Compressive Sensor Research Articles

Facile Fabrication of Highly Sensitive Thermoplastic Polyurethane Sensors with Surface- and Interface-Impregnated 3D Conductive Networks.

This research aims to develop a practical, scalable, and highly conductive flexible 3D printed piezoresistive sensor with low filler content. Here, we introduced a fused deposition modeling 3D printing combined in situ spray-coating technique to develop a conductive sensor in a single shot. The graphene suspension is sprayed over each layer during the 3D printing of the sensor, which helps develop a conductive network on the surface and at the interface of the printed system. Graphene deposited on the overall surface is often affected by nanoparticle delamination and loses its function over time. To avoid this, the prepared samples are subjected to foaming. The foaming process created a low-mass-density sensor by forming a microcellular structure, and the surface-deposited graphene is embedded well on the TPU surface. The method followed in this work reveals a stable and connected conduction path with excellent electrical resistance and resistance against harsh conditions (exposure to organic solvents). Besides, the compression sensor withstood its sensitivity over a severe compressive strain of 80% and showed a GF of 1.82 and a sensitivity of 2.316 kPa-1. The conductive network path varied based on the infill pattern, affecting its electrical sensitivity. The wiggle pattern shows good resistance; under stretching, the pattern generated a higher current and showed a delayed conductive path disconnection than other patterns. Thus, the embedded graphene/TPU conductive sensors show good stability and promising sensitivity. Furthermore, the developed sensor is used to monitor human motion and actions.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Multifunctional Superelastic, Superhydrophilic, and Ultralight Nanocellulose‐Based Composite Carbon Aerogels for Compressive Supercapacitor and Strain Sensor

AbstractDeveloping superelastic and superhydrophilic carbon aerogels with intriguing mechanical properties is urgently desired for achieving promising performances in highly compressive supercapacitors and strain sensors. Herein, based on synergistic hydrogen bonding, electrostatic interaction, and π–π interaction within regularly arranged layered porous structures, conductive carbon aerogels with cellulose nanofibrils (CNF), carbon nanotubes (CNT) and reduced graphene oxide (RGO) are developed via bidirectional freezing and subsequent annealing. Benefiting from the porous architecture and high surface roughness, CNF/CNT/RGO carbon aerogels exhibit ultralow density (2.64 mg cm–3) and superhydrophilicity (water contact angle ≈0° at 106 ms). The honeycomb‐like ordered porous structure can efficiently transfer stress in the entire microstructure, thereby endowing carbon aerogels with high compressibility and extraordinary fatigue resistance (10,000 cycles at 50% strain). These aerogels can be assembled into compressive solid‐state symmetric supercapacitors showing excellent area capacitance (109.4 mF cm–2 at 0.4 mA cm–2) and superior long cycle compression performance (88% after 5000 cycles at compressive strain of 50%). Furthermore, the aerogels reveal good linear sensitivity (S = 5.61 kPa–1) and accurately capture human bio‐signals as strain sensors. It is expected that such CNF/CNT/RGO carbon aerogels will provide a novel multifunctional platform for wearable electronics, electronic skin, and human motion monitoring.

Tunable Large‐Scale Compressive Strain Sensor Based on Carbon Nanotube/Polydimethylsiloxane Foam Composites by Additive Manufacturing

The compressive strain sensor is an extensively used flexible electronic device because of its capability to convert mechanical deformation to an electrical signal. However, the difficulty in tuning the performance of the strain sensor limits its further applications. Herein, the approach of fabricating a carbon nanotube (CNT)/polydimethylsiloxane (PDMS) compressive strain sensor, which has both tunable mechanical and electrical performances, is presented. CNT plays the role of reinforcement due to its outstanding mechanical strength and electrical conductivity. PDMS is a widely used matrix because of its softness and nontoxicity. The material extrusion 3D printing method is used to fabricate the composites, due to its advantages of design flexibility and compatibility with liquid‐based materials. The foam microstructure formed by removing sodium chloride provides a large‐scale deformation of at least 50% compressive strain and excellent elasticity. The strain sensor works durably over 10 000 cycles, with a gauge factor (GF) of 17.4. The compressive strain sensor in detecting both large‐ and small‐scale human motions due to the tunability of CNT/PDMS composites is also tested.

Multi-sensor optimal deployment based efficient and synchronous data acquisition in large three-dimensional physical similarity simulation

PurposeThis paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.Design/methodology/approachFirst, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.FindingsThe sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.Originality/valueThe proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

VTrust: Remotely Executing Mobile Apps Transparently With Local Untrusted OS

Increasingly, many security and privacy sensitive applications (apps for short) are running in the mobile platforms. However, as the mobile operating systems are becoming increasingly sophisticated, they are vulnerable to various attacks. In addressing the need of running high assurance mobile apps in a secure environment even though the operating systems are untrusted, this paper presents VTRUST, a new mobile app trusted execution environment, which offloads the general execution and storage of a mobile app to a trusted remote server (e.g., a VM running in a cloud) and secures the I/O between the server and the mobile device with the aid of a trusted hypervisor on the mobile device. Specifically, VTRUST establishes an encrypted I/O channel between the local hypervisor and the remote server, such that any sensitive data flowing through the mobile OS, which is hosted by the hypervisor, is encrypted from the perspective of the local mobile OS. To enhance the performance of VTRUST, we have also designed multiple optimizations, such as output data compression and selective sensor data transmission. We have implemented VTRUST and our evaluation shows that it has limited impact on both user experience and the app performance.

Compressible piezoresistive pressure sensor based on Ag nanowires wrapped conductive carbonized melamine foam

Compressible piezoresistive pressure sensor based on Ag nanowires wrapped conductive carbonized melamine foam

Ionic polymer metal composite compression sensors with 3D-structured interfaces

In this paper, we report the development of tailored 3D-structured (engineered) polymer-metal interfaces to create enhanced ‘engineered ionic polymer metal composite’ (eIPMC) sensors towards soft, self-powered, high sensitivity strain sensor applications. We introduce a novel advanced additive manufacturing approach to tailor the morphology of the polymer-electrode interfaces via inkjet-printed polymer microscale features. We hypothesize that these features can promote inhomogeneous strain within the material upon the application of external pressure, responsible for improved compression sensing performance. We formalize a minimal physics-based chemoelectromechanical model to predict the linear sensor behavior of eIPMCs in both open-circuit and short-circuit sensing conditions. The model accounts for polymer-electrode interfacial topography to define the inhomogeneous mechanical response driving electrochemical transport in the eIPMC. Electrochemical experiments demonstrate improved electrochemical properties of the inkjet-printed eIPMCs as compared to the standard IPMC sensors fabricated from Nafion polymer sheets. Similarly, compression sensing results show a significant increase in sensing performance of inkjet-printed eIPMC. We also introduce two alternative methods of eIPMC fabrication for sub-millimeter features, namely filament-based fused-deposition manufacturing and stencil printing, and experimentally demonstrate their improved sensing performance. Our results demonstrate increasing voltage output associated to increasing applied mechanical pressure and enhanced performance of the proposed eIPMC sensors against traditional IPMC based compression sensors.

Soft Magnetic Composites as Quantitative 3D Compression Sensors for Wearable Technology

AbstractDetecting the magnitude of compression or shear events can provide valuable data to assess forces experienced by sensitive equipment or even the human body. Compression sensors have historically had limited use in fields such as wearable technology due to the large size of the sensors, lack of 3D sensing capacity, need for rigid components, or signal quality issues associated with the orientation or deformation of soft composites under compression. In particular, soft magnetic composites paired with a magnetometer to sense material deformation have been hindered by such issues. Here, experiments evaluated soft silicone‐magnetic powder composites of various shapes, sizes, magnetic filler concentrations, and orientations to a magnetometer to optimize sensor performance. Sensors with smaller diameter (≤2.5 mm) cylinder‐shaped magnetic elements exhibited a linear response to compression when paired with a 3 mm × 3 mm magnetometer. The soft magnetic elements composed of 80% magnetic powder by weight, the highest concentration tested, had the greatest sensitivity. Parameters from these studies are used to design a sensor integrated into a football helmet, which successfully recorded 3D force data from head impacts while being worn, demonstrating the potential for such sensors in wearable technology applications.

Object Identification With Smart Glove Assembled by Pressure Sensors

The study and replication of human sensory abilities, such as visual, auditory, and tactile perception, depends on the availability of integrating electronics into flexible materials. Much previous research on pressure sensing films has a lack of flexibilities in its products and cannot be well applied to wearable device. In this letter, we first prepare Graphene-VSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -Ecoflex sponge, and then fabricate highly compressible Graphene-VSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -Ecoflex pressure sensors using this sponge, and finally construct an innovative glove with this highly compressible pressure sensor array. The Graphene-VSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -Ecoflex pressure sensor sustains high strain up to 85%. The sensor retains a good linearity performance under the cycling test. Moreover, the glove can identify and manipulate subjects at an over 89% recognition rate after trained by neural networks. The realization of the smart object identification glove may provide a new strategy for applying flexible devices to intelligent sensing systems.

Development and Laboratory Evaluation of an Online Controlling Algorithm for Precision Tillage.

Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of the tillage at various depths. For this purpose, an algorithm was written to investigate the subsoiler location and soil compaction. A program was also developed to implement this algorithm using Kinco Builder Software to control the subsoiler depth, which was evaluated on the experimental platform. In this study, four compression sensors were used at a distance of 10 cm up to a depth of 40 cm on the blade mounted at the front of the tractor. The data of these sensors were used as the input and compared with the pressure baseline limit (2.07 MPa), and with the priority to select the greater depth, the depth of subsoiler was determined. At all three modes of sensor activation (single, collective, and combined), this system was able to operate the hydraulic system of the tractor and place the subsoiler at the desired depth through the use of the position sensors.

2D and 3D Structuring of Freestanding Metallic Wires Enabled by Room-Temperature Welding for Soft and Stretchable Electronics.

In this work, a facile and cost-effective approach to assemble metallic wires into two-dimensional (2D) and three-dimensional (3D) freestanding geometries by room-temperature welding is demonstrated. The low melting point of gallium (29.8 °C) enables the welding at room temperature without the aid of high-energy sources required for high-melting-point metals and alloys. The welding enables assembly of solid gallium wires into 2D and 3D geometries that could create freestanding architectures with multiple junctions along any inclined direction. These 2D and 3D freestanding metallic structures are freeze-cast in soft elastomers to obtain stretchable and soft devices: a 2D stretchable resistive and capacitive sensor patterned with parallel metal lines, a 2D stretchable capacitive sensor patterned with an interdigitated metal structure with capacitive changes on stretching in both x- and y-axes, and a 3D compressive sensor by assembly of liquid metal helices, which could sense foot pressure compression. We also developed a facile method to interconnect between soft circuits and external electronics, suppressing stress during mechanical deformation.

Flexible longitudinal and transversal displacement sensors based on a composite of CI Disperse Orange 25 and carbon nanotubes

AbstractThis paper presents the proposal and characterisation of a flexible, shockproof, vibration‐free, longitudinal and transversal compressive displacement sensor based on a composite of CI Disperse Orange 25 and carbon nanotubes created with an energy‐free, drop‐casting technique. The structural morphology of each layer is studied briefly through X‐ray diffraction. Electric investigations of the sensors in the form of impedance were performed as a function of longitudinal and transversal compressive displacement at three different frequencies. The compressive displacements were measured from 0 to 250 µm at three fixed frequencies (ie, 1, 10 and 200 kHz). The impedance has an inverse relationship with longitudinal compressive displacement but a direct relationship with transversal compressive displacement. The frequency to impedance relationship is inverse with the sensitivity of the impedance as a function of longitudinal displacement. Because of their small size, light weight and simple fabrication technique and design, as well as their low cost and maintenance, these types of devices can be utilised across a wide range of medical applications, such as in rehabilitation centres, for machine status assessment during physiotherapy, and in robotic body parts, as well as for educational purposes in universities.

Shock-proof and supple multiplex sensor based on Silicon composite fabricated through an energy-free technology

Smart multi-purpose electronic devices are the utmost demanding products for sports, medical, and personal supervision. In smart appliances, silicon (Si) is one of the most common and widely utilized materials with remarkable properties in electronics. Still, the challenge is to exploit it for intelligent and innovative multi-functional applications under changing climate conditions. In this work, we proposed an all-in-one shock-proof and vibration-free smart, flexible multiplex, i.e., optical, thermal, compression, and stress sensor fabricated through energy-free rubbing-in technology. The p-type Si composite with carbon nanotubes (CNTs) and rubber acts as the device's active layer. The CNTs help to boost the conductivity of Si and rubber aid to enhance the flexibility as well as made the device shock and vibration-proof. Eventually, such multi-purpose devices are extremely encouraging for future smart wearable gadgets of the next generation not only due to their small size but also due to their cost-effectiveness.

An ultra-compressible piezoresistive strain and pressure sensor based on RGO-CNT-Melamine foam composite for biomedical sensing

Many heitherto reported compressive piezoresistive foam sensors suffer from variable sensitivity over large compression range, limiting the applicability of the sensor for a narrow range of motion. This is because the area of contact between conductive foam struts is very small and varies linearly over only a small compression range. We resolved this problem by fabricating an ultracompressive foam sensor with large area conductive flakes using facile technique of soak-drying Melamine foam with the conducting aqueous RGO-CNT ink. Sensor exhibited a constant gauge factor (GF) 1.6 over a large and highly linear compression range (∼2% to 60 %) and detected both small and large body movements with superior detectability. Besides that all vital features of a tiny signal such as pulse waveform were delineated due to the excellent low pressure (<5 kPa) sensitivity 0.22 kPa−1 and the small relaxation time of 19 ms. The sensor showed a stable and repeatable performance over 11000 cycles. Its operation is also tested over a wide range of ambient temperature (-7 ℃ to 140 ℃) and humidity (25%–82%, RH) and even under water with no major deterioration in performance.

GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction outcomes. To apply this technique, a set of selected atoms is initialized by setting fuzzy threshold. Subsequently, inappropriate atoms are excluded, and an iterative procedure is initiated to update the set so as to approximate the signal sparsity in a stepwise fashion. In comparison with commonly used algorithms, the proposed algorithm achieved the lowest signal recovery and reconstruction error. Findings from this study indicate that our proposed hybrid paradigm may lead to positive advancement towards the development of intelligent robotic vision systems for industrial applications.

Compressible and Stretchable Magnetoelectric Sensors Based on Liquid Metals for Highly Sensitive, Self-Powered Respiratory Monitoring.

Healthcare monitoring, especially for respiration, has attracted tremendous attention from academics considering the great significance of health information feedback. The respiratory rate, as a critical health indicator, has been used to screen and evaluate potential illness risks in early medical diagnoses. A self-powered sensing system for medical monitoring is critical and imperative due to needless battery replacement and simple assembly. However, the development of a self-powered respiratory sensor with highly sensitive performance is still a daunting challenge. In this work, a compressible and stretchable magnetoelectric sensor (CSMS) with an arch-shaped air gap is reported, enabling self-powered respiratory monitoring driven by exhaled/inhaled breath. The CSMS contains two key functional materials: liquid metals and magnetic powders both with low Young's modulus, allowing for sensing compressibility and stretchability simultaneously. More importantly, such a magnetoelectric sensor exhibits mechanoelectrical converting capacity under an external force, which has been verified by Maxwell numerical simulation. Owing to the air-layer introduction, the magnetoelectric sensors achieve high sensitivity (up to 17.73 kPa-1), fast response, and long-term stability. The highly sensitive and self-powered magnetoelectric sensor can be further applied as a noninvasive, miniaturized, and portable respiratory monitoring system with the aim of warning for potential health risks. We anticipate that this technique will create an avenue for self-powered respiratory monitoring fields.

Passive UHF RFID-based Knitted Wearable Compression Sensor.

One of the major challenges faced by passive on-body wireless Internet of Things (IoT) sensors is the absorption of radiated power by tissues in the human body. We present a battery-less, wearable knitted Ultra High Frequency (UHF, 902-928 MHz) Radio Frequency Identification (RFID) compression sensor (Bellypatch) antenna and show its applicability as an on-body respiratory monitor. The antenna radiation efficiency is satisfactory in both free-space and on-body operations. We extract RF (Radio Frequency) sheet resistance values of three knitted silver-coated nylon fabric candidates at 913 MHz. The best type of fabric is selected based on the extracted RF sheet resistance. Simulated and measured performance of the antenna confirm suitability for on-body applications. The proposed Bellypatch antenna is used to measure the breathing activity of a programmable infant patient emulator mannequin (SimBaby) and a human subject. The antenna is highly sensitive to respiratory compression and relaxation. Fluctuations in the backscatter power level/Received Signal Strength Indicator (RSSI) in both cases range from 6 dB to 15 dB. The improved on-body read range of the proposed sensor antenna is 5.8 m, about 10 times higher than its predecessor wearable knitted strain sensing Bellyband antenna (0.6 m). The maximum simulated Specific Absorption Rate (SAR) on a human torso model is 0.25 W/kg, lower than the maximum allowable limit of 1.6 W/kg.

Pressure sensor based on porous polydimethylsiloxane with embedded gold nanoparticles

A compressible capacitive mechanical pressure sensor has been developed. Porous polydimethylsiloxane (p-PDMS) has been chosen as dielectric insulator because of its dielectric constant value. Gold nanoparticles have been embedded in p-PDMS to change the dielectric properties and to tune its elasticity. p-PDMS and its nanocomposite have been synthesized using the sugar leaching process. The p-PDMS physical characterization, with and without the gold nanoparticles, has been conducted to investigate its elastic response to compressive stresses as a function of both the polymer preparation thermal treatment and the gold nanoparticle concentration. A sensor operating in a low-pressure range between about 100 Pa and 10 kPa with a strain ranging between about 5% and 95% has been realized. Dielectric constant and electrical resistivity measurements have been performed using samples with a starting volume of the order of 1 cm3. The relationship between the dielectric constant, the electrical resistivity and the compressive stress/strain has been also deduced. The described sensor is flexible, biocompatible, water equivalent and can have applications in biomedicine (orthopedic, dentistry), engineering (stress–strain measurements, robotics), and microelectronics (microbalances, stress test on electronic devices).

Sensitive compressible electrochemical-driven asymmetric supercapacitor-type sensor based on cross-linked honeycomb naofoam and resilient graphene aerogel framework

With the rapid development of intelligent portable electronics, the multifunction requirements for electronic devices get higher and higher. Here, we introduce a novel electrochemical-driven asymmetric supercapacitor-type tactile sensor using cross-linked honeycomb CoV2O6/rGO nanofoam as positive electrode and 1D Ag nanowires integrated with resilient graphene aerogel framework as negative electrode. The novel sensor is integrated with electrochemical-storing and sensing functions, simultaneously. The positive honeycomb CoV2O6/rGO naofoam reveals a high specific capacitance of 1687.7 F g−1 at 1 A g−1, meanwhile negative porous graphene aerogel framework demonstrates high reversible compressibility under strain from 10% to 90%. This electrochemical-driven sensor can realize self-driven and behaves rapid sensitive responses current in the case of detecting strain. The elastic electrochemical-driven sensor not only realizes multi-functionalization of supercapacitor storage devices, but also offers a promising candidate for flexible sensor devices.