Integration Of Sensors Research Articles

Potensi precision farming dalam penerapan prinsip reduce untuk mengurangi limbah sumber daya pertanian

Background: The increase in agricultural production is often accompanied by excessive use of chemical inputs, contributing to environmental pollution. Precision farming is a modern approach that can address this challenge by optimizing input use through advanced technology. This paper explores the application of the reduce principle in precision farming to support efficient use of fertilizers and other resources. Method: The method used is a literature review of various published studies related to precision farming and agricultural land management. Findings: The application of the reduce principle in fertilization activities with precision farming can reduce fertilizer waste by up to 50%, increase land productivity by 10%, and minimize harmful environmental effects, such as groundwater pollution and greenhouse gas emissions. Precision farming also improves irrigation efficiency through precision irrigation technology. Conclusion: This study confirms that applying precision farming with a focus on the reduce principle not only supports sustainable agriculture but also provides economic benefits for farmers by reducing production costs. Innovations in precision farming technology, such as the integration of AI-based sensors and IoT, further enhance potential efficiencies in the future. Novelty/Originality of this article: Emphasizing the use of the reduce principle in precision farming as a primary strategy to achieve more efficient and environmentally friendly agriculture aligns with the goals outlined in the Sustainable Development Goals (SDGs) for responsible production.

Enhancing Leafy Greens’ Production: Nutrient Film Technique Systems and Automation in Container-Based Vertical Farming

Urban agriculture has emerged as a crucial strategy to address food security and sustainability challenges, particularly in densely populated areas. This study focused on enhancing leafy greens’ production, specifically lettuce (Lactuca sativa L.) and arugula or rocket (Eruca sativa L.), using Nutrient Film Technique (NFT) systems and automation in container-based vertical farming. The study utilized a 20-foot shipping container retrofitted to create a thermally insulated and automated growth environment equipped with energy-efficient LED lighting and precise climate control systems. The results demonstrated significant improvements in crop yields, with the NFT systems achieving productivity up to 11 times higher than traditional methods in protected horticulture. These systems enabled continuous cultivation cycles, responding to the high market demand for fresh local produce. Moreover, the integration of low-cost sensors and automation technologies, each costing under USD 300, ensured that the environmental conditions were consistently optimal, highlighting this approach’s economic feasibility and scalability. This low-cost framework aligns with industry standards for affordable technology, making it accessible for small- to medium-sized urban agriculture enterprises. This study underscores the potential of vertical farming as a sustainable solution for urban food production. It provides a model that can be replicated and scaled to meet the growing demand for healthy, locally grown vegetables.

Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms.

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.

Empowering Ageing with Ingenuity: A Scoping Review on the Methods and Technologies Used into Elderly Health Monitoring.

With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of "Healthy Ageing" as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy.

Development and characterization of flexible carbon temperature sensors: a study on different inks and sensor designs

Several industries and applications rely on the accurate monitoring of temperature. Consequently, the development and integration of reliable and flexible temperature sensors that are able to conform to different surfaces is desirable. Despite being a known fact that the used materials, chosen technologies, sensor size, and design tend to impact the performance of said sensors, not many studies focus on the analysis of the influence of these factors. As a result, this work intended to explore how the performance of carbon-based screen-printed temperature sensors could be tuned or optimized by changing the ink and the design of the printed sensors. Therefore, two commercial carbon inks and three different designs were used to create the herein-studied temperature sensors. Firstly, to explore potential differences between the used inks, their morphologic, chemical, and electrical properties were evaluated, allowing further insights to be gathered regarding the filler type, composition, and bulk resistivity. After printing the sensors, they were also submitted to electrical impedance spectroscopy, which allowed for a circuit equivalent model to be proposed for each sensor type, unraveling how current flew across the printed materials. Then, a full factorial statistical analysis was outlined and conducted to uncover which combination of factors allowed to optimize the sensors’ performance, i.e. lower initial resistance, higher thermal coefficient of resistance (TCR), and higher correlation coefficient (R2) of the responses. To gather the experimental data, the electrical response of the sensors (resistance variation) to increasing temperature was collected. As a result of these experiments, it was revealed that ink 1 (based upon carbon nanoparticles) allowed to obtain less resistive sensors and, even though ink 2 (based upon carbon nanotubes) presented higher sensitivity, ink 1 resulted in higher linearity of the sensors. Overall, the factor design had less influence on the results than the factor ink. Notwithstanding, design 1 could be used to obtain sensors with lower native resistance and higher linearity. To sum up, using ink 1 combined with design 1 (fewer hoops and thicker line width) allowed to obtain temperature sensors with average resistance at room temperature of 7.8 kΩ, a TCR of 1.13 %.ºC−1, and R2 of 0.99, which means the herein studied sensors present a very promising behavior when compared to the sensors in the preexisting literature. Thus, the proposed optimized sensors presented attractive characteristics as flexible printed temperature sensors.

Random memristor-based dynamic graph CNN for efficient point cloud learning at the edge

The broad integration of 3D sensors into devices like smartphones and AR/VR headsets has led to a surge in 3D data, with point clouds becoming a mainstream representation method. Efficient real-time learning of point cloud data on edge devices is crucial for applications such as autonomous vehicles and embodied AI. Traditional machine learning models on digital processors face limitations, with software challenges like high training complexity, and hardware challenges such as large time and energy overheads due to von Neumann bottleneck. To address this, we propose a software-hardware co-designed random memristor-based dynamic graph CNN (RDGCNN). Software-wise, we transform point cloud into graph, and propose random EdgeConv for efficient hierarchical and topological features extraction. Hardware-wise, leveraging memristor’s intrinsic stochasticity and in-memory computing capability, we achieve significant reductions in training complexity and energy consumption. RDGCNN demonstrates high accuracy and efficiency across various point cloud tasks, paving the way for future edge 3D vision.

Digitalisation opportunities for livestock welfare monitoring with a focus on heat stress

Rising global temperatures due to climate change pose an increased risk of heat stress (HS) in livestock, which requires accurate prediction methods to improve animal welfare and mitigate heat-related economic losses. In this literature review, we aimed to critically examine the potential of digitalisation in precision livestock farming from the perspective of assessing heat stress in cattle. Particular attention was paid to the possibility of using sensors to record changes in behaviour, physiological parameters, and feed and water consumption as predictors for detecting and preventing heat stress and its consequences in animals. The use of already proven sensors, such as those that record animal behaviour, to assess physiological parameters related to heat stress may be promising, using innovative data processing technologies, which requires additional investigation in future studies. Precision livestock farming (PLF) technologies that enable real-time monitoring of herds under specific conditions, such as disease or stress, play a key role in increasing the efficiency of herd management. Sensors, cameras, microphones, GPS, accelerometers and RFID tags are commonly used to collect data on animal behaviour and physiological parameters, helping to detect stress. Machine learning algorithms applied to sensor data can distinguish between different states of declining cow welfare, helping farmers make quick decisions. In summary, the integration of sensors, artificial intelligence and precision devices can help prevent heat stress in farm animals, improving animal welfare and productivity, while mitigating the negative effects of heat stress caused by climate change.

Revolutionizing Grain and Particle Size Measurement in Metals: The Role of Sensor-Assisted Metallographic Image Analysis.

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers. A final product's quality, durability, and lifespan are determined via the quality analysis of properties of a material with proper tolerances. A flaw in a single component can cause the breakdown of the entire finished product. To ensure the good quality of a material, micro-structural analysis is necessary, which includes the routine measurement of inclusions. The particle and grain sizes of particulate samples are the most crucial physical characteristics of metals. Their measurement is routinely conducted across various industries, and they are frequently considered essential parameters in the creation of many products. This paper discusses the role of sensors in enhancing the accuracy and efficiency of metallographic image analysis, as well as the challenges and limitations associated with this technology. The paper also highlights the potential applications of sensor-assisted metallographic image analysis in various industries, such as aerospace, automotive, and construction. The paper concludes by identifying future research directions for this emerging field, including the development of more sophisticated algorithms for grain and particle size estimation, the integration of multiple sensors for more accurate measurements, and the exploration of new sensing modalities for metallographic image analysis.

Organ chips with integrated multifunctional sensors enable continuous metabolic monitoring at controlled oxygen levels

Despite remarkable advances in Organ-on-a-chip (Organ Chip) microfluidic culture technology, recreating tissue-relevant physiological conditions, such as the region-specific oxygen concentrations, remains a formidable technical challenge, and analysis of tissue functions is commonly carried out using one analytical technique at a time. Here, we describe two-channel Organ Chip microfluidic devices fabricated from polydimethylsiloxane and gas impermeable polycarbonate materials that are integrated with multiple sensors, mounted on a printed circuit board and operated using a commercially available Organ Chip culture instrument. The novelty of this system is that it enables the recreation of physiologically relevant tissue-tissue interfaces and oxygen tension as well as non-invasive continuous measurement of transepithelial electrical resistance, oxygen concentration and pH, combined with simultaneous analysis of cellular metabolic activity (ATP/ADP ratio), cell morphology, and tissue phenotype. We demonstrate the reliable and reproducible functionality of this system in living human Gut and Liver Chip cultures. Changes in tissue barrier function and oxygen tension along with their functional and metabolic responses to chemical stimuli (e.g., calcium chelation, oligomycin) were continuously and noninvasively monitored on-chip for up to 23 days. A physiologically relevant microaerobic microenvironment that supports co-culture of human intestinal cells with living Lactococcus lactis bacteria also was demonstrated in the Gut Chip. The integration of multi-functional sensors into Organ Chips provides a robust and scalable platform for the simultaneous, continuous, and non-invasive monitoring of multiple physiological functions that can significantly enhance the comprehensive and reliable evaluation of engineered tissues in Organ Chip models in basic research, preclinical modeling, and drug development.

Skin-inspired multimodal tactile sensor aiming at smart space extravehicular multi-finger operations

Tactile sensing of skin shapes the interactions between hand and the surrounding world, owing to the remarkable natural sensory system. But for astronauts, tactile feedback cannot be obtained biologically due to the very thick protective gloves, which seriously hinders the flexibility of the extravehicular activity. In this work, inspired by human skin, we develop a biomimetic multi-layer tactile sensor (BMLTS) that can work like the fast adaptive and slow adaptive receptors of fingertip to achieve the multimodal tactile perception based on the fusion of thermosensitive, piezoelectric, triboelectric and piezoresistive materials. Based on the BMLTS, the tactile perception for temperature, surface roughness discrimination and smart object grasping is successfully achieved, which fully simulates the basic tactile perceptions in typical extravehicular operations. Furthermore, by the combination of BMLTS and deep learning, a biomimetic intelligent perception system (BIPS) that can make decisions to the tactile signal just like human brain is constructed, which can provide real-time tactile information for astronauts. Intelligent real-time object material identification, writing and recording are conducted using BIPS, reaching the recognition accuracy rate higher than 95%. This work lays the foundation for the systematization and integration of tactile sensors, and paves the way for the development of dexterous tactile perception for smart extravehicular activities of astronauts.

Fabrication of organogel strain sensor with self-healing property and extreme temperature tolerance by using phytic acid-liquid metal as initiator

Wearable strain sensors have aroused myriad of interest in the domain of electronic skin, soft robotics, and biomedicine. Organogels for wearable strain sensors require desirable stretchability, sensing stability, self-healing property, and a wide working temperature range. However, the synthesis of such organogels typically involves complex and time-consuming processes. Herein, we proposed a rapid copolymerization method for organogels composed of poly(acrylic acid-co-acrylic amide) (P(AA-co-AM)), phytic acid (PA), liquid metal (LM), and polyethylene oxide (PEO). The dispersion of GaInSn in PA solution (GaInSn-PA) was synthesized and employed to initiate the copolymerization of acrylic acid (AA) and acrylic amide (AM), which significantly reduced polymerization time to 2–3 min at room temperature. The release of Ga3+ ions from the LM facilitated cross-linking of P(AA-co-AM) chains through coordination bonds, while also imparted conductivity to the organogel. The synergistic effect between hydrogen bonds and metal coordination bonds extensive within the polymer chains imparted exceptional stretchability (1079 % strain) and self-healing efficiency (98 % at 60 °C) to the organogel. Consequently, the resulting strain sensor demonstrated the ability to accurately monitor both subtle and large human motion, exhibiting remarkable repeatability and durability across a wide temperature range (-20 °C∼40 °C). In addition, the integration of strain sensors on limbs for robotic control through limb movements underscored its practical viability. This optimized fabrication approach and structural design strategy set a new precedent for the development of organogel strain sensors in various applications.

An in-situ hybrid laser-induced integrated sensor system with antioxidative copper

Integration of sensors with engineering thermoplastics allows to track their health and surrounding stimuli. As one of vital backbones to construct sensor systems, copper (Cu) is highly conductive and cost-effective, yet tends to easily oxidize during and after processing. Herein, an in-situ integrated sensor system on engineering thermoplastics via hybrid laser direct writing is proposed, which primarily consists of laser-passivated functional Cu interconnects and laser-induced carbon-based sensors. Through a one-step photothermal treatment, the resulting functional Cu interconnects after reductive sintering and passivation are capable of resisting long-term oxidation failure at high temperatures (up to 170 °C) without additional encapsulations. Interfacing with signal processing units, such an all-in-one system is applied for long-term and real-time temperature monitoring. This integrated sensor system with facile laser manufacturing strategies holds potentials for health monitoring and fault diagnosis of advanced equipment such as aircrafts, automobiles, high-speed trains, and medical devices.

A Review On Revolutionizing Waste Water Collection and Recycling Processes with IoT

This paper provides an extensive review of IoT-driven solutions addressing critical challenges in sectors like wastewater treatment, agriculture, waste management, e-waste recycling, and water quality monitoring. Innovative models leverage IoT technology for real-time monitoring, control, and optimization. Examples include an industrial IoT cloud-based model for wastewater treatment, sensor integration in aquaculture systems for improved resource management, and intelligent waste collection systems in smart cities. The review identifies gaps and opportunities for advancements, highlighting the importance of real-time data collection, cloud-based analytics, and machine learning for informed decision- making in environmental and resource management

Prototype smart integrated fire detection based on deep learning YOLO v8 and IoT (internet of things) to improve early fire detection

The high incidence of fires in Indonesia in 2018-2023 is 5,336 fire incidents have caused many deaths and enormous material losses. This system is designed to identify early signs of fire through object detection and sensor technology, which is integrated with the Blynk IoT platform for real-time sensor monitoring and Telegram for instant notifications to users. The waterfall prototype method was designed through observation, system design, program code creation, tool testing, and tool implementation. This research uses Deep Learning YOLOv8 technology and IoT using ESP 32 as a microcontroller. Based on the training datasets, it produces precision=0.95872; recall=0.91; mAP50=0.97; mAP50-95 =0.66. The system uses the integration of a multisensor KY-026 flame sensor, DHT 22 temperature and humidity sensor, and MQ-2 sensors can detect CO, LPG, and smoke gas. All these multisensors can be monitored on Blynk IoT and Telegrambot in real time.

THERMAL APPLICATION USING PYRAMID SOLAR STILL TO ENHANCE THE PRODUCING OF CLEAN WATER

Solar stills have become a viable and environmentally responsible method of water purification in the context of tackling the urgent worldwide issue of water scarcity. Solar stills are a practical solution for areas with poor access to clean drinking water since they use the sun's limitless energy to filter contaminants from water. Standard solar still models have been used for this purpose, however their effectiveness and output quality frequently fell short of expectations. Consequently, there is a compelling need to investigate novel designs and technologies in order to improve the functionality of solar stills and the quality of the filtered water. This study examines solar stills as a means of removing contaminants from water within the context of water purification utilizing solar energy. The study specifically highlights a pyramid solar still's inventive design and evaluation because it deviates from regularly used standard models. This pyramid solar still has been designed in a special way, and a thermoelectric system has also been added to improve performance. Furthermore, real-time monitoring of critical water parameters is made possible by the integration of sensors like the DHT11. A pH sensor, a water level sensor, and an Arduino Uno microcontroller are used to provide this real-time monitoring, which provides ongoing information on the purifying process. A heating plate has been added to the pyramid solar still to improve the evaporation process and increase efficiency.

Cephalopods' Skin-Inspired Design of Nanoscale Electronic Transport Layers for Adaptive Electrochromic Tuning.

Cephalopods can change their skin color by using high-speed electron transduction among receptors, neural networks, and pigmentary effectors. However, it remains challenging to realize a neuroelectrical transmission system like that found in cephalopods, where electrons/ions transmit on nanoscale, which is crucial for fast adaptive electrochromic tuning. Inspired by that, hereby an ideal, rapidly responsive, and multicolor electrochromic biomimetic skin is introduced. Specifically, the biomimetic skin comprises W18O49 nanowires (NWs)that are either colorless or blue, Au nanoparticles@polyaniline (Au NPs@PANI) ranging from green to pink, and a flexible conductive substrate. As the applied voltage changes from 0.4 V to -0.7 V and back to 0V, the color of the biomimetic skin transforms from green to blue and ultimately to pink. This color change is attributed to the electrically induced redox reaction of Au NPs@PANI and W18O49 NWs, triggered by the transfer of electrons and ions. Furthermore, the high versatility and adaptability of electrical stimulus enable the creation of a highly interactive electrochromic biomimetic skin system through the integration of sensitive acoustic sensors, providing a perfect environment-responsive platform. This work provides a biomimetic multicolor electrochromic skin that depends on electron/ion transfer on nanoscale, expands potential uses for camouflage skin.

(Invited) 3D Printing for Tissue Scaffolds with Tunable Mechanical Durability and Sensing Capabilities

Pelvic organ prolapse (POP) is a prevalent condition among senior women, characterized by the descent or herniation of pelvic organs into the vaginal canal. It is a widespread issue, affecting a significant portion of the elderly female population, and yet, effective medical solutions for this condition remain elusive. This can be attributed to the complex nature of POP, where multiple factors, including tissue weakening and structural changes, contribute to its development. Despite the significant impact on women's quality of life, the absence of comprehensive treatment options underscores the urgency for innovative approaches to address this pressing health concern. In this context, 3D printing emerges as a valuable rapid prototyping tool, offering unique capabilities for designing and fabricating smart sensors and actuators. Its precision and versatility enable the creation of intricate devices that can be customized to specific medical applications. Moreover, 3D printing allows for the integration of sensors directly into biomedical devices, facilitating seamless monitoring and feedback mechanisms. Leveraging this technology, the research endeavors to harness the potential of 3D printing to develop advanced thermoelectric devices that can be seamlessly embedded within POP tissue scaffolds. These innovative devices will serve a dual purpose—detecting the health status of the tissue scaffolds and assessing their mechanical durability. In the realm of embedded medical devices, self-powered sensors hold particular significance due to their wireless powering capabilities and in-situ monitoring capabilities of human health. These sensors can operate without the need for external power sources or frequent battery replacements, making them ideal for long-term implantation within the human body. By tapping into the body's own energy sources or ambient environmental factors, self-powered sensors ensure continuous data collection and transmission, enhancing the efficiency and reliability of healthcare monitoring systems. Consequently, this talk aims to demonstrate the feasibility of 3D-printed thermoelectric devices as self-powered sensors, integrated into POP tissue scaffolds. These innovative devices will play a pivotal role in monitoring the tissue scaffold's condition, ensuring its mechanical integrity, and ultimately contributing to improved healthcare outcomes for women affected by POP.

3D printing of customized Li-S microbatteries

Microbatteries serve as essential energy supply components in microelectronic, wearable and implantable devices. Despite recent efforts, developing lithium metal microbatteries with high energy densities and mechanical flexibility in structural design is still challenging, because of the poor interfacial stability and poor processability of lithium foil. Herein, we developed lithium-sulfur (Li-S) microbatteries with customized configurations using 3D printing techniques, which consist of a printable Li anode hosted within a graphene aerogel framework and a printable S cathode based on a carbon nanocages framework, featuring abundant porous structures and large specific surface areas. Consequently, the 3D-printed Li anode achieves over 900 hours of cycling with low overpotentials at an ultrahigh current density of 10 mA cm−2, and the 3D-printed S cathode delivers an ultrahigh capacity of 21.9 mAh cm−2 at the high thickness of 2.3 mm. Furthermore, the 3D-printed Li-S cell delivers an ultrahigh areal energy density of 48.4 mWh cm−2 at a high power density of 3.5 mW cm−2. The application potential of the 3D-printed customized Li-S microbatteries with arbitrary form factors is demonstrated by LED lighting and powering wearable sensor integration system, paving the way for their applications in miniaturized devices.

A Study on the Factors Controlling the Kinematics of a Reactivated and Slow-Moving Landslide in the Eastern Liguria Region (NW Italy) through the Integration of Automatic Geotechnical Sensors

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour.

Exploring polymeric nanotextile devices: progress and outlook

PurposeNanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.Design/methodology/approachReview of a series of state of the art research papers on the subject matter.FindingsThis paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.Research limitations/implicationsAs a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).Practical implicationsPPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.Social implicationsWhen used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.Originality/valueNT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.