Carbon nanofiber-reinforced Pt thin film-based airflow sensor for respiratory monitoring

Moshizi, S.A.; Abedi, Abolfazl; Pastras, Christopher J.; Peng, Shuhua; Wu, Shuying; Sanaeepur, Majid; Asadnia, Mohsen

doi:10.1016/j.sna.2022.113969

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…The sensing capabilities of Pt/CNFs sensors exhibited a remarkable enhancement in comparison to the pure Pt coating. The sensing range achieved an ultrahigh sensitivity of 431 and a linearity of 99% [16]. These values surpass those reported for strain sensors based on cracks.…”

Section: Methodsmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

“…[12]. Taking inspiration from sensory hair cells is a well-established technique to fabricate flow sensors for a broad range of applications such as microfluidic devices [13], leakage detection systems [14], navigation systems [15] and respiratory monitoring [16] to measure flow velocity and frequency. It is no surprise the elegance and simplicity of the biological transduction apparatus (the hair bundle/cell system) has inspired engineers looking to fabricate sensitive signal detection systems over a range of research fields, from underwater sensing [17], force de- It is no surprise the elegance and simplicity of the biological transduction apparatus (the hair bundle/cell system) has inspired engineers looking to fabricate sensitive signal detection systems over a range of research fields, from underwater sensing [17], force detection [18], to respiratory flow sensing [19].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Hair Cell Sensor Based on Nanofiber-Reinforced Thin Metal Films

A. Moshizi,

Pastras,

Peng

et al. 2024

Biomimetics

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Engineering artificial mechanosensory hair cells offers a promising avenue for developing diverse biosensors spanning applications from biomedicine to underwater sensing. Unfortunately, current artificial sensory hair cells do not have the ability to simultaneously achieve ultrahigh sensitivity with low-frequency threshold detection (e.g., 0.1 Hz). This work aimed to solve this gap by developing an artificial sensory hair cell inspired by the vestibular sensory apparatus, which has such functional capabilities. For device characterization and response testing, the sensory unit was inserted in a 3D printed lateral semicircular canal (LSCC) mimicking the environment of the labyrinth. The sensor was fabricated based on platinum (Pt) thin film which was reinforced by carbon nanofibers (CNFs). A Pi-shaped hair cell sensor was created as the sensing element which was tested under various conditions of simulated head motion. Results reveal the hair cell sensor displayed markedly higher sensitivity compared to other reported artificial hair cell sensors (e.g., 21.47 mV Hz−1 at 60°) and low frequency detection capability, 0.1 Hz < f < 1.5 Hz. Moreover, like the LSCC hair cells in biology, the fabricated sensor was most sensitive in a given plane of rotational motion, demonstrating features of directional sensitivity.

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Section: Methodsmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Hair Cell Sensor Based on Nanofiber-Reinforced Thin Metal Films

A. Moshizi,

Pastras,

Peng

et al. 2024

Biomimetics

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“…In their method, heart rate is monitored, compared to a predetermined threshold, and transmitted using radio frequency (RF) with a range of 5–6 m. The device may be attached to the swimmer’s head or hand for movement assessment. Kulkarni et al [ 64 ] devised a system for detecting drowning utilising non-invasive oxygen saturation, respiration, and water sensors [ 65 , 66 , 67 , 68 , 69 ]. These measure blood oxygen saturation, breathing, and submersion.…”

Section: Sensor-based Techniques For Drowning Detectionmentioning

confidence: 99%

Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection

Jalalifar,

Belford,

Erfani

et al. 2024

Sensors

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Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer’s condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.

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“…Carbon nanotubes (CNTs) have attracted much attention in the field of airflow sensing due to their high flexibility, high electrical conductivity, high toughness, lightweight, and one-dimensional nanostructures. ,− Inspired by the sensing behavior of several animals, many researchers designed and synthesized biomimetic CNT-containing composites to realize the airflow sensing, − of which the working mechanisms are generally based on the changes of connectivity in conductive networks under external stimuli . However, as the key functional parts in these sensors, CNTs are usually anchored in bulk matrices with high rigidity, which imposes many constraints on the deformation and displacement of CNTs, thus seriously degrading the overall sensing performance. , In addition, the CNTs used in previous works usually possess defective and multiwalled structures, − which may cause the deterioration of their mechanical and electrical properties, thus lowering the overall performance of airflow sensors.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Airflow Sensors Based on Suspended Ultralong Carbon Nanotube Crossed Networks

Jiang,

Leu,

Gong

et al. 2024

ACS Appl. Mater. Interfaces

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Airflow sensors are in huge demand in many fields such as the aerospace industry, weather forecasting, environmental monitoring, chemical and biological engineering, health monitoring, wearable smart devices, etc. However, traditional airflow sensors can hardly meet the requirements of these applications in the aspects of sensitivity, response speed, detection threshold, detection range, and power consumption. Herein, this work reports high-performance airflow sensors based on suspended ultralong carbon nanotube (CNT) crossed networks (SCNT-CNs). The unique topologies of SCNT-CNs with abundant X junctions can fully exhibit the extraordinary intrinsic properties of ultralong CNTs and significantly improve the sensing performance and robustness of SCNT-CNs-based airflow sensors, which simultaneously achieved high sensitivity, fast response speed, low detection threshold, and wide detection range. Moreover, the capability for encapsulation also guaranteed the practicality of SCNT-CNs, enabling their applications in respiratory monitoring, flow rate display and transient response analysis. Simulations were used to unveil the sensing mechanisms of SCNT-CNs, showing that the piezoresistive responses were mainly attributed to the variation of junction resistances. This work shows that SCNT-CNs have many superiorities in the fabrication of advanced airflow sensors as well as other related applications.

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Carbon nanofiber-reinforced Pt thin film-based airflow sensor for respiratory monitoring

Cited by 6 publications

References 47 publications

Artificial Hair Cell Sensor Based on Nanofiber-Reinforced Thin Metal Films

Artificial Hair Cell Sensor Based on Nanofiber-Reinforced Thin Metal Films

Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection

High-Performance Airflow Sensors Based on Suspended Ultralong Carbon Nanotube Crossed Networks

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