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Neural network-assisted carbon nanotube electrochemical sensors for automated environmental risk assessment
The current research proposes an intelligent network system that continuously tracks the quality of water flow, with particular attention to pollutants frequently occurring in runoff water from agricultural practices. It deploys high-performance electrochemical sensors based on carbon nanotubes (CNT) combined with a small neural network that functions directly on the built-in microcontroller. It is deployed on floating buoys powered by solar energy, where it can detect some critical contaminants in the rural water bodies, including nitrates, phosphates, atrazine, cadmium, and lead. The sensors work by transmitting their electrical signals through the sensors to the neural network, which provides precise identification of the level of pollutants as one of three risk levels: safe (below detection levels), manageable (within regulatory levels), and hazardous (exceeding regulations). Regarding power performance, results can be delivered over a relatively small-time delay (1.2 milliseconds per reading) and with low memory usage (1.8 MB), making it ideal for remote and low-powered sensors. It is more accurate (93.6 %) than typical machine learning models. Should pollutants exceed the above-prescribed limits, an automated warning will be generated, and the information will be immediately uploaded to a cloud-based dashboard. The dashboard will be closely monitored via remote control, and trend analysis will be conducted. By eliminating the need for manual water sampling, the system offers a scalable and energy-saving method for autonomous environmental testing, particularly in inaccessible locations. In the future, the study will focus on the use of federated learning, a technique that retains data locally to protect privacy, enabling more intelligent and collaborative conclusions across sensor networks. This prepares the ground for more intelligent and secure environmental surveillance systems in the future. 2025 Elsevier B.V. -
Balancing Innovation and Tradition: An Analytical Study of the Interface between Intellectual Property and Cultural Appropriation in India
India, with its rich and diverse cultural heritage, is vulnerable to practices which may undermine its traditional knowledge. Intellectual Property (IP) rights, while designed to protect innovation and creativity, can also play a crucial role in safeguarding traditional knowledge. Cultural appropriation, which essentially refers to the unauthorized use of indigenous knowledge and practices by foreign entities who exploit these lesser-known art forms, expression or traditional knowledge relating to lifestyle and well being with an objective of commercial exploitation, has increasingly become a concern in the global south, including India. There have been several instances of cultural appropriation like patent claims on Indian traditional knowledge which have been part of local customs since time immemorial by misrepresenting the origin of the product or diluting the traditional knowledge and portraying it as a scientific breakthrough. Therefore, this paper explores the intersection of cultural misappropriation and protection under IP laws for appropriators in the context of India's heritage. This paper examines the challenges faced by indigenous communities in protecting their traditional knowledge such as loss of traditional knowledge, economic disadvantages and disrespect to the community. This paper highlights the importance of balancing the rights of indigenous communities with the broader public interest in accessing and enjoying cultural heritage. The study aims to focus on bottlenecks in the current IP system that fail to adequately protect traditional knowledge from misappropriation. It also explores the ongoing debates around fair benefit-sharing mechanisms, the importance of maintaining a traditional digital library that is accessible to all and the need for a sui generis system that aligns with the unique characteristics of traditional knowledge. The study advocates for stronger legal protections, ethical considerations and raising awareness across nations about the value of traditional knowledge which are crucial for fostering respect and preventing further exploitation. Moreover, recommendations are explored to suggest safeguarding the cultural heritage of indigenous communities in India against the backdrop of global intellectual property regimes by implementing robust IPR protection for traditional knowledge that can safeguard against unauthorized use and exploitation. 2025 Department of Law, University of North Bengal. All rights reserved. -
A Three-Species Model With Predator-Taxis Sensitivity: Hopf Bifurcation and Active Control Stabilization
This study presents an analysis of a novel fractional two preyone predator model incorporating predator-taxis sensitivity. We conduct a comprehensive stability analysis, explore the model's chaotic nature through period-doubling bifurcations, and also show the existence of limit cycles through fractional Hopf bifurcation. It is observed that the fractional-order parameter brings in a stabilizing effect and, simultaneously, a shift of the Hopf bifurcation point. At the Hopf bifurcation point, the system moves from stable equilibria to sustained oscillations. In addition, regardless of initial conditions, the system approaches a stable limit cycle, showing the robustness of the method. We also demonstrate the effectiveness of the active control method to eliminate the periodicity of the fractional system and also unravel the decelerating influence of the fractional-order parameter on the convergence time to equilibrium. These results provide valuable insights into the stabilization of ecosystem dynamics and contribute more broadly to our understanding of population dynamics in ecological systems. 2025 John Wiley & Sons Ltd. -
Diffusive instability, patterns and limit cycles in a slow-fast generalized SamardzijaGreller model: a multiscale approach
The SamardzijaGreller model is an extension of the classical LotkaVolterra predatorprey system, and this paper investigates the multiscale dynamics in a modified SamardzijaGreller model to take into account the slower timescales in predator reactions rather than prey. We present a comprehensive local stability analysis, pattern formation through diffusive instability and fractional Hopf bifurcations. The analysis of the spatio-temporal model reveals the effects of diffusion coefficients and parameter variations on the dynamical behavior of the slow-fast system. By analyzing the systems response to changes in the self-diffusion rate of the prey (dX), the intra-species competition rate of the first predator (d1) and the interaction parameter a, we observe chaotic patterns for small values of a, particularly when the prey exhibits strong diffusion. Increases in a lead to the emergence of regular, periodic patterns that are homogeneous in space. We discuss in detail how fractional-order models create memory effects that inhibit chaotic transitions, potentially being delayed or avoided in the temporal model. The study shows clear differences in the dynamical regimes between the integer-order and the fractional-order models. The latter model gives more significance to the stabilization effect of the fractional-order derivative on ecological systems and improves our understanding of predatorprey interactions under different parameter settings. Findings clarify the potential to derive ecological stability from emergent patterns and transition into a better understanding of complex ecological processes. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Synthesis and Characterization of Eu2+/Nd3+ Activated CaSi2O5 Phosphor for Bioimaging Applications
The advancement of non-invasive diagnostic tools has propelled the development of luminescent nanomaterials with enhanced imaging capabilities. In this study, Eu2+/Nd3+ codoped CaSi2O5 phosphors were synthesized via a conventional solid-state reaction route under a reducing environment to explore their potential for bioimaging applications. Calcium silicate, known for its intrinsic biocompatibility, served as the host matrix, whereas Eu2+ acted as the primary luminescent centre and Nd3+ was used as the near-infrared (NIR) sensitizer to support deep-tissue excitation. Structural analysis via X-ray diffraction (XRD) verified the formation of a triclinic crystal structure and the average crystallite size was validated through both Scherrer equation and WilliamsonHall analyses. Field emission gun scanning electron microscopy (FEG-SEM) images revealed flower-like microstructures with embedded fine white particles. Energy-dispersive X-ray spectroscopy (EDX) detected the existence of expected chemical components of the phosphor, whereas Fourier-transform infrared (FTIR) spectra provided evidence of successful dopant incorporation through characteristic vibrations corresponding to CaO, SiOSi, EuO and NdO bonds. Photoluminescence studies showed an excitation spectrum with distinct and intense absorption bands within the range of 7001000 nm, attributed to the 4f4f transitions of Nd3+ ions, and upon excitation at approximately 800 nm, the phosphor exhibited dual emission bands around 410 and 440 nm with optimal intensity corresponding to the characteristic 4f65d1 ? 4f7 transitions of Eu2+. The afterglow decay analysis showed persistent luminescence exceeding 15 min, and CIE chromaticity analysis confirmed that the emission lies within the blue spectral range (x = 0.155, y = 0.059), indicating high potential for background-free bioimaging with high chromatic accuracy. These findings suggest that Eu2+/Nd3+ doped CaSi2O5 can be a promising luminescent material for advanced biomedical imaging applications. 2025 John Wiley & Sons Ltd. -
Supervised Learning-Based Data Classification and Incremental Clustering
Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, Tell me who your neighbors are, and it will tell you who you are. The supervised learning-based data classification and incremental clustering technique is a simple yet powerful approach with applications in computer vision, pattern recognition, optical character recognition, facial recognition, genetic pattern recognition, and other fields. Its also known as a slacker learner because it doesnt develop a model to classify a given test tuple until the very last minute. When we say yes or no, there may be an element of chance involved. However, the fact that a diner can recognise an invisible food using his senses of taste, flavour, and smell is highly fascinating. At first, there can be a brief data collection phase: what are the most noticeable spices, aromas, and textures? Is the flavour of the food savoury or sweet? This information can then be used by the diner to compare the bite to other items he or she has had in the past. Earthy flavours may conjure up images of mushroom-based dishes, while briny flavours may conjure up images of fish. We view the discovery process through the lens of a slightly modified adage: if it smells like a duck and tastes like a chicken, youre probably eating chicken. This is a case of supervised learning in action. Machine learning can benefit from supervised learning, which is a concept that can be applied to it (ML). 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Real-Time Application with Data Mining and Machine Learning
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Brief Concept on Machine Learning
Machine learning is a subset of AI. Its a research project aimed at gathering computer programscapable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the datas underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Classification and Incremental Clustering Using Unsupervised Learning
Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Introduction to Data Mining and Knowledge Discovery
Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. Its an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Feature Subset Selection Techniques with Machine Learning
Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapterdescribes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learningtasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizesthe performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Mining-Based Variant Subset Features
A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Research Intention Towards Incremental Clustering
Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. Thats why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges.To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Factors Affecting Data-Privacy Protection and Promotion of Safe Digital Usage
India is facing the problem of the digital divide. Being developing countries and with low literacy rates, digital knowledge among the public is weak. Those who know a bit about digital operations on smartphones and computers are not having complete knowledge of data security and its peculiarities. Therefore, this study aimed to find determinants of data-privacy anxiety among Indians and to understand their stress and anxiety during the use of digital applications in their daily routines, especially amid the COVID-19 scenario. The current study adopted an inductive qualitative exploratory approach to delve into the above issues. This study employed a reflexive thematic analysis method to analyse interview data of 10 participants across young-adult to middle-adult age groups of male and female gender. Participants belonged to middle socio-economic status having urban background. The study found 6 themes and 26 subordinate themes as determinants of data-privacy anxiety. Emerging themes from the data indicated at the systemic determinants of data-security anxiety, the paradox of learned helplessness and convenience preference among participants. This paper employed the Foucauldian lens of bio-power to discuss the circumscribing function of ill-structured knowledge dissemination approaches. This paper argues in favor of a critical pedagogy approach in educating people about digital security, dealing with data-privacy anxiety, and promoting safe digital usage among all generations of Indians. It also suggests measures of modifications in policies and documentation processes of major online platforms and apps to curb uncertainty and sense of insecurity among users. 2022 Copyright for this paper by its authors. -
Children Witnessing Violence in India: Nature, Risk Factors, Impact and Prevention Strategies
Children witness various degrees and intensities of violations and violence along with a hoard of environmental stressors. Such a spectrum of violence includes disturbing family environments, witnessing adults, including parents and family members, indulge in violence and abusive behaviours and direct or vicarious exposure to violence outside the home. The chapter aims to provide an overview of the nature and impact of witnessing violence. The frequency, type, intensity and the child's relationship with the people involved or impacted by the violence can determine the impact on a child's mental health and development. Children may witness distressing events in their daily lives like the loss of a loved one or watching adults take up challenging tasks, which may help them be resilient and learn coping skills with appropriate support. Long-term exposure to witnessing violence and trauma can lead to severe emotional and developmental difficulties. Such direct or vicarious exposure to varying degrees of violence may cultivate a culture of fear, repression and silence around the children. These difficulties may be similar to those of children who are direct victims of abuse. Witnessing violence has also been linked to anxiety and depression. Children growing up in such environments are at higher risk of normalizing violence and growing into abusive adults. Poverty, cultural factors, parenting, schooling, and policies can largely determine such risks for children. The paper discusses the preventive and promotive approaches at the school, family and community levels. Education and empowerment of adults in the child's environment can be the best preventative approach. Existing policies and programmes in India for children need to bring in more robust initiatives to identify, report, prevent and protect children witnessing violence. The needs and concerns of children witnessing violence and prevention approaches should be part of courses in helping professionals training and curriculum. The chapter calls for the necessity of individual and community-based interventions in terms of need-based models for addressing the mental health needs of children. The chapter strongly recommends the need for addressing mental health education for families and schools. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Camouflaged Protocols of Womanhood: Inherent Paradoxes of Bengal
Contemporary India is part of a global community where modernizationand urbanization including consumerism is on the rise; but not leaving behind its historic cultural essence. Old scriptures, myths, folklore, literature, cultural proverbs as well as media, have a noteworthy role in shaping the perspectives of Indian people. The current study was done in parts of urban Bengal to explore the influence of these forces from socio-political, psychological as well as philosophical registers, in shaping the self-image and identity of contemporary women. 50 participants were interviewed using a semi-structured in-depth interview method and data were analyzed following qualitative analytical methods keeping the cultural-relational-social context in perspective. The method of pluralism in the qualitative analysis was followed using three data analysis methodsGrounded Theory, Interpretative Phenomenological Approach, and Narrative Analysis. Analyses revealed anintriguing social-crafting of theconcepts of gender and identities of women through idealization and internalization of traits and notions incessantly presented through celebrated literature, myths, folklore, proverbs, and media. Furthermore, the analysis showed how the deep-rooted paradox, inherent in Bengals cultural discourse, is internalized even by educated urban women and infuses a sense of lack, vulnerability, self-loathing, blame, and stigma in their identity. On the contrary, it was also observed in a few instances that optimum use of media and global knowledge led to the construction of a new-age individual and subjective discourse. 2022, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India. -
A Survey of Sentiment Analysis from Social Media Data
In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future. 2014 IEEE. -
Sentiment Analysis of COVID-19 tweets by Deep Learning ClassifiersA study to show how popularity is affecting accuracy in social media
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%. 2020 Elsevier B.V. -
An insight into microscopy and analytical techniques for morphological, structural, chemical, and thermal characterization of cellulose
Cellulose obtained from plants is a bio-polysaccharide and the most abundant organic polymer on earth that has immense household and industrial applications. Hence, the characterization of cellulose is important for determining its appropriate applications. In this article, we review the characterization of cellulose morphology, surface topography using microscopic techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Other physicochemical characteristics like crystallinity, chemical composition, and thermal properties are studied using techniques including X-ray diffraction, Fourier transform infrared, Raman spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, and thermogravimetric analysis. This review may contribute to the development of using cellulose as a low-cost raw material with anticipated physicochemical properties. Highlights: Morphology and surface topography of cellulose structure is characterized using microscopy techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Analytical techniques used for physicochemical characterization of cellulose include X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. 2022 Wiley Periodicals LLC.