Advances in Piezoelectret Materials-Based Bidirectional Haptic Communication Devices.

Bidirectional haptic communication devices accelerate the revolution of virtual/augmented reality and flexible/wearable electronics. As an emerging kind of flexible piezoelectric materials, piezoelectret materials can effortlessly convert mechanical force into electrical signals and respond to electrical fields in a deformation manner, exhibiting enormous potential in the construction of bidirectional haptic communication devices. Existing reviews on piezoelectret materials primarily focus on flexible energy harvesters and sensors, and the recent development of piezoelectret-based bidirectional haptic communication devices has not been comprehensively reviewed. Herein, a comprehensive overview of the materials construction, along with the recent advances in bidirectional haptic communication devices, is provided. First, the development timeline, key characteristics, and various fabrication methods of piezoelectret materials are introduced. Subsequently, following the underlying mechanisms of bidirectional electromechanical signal conversion of piezoelectret, strategies to improve the d33 coefficients of materials are proposed. The principles of haptic perception and feedback are also highlighted, and representative works and progress in this area are summarized. Finally, the challenges and opportunities associated with improving the overall practicability of piezoelectret materials-based bidirectional haptic communication devices are discussed.

High-performance flexible resistive random-access memory based on SnS2 quantum dots with a charge trapping/de-trapping effect.

The application of resistive random-access memory (RRAM) in storage and neuromorphic computing has attracted widespread attention. Benefitting from the quantum effect, transition metal dichalcogenides (TMD) quantum dots (QDs) exhibit distinctive optical and electronic properties, which make them promising candidates for emerging RRAM. Here, we show a high-performance forming-free flexible RRAM based on high-quality tin disulfide (SnS2) QDs prepared by a facile liquid phase method. The RRAM device demonstrates high flexibility with a large on/off ratio of ∼106 and a long retention time of over 3 × 104 s. The excellent switching behavior of the memristor is elucidated by a charge trapping/de-trapping mechanism where the SnS2 QDs act as charge trapping centers. This study is of significance for the understanding and development of TMD QD-based flexible memristors.

A Review of Conductive Hydrogel-Based Wearable Temperature Sensors.

Conductive hydrogel has garnered significant attention as an emergent candidate for diverse wearable sensors, owing to its remarkable and tailorable properties such as flexibility, biocompatibility, and strong electrical conductivity. These attributes make it highly suitable for various wearable sensor applications (e.g., biophysical, bioelectrical, and biochemical sensors) that can monitor human health conditions and provide timely interventions. Among these applications, conductive hydrogel-based wearable temperature sensors are especially important for healthcare and disease surveillance. This review aims to provide a comprehensive overview of conductive hydrogel-based wearable temperature sensors. First, this work summarizes different types of conductive fillers-based hydrogel, highlighting their recent developments and advantages as wearable temperature sensors. Next, this work discusses the sensing characteristics of conductive hydrogel-based wearable temperature sensors, focusing on sensitivity, dynamic stability, stretchability, and signal output. Then, state-of-the-art applications are introduced, ranging from body temperature detection and wound temperature detection to disease monitoring. Finally, this work identifies the remaining challenges and prospects facing this field. By addressing these challenges with potential solutions, this review hopes to shed some light on future research and innovations in this promising field.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.

BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. METHODS: A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. RESULTS: Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. CONCLUSION: This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study.

Carbon nanotube (CNT) fiber electrodes have demonstrated exceptional spatial selectivity and sustained reliability in the context of intrafascicular electrical stimulation, as evidenced through rigorous animal experimentation. A significant presence of unmyelinated C fibers, known to induce uncomfortable somatosensory experiences, exists within peripheral nerves. This presence poses a considerable challenge to the excitation of myelinated Aß fibers, which are crucial for tactile sensation. To achieve nuanced tactile sensory feedback utilizing CNT fiber electrodes, the selective stimulation of Aß sensory afferents emerges as a critical factor. In confronting this challenge, the present investigation sought to refine and apply a rat sciatic-nerve model leveraging the capabilities of the COMSOL-NEURON framework. This approach enables a systematic evaluation of the influence exerted by stimulation parameters and electrode geometry on the activation dynamics of both myelinated Aß and unmyelinated C fibers. The findings advocate for the utilization of current pulses featuring a pulse width of 600 µs, alongside the deployment of CNT fibers characterized by a diminutive diameter of 10 µm, with an exclusively exposed cross-sectional area, to facilitate reduced activation current thresholds. Comparative analysis under monopolar and bipolar electrical stimulation conditions revealed proximate activation thresholds, albeit with bipolar stimulation exhibiting superior fiber selectivity relative to its monopolar counterpart. Concerning pulse waveform characteristics, the adoption of an anodic-first biphasic stimulation modality is favored, taking into account the dual criteria of activation threshold and fiber selectivity optimization. Consequently, this investigation furnishes an efficacious stimulation paradigm for the selective activation of touch-related nerve fibers, alongside provisioning a comprehensive theoretical foundation for the realization of natural tactile feedback within the domain of prosthetic hand applications.

Electrostatic Smart Textiles for Braille-To-Speech Translation.

A wearable Braille-to-speech translation system is of great importance for providing auditory feedback in assisting blind people and people with speech impairment. However, previous reported Braille-to-speech translation systems still need to be improved in terms of comfortability or integration. Here, a Braille-to-speech translation system that uses dual-functional electrostatic transducers which are made of fabric-based materials and can be integrated into textiles is reported. Based on electrostatic induction, the electrostatic transducer can either serve as a tactile sensor or a loudspeaker with the same design. The proposed electrostatic transducers have excellent output performances, mechanical robustness, and working stability. By combining the devices with machine learning algorithms, it is possible to translate the Braille alphabet and 40 commonly used words (extensible) into speech with an accuracy of 99.09% and 97.08%, respectively. This work demonstrates a new approach for further developments of advanced assistive technology toward improving the lives of disabled people.

Realizing multiferroics in α-Ga2S3via hole doping: a first-principles study.

Using first-principles calculations, we report the realization of multiferroics in an intrinsic ferroelectric α-Ga2S3 monolayer. Our results show that the presence of intrinsic gallium vacancies, which is the origin of native p-type conductivity, can simultaneously introduce a ferromagnetic ground state and a spontaneous out-of-plane polarization. However, the high switching barrier and thermodynamic irreversibility of the ferroelectric reversal path disable the maintenance of ferroelectricity, suggesting that the defect-free form should be a prerequisite for Ga2S3 to be multiferroic. Through applying strain, the behavior of spontaneous polarization of the pristine α-Ga2S3 monolayer can be effectively regulated, but the non-magnetic ground state does not change. Strikingly, via an appropriate concentration of hole doping, stable ferromagnetism with a high Curie temperature and robust ferroelectricity can be concurrently introduced in the α-Ga2S3 monolayer. Our work provides a feasible method for designing 2D multiferroics with great potential in future device applications.

Single Channel Based Interference-Free and Self-Powered Human-Machine Interactive Interface Using Eigenfrequency-Dominant Mechanism.

The recent development of wearable devices is revolutionizing the way of human-machine interaction (HMI). Nowadays, an interactive interface that carries more embedded information is desired to fulfill the increasing demand in era of Internet of Things. However, present approach normally relies on sensor arrays for memory expansion, which inevitably brings the concern of wiring complexity, signal differentiation, power consumption, and miniaturization. Herein, a one-channel based self-powered HMI interface, which uses the eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is reported. When manually vibrated, the inherent recovery of the MMP causes a damped oscillation that generates current signals because of Faraday's Law of induction. The time-to-frequency conversion explores the MMP-related eigenfrequency, which provides a specific solution to allocate diverse commands in an interference-free behavior even with one electric channel. A cylindrical cantilever model is built to regulate the MMP eigenfrequencies via precisely designing the dimensional parameters and material properties. It is shown that using one device and two electrodes, high-capacity HMI interface can be realized when the magnetic micropillars (MMPs) with different eigenfrequencies have been integrated. This study provides the reference value to design the future HMI system especially for situations that require a more intuitive and intelligent communication experience with high-memory demand.

Precise Engineering of the Electrocatalytic Activity of FeN4-Embedded Graphene on Oxygen Electrode Reactions by Attaching Electrides.

Using first-principles calculations combined with a constant-potential implicit solvent model, we comprehensively studied the activity of oxygen electrode reactions catalyzed by electride-supported FeN4-embedded graphene (FeN4Cx). The physical quantities in FeN4Cx/electrides, i.e., work function of electrides, interlayer spacing, stability of heterostructures, charge transferred to Fe, d-band center of Fe, and adsorption free energy of O, are highly intercorrelated, resulting in activity being fully expressed by the nature of the electrides themselves, thereby achieving a precise modulation in activity by selecting different electrides. Strikingly, the FeN4PDCx/Ca2N and FeN4PDCx/Y2C systems maintain a high oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) activity with the overpotential less than 0.46 and 0.62 V in a wide pH range. This work provides an effective strategy for the rational design of efficient bifunctional catalysts as well as a model system with a simple activity-descriptor, helping to realize significant advances in energy devices.

Crosstalk-Free Position Mapping for One-Step Reconstruction of Surface Topological Information via Eigenfrequency-Registered Wearable Interface.

Exploring flexible tactile sensors capable of recognizing surface information is significant for the development of virtual reality, artificial intelligence, soft robotics, and human-machine interactions (HMI). However, it is still a challenge for current tactile sensors to efficiently recognize the surface pattern information while maintaining the simplicity of the overall system. In this study, cantilever beam-like magnetized micropillars (MMPs) with height gradients are assembled as a position-registered array for rapid recognition of surface pattern information. After crossing the surface location with convex patterns, the deformed MMPs undergo an intrinsic oscillating process to induce damped electrical signals, which can then be converted to a frequency domain for eigenfrequency extraction. Via precisely defining the specific eigenfrequencies of different MMPs, position mapping is realized in crosstalk-free behavior even though all signals are processed by one communication channel and a pair of electrodes. With a customized LabVIEW program, the surface information (e.g., letters, numbers, and Braille) can be accurately reconstructed by the frequency sequence produced in a single scanning procedure. We expect that the proposed interface can be a convenient and powerful platform for intelligent surface information perception and an HMI system in the future.

A Flexible Self-Powered Noncontact Sensor with an Ultrawide Sensing Range for Human-Machine Interactions in Harsh Environments.

Noncontact human-machine interactions (HMIs) provide a hygienic and intelligent approach to communicate between humans and machines. However, current noncontact HMIs are generally hampered by the interaction distance, and they lack the adaptability to environmental interference such as high humidity conditions. Here, we explore a self-powered electret-based noncontact sensor (ENS) with moisture-resisting ability and ultrawide sensing range exceeding 2.5 m. A megascopic air-bubble structure is designed to enhance charge-storage stability and charge-recovery ability of the ENS based on the heterocharge-synergy effect in electrets. Besides, multilayer electret films are introduced to strengthen the electric field by utilizing the electrostatic field superposition effect. Thanks to the above improved performances of the ENS, we demonstrate various noncontact HMI applications in harsh environments, including noncontact appliances, a moving trajectory and accidental fall tracking system, and a real-time machine learning-assisted gesture recognition system with accuracy as high as 99.21%. This research expands the way for noncontact sensor design and may further broaden applications in noncontact HMIs.

Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma.

OBJECTIVES: Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. METHODS: 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. RESULTS: The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (tradscore = -5.829, χ2ALP = 97.137, tsize = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). CONCLUSION: The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.

Machine-Learning Assisted Electronic Skins Capable of Proprioception and Exteroception in Soft Robotics.

Inspired by natural biological systems, soft robots have recently been developed, showing tremendous potential in real-world applications because of their intrinsic softness and deformability. The confluence of electronic skins and machine learning is extensively studied to create effective biomimetic robotic systems. Based on a differential piezoelectric matrix, this study presents a shape-sensing electronic skin (SSES) that can recognize surface conformations with minimal interference from pressing, stretching, or other surrounding stimuli. It is then integrated with soft robots to reconstruct their shape during movement, serving as a proprioceptive sense. Additionally, the robot can utilize machine learning to identify various terrains, demonstrating exteroception and pointing toward more advanced autonomous robots capable of performing real-world tasks in challenging environments.

Washable and Breathable Electret Sensors Based on a Hydro-Charging Technique for Smart Textiles.

Flexible electromechanical sensors based on electret materials have shown great application potential in wearable electronics. However, achieving great breathability yet maintaining good washability is still a challenge for traditional electret sensors. Herein, we report a washable and breathable electret sensor based on a hydro-charging technique, namely, hydro-charged electret sensor (HCES). The melt-blown polypropylene (MBPP) electret fabric can be charged while washing with water. The surface potential of MBPP electret fabric can be improved by optimizing the type of water, water pressure, water temperature, drying temperature, drying time, ambient air pressure, and ambient relative humidity. It is proposed that the single fiber has charges of different polarities on the upper and lower surfaces due to contact electrification with water, thereby forming electric dipoles between fibers, which can lead to better surface potential stability than the traditional corona-charging method. The HCES can achieve a high air permeability of ∼215 mm/s and sensitivity up to ∼0.21 V/Pa, with output voltage remaining stable after over 36,000 working cycles and multiple times of water washing. As a demonstration example, the HCES is integrated into a chest strap to monitor human respiration conditions.

TM2 -B2 Quadruple Active Sites Supported on a Defective C3 N Monolayer as Catalyst for the Electrochemical CO2 Reduction: A Theoretical Perspective.

Developing high-performance electrocatalysts for the CO2 reduction reaction (CO2 RR) holds great potential to mitigate the depletion of fossil feedstocks and abate the emission of CO2 . In this contribution, using density functional theory calculations, we systematically investigated the CO2 RR performance catalyzed by TM2 -B2 (TM=Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu) supported on a defective C3 N monolayer (V-C3 N). Through the screening in terms of stability of catalyst, activity towards CO2 adsorption, and selectivity against hydrogen evolution reaction, Mn2 -, Fe2 -, Co2 -, and Ni2 -B2 @V-C3 N were demonstrated to be a highly promising CO2 RR electrocatalyst. Due to quadruple active sites, these candidates can adsorb two or three CO2 molecules. Strikingly, different products, distributing from C1 to C2+ , can be generated. The high activity originates from the synergistic effect of TM and B atoms, in which they serve as adsorption sites for the C- and O-species, respectively. The high selectivity towards C2+ products at the Fe2 -, and Ni2 -B2 sites stems from moderate C adsorption strength but relatively weak O adsorption strength, in which a universal descriptor, that is, 0.6 ΔEC -0.4 ΔEO =-1.77 eV (ΔEC /ΔEO is the adsorption energy of C/O), was proposed. This work would offer a novel perspective for the design of high active electrocatalysts towards CO2 RR and for the synthesis of C2+ compounds.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

Multiplying the Stable Electrostatic Field of Electret Based on the Heterocharge-Synergy and Superposition Effect.

Owing to magic charge storage behavior, an electret can exhibit an external electrostatic field, which is widely used in numerous domains such as electronics, energy, healthcare, and environment. However, the theory of the charge storage mechanism still needs further development to enhance the performance and stability of the electret. Herein, a stable charge storage model known as the heterocharge-synergy model (HSM) in electrets is proposed and verified, and the electrostatic field superposition effect of electrets is also proved. Based on the HSM and superposition effect, the stable electrostatic field intensity (average of ≈22.49 kV cm-1 and maximum of ≈29.58 kV cm-1 , which is close to the minimum air breakdown field intensity of ≈30 kV cm-1 ) of the composite electret film is multiplied by simple layer-by-layer stacking. Utilizing the multilayer composite electret films and designing a two-sided electrostatic induction structure, a two-sided bipolar single-electrode non-contact nanogenerator is constructed with transferred charge density up to ≈132.61 µC m-2 , which is twice as large as that of the non-contact nanogenerators with one-sided electrostatic induction structure. Clearing and utilizing the charge behaviors of the electret can boost the performance and enhance the stability of electret-based devices in various domains.

A Moisture-Resistant Soft Actuator with Low Driving Voltages for Haptic Stimulations in Virtual Games.

Strong and robust stimulations to human skins with low driving voltages under high moisture working conditions are desirable for wearable haptic feedback applications. Here, a soft actuator based on the "air bubble" electret structure is developed to work in high-moisture environments and produce haptic sensations to human skin with low driving voltages. Experimentally, the water soaking and drying process has been conducted repeatedly for the first time and the 20th time to test the antimoisture ability of the actuator as it recovers its output force up 90 and 65% of the initial value, respectively. The threshold voltages for sensible haptic sensations for the fingertip and palm of volunteers have been characterized as 7 and 10 V, respectively. Furthermore, a demonstration example has been designed and conducted in a virtual boxing game to generate the designated haptic sensations according to the gaming conditions with an accuracy of 98% for more than 100 tests. As such, the design principle, performance characteristic, and demonstration example in this work could inspire various applications with improved reliability for wearable haptic devices.

A miniaturized mechanical antenna based on FEP/THV unipolar electrets for extremely low frequency transmission.

An electret-based mechanical antenna (EBMA), which can transmit extremely low frequency (ELF) electromagnetic signals, has the advantages of miniaturization and high transmitting efficiency, with great potential applications in air, underwater, and underground communications. To improve the charge density of the electret, which is a key factor in determining the radiation performance of an EBMA, this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene, hexafluoropropylene and vinylidene fluoride (FEP/THV) unipolar electret exhibiting negative polarity, reaching a total charge density up to -0.46 mC/m2 for each layer of electret. Long transmission distances can be achieved in sea water, soil, and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5 × 10-4 m3. As an application demonstration, binary ASCII-coded ELF information of "BUAA" is successfully transmitted with a power consumption < 5 W.

Smart Face Mask Based on an Ultrathin Pressure Sensor for Wireless Monitoring of Breath Conditions.

A smart face mask that can conveniently monitor breath information is beneficial for maintaining personal health and preventing the spread of diseases. However, some challenges still need to be addressed before such devices can be of practical use. One key challenge is to develop a pressure sensor that is easily triggered by low pressure and has excellent stability as well as electrical and mechanical properties. In this study, a wireless smart face mask is designed by integrating an ultrathin self-powered pressure sensor and a compact readout circuit with a normal face mask. The pressure sensor is the thinnest (totally compressed thickness of ≈5.5 µm) and lightest (total weight of ≈4.5 mg) electrostatic pressure sensor capable of achieving a peak open-circuit voltage of up to ≈10 V when stimulated by airflow, which endows the sensor with the advantage of readout circuit miniaturization and makes the breath-monitoring system portable and wearable. To demonstrate the capabilities of the smart face mask, it is used to wirelessly measure and analyze the various breath conditions of multiple testers.