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Flexible piezocapacitive sensors utilizing nanomaterial-polymer composite based nanofibrous membranes offer an attractive alternative to more traditional piezoelectric and piezoresistive wearable sensors owing to their ultralow powered nature, fast response, low hysteresis, and insensitivity to temperature change. In this work we propose a facile method of fabricating electrospun graphene dispersed PVAc nanofibrous membrane based piezocapacitive sensors for applications in IoT enabled wearables and human physiological function monitoring. A series of electrical and material characterization experiments were conducted on both the pristine and graphene dispersed PVAc nanofibers to understand the effect of graphene addition on nanofiber morphology, dielectric response, and pressure sensing performance. Dynamic uniaxial pressure sensing performance evaluation tests were conducted on the pristine and graphene loaded PVAc nanofibrous membrane-based sensors for understanding the effect of two-dimensional (2D) nanofiller addition on pressure sensing performance. Almost twofold increase in dielectric constant and pressure sensing performance was observed for graphene loaded nanofiber sensors and subsequently, micro dipole formation model was invoked to explain the nanofiller induced dielectric constant enhancement. The robustness and reliability of the sensor has been underscored by conducting accelerated lifetime assessment experiments entailing at least 3000 cycles of periodic tactile force loading. A series of tests involving human physiological parameters monitoring were conducted to underscore the applicability of the proposed sensor for IoT enabled personalized health care, soft robotics, and next generation prosthetic devices. Finally, easy degradability of the sensing elements is demonstrated to emphasize their suitability for transient electronics applications.

During the past few decades, a significant amount of research effort has been dedicated toward developing skin-inspired sensors for real-time human motion monitoring and next-generation robotic devices. Although several flexible and wearable sensors have been developed in the past, the need of the hour is developing accurate, reliable, sophisticated, facile yet inexpensive flexible sensors coupled with neuromorphic systems or spiking neural networks to encode tactile information without the need for complex digital architectures, thus achieving true skin-like sensing with limited resources. In this work, we propose an approach entailing carbon nanofiber-polydimethylsiloxane composite-based piezoresistive sensors, coupled with spiking neural networks, to mimic skin-like sensing. The strain and pressure sensors have been combined with appropriately designed neural networks to encode analog voltages to spikes to recreate bioinspired tactile sensing and proprioception. To further validate the proprioceptive capability of the system, a gesture tracking smart glove, combined with a spiking neural network, was demonstrated. Wearable and flexible sensors with accompanying neural networks such as the ones proposed in this work will pave the way for a future generation of skin-mimetic sensors for advanced prosthetic devices, apparel integrable smart sensors for human motion monitoring, and human-machine interfaces.

Purpose This paper studies the efficiency of Indian coal-fired thermal power plants (CTPPs) in by-production of electricity and particulates also known as Suspended Particulate Matter (SPM). Design/methodology/approach A non-radial directional distance function is optimized using data envelopment analysis to enumerate the overall inefficiency of CTPPs and its components in recent times. Further, second-stage regression analysis is conducted to identify factors that affect the inefficiency of plants. Findings The low inefficiency score for electricity generation suggests that most CTPPs operate close to the good output frontier. A high degree of emissions inefficiency is a challenge for Indian CTPPs. Ever-rising coal use inefficiency is a hindrance to control SPM emissions. The second stage regression analysis concludes that factors like ownership and capacity utilization play vital roles in determining a plant’s inefficiency level. Privately owned CTPPs have performed better in terms of technical inefficiency and emission inefficiency than plants owned by Central and State governments. Originality/value To the best of the authors’ knowledge, this study is one of the few published works that benchmark the productive and environmental performance of Indian CTPPs.

Recently, 3D porous graphene–polymer composite-based piezoresistive sensors have gained significant traction in the field of flexible electronics owing to their ultralightweight nature, high compressability, robustness, and excellent electromechanical properties. In this work, we present an improved facile recipe for developing repeatable, reliable, and linear 3D graphene–polydimethylsiloxane (PDMS) spongy sensors for internet of things (IoT)-enabled wearable systems and smart consumer products. Fundamental morphological characterization and sensing performance assessment of the piezoresistive 3D graphene–polymer sensor were conducted to establish its suitability for the development of squeezable, flexible, and skin-mountable human motion sensors. The density and porosity of the sponges were determined to be 250 mg cm−3 and 74% respectively. Mechanical compressive loading tests conducted on the sensors revealed an average elastic modulus as low as ∼56.7 kPa. Dynamic compressive force-resistance change response tests conducted on four identical sensors revealed a linear piezoresistive response (in the compressive load range 0.42–2.18 N) with an average force sensitivity of 0.3470 ± 0.0794 N−1. In addition, an accelerated lifetime test comprising 1500 compressive loading cycles (at 3.90 N uniaxial compressive loading) was conducted to demonstrate the long-term reliability and stability of the sensor. To test the applicability of the sensors in smart wearables, four identical graphene–PDMS sponges were configured on the fingertip regions of a soft nitrile glove to develop a pressure sensing smart glove for real-time haptic pressure monitoring. Similarly, the sensors were also integrated into the Philips 9000 series electric shaver to realize smart shaving applications with the ability to monitor shaving motions. Furthermore, the readiness of our system for next-generation IoT-enabled applications was demonstrated by integrating the smart glove with an embedded system software utilizing the an open source microcontroller platform. The system was capable of identifying real-time qualitative pressure distribution across the fingertips while grasping daily life objects, thus establishing the suitability of such sensors for next-generation wearables for prosthetics, consumer devices, and personalized healthcare monitoring devices.

In this work, we report a class of fabric-like flexible and wearable capacitive sensors utilizing electrospun polyvinyl acetate (PVAc)-graphene nanofibers for human movement monitoring applications. The sensor reported in this work features flexible PVAc-graphene thin film sandwiched between two layers of conductive fabric and fully encapsulated in transparent adhesive films. The fabricated pressure sensor demonstrates two linear regimes with sensitivity figures of 0.0136 kPa-1 (pressure < 75 kPa) and 0.0063 kPa-1 (pressure > 75 kPa), respectively. The realized pressure sensor is flexible, apparel integrable, and the facile fabrication method will enable reliable and repeatable performance over roll-to-roll manufacturing suitable for mass production.

In this work, we report a class of wearable, stitchable, and sensitive carbon nanofiber (CNF)-polydimethylsiloxane (PDMS) composite-based piezoresistive sensors realized by carbonizing electrospun polyacrylonitrile (PAN) nanofibers and subsequently embedding in PDMS elastomeric thin films. Electro-mechanical tactile sensing characterization of the resulting piezoresistive strain sensors revealed a linear response with an average force sensitivity of ~1.82 kN−1 for normal forces up to 20 N. The real-time functionality of the CNF-PDMS composite sensors in wearable body sensor networks and advanced bionic skin applications was demonstrated through human motion and gesture monitoring experiments. A skin-inspired artificial soft sensor capable of demonstrating proprioceptive and tactile sensory perception utilizing CNF bundles has been shown. Furthermore, a 16-point pressure-sensitive flexible sensor array mimicking slow adapting low threshold mechanoreceptors of glabrous skin was demonstrated. Such devices in tandem with neuromorphic circuits can potentially recreate the sense of touch in robotic arms and restore somatosensory perception in amputees.

Self-powered wearable devices are gaining increasing attention to fulfil the sensing and energy harvesting needs for the next-generation wearable systems and body sensor networks. Here, we developed a wearable, flexible and lightweight nanofiber-based triboelectric nanogenearator (NF-TENG) for body motion energy harvesting. Polyvinylidene difluoride (PVDF) and nylon were electrospun into nanofiber films to form the two triboelectric materials. Under a load force of 10 N, the NF-TENG could generate an output voltage of 140 V, current of 0.5 µA and transferred charges of 50 nC. Preliminary body energy harvesting applications of the proposed NF-TENG were demonstrated, wherein the NF-TENG was able to generate an output voltage of 8 V and 10 V during typing and elbow bending, respectively.

The growing demand for flexible, ultrasensitive, squeezable, skin-mountable and wearable sensors tailored to the requirements of personalized health care monitoring has fueled the necessity to explore novel nanomaterial-polymer composite-based sensors. Herein, we report a sensitive, 3D squeezable graphene-polydimethylsiloxane (PDMS) foam-based piezoresistive sensor realized by infusing multi-layered graphene nanoparticles into a sugar scaffolded porous PDMS foam structure. Static and dynamic compressive strain testing of the resulting piezoresistive foams sensors revealed two linear response regions with an average gauge factor of 2.87 ~ 8.77 over a strain range of 0-50 %. Furthermore, the dynamic stimulus-response revealed the ability of the sensors to effectively track dynamic pressure up to a frequency of 70 Hz. In addition, the sensors displayed a high stability over 36000 cycles of cyclic compressive loading and 100 cycles of complete human gait motion. The 3D sensing foams were applied to experimentally demonstrate accurate human gait monitoring through both simulated gait models and real-time gait characterization experiments. The real-time gait experiments conducted demonstrate that the information of the pressure profile obtained at three locations in the shoe sole could not only differentiate between different kinds of human gait including walking and running, but also identify possible fall conditions. This work also demonstrates the capability of the sensors to differentiate between foot anatomies, such as a flat foot (low central arch) and a medium arch foot which is biomechanically more efficient. Furthermore, the sensors were able to sense various basic joint movement responses demonstrating their suitability for personalized healthcare applications.

This chapter employs a single referendum format to evaluate an individual’s willingness to pay for climate change–related research and development fund. Based on primary data, the chapter analyzes the awareness and knowledge on climate issues and the willingness to make contributions. This chapter used a logit model to estimate the parameters and found that gender, income group and the bid amount significantly influenced an individual’s choice to make contributions. It also provides a review of corresponding literature for both developed and developing countries and the methodology behind referendum format followed by empirical analysis.

This work reports a facile method of fabricatingultralight weight (density of 0.305 g/cm3) and squeezablemicroporous graphene-PDMS piezoresistive sensors for humangait monitoring applications. The sensor reported in this workdemonstrates piezoresistivity by utilizing the conductive domaindiscontinuity mechanism demonstrated by multilayer graphenenanoflakes populating the inner pore walls of microporousPDMS sponges. Quasi-static compressive strain characterizationexperiments conducted on the sensor revealed a linear responsewith a gauge factor of 8.77 for compressive strains up to 9.5%.Two identical graphene-PDMS sponge sensors embedded into apair of soft shoe-soles were used to demonstrate comprehensivereal-time gait monitoring, which includes pressure profiling ofthe heels of both the legs.

Suspended particulate matter (SPM) emissions from coal-based thermal power plants (CTPPs) cause respiratory illness. However, this has not been given its due importance in the efficiency assessment of CTPPs. This study contributes to the literature by incorporating suspended particulate matter in the benchmarking exercise for Indian CTPPs. In such a study, the theoretical assumptions regarding pollution generating technology or the choice of evaluation tool may impact the ranking of CTPPs. To draw robust inferences, we present a comparative study of two alternative microeconomic approaches (joint and by-production technologies) and two types of data envelopment analysis tools (graph–hyperbolic and directional distance function) applied on two representative samples of Indian CTPPs. Results indicate that Indian CTPPs are moderately inefficient. Choice of technological assumption or data envelopment analysis model does not impact the ranking of CTPPs. Ownership and plant load factor play vital roles in determining inefficiency, and impacts of these factors remain stable across models. JEL Classifications: C61, D22, Q40, Q50

This book explores the fabrication of soft material and biomimetic MEMS sensors, presents a review of MEMS/NEMS energy harvesters and self-powered sensors, and focuses on the recent efforts in developing flexible and wearable piezoelectric nanogenerators. It also includes a critical analysis of various energy harvesting principles, such as electromagnetic, piezoelectric, electrostatic, triboelectric, and magnetostrictive. Included are chapters that:Describe self/low-powered MEMS devices that are developed through biomimetic and bio-inspired approaches;Review the recent progress in kinetic MEMS/NEMS-enabled energy harvesters as self-powered sensors;Comprehensively review the ongoing research done in the field of nanofiber-based flexible and wearable energy harvesters;Explore the current trends in the field of soft materials research and future challenges.This multidisciplinary book is appropriate for students and professionals in the fields of material science, mechanical engineering, electrical engineering, and bioengineering.

This work reports the use of graphitized Polyacrylonitrile (PAN) nanofiber bundle for strain sensing applications. Strain sensors employing piezoresistive properties of carbon nanofiber is relatively new compared to conventional piezoelectric electrospun nanofiber based devices. The graphitization of PAN nanofiber bundles annealed at 500°C, 750°C, and 950°C are studied by means of spectroscopic analysis techniques including Raman and X-ray photoelectron spectroscopy (XPS). The nanofiber bundle annealed at 750°C exhibits disordered (D) and ordered (G) peaks similar to the nanofiber bundle annealed at 950°C. This is confirmed by XPS analysis which shows the asymmetric C1s peak at 284.8 eV. Raman analysis has also been performed on the as-spun, stabilized and annealed samples to show the change in D and G peaks. Finally, a strain sensor utilizing the nanofiber bundle annealed at 950°C is fabricated which is sensitive enough to detect overall bending of less than 3° without the use of any external amplifier circuit.

Neuromast sensors found in fishes detect flows around their body and are known to generate hydrodynamic awareness. This work reports the development of superficial neuromast (SN) and canal neuromast (CN) inspired MEMS flow sensors. The MEMS artificial neuromast sensors mimic the key features of the SN and the CN such as the shape and the mechanical properties of the cupula. In case of both SN and CN MEMS sensors, hydrogels with varying initiator concentrations were used to replicate the Young’s modulus of the biological cupula. The MEMS SN and CN sensors were experimentally characterized in steady-state and oscillatory flows respectively. Keywords: Superficial Neuromasts (SNs); Canal Neuromasts (CNs); MEMS; biomimetics; flow sensing

Evolving over millions of years, hair-like natural flow sensors called cilia, which are found in fish, crickets, spiders, and inner ear cochlea, have achieved high resolution and sensitivity in flow sensing. In the pursuit of achieving such exceptional flow sensing performance in artificial sensors, researchers in the past have attempted to mimic the material, morphological, and functional properties of biological cilia sensors, to develop MEMS-based artificial cilia flow sensors. However, the fabrication of bio-inspired artificial cilia sensors involves complex and cumbersome micromachining techniques that lay constraints on the choice of materials, and prolongs the time taken to research, design, and fabricate new and novel designs, subsequently increasing the time-to-market. In this work, we establish a novel process flow for fabricating inexpensive, yet highly sensitive, cilia-inspired flow sensors. The artificial cilia flow sensor presented here, features a cilia-inspired high-aspect-ratio titanium pillar on an electrospun carbon nanofiber (CNF) sensing membrane. Tip displacement response calibration experiments conducted on the artificial cilia flow sensor demonstrated a lower detection threshold of 50 &micro, m. Furthermore, flow calibration experiments conducted on the sensor revealed a steady-state airflow sensitivity of 6.16 mV/(m s&minus, 1) and an oscillatory flow sensitivity of 26 mV/(m s&minus, 1), with a lower detection threshold limit of 12.1 mm/s in the case of oscillatory flows. The flow sensing calibration experiments establish the feasibility of the proposed method for developing inexpensive, yet sensitive, flow sensors, which will be useful for applications involving precise flow monitoring in microfluidic devices, precise air/oxygen intake monitoring for hypoxic patients, and other biomedical devices tailored for intravenous drip/urine flow monitoring. In addition, this work also establishes the applicability of CNFs as novel sensing elements in MEMS devices and flexible sensors.

The use of Polyvinylidene Fluoride (PVDF) based piezoelectric nanofibers for sensing and actuation has been reported widely in the past. However, in most cases, PVDF piezoelectric nanofiber mats have been used for sensing applications. This work fundamentally characterizes a single electrospun PVDF nanofiber and demonstrates its application as a sensing element for nanoelectromechanical sensors (NEMS). PVDF nanofiber mats were spun by far field electrospinning (FFES) process and complete material characterization was conducted by means of scanning electron microscope (SEM) imaging, Raman Spectroscopy and FTIR spectroscopy. An optimized recipe was developed for spinning a single suspended nanofiber on a specially designed MEMS substrate which allows the nano-mechanical and electrical characterization of a single PVDF nanofiber. Electrical characterization is conducted using a single suspended nanofiber to determine the piezoelectric coefficient (d33) of the nanofiber to be -58.77 pm/V. Also the mechanical characterization conducted using a nanoindenter revealed a Young’s Modulus and hardness of 2.2 GPa and 0.1 GPa respectively. Finally, an application that utilizes the single PVDF nanofiber as a sensing element to form a NEMS flow sensor is demonstrated. The single nanofiber flow sensor is tested in presence of various oscillatory flow conditions. NRF (Natl Research Foundation, S’pore) Published version

This work reports the optimization of various experimental parameters for electrospinning Polyvinylidene Fluoride nanofiber mats in order to maximize their performance in sensing applications. Due to growing demand for ultra-low power wearable technology, piezoelectric nanofibers are getting special attention for energy harvesting applications. Polyvinylidene Fluoride based piezoelectric nanofibers are particularly promising because of their bio-compatibility and low cost. In this work, a detailed study is carried out to optimize the electrospinning parameters to control the morphology and maximizer the piezoelectric property of Polyvinylidene Fluoride nanofibers mats. Application of optimized nanofiber mat has been demonstrated in the end which will find applications in wearable technology and other passive sensing applications.