution combustion method. The composite formation is studied by structural, morphological, and optical characterization techniques. Among the three nanofluids ZnO, MWCNT, and ZnO-MWCNT, the composite exhibits a drastic enhancement in thermal diffusivity at a lower solid volume fraction of 0.047 mg/ml containing 0.009 mg/ml of MWCNT. All the nanofluids show an optimum concentration beyond which the thermal diffusivity decreases with the nanoparticle concentration. Thus, this study suggests the potential application of ZnO-MWCNT hybrid nanofluids in thermal system design to enhance internal combustion engines' efficiency during cold-start.
Ključne besede: Zinc Oxide, MWCNT, hybrid nanofluid, thermal lens, diffusivity, engine efficiency
Objavljeno v RUNG: 30.06.2022; Ogledov: 1207; Prenosov: 0
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85.
Graph based feature extraction and classification of wet and dry cough signals: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek

Opis: This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.
Ključne besede: wet cough, dry cough, complex network, quadratic SVM, neural net
Objavljeno v RUNG: 30.06.2022; Ogledov: 1012; Prenosov: 0
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86.
RF sputtered boron carbide thin film for UVB and UVC shielding: A greener approach
Mohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek

Opis: The paper reports the development of RF sputtered boron carbide coatings as refractory and UV-shielder for high-temperature goggles and spacecraft applications. The advancement in the design and fabrication of machinery and UV optics necessitates the development of low-cost, eco-friendly preparation of wear-resistant refractory coatings with strong absorption in the UV region. Boron carbide coatings have proven their potential as abrasives besides their electronic applications. In the present work, boron carbide coatings are prepared by RF sputtering technique using the target prepared by low-temperature hydrothermal synthesis using cotton as carbon precursor. The sample synthesized and the film prepared are subjected to structure, morphological, and optical characterizations. The X-ray diffraction, Fourier transform infrared, micro-Raman and X-ray photoelectron studies confirm the formation of boron-rich boron carbide with the thermal stability of 87% at 800 C, revealed through the thermogravimetric analysis. The Tauc plot analysis gives the bandgap energy of the boron carbide target and film as 2.66 eV and 2.70 eV, respectively. The UV–Vis spectroscopic study also reveals the potential of the sample and the film in blocking UVB and UVC. The CIE plot from the photoluminescence study suggests the sample to be a blue light emitter.
Ključne besede: Boron carbide, RF sputtering, uv shielding, thin films
Objavljeno v RUNG: 30.06.2022; Ogledov: 1046; Prenosov: 0
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87.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek

Opis: Objectives: The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Findings: The spectral analyses reveal fewer, low-intensity frequency components along with its overtones in the intermittent and rapidly damping RB signal. The complexity in the dynamics of airflow in BB and RB is investigated through the fractal dimension, Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the RB signal provides an insight into the internal morphology of the airways containing mucous and other obstructions. The Principal Component Analysis (PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along with Pattern Recognition Neural Network (PRNN) uses non-linear measures for predicting BB and RB. Signal classification based on phase portrait features evaluates the multidimensional aspects of signal intensities, whereas that based on PSD features considers mere signal intensities. The principal components in PCA cover about 86.5% of the overall variance of the data class, successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100% accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the potential of non-linear measures and PSD features in classifying these signals enabling its application to be extended for low-cost, non-invasive COVID-19 detection and real-time health monitoring.
Ključne besede: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 1251; Prenosov: 0
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88.
Fractal and inertia moment analyses for thin film quality monitoring
Mohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek

Opis: The widespread applications of thin films in optronics demand innovative techniques for its characterizations. The work reported here proposes electronic speckle pattern interferometry and fractal-based methods for assessing the quality of thin films taking the industrially relevant molybdenum oxide (MoO3) incorporated niobium oxide (Nb2O5) films. The films with different levels of MoO3 incorporation (1, 2, 3, 5, and 10 wt. %) are prepared by radio frequency sputtering. The study reveals the structure modifications of Nb2O5 from the orthorhombic to monoclinic phases with an associated morphological variation revealed through atomic force microscopy and field-emission scanning electron microscopy analyses. The films’ specklegrams are recorded under thermal stress; the inertia moment (IM) and fractal analyses are computed and compared with the root-mean-square surface roughness of the films. The lacunarity analysis of the AFM films agrees well with the specklegrams. Thus, the lower IM and lacunarity values of the specklegrams can be regarded as indicators of the good quality of thin films.
Ključne besede: cross-correlation, fractal dimension, inertia moment, lacunarity, speckle, surface roughness.
Objavljeno v RUNG: 30.06.2022; Ogledov: 1136; Prenosov: 0
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89.
Fluorescent emission from a natural carbon matrix incorporating sodium
Mohanachandran Nair Sindhu Swapna, 2019, izvirni znanstveni članek

Opis: The process of functionalization of metals in natural carbon matrices has become an important area of research due to its improved properties and applications. Carbon materials possessing photoluminescence (PL) properties find a wide range of applications in photonics. Among the various carbon materials available in nature, cellulose has critical importance since it is the most abundant and wide-spread biopolymer on Earth, and also, the important component in plants’ skeleton. In the present work, the functionalized carbonaceous material is prepared by the hydrothermal treatment of natural cellulosic source Aloe Vera and the metallic element sodium is properly incorporated into it by adding sodium borohydride to observe the fluorescence emission changes. The incorporation of metal ions in the carbon matrix leads to structural modifications and properties as evidenced by field emission scanning electron microscopy, Energy dispersive spectroscopy, X-ray dot mapping, X-ray Photoelectron spectroscopy, and X-ray diffraction analysis. The optical emission characteristics are studied using Photoluminescence spectroscopy, CIE plot, power spectrum, color purity, and quantum yield. The excitation wavelength dependent photoluminescence emission mechanism shown by the carbon–metal incorporated products obtained from the cellulosic raw materials makes them suitable for biomedical and biosensing applications because of the non-toxic and eco-friendly nature.
Ključne besede: Fluorescent emission, sodium carbide, cellulose, carbon matrix
Objavljeno v RUNG: 30.06.2022; Ogledov: 1059; Prenosov: 0
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90.
Pharmacological application of thermal Lens technique - A thermal diffusivity study
Mohanachandran Nair Sindhu Swapna, 2018, izvirni znanstveni članek

Opis: The photothermal phenomenon has emerged as a potential tool for the nondestructive evaluation of thermal and optical properties of materials. Thermal analysis of drugs is an unavoidable part of preformulation study, as temperature variations can induce structural changes of the constituents of drugs. Techniques based on photothermal phenomena are highly sensitive, as only the absorbed radiation contributes to the signal. Periodic illumination and subsequent nonradiative de-excitation generate thermal lens signals of various types within and around the sample. Variation of thermal diffusivity with a concentration of the commonly used drug terbutaline is studied through the single-beam thermal lens technique. The ultraviolet–visible spectrum of the drug shows strong absorption around 500 nm, which suggests the possible wavelengths that can be used for the study. It is found that concentration of the drug in liquid form decides its thermal stability, as its thermal diffusivity varies with concentration. The study gives information about the optimum value for the concentration of the drug noted above for which the chance of thermal stability is high.
Ključne besede: thermal lens, thermal diffusivity, pharmacology, drug
Objavljeno v RUNG: 30.06.2022; Ogledov: 1030; Prenosov: 0
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