Sarma, Shrutidhara

This work presents a comprehensive exploration of printed strain sensor array fabrication encompassing selection of substrate material, sensing layer deposition, design and fabrication of the sensor array, and the electronic component used to obtain sensor output. The objective of this study is to design a strain sensor array that exhibits highest sensitivity against bending conditions without degradation. Three distinct designs of resistance element arrays were tested rigorously to identify the most suitable sensor for application involving repetitive bending conditions such as respiration monitoring.

Metallic nanoparticles and carbon nanotubes are two of the most promising nanomaterials, due to their distinctive properties occurring from spatial confinement of electron-hole pairs. The unique combination of metallic nanoparticles and carbon nanotubes (CNTs) in a polymer matrix offers unparalleled advantages, making them highly desirable in various fields. Advanced methods and techniques for synthesizing and characterizing hybrid metal-CNT-polymer nanocomposites have undergone significant progress in recent years, paving their integration into various fields, including aerospace, electronics, energy, water treatment and environmental remediation. These advances have allowed better understanding of nanocomposite properties and imparted ability to tune specific properties through size, shape, and distribution control of the nanofillers within the matrix material or by altering filler properties through functionalization. This study aims to critically judge the emerging tools, techniques and methods used in polymer nanocomposites with specific focus on metal-CNT based hybrid polymer nanocomposites, and suggest new avenues for research in the field. Furthermore, by examining the mechanisms affecting the performance of these composites, we can understand how the inclusion of fillers alters the microstructure and overall behavior of the material. Ultimately, this knowledge could lay the foundation for the development of novel nanocomposites with tailored properties and enhanced performance in a plethora of applications.

In this work, a strain sensor was fabricated using conductive electrospun polyvinylidene fluoride (PVDF) nanofiber film coated with carbon nanotubes (CNTs) on a polyimide (PI) substrate. An investigation of the sensor’s properties during stretching and relaxation under varying experimental conditions was undertaken. The morphologies of the sensing films were examined to gain an understanding of their structure. The experimental design was complemented by simulation to optimize the shape and size of the substrate to ensure uniform and consistent straining under loading conditions. The insight derived from the computational analysis played an instrumental role in selecting the appropriate substrate design and determining the ideal location for placing the PVDF/CNTs sensing film. The difference in the deformation value of the substrate with the selected design during the computational and experimental analysis was 17.5%. An integrated approach, combining both experimental and computational methodologies, was used to significantly enhance the study’s value and facilitate the development of an efficient and effective strain sensor. The sensor displayed a gauge factor (GF) of 1.83 and 1.19 under the incremental loads of 0.98 and 1.96 N, respectively. In conclusion, this study contributes to an improved understanding of the strain sensor’s characteristics and potential applications in structural health monitoring technologies.

Touch is a fundamental mode of human-machine interaction and ability to monitor tactile pressure, recognize gestures and location of touch are crucial for touch-based technologies. However, achieving reliable touch sensing on curved surfaces remains challenging as flexing often disrupts the stability of sensor outputs and diminishes sensitivity, especially in dynamic environments. This study presents the development of a flexible multi-element touch sensing patch that can monitor its bending state as well as detect pressure with a sensitivity of 0.827 kPa−1. The patch is fabricated using resistive strain sensors, screen printed onto a PET sheet with a foam backing. Evaluation electronics were integrated to ensure stable, noise-free signal acquisition, and output was processed with machine learning (ML) algorithms to classify gestures such as single and double finger taps, swipes, and touch locations, with 93 % accuracy, on both flat and curved surfaces. Based on the identified gesture, the system enables users to type text or control external devices with minimal physical effort. Its scalable fabrication, high sensitivity, mechanical resilience and seamless ML integration establishes it as a powerful and efficient tool for assistive technologies, designed to support individuals with limited speech and mobility, such as those with quadriplegia or paralysis. © 2025

This study presents the development and characterization of a novel textile-based wristband sensor for continuous temperature monitoring. The sensor, woven with a blend of polyester-stainless steel and polyester-cotton yarns, exhibited high sensitivity (0.0315/°C) and a rapid response time (∼35 sec). Stainless steel was selected for its excellent electrical conductivity and biocompatibility, crucial for accurate and safe skin contact applications, while polyester and cotton contribute its lightweight, breathable, and durable properties, ensuring prolonged comfort and wearability. Through systematic testing and optimization, the sensor’s sensitivity was fine-tuned by adjusting the number and length of conductive yarns. The study also introduced a theoretical resistance equivalent model, comparing its theoretical sensitivity with experimental findings. The sensor, lightweight and cost-effective, proved comfortable during 8–10 h of continuous wear. This study addresses challenges faced by existing textile temperature sensors and offers a reliable alternative with high linearity, repeatability, and suitability for individuals with sensitive skin.

Effective transmission of stress between fillers and the host matrix is instrumental in enhancing the properties of nanocomposites and optimizing filler reinforcement. This study delves into understanding stress transfer in materials, exploring the impact of synthesis methods and matrix materials through a comprehensive examination of a bilayer graphene (BLG)-polydimethylsiloxane (PDMS) nanocomposite. BLG was synthesized via chemical exfoliation and subjected to various strain conditions within a PDMS matrix. A precisely controlled strain, ranging from 0% to 0.19%, was applied using a meticulous 4-point bending method, monitored with a standard strain gauge. The study unveiled significant shifts in the Raman spectra's 2D band, indicating a direct linear relationship with applied strain. Utilizing contour mapping techniques on well-defined Raman spectra and stress-induced band shifts, we mapped strain distribution within the BLG layer. The results revealed a gradual increase in strain from the lower to the upper end of the BLG structure, resembling strain behaviors observed in differently shaped fibers. These compelling findings advance our understanding of strain dynamics within BLG-PDMS nanocomposites, highlighting the crucial role of strain mapping methodologies in scrutinizing the material's structural response to deformation. Highlights: The study reports successful synthesis of high-quality bilayer graphene (BLG) using a chemical exfoliation method, as evidenced by the sharp 2D and G Raman peaks. Significant shifts in 2D band exhibited linear correlation between applied strain; observed within precise strain levels of 0% to 0.19%. Contour maps revealed a gradual increase in strain from the lower to the upper end of the BLG structure, akin to strain behaviors observed in differently shaped fibers.

This study introduces a flexible skin thermometer for personalized body temperature monitoring, enabling continuous measurement and offering advantages over conventional mouth-based techniques that are limited to point-in-time readings. Such personalization is critical for medical applications, particularly in environments with varying climatic conditions. Our in-house developed flexible thermometer FLEXTEM provides continuous real-time temperature readings, can be used on different body sites (such as forehead and forearm), and adapts to diverse physiological and environmental factors. In a novel study involving ∼470 individuals from Rajasthan, a region characterized by extreme seasonal variations, FLEXTEM was tested alongside standard digital and infrared thermometers, to examine the influence of environmental (such as ambient temperature) and physiological (such as gender) factors on body temperature. Based on the findings, normal body temperature ranges were established for different demographic groups based on gender and work conditions. However, defining ranges based on health status was constrained by the smaller sample size. Our research highlights the potential of flexible skin-based thermometers for tailored health assessments and demonstrates how environmental factors can lead to variations in body temperature ranges. © 2025 Elsevier B.V., All rights reserved.

Bead formation is a typical ramification of electrospun fibers during electrospinning. Presence or absence of beads controls the fiber properties, which dictates its usefulness in diverse applications. However, bead formation is a complex non-linear process influenced by solution properties as well as electrospinning process parameters, mostly explored through trial-and-error experiments. Thus, being able to predict bead formation and identify its causal properties could have tremendous techno-economic value by reducing cost of experimentation. This is challenging as these structure-property relationships between experimental features and bead formation are inherently complex and modelling them requires large datasets from diverse experiments with multiple solvents, which are not commonly available. Here, we developed our own Electrospun Fiber Experimental Attributes Dataset (FEAD) dataset, a curated meta-database of experimental data available in literature, supplemented with our own experiments. Combining it with multiple machine learning models, we showed that while an increase in polymer concentration and applied voltage leads to fewer beads, a higher Flory-Huggins parameter supports increased bead formation. Further, we adopted a game theory-based model agnostic interpretation technique called SHAP (SHapley Additive exPlanations) to identify features contributing towards the occurrence of beads and their relative significance. This novel framework successfully predicted bead formation across various PVDF-polymer solvent systems and demonstrates how community meta datasets, cutting-edge machine learning techniques, and model interpretability methods could be seamlessly integrated to reduce the number of experiments required for developing high quality PVDF fibers. © 2024 The Author(s)