Search

In recent years, the convergence of smart electronic textile (e‐textile) and digital technology has emerged as a transformative shift in healthcare, offering innovative solutions for point‐of‐care diagnostics. However, the development of textile electronics with exceptional functionality and comfort still remains challenging. Here, all‐electrospun piezoelectric smart e‐textile empowered is reported by Internet of Things (IoT) and machine learning for advanced point‐of‐care diagnostics. The resulting e‐textile exhibits exceptional breathability (b ≈ 4.13 kg m−2 d−1), flexibility, water‐resistive properties (water contact angle ≈137°), and mechano‐sensitivity of 1.5 V N−1 due to its mechanical‐to‐electrical energy conversion abilities. It can efficiently monitor different critical biomedical healthcare signals, such as, arterial pulse and respiration rate. Importantly, the e‐textile sensor demonstrates remarkable attributes, generating an open circuit voltage of 10.5 V, a short circuit current of 7.7 µA, and power density of 4.2 µW cm−2. Moreover, the e‐textile provides real‐time, non‐invasive monitoring of human physiological movements through IoT. It is worth highlighting that the machine learning showcases an impressive 96% of accuracy in detecting respiratory signals, representing a significant accomplishment. Thus, this e‐textile has enormous potential in remote patient monitoring and early disease detection, aiming to reduce healthcare costs, enhance patient outcomes, and improve the overall quality of medical care. [ABSTRACT FROM AUTHOR]

Vascular grafts, widely utilized in managing cardiovascular diseases (CVD), are susceptible to postoperative complications. Recent strides in intelligent vascular grafts leveraging flexible bioelectronics enable hemodynamic and vascular health monitoring. However, their practical application faces challenges, notably biomechanical compatibility and endothelialization. Here, an all‐in‐one piezoelectric vascular graft (PVG) constructed by encapsulating a polyvinylidene fluoride (PVDF) nanofiber mat with patterned silver nanowire (AgNW) electrodes between two layers of polycaprolactone (PCL) nanofiber mats is presented. The meticulously optimized PVG, featuring PVDF and PCL nanofibers with average diameters of ≈950 and 250 nm, respectively, showcases remarkable endothelialization and mechanical performance akin to native blood vessels. The exquisite piezoelectric properties of PVDF nanofibers imbue PVG with outstanding mechanical sensing capabilities, boasting a sensitivity of 11 mV kPa−1 and stability exceeding 50 000 cycles, facilitating precise hemodynamic monitoring. Notably, artificial artery model tests demonstrate PVG's ability to diagnose vascular health status accurately based on detected hemodynamic data. Furthermore, the developed PVG exhibits nontoxicity, good hemocompatibility (hemolytic ratio < 1%), and histocompatibility. This pioneering technology, validated through ex vivo and in vivo experiments, represents a significant stride in precise vascular health management, unlocking diverse potential applications. [ABSTRACT FROM AUTHOR]

In this research work, a piezoelectric sensor and actuator model was designed. Carbon fibre-reinforced polylactic acid (CF/PLA) composites were fabricated using the fused deposition modelling (FDM) technique using many parameters to achieve this goal. For instance, the primary key parameters were 0.1 mm (layer height) and 25 mm/s (print head). Besides, the fabricated samples were subjected to a tensile study. Besides, MATLAB® Partial Differential Equation (PDE) tool was used to develop a finite element analysis (FEA) model for the piezoelectric actuator using the experimentally tested results. The MATLAB® was also used to examine the geometry and tip deflection. The experimental works were carried out to measure the sample's strain using piezoelectric sensors. This approach could be used to establish the accuracy of the model. The CF/PLA composites were fabricated using 20 wt.% of reinforcements. The experimental results and finite element simulation were good agreement on results. Results reported that the deflections of 6.5765 mm, 5.197 mm, and 7.6328 mm, respectively embedded with PZT 5J, PZT 5H, and PZT 5A. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U * 7 7 4 was applied, considering weight percentage (1–13 wt%), the distance between needle and disk collector (2.1–3.9 mm), and applied voltage (14.5–17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 µS/cm for pure PVDF to 187.5 µS/cm. The fiber diameter decreased from 21.4 µm in PVDF/1% graphene to 9.1 µm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. The results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis. [ABSTRACT FROM AUTHOR]

In machining processes, the self-excited vibration between the cutting tool and the workpiece is an important issue that can result in undesirable effects, for example, poor quality of the final surface, low dimensional accuracy, breakage of the tool, and excessive noise. To anticipate this problem, statistical features of the vibration signal, such as mean, variance, and standard deviation, have been extracted from online measurements. The synthesis criterion (SC), which is based on the standard deviation (STD) and the one-step autocorrelation function (OSAF), has been employed to detect quickly the threshold of chatter vibration. In this article, flexible workpieces with varying cutting depths have been selected to detect online chatter vibrations during milling operations. In order to collect an analog vibration signal, an STM32 card has been selected with a sampling rate up to 20 kSPS. A high-bandwidth, lightweight film piezoelectric sensor is attached to the workpiece. Unlike other sensors, such as load cells or acceleration sensors, the film piezoelectric sensors do not alter the dynamics of the system. In this research, cost-effective hardware is also developed to capture vibration signals reliably and efficiently. The experimental results confirm that the developed SC algorithm can efficiently predict the onset of chatter vibration as it was able to detect the onset of chatter vibrations within 0.18 sec. Thus, the SC algorithm can considerably enhance the milling operations of flexible parts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In machining processes, the self-excited vibration between the cutting tool and the workpiece is an important issue that can result in undesirable effects, for example, poor quality of the final surface, low dimensional accuracy, breakage of the tool, and excessive noise. To anticipate this problem, statistical features of the vibration signal, such as mean, variance, and standard deviation, have been extracted from online measurements. The synthesis criterion (SC), which is based on the standard deviation (STD) and the one-step autocorrelation function (OSAF), has been employed to detect quickly the threshold of chatter vibration. In this article, flexible workpieces with varying cutting depths have been selected to detect online chatter vibrations during milling operations. In order to collect an analog vibration signal, an STM32 card has been selected with a sampling rate up to 20 kSPS. A high-bandwidth, lightweight film piezoelectric sensor is attached to the workpiece. Unlike other sensors, such as load cells or acceleration sensors, the film piezoelectric sensors do not alter the dynamics of the system. In this research, cost-effective hardware is also developed to capture vibration signals reliably and efficiently. The experimental results confirm that the developed SC algorithm can efficiently predict the onset of chatter vibration as it was able to detect the onset of chatter vibrations within 0.18 sec. Thus, the SC algorithm can considerably enhance the milling operations of flexible parts.

As an emerging sensor technology, the flexible piezoelectric sensor has a very wide range of applications. Therefore, in order to achieve better output performance of flexible piezoelectric sensors, it is particularly important to choose piezoelectric materials with good piezoelectric performance. In this study, polyvinylidene difluoride (PVDF)/polyvinyl chloride (PVC)/graphene oxide (GO) composite fibers were fabricated by the electrospinning technique. FTIR and XRD studies showed that the β-phase content of the composite fibers reached 94.6% with the addition of PVC and GO. The mechanical properties of the composite films doped with PVC and GO were also significantly improved. The sensitivity of the sensor made of composite fibers doped with 0.2 % GO reached 16.2 V/N in the range of 1 N. Compared to other systems, the PVDF/PVC/GO system showed excellent piezoelectric output performance with output voltages of up to 6 V and 3 μA by the action of palm tapping. And after 2000 cycles, the sensor system showed good stability. The resulting flexible piezoelectric sensor can monitor actions such as impacts and bending and has great potential for motion monitoring. [ABSTRACT FROM AUTHOR]

The emergence of curved shield tunnels poses a significant construction challenge. If the quality of the segment assembly is not guaranteed, many segment cracks and damage will result from the stress concentration. Sensing the contact stresses between segmental joints is necessary to improve the quality of segments assembled for shield tunnel construction. Polyvinylidene difluoride (PVDF) piezoelectric material was chosen for the sensor because it can convert contact stresses into electrical signals, allowing the state of the segmental joints to be effectively sensed. It matches the working environment between the segmental joints of the shield tunnel, where flexible structures such as rubber gaskets and force transfer pads are present. This study proposes a piezoelectric sensing method for segmental joints in shield tunnels and conducts laboratory tests, numerical analyses, and field tests to validate the feasibility of the method. The results indicate that the PVDF film sensor can effectively sense the entire compression process of the gasket with different amounts of compression. The piezoelectric cable sensor can effectively sense the joint offset direction of the gasket. For differently shaped sections, the variation in the force sensed by the piezoelectric cable sensors was different, as verified by numerical simulation. Through the field test, it was found that the average contact stress between the segmental joints was in the range of 1.2–1.8 MPa during construction of the curved shield tunnels. The location of the segmental joints and the type of segment affect the contact stress value. The field monitoring results show that piezoelectric sensing technology can be successfully applied during assembly of the segments for effective sensing of the contact stress.

Abstract This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection.

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly. [ABSTRACT FROM AUTHOR]

Nerve injury can lead to defects in related motor functions. It is critical to achieve long‐term and convenient real‐time evaluation of motor function recovery status during nerve injury repair. In this study, an implantable PLLA/BTO piezoelectric sensor (PBPS) with good biodegradability and biocompatibility for real time evaluation of the motor function recovery after nerve injury is developed. PLLA fibers doped with BTO are employed as piezoelectric material in PBPS, which can convert the biomechanical signals generated by motion into electrical signals. PBPS can be implant simultaneously with commonly used tissue scaffolds for treatment in the rats with sciatic nerve injury. The linearity of the pressure and the output voltage of PBPS is ≈0.9445. For the evaluation effectiveness, as the treatment process progresses, the signals generated by PBPS exhibited good consistency with EMG signals, indicating effectively evaluation of the motor function. Moreover, the integration of PBPS and wireless module can break the limitations of time and space for sensing and realize wireless evaluation of motor function in rat. The implantable sensor based on PBPS may bring new ideas for the development of implantable bioelectronics. [ABSTRACT FROM AUTHOR]

The multilayer composite film system was designed in this study. The multilayer composite film was constructed by alternating electrospinning of MXene doped with polyacrylonitrile (PAN) nanofilm and thermoplastic polyurethane (TPU) nanofilm as piezoelectric functional layer. After utilizing nanofilm material, the piezoelectric sensor was prepared and subsequently examined for its piezoelectric sensitivity, piezoelectric output, and mechanical characteristics. MXene/PAN/TPU multilayer composite films showed good piezoelectric output performance. The final experimental data showed that the piezoelectric sensor of the MXene/PAN/TPU-based system achieved 0.16 kPa−1 piezoelectric sensitivity, 150ms/140ms response time and recovery time, good output voltage and output current, and good output stability. In human biological motion detection, the generator produced a maximum output voltage of 30 V when pressed by the palm. Tests such as palm pressing, finger tapping, and footstomping demonstrated the potential of piezoelectric sensors in human energy harvesting and motion monitoring. Multilayer composite films had flexible mechanical properties and sensitive piezoelectric response and had broad application prospects in piezoelectric sensors and piezoelectric nanogenerators. [ABSTRACT FROM AUTHOR]

This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection. [ABSTRACT FROM AUTHOR]

Flexible bending sensors are crucial components of flexible/wearable electronics. However, their broad application is restricted by their difficulty in sensing different bending directions and, more importantly, their significant output attenuation after long cycles. Herein, a new design of an All‐in‐One piezoelectric bending sensor is proposed, in which double‐deck, cross‐over 3D interdigital microelectrodes (silver nanowires (Ag NWs)) are embedded in a piezoelectric polymer film (poly(vinylidene fluoride‐trifluoroethylene (P(VDF‐TrFE))). The piezoelectric film with the embedded 3D microelectrodes exhibits a high anisotropy coefficient (η90∘${{\eta }_{90^\circ }}$ > 1) based on dual piezoelectric modes (d31 and d33), which renders it more sensitive to different bending directions. More importantly, the homogeneous interconnected interface and heterogeneous microinterlocked interface formed inside the sensor significantly enhance its interface mechanical properties, with at least a tensile strength of 51 MPa, a shear strength of 28 MPa, and an interfacial toughness of 300 J m−2, which are nearly two orders of magnitude greater than those of conventional sandwich architectures. The prepared All‐in‐One sensor shows an extremely stable piezoelectric output over as many as 16 00 000 bending cycles, which represents a remarkable breakthrough. Furthermore, a single sensor can be used to remotely control the multidirectional motion of a smart car, demonstrating enormous potential in practical applications. [ABSTRACT FROM AUTHOR]

As a highly ductile concrete, engineered cementitious composites (ECC) can be used as pavement to form a lightweight composite bridge deck system. However, the structural damage introduced by fatigue load in operation might lead to the degradation of structural performance. In this paper, piezoelectric sensors and hierarchical clustering algorithm are used to identify structural damage of steel-ECC composite deck. First, three steel-ECC composite decks were tested under four-point loading, and the electrical impedance signals were measured. The root mean square deviation (RMSD) was extracted to quantify the structural damage severities and locations. Then the frequency interval is divided into nine sub-frequency range to employ the sensitivity analysis. On this basis, a hierarchical clustering algorithm was introduced to analyze the impedance signal to identify the damage of steel-ECC composite deck. The results show that the development of the structural damage can be continuously monitored using impedance methodology and hierarchical clustering algorithm even in the case of small unlabeled datasets. [ABSTRACT FROM AUTHOR]

Self-powered wearable pressure sensors based on flexible electronics have emerged as a new trend due to the increasing demand for intelligent and portable devices. Improvements in pressure-sensing performance, including in the output voltage, sensitivity and response time, can greatly expand their related applications; however, this remains challenging. Here, we report on a highly sensitive piezoelectric sensor with novel light-boosting pressure-sensing performance, based on a composite membrane of copper phthalocyanine (CuPC) and graphene oxide (GO) (CuPC@GO). Under light illumination, the CuPC@GO piezoelectric sensor demonstrates a remarkable increase in output voltage (381.17 mV, 50 kPa) and sensitivity (116.80 mV/kPa, <5 kPa), which are approximately twice and three times of that the sensor without light illumination, respectively. Furthermore, light exposure significantly improves the response speed of the sensor with a response time of 38.04 µs and recovery time of 58.48 µs, while maintaining excellent mechanical stability even after 2000 cycles. Density functional theory calculations reveal that increased electron transfer from graphene to CuPC can occur when the CuPC is in the excited state, which indicates that the light illumination promotes the electron excitation of CuPC, and thus brings about the high polarization of the sensor. Importantly, these sensors exhibit universal spatial non-contact adjustability, highlighting their versatility and applicability in various settings. [ABSTRACT FROM AUTHOR]

Sarcopenia recognition is very crucial in the early diagnosis of sarcopenia. However, the commonly used screening methods are limited by real‐time property, portability, and convenient usability at home. Herein, an electrospun BaTiO3 film is proposed and a piezoelectric sensor with silver electrodes and polyimide substrates is fabricated. The sensor exhibits high piezoelectricity (74.2 pC N−1), sensitivity, linearity, low detection limit (0.2 mN), and significant bending ability (bending angle can exceed 90°), maintaining stable output after more than 20 000 cycles during a week. Due to its excellent performance, the piezoelectric sensor to the recognition of sarcopenia is applied and a wearable system to collect piezoelectric signals from the lower limb movements of the elderly is developed. By selecting features from these signals, eight kinds of machine learning models are employed and their performances in recognizing sarcopenia are compared in both male and female groups. The results indicate that the artificial neural network (ANN) model has the highest performance, with accuracies of 92.9% in males and 98.1% in females. This piezoelectric sensor, combined with a wireless communication module, is expected to provide crucial evidence for sarcopenia detection, offering a new, convenient, and household screening solution for early diagnosis and prevention of sarcopenia. [ABSTRACT FROM AUTHOR]

Flexible piezoelectric devices have attracted much attention in the fields of intelligent devices and biomedicine because of their high sensitivity, stability, and flexibility. In this paper, a multifunctional flexible pressure sensor was prepared by adding polyacrylonitrile (PAN) and carboxylic-terminated multi-walled carbon nanotubes (c-MWCNTs) with polyvinylidene difluoride (PVDF) as the substrate. The β -phase content of PVDF/PAN blended fibers compounded with c-MWCNT was up to 95%. At the same time, when PAN was added, the mechanical properties of the composite fibers were constantly improved. The results show that the polymer blending method can improve the comprehensive properties of PVDF composite. The flexible sensor prepared from the PVDF/PAN/c-MWCNT composite film has an output voltage of 2.1 V and a current of 7 μ A. The addition of c-MWCNT can largely improve the sensitivity of the sensor (4.19 V N−1). The sensor is attached to the finger and shows good output performance under different degrees of bending of the finger. The maximum output voltage of the sensor is 0.4 V, 0.56 V and 1.15 V when the finger bending angle is 30°, 60°, and 90°, respectively. Moreover, the developed piezoelectric sensor can monitor large-scale movements of various parts of the human body. Therefore, this composite material shows potential in areas such as motion monitoring and energy storage devices. [ABSTRACT FROM AUTHOR]

An outstanding event related to the understanding of the physics of mechanical sensors occurred and was announced in 1954, exactly seventy years ago. This event was the discovery of the piezoresistive effect, which led to the development of semiconductor strain gauges with a sensitivity much higher than that obtained before in conventional metallic strain gauges. In turn, this motivated the subsequent development of the earliest micromachined silicon devices and the corresponding MEMS devices. The science and technology related to sensors has experienced noteworthy advances in the last decades, but the piezoresistive effect is still the main physical phenomenon behind many mechanical sensors, both commercial and in research models. On this 70th anniversary, this tutorial aims to explain the operating principle, subtypes, input–output characteristics, and limitations of the three main types of mechanical sensor: strain gauges, capacitive sensors, and piezoelectric sensors. These three sensor technologies are also compared with each other, highlighting the main advantages and disadvantages of each one. [ABSTRACT FROM AUTHOR]

Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors' data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm3. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed. [ABSTRACT FROM AUTHOR]

Introduction/purpose: SMART orthopedic systems use fixators with remote monitoring, processing, and communication capabilities to leverage healing progression data for personalized, real-time monitoring of a healing process. The fixators incorporate small and compact piezoelectric sensors that generate electrical signals upon the application of force to the piezoelectric diaphragm. This enables doctors to remotely guide fixation devices using indirectly and remotely controlled stepper motors known for their precision and accuracy. Reliability of stepper motors makes them a viable alternative for the mechanical tools traditionally used by doctors for fixator extension. Methods: This study focuses on the evaluation of sensor-based technology in orthopedic applications. The paper presents a theoretical framework for the application of SMART devices in the bone fracture healing process. It delves into the structure and functionality of piezoelectric transducers, offering a comprehensive insight into this technology and various engineering aspects of SMART systems. Results: The implementation of SMART systems has significantly enhanced doctor-patient communication. This improvement is facilitated through a dual-phase process involving gathering, processing, and transmitting the data wirelessly from the patient's (sensor) interface to the doctor who uses specialized software for data analysis and wireless transmission to the stepper motor actuator. Subsequently, the data is forwarded to the decoder at the motor site, where a motor controller generates the control signal for the stepper motor driver. Conclusion: SMART implants can provide doctors with quantitative data that can be used in directing a rehabilitation plan. The sensor-based technology offers insights into the stress induced by the callus formation enabling bidirectional communication between the doctor and the patient. The stepper motor is a tool that aids in personalized treatment from the distance.

This paper presents a method to optimize and simplify the electrode profile of a piezoelectric modal sensor. At the same time that the electrode profile is optimized to maximize the charge, a null-polarity phase is introduced. This gap-phase is modeled using the normalized norm of the spatial gradient of the polarity profile along with a two-step filtering and projection technique. High-order vibration modes generate a complex polarization profile that makes the manufacturing process difficult. The novelty of the proposed method is the addition of a constraint on the length of the interface in the topology optimization problem. This constraint simplifies the optimal designs and facilitates the fabrication process. Several examples show the simplified electrode profile that maximizes the electrical charge produced by a vibration mode, while reducing the number of different polarization regions by means of the gap-length constraint. [ABSTRACT FROM AUTHOR]

A vital sign of the human body is respiration rate defined by the number of breaths a person takes per minute. In Bangladesh, respiratory diseases are very common. To find the respiration rate of patients, doctors use a traditional manual counting method. Sometimes this method is not accurate enough and is troublesome for them and it is time-consuming. Critical patients have very low breathing rates which makes it difficult to be counted and detected manually. That is why we developed a non-contact ultrasonic sensor-based method as well as a contact piezoelectric sensor-based method for obtaining respiration rate, and a comparison between them was demonstrated. The piezoelectric sensor was found to be more efficient and accurate and that is why we selected this sensor for the final design. We placed the sensor onto the subject's body and collected data from three positions: chest, upper and lower abdomen. We got two best positions based on body mass index (BMI). For low-BMI subjects, the best position was the chest and upper abdomen; for high-BMI subjects, it was the upper and lower abdomen. The accuracy of the device was 96.58%. Respiratory rate, heart rate, oxygen saturation, and BMI data were collected from 49 normal and respiratory disease patients of Chittagong Medical College Hospital and some volunteers to detect respiratory diseases. A logistic regression model was used for binary classification of this dataset to check whether the patient has respiratory diseases or not and found 88% accuracy for this model after five-fold cross-validation. [ABSTRACT FROM AUTHOR]

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals. [ABSTRACT FROM AUTHOR]

With the advent of bonded composites in today's aircraft, there is a need to verify the structural integrity of the bonded joints that comprise their structure. To produce adequate joint integrity, strict process control is required during bonding operations. The latest non-destructive joint inspection techniques cannot quantify the strength of the bond and only indicate the presence of disbonds or delaminations. Expensive and timely proof-load testing of the joints is required to demonstrate structural performance. This work focuses on experimentally evaluating joint-health monitoring with piezoelectric sensors exposed to repeated loadings until failure. Single-lap-shear composite joints are structurally tested while using sensor electromechanical impedance response as a health indicator. Based on these experiments, validation of this novel method is achieved through detailed evaluation of the sensor impedance response characteristics during loading, which enable initial and prognostic joint health assessments. The experimental results indicate that the embedded piezoelectric sensors are able to measure the sensor impedance radial and thickness resonance response changes prior to joint failure, without sacrificing the joints' structural performance. [ABSTRACT FROM AUTHOR]

This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, quantifies cavitation under assorted power settings. Two acoustic radiator shape configurations, one with holes and another solid, were examined. The piezoelectric sensor demonstrated efficacy, corroborating with existing literature, in measuring acoustic cavitation. This was achieved through the Fast Fourier Transform (FFT) analysis of voltage data, specifically targeting sub-harmonic patterns, thereby providing a robust method for cavitation detection. Results demonstrate that perforated geometries enhance cavitation intensity at lower power levels, while solid shapes predominantly affect cavitation axially, exhibiting decreased activity at minimal power. The findings recommend using two different shape geometries on the acoustic radiator for efficient cavitation detection, highlighting intense cavitation on radial walls and cavitation generation on the bottom. Due to the stochastic nature of cavitation, averaging data is critical. The spatial limitation of the sensor necessitates prioritizing specific areas over complete coverage, with multiple sensors recommended for comprehensive cavitation pattern analysis. [ABSTRACT FROM AUTHOR]

Introduction/purpose: SMART orthopedic systems use fixators with remote monitoring, processing, and communication capabilities to leverage healing progression data for personalized, real-time monitoring of a healing process. The fixators incorporate small and compact piezoelectric sensors that generate electrical signals upon the application of force to the piezoelectric diaphragm. This enables doctors to remotely guide fixation devices using indirectly and remotely controlled stepper motors known for their precision and accuracy. Reliability of stepper motors makes them a viable alternative for the mechanical tools traditionally used by doctors for fixator extension. Methods: This study focuses on the evaluation of sensor-based technology in orthopedic applications. The paper presents a theoretical framework for the application of SMART devices in the bone fracture healing process. It delves into the structure and functionality of piezoelectric transducers, offering a comprehensive insight into this technology and various engineering aspects of SMART systems. Results: The implementation of SMART systems has significantly enhanced doctor-patient communication. This improvement is facilitated through a dual-phase process involving gathering, processing, and transmitting the data wirelessly from the patient’s (sensor) interface to the doctor who uses specialized software for data analysis and wireless transmission to the stepper motor actuator. Subsequently, the data is forwarded to the decoder at the motor site, where a motor controller generates the control signal for the stepper motor driver. Conclusion: SMART implants can provide doctors with quantitative data that can be used in directing a rehabilitation plan. The sensor-based technology offers insights into the stress induced by the callus formation enabling bidirectional communication between the doctor and the patient. The stepper motor is a tool that aids in personalized treatment from the distance. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A piezoelectric sensor is a type of transducer that utilizes the piezoelectric effect to convert changes in pressure, acceleration temperature, or force into an electrical charge. This unique property makes piezoelectric sensors valuable for a wide range of applications in various industries. In this work, the main focus is on studying the effects of piezoelectric materials and exploring the functionality of piezoelectric sensors. The physical behavior of the sensor is thoroughly examined and a mathematical formula relating the accuracy of the sensor to relative movement or vibratory displacement is derived. The developed model is verified through simulations and experimental tests. By carefully selecting the appropriate damping rate, it is possible to enhance the parameters of the piezoelectric sensor and advance the technique of vibratory analysis. Overall, this research aims to enhance our understanding of piezoelectric materials and sensors, and how they can be effectively utilized in various applications involving vibratory analysis. The findings from this study can contribute to better design and implementation of piezoelectric sensors, improving their accuracy and effectiveness in capturing and analyzing vibratory movements. [ABSTRACT FROM AUTHOR]

Piezoelectric polymer poly(vinylidene fluoride) (PVDF) is extensively used as sensor and actuator devices owing their excellent piezoelectric and pyroelectric properties. However, the melting point of PVDF is relatively low and the service temperature of PVDF is up to 100°C. This restricts the use of PVDF in high temperature applications. This can be improved by adding appropriate fillers to the PVDF. In the present study, a composite of PVDF-Onium salt was developed and characterized to improve the thermal efficiency of PVDF for high temperature applications. In order to investigate the presence of β-phase, which is necessary for sensor applications, X-ray diffraction, scanning electron microscopy, differential scanning calorimetry, Raman and infrared spectra, and dielectric test were used to characterize PVDF-Onium salt composite films made using the solvent cast method. The melting point of PVDF-Onium salt composite was found to be higher (175°C) as compared to the PVDF polymer alone (168.2°C) which has been discussed in detail. The PVDF-Onium salt composite sensors were further tested for dynamic strain sensing application for the first time. The modes of cantilever beam vibrations and the impact loading effects were recorded in order to assess the performance of these sensors. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Analyzing floor surface materials is critical for controlling the motion and tasks of mobile robots. In this study, we propose a novel method for classifying floor materials for indoor mobile robots using a piezoelectric actuator–sensor pair and deep learning. This method can classify the floor properties itself with isolated sensing system while the mobile robot is moving. The piezoelectric pair is a thin-film type. It consists of an actuator and a sensor. The sensing pair is positioned at the bottom of the robot. When the robot moves forward, the sensing part collects the electrical responses from the actuator. Since one-dimensional data is collected through the piezoelectric actuator-sensor pair, the size of the system is small and the data processing speed can be reduced. Using this mechanism, experiments were conducted to classify various materials of floor surfaces in indoor environments. The sensing data were processed by fast Fourier transform, high-pass filter, polynomial fitting, and sampling to be used as inputs for machine learning of the classification model. Specifically, the trained model achieved a high accuracy of 95.4%. In addition, the training data were verified using the k-means clustering method. Moreover, the effect of the physical properties on the sensor data was analyzed to investigate the relationship between the materials and the sensing outputs.

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U*774 was applied, considering weight percentage (1–13 wt%), the distance between needle and disk collector (2.1–3.9 mm), and applied voltage (14.5–17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 µS/cm for pure PVDF to 187.5 µS/cm. The fiber diameter decreased from 21.4 µm in PVDF/1% graphene to 9.1 µm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. The results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis.

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F 1 -scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model for the electrical responses of a soft cylindrical structure with shear deformations using piezoelectric sensors. Specifically, to analyze the cylindrical structure during movement, we assumed that a shear force was applied to the flat surface on the top of the structure. Using this force, we established a model using the Euler–Bernoulli beam theory and estimated the electrical responses of the sensors generated by its deformation. To validate the theoretical analysis, a soft cylindrical structure was fabricated using silicone containing piezoelectric sensors. Moreover, a series of tests were performed by applying a tensile testing machine and vibration exciter to the soft structure. During the experiments, we observed the sensor outputs while generating vibrations in the form of triangular and sinusoidal waves. The experimental outputs demonstrate that the sensors can distinguish the displacements and directions of the structural deformations, similar to our predictive model, through the voltage outputs and phase variations of the sensors. Moreover, parametric studies were performed to investigate the sensor responses under structural deformations affected by four parameters related to the material and external forces: the Young's modulus, radius, mass density, and frequency of the sinusoidal shear force. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The piezoelectric sensors modified with a molecularly imprinted polymer (MIP) with potassium sorbate (MIP-E202) and sodium benzoate (MIP-E211) imprints are tested and implemented in the determination of preservatives in soft drinks. Molecularly imprinted polymers were synthesized by noncovalent imprinting on the base of copolymer of 1,2,4,5-benzene tetracarboxylic acid dianhydride and 4,4'-diaminodiphenyl oxide in N,N-dimethylformamide (DMF) in the presence of templates. Piezoelectric sensors based on MIP and non-imprinted polymer (polyimide) were compared. High values of the imprinting factor (IF) and selectivity coefficient (k) obtained for MIP-E202 (IF = 5.4) and MIP-E211 (IF = 6.0) sensors indicated better selectivity and ability of MIP-based sensors to recognize target molecules than piezoelectric sensors modified with a reference polymer. The detectable concentrations range within 5–500 mg/L; the detection limits for potassium sorbate and sodium benzoate are 1.6 and 2.0 mg/L, respectively. The correctness of the determination of preservatives in model solutions was verified using the spike test. MIP-based sensors appeared sensitive to the determination of preservatives and insensitive to interfering substances. The matrix composition of the nonalcoholic drinks did not affect the value of the analytical signal of the piezoelectric sensor. High performance liquid chromatography (HPLC) was used as a reference method. The results of potassium sorbate and sodium benzoate determination in nonalcoholic drinks using piezoelectric sensors match the HPLC data rather well, their content in the studied soft drinks being 130–176 and 129–146 mg/L, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the control of a powered prosthetic hand, this paper compares methods for the classification of intended hand motions using muscle bulging patterns caused by muscle contraction. Two sheets of Polyvinylidene Difluoride (PVDF) film were used as sensors to detect the muscle bulging on the forearm caused by intended hand motions. A neural network had been successfully trained for the classification of six types of hand motions using the muscle bulging pattern detected by the two PVDF sensors. In this paper, we further studied the motion classification methods of back propagation neural network (BPNN), k‐nearest neighbor algorithm (k‐NN), and support vector machine (SVM) to compare their classification performance. We found that all three methods had a similar classification rate of about 95% for six types of hand motions. Moreover, a regressive analysis comparison of the time for each classification method to converge to 95% of the total classification rate showed that SVM converged significantly earlier than BPNN and k‐NN. The time it takes for SVM to converge the classification rate to 95% is less than 0.1 s, suggesting that real‐time motion classification is possible by using SVM. In a similar manner, we found that SVM requires the least training data of the three methods at only nine trials for a type of motion. Furthermore, SVM had the highest classification rate at about 90% in practical experimental conditions. In conclusion, SVM was found to be the most practical of the three methods. [ABSTRACT FROM AUTHOR]

Wearable flexible electronic devices are increasingly becoming an important part of the future rolling touch screen, health care equipment, electronic skin and other fields. It is worth noting that piezoelectric sensors are widely used because of their self‐powered capability, fast response, and relatively simple circuits. Among them, Indium‐tin‐zinc oxide thin‐film transistor (ITZO TFT) has high field effect mobility, low energy consumption, and can be used as an amplifier for signal amplification of flexible sensing systems. In this paper, we constructed a flexible piezoelectric sensor (PZ) based on electrospun PVDF and hydrothermally assisted growth of ZnO nanorods. Besides, this PZ sensor has higher sensitivity than the individual PVDF piezoelectric sensor. The sensor can not only be used for detection of static objects, but also for the capture of tiny physiological signals of human body, such as vibration of vocal cords, pulse beat, etc., showing superiority in dynamic and static signal detection. At the same time, the sensor can be integrated with ITZO TFT, which can still achieve stable signal acquisition and show application prospects as future brain‐computer interface and intelligent robotics manufacturing. [ABSTRACT FROM AUTHOR]

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly.

Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors’ data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm3. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed.

An outstanding event related to the understanding of the physics of mechanical sensors occurred and was announced in 1954, exactly seventy years ago. This event was the discovery of the piezoresistive effect, which led to the development of semiconductor strain gauges with a sensitivity much higher than that obtained before in conventional metallic strain gauges. In turn, this motivated the subsequent development of the earliest micromachined silicon devices and the corresponding MEMS devices. The science and technology related to sensors has experienced noteworthy advances in the last decades, but the piezoresistive effect is still the main physical phenomenon behind many mechanical sensors, both commercial and in research models. On this 70th anniversary, this tutorial aims to explain the operating principle, subtypes, input–output characteristics, and limitations of the three main types of mechanical sensor: strain gauges, capacitive sensors, and piezoelectric sensors. These three sensor technologies are also compared with each other, highlighting the main advantages and disadvantages of each one.