Accurate measurement of explosive blast events is essential for understanding blast loading and evaluating sensing technologies for harsh, transient environments. Conventional blast pressure measurements rely on rigid, externally powered transducers that are difficult to deploy on compliant or distributed surfaces. This study investigates the response of a flexible, self-powered ferro-electret nanogenerator (FENG) subjected to controlled explosive blast loading within a large cross-section blast chamber. The FENG exhibited clear and repeatable transient voltage responses across all blast levels. Analysis of the time-integrated FENG voltage revealed pressure-like waveform characteristics, with the peak integrated response scaling linearly with measured static overpressure (R² = 0.971), indicating sensitivity to blast severity through cumulative mechanical loading. The FENG rise time was consistently longer than that of the reference pressure transducer, reflecting differences in sensing mechanisms rather than reduced repeatability. Additional experiments demonstrated that the FENG detected blast events from multiple orientations, including configurations without direct line-of-sight exposure to the explosion and when fully covered or shielded. These results indicate that flexible FENG devices can reliably capture both rapid deformation dynamics and accumulated blast loading, supporting their potential use in distributed, conformal, or wearable blast-monitoring applications where traditional rigid pressure sensors are impractical.

This work presents the application of a flexible, self-powered sensor designed to predict angular velocity and acceleration during head kinematics associated with concussions. This paper-thin, flexible device, which exhibits piezoelectric-like properties, is strategically placed on the back of a human head substitute to capture stress and strain in this region during whiplash events. The mechanical energy generated by varying magnitudes of whiplash is converted into electrical pulses, which are then integrated with multiple machine learning models. These models were tested and compared, demonstrating their ability to accurately predict angular velocity and acceleration of the head. This predictive capability can be utilized to assess the probability of brain injury. The findings demonstrate that this system not only enhances the understanding of head impact dynamics, but also opens avenues for developing more effective injury risk assessment tools. By combining innovative sensor technology with advanced machine learning techniques, this study contributes to improved safety monitoring in high-risk environments, such as high-contact and automotive sports.

Active noise control has the potential to address the growing problem of noise pollution. Modern applications of noise control integrate the use of adaptive algorithms and "antinoise" speakers. This letter explores thin-film polypropylene ferroelectret being used as an antinoise speaker for noise control with a focus on the high end of the audible spectrum and ultrasonics (15-40 kHz). The experiments conducted demonstrate the film's ability to cancel or amplify target acoustic noise frequencies while simultaneously delivering user-defined acoustic signals. Noise control is performed by using polypropylene ferroelectret to attenuate/amplify a 10 kHz signal while sustaining a user-defined 5 kHz signal, and vice versa. Additional experiments demonstrate that the polypropylene ferroelectret material cancels signals from 15 to 40 kHz. The differences in sound pressure levels were measured and used to calculate an effective antinoise amplitude sound pressure level. These results can be used in the future to compare other potential antinoise speaker candidates.

Iris recognition systems, operating in the near infrared spectrum (NIR), have demonstrated vulnerability to presentation attacks, where an adversary uses artifacts such as cosmetic contact lenses, artificial eyes or printed iris images in order to circumvent the system. At the same time, a number of effective presentation attack detection (PAD) methods have been developed. These methods have demonstrated success in detecting artificial eyes (e.g., fake Van Dyke eyes) as presentation attacks. In this work, we seek to alter the optical characteristics of artificial eyes by affixing Vanadium Dioxide (VO2) films on their surface in various spatial configurations. VO2 films can be used to selectively transmit NIR light and can, therefore, be used to regulate the amount of NIR light from the object that is captured by the iris sensor. We study the impact of such images produced by the sensor on two state-of-the-art iris PA detection methods. We observe that the addition of VO2 films on the surface of artificial eyes can cause the PA detection methods to misclassify them as bonafide eyes in some cases. This represents a vulnerability that must be systematically analyzed and effectively addressed.

Ferroelectret nanogenerators (FENGs), recognized for their porous structures that facilitate charge retention, thereby creating giant electric dipoles and exhibiting remarkable piezoelectric properties, are utilized in the development of various flexible transducers. However, despite their flexibility, most developed ferroelectret nanogenerators lack adequate stretchability and satisfactory transverse piezoelectric properties, significantly inhibiting their widespread deployment in wearable or skin-mounted electronics. Here, we introduce a highly stretchable ferroelectret nanogenerator (HS-FENG) built from laser-induced graphene (LIG), Ecoflex and anhydrous ethanol, demonstrating exceptional flexibility and stretchability, along with longitudinal and transverse piezoelectric effects. The stretchability of HS-FENG can reach a record of 468 %, while the quasi-static piezoelectric coefficients d33 and d31 are approximately 120 pC/N and 70 pC/N, respectively. To our knowledge, this is the first demonstration of the developed FENG with remarkably high stretchability. Furthermore, leveraging the performance of the created HS-FENG, we construct a skin-mounted intelligent kinesiology tape capable of effectively monitoring motion signals from human muscles and joints, thereby offering a deeper understanding of movement for users across different levels of physical activity, from professional athletes to individuals undergoing rehabilitation. The development of intelligent kinesiology tape exemplifies the potential of HS-FENG technology in enhancing professional athletic training and personalized healthcare. It contributes to the advancement of inconspicuous skin-mounted biomechanical feedback systems and human-machine interfaces, marking progress in the field.

Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86

A long-standing challenge in lab-on-chip and biomicrofluidic sensing modes is the formation of reliable, leak-free bonding on the surface of wafers or CMOS chips having sensing electrodes typically formed by thin film metal deposition. This challenge is particularly evident in the design of polydimethylsiloxane (PDMS) flow channels for electrochemical analysis where the need for high-density electrodes increases the number of metal-PDMS interface points. This work presents a fabrication method for creating leak free bonding of PDMS on Au electrodes by coating the substrate in a low-temperature plasma enhanced chemical vapor deposited dielectric material, thereby exposing only the sensing area within the channel. Furthermore, this fabrication process was used to create the first-known impact electrochemistry flow cell capable of continuous measurement of air-borne particulate matter using microfluidics over a surface containing microfabricated gold electrodes. Functionality of the device for air pollution monitoring was validated by detecting black carbon particles using impact electrochemistry at 0.8V.

As a critical element of the technological infrastructure of body sensor networks (BSNs), wearable electromagnetic vibration energy harvesters (EMVEHs) are a competitive candidate for breaking through the development bottleneck of BSNs’ sustainability, and thus facilitating their self-sustained operations with versatile functions. To this end, the prior concern of wearable EMVEHs is to enhance their adaptability to complex biomechanics of human motions for better power generation performance. Given the state-of-the-art progress of this BSN enabling technology, we provide a comprehensive and in-depth summary of recent excitation-adaptive designs of miniaturized wearable EMVEHs focusing on their insightful vibration pick-up structures here, to systematically clarify a developing roadmap of this branch of science and then offer inspirations for the underway endeavors focused on energy harvesting from human motions. In this way, we try to lift the impacts of current innovative efforts in this field and corresponding BSN achievements to a higher level.

In wearable or implantable biomedical devices that typically rely on battery power for diagnostics or operation, the development of flexible piezoelectric nanogenerators (NGs) that enable mechanical-to-electrical energy harvesting is finding promising applications. Here, we present the construction of a flexible piezoelectric nanogenerator using a thin film of room temperature deposited nanocrystalline aluminium nitride (AlN). On a thin layer of aluminium (Al), the AlN thin film was grown using pulsed laser deposition (PLD). The room temperature grown AlN film was composed of crystalline columnar grains oriented in the (100)-direction, as revealed in images from transmission electron microscopy (TEM) and X-ray diffraction (XRD). Fundamental characterization of the AlN thin film by piezoresponse force microscopy (PFM) indicated that its electro-mechanical energy conversion metrics were comparable to those of c-axis oriented AlN and zinc oxide (ZnO) thin films. Additionally, the AlN-based flexible piezoelectric NG was encapsulated in polyimide to further strengthen its mechanical robustness and protect it from some corrosive chemicals.

Bioengineering devices and systems will become a practical and versatile technology in society when sustainability issues, primarily pertaining to their efficiency, sustainability, and human-machine interaction, are fully addressed. It has become evident that technological paths should not rely on a single operation mechanism but instead on holistic methodologies that integrate different phenomena and approaches with complementary advantages. As an intriguing invention, the ferroelectret nanogenerator (FENG) has emerged with promising potential in various fields of bioengineering. Utilizing the changes in the engineered macro-scale electric dipoles to create displacement current (and vice versa), FENGs have been demonstrated to be a compelling strategy for bidirectional conversion of energy between the electrical and mechanical domains. Here we provide a comprehensive overview of the latest advancements in integrating FENGs in bioengineering systems, focusing on the applications with the most potential and the underlying current constraints.

Our knowledge of traumatic brain injury has been fast growing with the emergence of new markers pointing to various neurological changes that the brain undergoes during an impact or any other form of concussive event. In this work, we study the modality of deformations on a biofidelic brain system when subject to blunt impacts, highlighting the importance of the time-dependent behavior of the resulting waves propagating through the brain. This study is carried out using two different approaches involving optical (Particle Image Velocimetry) and mechanical (flexible sensors) in the biofidelic brain. Results show that the system has a natural mechanical frequency of [Formula: see text] 25 oscillations per second, which was confirmed by both methods, showing a positive correlation with one another. The consistency of these results with previously reported brain pathology validates the use of either technique, and establishes a new, simpler mechanism to study brain vibrations by using flexible piezoelectric patches. The visco-elastic nature of the biofidelic brain is validated by observing the the relationship between both methods at two different time intervals, by using the information of the strain and stress inside the brain from the Particle Image Velocimetry and flexible sensor, respectively. A non-linear stress-strain relationship was observed and justified to support the same.

Flexible pressure sensor arrays are of increasing interest, but their fabrication tends to be complex and time-consuming. In this work, we propose an inexpensive, rapid fabrication approach for flexible pressure sensor arrays. The sensor consists of a matrix of pressure-sensitive resistive elements sandwiched by patterned electrode strips that are bonded to flexible tape substrates. The fabrication uses a commercially available vinyl-cutting machine to cut out patterned electrodes and sensing "pixels," from a copper tape sheet and a carbon-impregnated polyethylene sheet (Velostat), respectively, and it uses transfer tapes to facilitate the bonding of the electrodes to the adhesive tape substrates. The entire fabrication takes approximately 15 min, in contrast to several hours required with a previously reported approach for fabricating similar sensors. The spatial resolution achieved by the proposed method is also higher, with each sensing unit occupying an area as small as 3 mm × 3 mm. Due to the programmable nature of the cutting machine, the proposed method can be used to readily fabricate flexible sensors with versatile patterns. Various tests have been conducted to demonstrate the effectiveness of the fabricated sensors, with a 14×14 sensor array successfully mapping the pressure profiles under finger touch, loading of a hand sanitizer bottle, and grasping around a cup.

Using a dual-probe setting to simultaneously obtain the structural and electronic responses upon photo-excitations we reveal a new many-body physics mechanism responsible for the insulator-metal phase transitions of VO 2 .

High-sensitivity flow measurement technology is a prerequisite for precise dynamic control of microfluidics. Despite the advances in structure optimization, a more efficient approach to improve device sensitivity can be realized by leveraging materials with a higher temperature coefficient of resistance (TCR). This work presents the design and simulation of a vanadium dioxide (VO2)-based microfluidic thermal flow sensor with record high sensitivity. Owing to the phase change property, VO2 demonstrates the maximum TCR of -0.703 and -0.63 K-1 in the major heating and cooling curves, respectively, which is more than two orders of magnitude higher than commonly used thermal-sensitive materials. To fully utilize the high thermal sensitivity of VO2, a dual-heater configuration with enhanced thermal differential effect is proposed, and its sensing performance is evaluated in the flow range below 10 mu L.min(-1). By individually operating the VO2 thermal sensors at critical transition temperatures in the major hysteresis loop, the sensitivity can reach as high as 2.79 V/mu L.min(-1), which is about 187.88 times and 277.89 times higher than the VO2-based anemometer and the Pt-based dual-heater calorimetric (DHC) sensor, respectively. The research in the present work may enable a breakthrough in the improvement of high-performance microfluidic thermal flow sensors in the ultralow flow region using nonstandard metamaterials.

A microfluidic thermal mass flow sensor with ultra-high thermal sensitivity, based on planar micro-machining technology and a phase-change material is developed.

This letter reports the thermal-mechanical tuning capability of an in-plane comb drive resonator using VO2 phase transition material. By inducing the insulator-to-metal transition (IMT) using a heat conduction method, a shift of approximately 2% was observed. The frequency tuning was attributed to the induced stresses during the IMT. [2022-0200]

Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor ("compression" pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth ("suction" pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor's memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.

This work presents a prototype of a wireless, flexible, self-powered sensor used to analyze head impact kinematics relevant to concussions, which are frequent in high-contact sports. Two untethered, paper-thin, and flexible sensing devices with piezoelectric-like behavior are placed around the neck of a human head substitute and used to monitor stress/strain in this region during an impact. The mechanical energy exerted by an impact force --varied in locations and magnitudes-- is converted to pulses of electric energy which are transmitted wirelessly to a smart device for storage and analysis. The wireless prototype system is presented using a microcontroller with an integrated Bluetooth Low Energy module. The static and dynamic characteristics of the transmitted signal are then compared to signals from accelerometers embedded in a head substitute, to map the sensor’s output to the angular velocity and acceleration during impacts. It is demonstrated that using only two sensors is enough to detect impacts coming from any direction; and that placing multiple external sensors around the neck region could provide accurate information on the dynamics of the head, during a collision, which other sensors fail to capture.