A Signal Amplitude-Insensitive Triboelectric Touch Panel with a Significantly Reduced Signal Channel and Deep-Learning-Enhanced Robustness.

The self-powered triboelectric touch panel has garnered considerable research attention due to its potential to reduce system energy consumption and its applications in human-machine interfaces, e-skin, and the Internet of Things. Current methods for achieving triboelectric-based touch positioning in an M × N detection pixel array typically require signal amplitude comparison across at least M + N signal channels, thereby limiting lightweight design possibilities. In contrast, our novel "resistor ladder" approach necessitates only 4 signal channels for touch positioning. This method leverages a lookup table correlating touch positions with amplitude ratios from different channels, rendering it insensitive to signal amplitude and significantly enhancing robustness. We fabricated a transparent touch panel using PET tribomaterial, where the surface roughness was enhanced through plasma treatment. The panel successfully demonstrated touch positioning for 128 taps within a 4 × 4 pixel detection array and sliding positioning using a predefined lookup table. To further enhance device robustness, a 2D convolutional neural network was implemented, which achieved an impressive touch positioning accuracy of 97.7% even under artificially introduced signal defects. This study represents an initial exploration of amplitude-insensitive touch and sliding positioning methods, significantly reducing the number of required signal channels and enhancing the robustness of triboelectric touch panels.

Self-Powered Terahertz Modulators Based on Metamaterials, Liquid Crystals, and Triboelectric Nanogenerators.

6G communication mainly occurs in the THz band (0.1-10 THz), which can achieve excellent performance. Self-powered THz modulators are essential for achieving better conduction, modulation, and manipulation of THz waves. Herein, a self-powered terahertz modulator, which is based on metamaterials, liquid crystals (LCs), and rotary triboelectric nanogenerators (R-TENGs), is proposed to realize the driving of different array elements. The corresponding designs can achieve an integrated design and preparation method for dynamic spectrum-reconfigurable liquid crystal metamaterials. In addition, for the type of cross-structure metamaterial liquid crystal box, a phase modulation of 1 GHz is achieved at frequencies of 0.117 and 0.161 THz with modulation depths of 13 and 11%, respectively. Because the R-TENG with a multifan blade and circular electrodes can generate 18 peaks of electric output in every rotation, it can successfully provide sufficient frequency alternating-current electric energy to drive the terahertz modulator and achieve a self-powered function. Our findings lay a solid theoretical foundation for further building self-powered THz communication systems and promote the development of a theoretical system for LC-driving spectrum-reconfigurable devices in the THz domain.

A Rotating Triboelectric Nanogenerator Driven by Bidirectional Swing for Water Wave Energy Harvesting.

Due to the simple installation and convenient maintenance, the floating water wave energy harvesting devices have significant economic advantages. Mass power density is the most important index to evaluate the advancement of floating wave energy harvesting devices. Herein, a self-adaptive rotating triboelectric nanogenerator (SR-TENG) with a compound pendulum and a functional gear-set is provided for wave energy harvesting. First, a compound pendulum structure with a low center of gravity and high moment of inertia is obtained by the geometric design and mechanical study. Besides, compared with the previous triboelectric nanogenerator with one-way clutch, SR-TENG can harvest twice the kinetic energy utilization through the functional gear-set. Importantly, depending on the structure design, the SR-TENG obtains the average mass power density of 45.18 mW kg-1 under low frequency driving conditions, which is about 10 times the reference electromagnetic generator with a similar structure and size. This result shows that the SR-TENG has a significant advantage in small water wave energy harvesting. These findings provide an important example of the floating water wave energy harvesting devices.

Realgar-Induced Neurotoxicity: Crosstalk Between the Autophagic Flux and the p62-NRF2 Feedback Loop Mediates p62 Accumulation to Promote Apoptosis.

Realgar is a traditional Chinese medicine that contains arsenic. It has been reported that the abuse of medicine-containing realgar has potential central nervous system (CNS) toxicity, but the toxicity mechanism has not been elucidated. In this study, we established an in vivo realgar exposure model and selected the end product of realgar metabolism, DMA, to treat SH-SY5Y cells in vitro. Many assays, including behavioral, analytical chemistry, and molecular biology, were used to elucidate the roles of the autophagic flux and the p62-NRF2 feedback loop in realgar-induced neurotoxicity. The results showed that arsenic could accumulate in the brain, causing cognitive impairment and anxiety-like behavior. Realgar impairs the ultrastructure of neurons, promotes apoptosis, perturbs autophagic flux homeostasis, amplifies the p62-NRF2 feedback loop, and leads to p62 accumulation. Further analysis showed that realgar promotes the formation of the Beclin1-Vps34 complex by activating JNK/c-Jun to induce autophagy and recruit p62. Meanwhile, realgar inhibits the activities of CTSB and CTSD and changes the acidity of lysosomes, leading to the inhibition of p62 degradation and p62 accumulation. Moreover, the amplified p62-NRF2 feedback loop is involved in the accumulation of p62. Its accumulation promotes neuronal apoptosis by upregulating the expression levels of Bax and cleaved caspase-9, resulting in neurotoxicity. Taken together, these data suggest that realgar can perturb the crosstalk between the autophagic flux and the p62-NRF2 feedback loop to mediate p62 accumulation, promote apoptosis, and induce neurotoxicity. Realgar promotes p62 accumulation to produce neurotoxicity by perturbing the autophagic flux and p62-NRF2 feedback loop crosstalk.

Exploring the role of learning through play in promoting multimodal learning among children: a pilot study in Chinese first-tier cities.

Play is an effective approach to engaging children in learning as an alternative to traditional lecturing. The Learning through Play (LtP) approach involves various modes of learning participation, including multi-sensory participation, interpersonal interaction, and hands-on operation, which can effectively motivate children to learn. This study implemented an LtP pilot survey in several first-tier cities of China, which included questionnaires and interviews. The results present the basic ecology of LtP in China, LtP effects on children's multimodal learning. We found that LtP has gained great popularity, both conceptually and practically, in China. LtP stakeholders recognize the behavioral, cognitive, and affective effectiveness of LtP for children's learning. The factors influencing the effectiveness of LtP include its structural weaknesses, the subjects involved, the environment, and culture. This study provides a reference for promoting the theory and practice of children's multimodal learning with a playful approach.

Computational simulation-driven discovery of novel zeolite-like carbon materials as seawater desalination membranes.

Freshwater is a scarce and vulnerable resource that has never encountered such an extensive focus on a nearly worldwide scale as it does today. Recently, it has been found that desalination powered by two-dimensional (2D) carbon materials as separation membranes has significantly reduced the operational costs and complexity but presents heavy requirements for the structural stability and separation properties of the membrane materials. Here, we combined carbon materials with promising adsorption properties and zeolites characterized by a regular pore structure to obtain a zeolite-like structured carbon membrane Zeo-C and investigated the suitability of the Zeo-C membrane for seawater desalination based on the computational-simulation-driven approach. The results of molecular dynamics (MD) simulations and density functional theory (DFT) calculations revealed that the periodic pore distribution conferred favorable structural stability and mechanical strength to the Zeo-C desalination membrane. The rejection rate of Na+ and Cl- is ensured at 100% under a pressure of 40-70 MPa, and that of Na+ could reach 97.85% even though the pressure increases to 80 MPa, exhibiting superior desalination properties. The porous nature of the zeolite-like structure and the low free energy potential barrier are conducive for reliable adsorption and homogeneous diffusion of salt ions, which facilitates the acquisition of desirable water molecule permeability and salt ion selectivity. In particular, the interlinked delocalized π-network imparts inherent metallicity to Zeo-C for self-cleaning in response to electrical stimulation, thereby extending the lifetime of the desalination membrane. These studies have greatly encouraged theoretical innovations and serve as a guiding reference for desalination materials.

Double total variation (DTV) regularization and Improved adaptive moment estimation (IADAM) optimization method for fast MR image reconstruction.

BACKGROUND AND OBJECTIVE: Compressed sensing has been extensively studied as an advanced technique for fast MR image reconstruction. Current reconstruction algorithms often use total variation as the regularization term. Traditional total variation can easily lead to a staircase effect because it only pays attention to the variational information of the horizontal and vertical subbands. METHODS: In this paper, we propose a novel algorithm to reduce the staircase effect by increasing the variational information of the two diagonal subbands, which named Double Total Variation (DTV). We optimize the conjugate gradient algorithm by Improved Adaptive Moment Estimation (IADAM) as the solution algorithm. RESULTS: MR images of three body parts (head, knee and ankle) were used for simulations under different acceleration factor conditions. The conjugate gradient and fast conjugate gradient series algorithms were selected for comparison experiments. The results showed that the improved adaptive moment estimation conjugate gradient combined with DTV achieves the best reconstruction performance, therefore proved the superiority of DTV. After that, 64 different MR images of the three body parts were further simulated and the results demonstrated the general superiority from the proposed algorithm. CONCLUSIONS: The results of this study support that the proposed method may facilitate the development of the research field of image reconstruction algorithms and provide ideas for other algorithmic improvements.

Crosstalk between autophagy and the Keap1-Nrf2-ARE pathway regulates realgar-induced neurotoxicity.

ETHNOPHARMACOLOGICAL RELEVANCE: Realgar, the main component of which is As2S2 or As4S4 (≥90%), is a traditional Chinese natural medicine that has been used to treat carbuncles, furuncles, snake and insect bites, abdominal pain caused by parasitic worms, and epilepsy in China for many years. Because realgar contains arsenic, chronic or excessive use of single-flavor realgar and realgar-containing Chinese patent medicine can lead to drug-induced arsenic poisoning, but the exact mechanism underlying its toxicity to the central nervous system is unclear. AIM OF THE STUDY: The aim of this study was to clarify the mechanism of realgar-induced neurotoxicity and to investigate the effects of realgar on autophagy and the Keap1-Nrf2-ARE pathway. MATERIALS AND METHODS: We used rats treated with the autophagy inhibitor 3-methyladenine (3-MA) or adeno-associated virus (AAV2/9-r-shRNA-Sqstm1, sh-p62) to investigate realgar-induced neurotoxicity and explore the specific relationship between autophagy and the Keap1-Nrf2-ARE pathway (the Nrf2 pathway) in the cerebral cortex. Molecular docking analysis was used to assess the interactions among the Nrf2, p62 and Keap1 proteins. RESULTS: Our results showed that arsenic from realgar accumulated in the brain and blood to cause neuronal and synaptic damage, decrease exploratory behavior and spontaneous movement, and impair memory ability in rats. The mechanism may have involved realgar-mediated autophagy impairment and continuous activation of the Nrf2 pathway via the LC3-p62-Keap1-Nrf2 axis. However, because this activation of the Nrf2 pathway was not sufficient to counteract oxidative damage, apoptosis was aggravated in the cerebral cortex. CONCLUSIONS: This study revealed that autophagy, the Nrf2 pathway, and apoptosis are involved in realgar-induced central nervous system toxicity and identified p62 as the hub of the LC3-p62-Keap1-Nrf2 axis in the regulation of autophagy, the Nrf2 pathway, and apoptosis.

Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms.

A multifunctional wearable tactile sensor assisted by deep learning algorithms is developed, which can realize the functions of gesture recognition and interaction. This tactile sensor is the fusion of a triboelectric nanogenerator and piezoelectric nanogenerator to construct a hybrid self-powered sensor with a higher power density and sensibility. The power generation performance is characterized with an open-circuit voltage VOC of 200 V, a short-circuit current ISC of 8 µA, and a power density of 0.35 mW cm-2 under a matching load. It also has an excellent sensibility, including a response time of 5 ms, a signal-to-noise ratio of 22.5 dB, and a pressure resolution of 1% (1-10 kPa). The sensor is successfully integrated on a glove to collect the electrical signal output generated by the gesture. Using deep learning algorithms, the functions of gesture recognition and control can be realized in real time. The combination of tactile sensor and deep learning algorithms provides ideas and guidance for its applications in the field of artificial intelligence, such as human-computer interaction, signal monitoring, and smart sensing.

Sparse reconstruction of magnetic resonance image combined with two-step iteration and adaptive shrinkage factor.

As an advanced technique, compressed sensing has been used for rapid magnetic resonance imaging in recent years, Two-step Iterative Shrinkage Thresholding Algorithm (TwIST) is a popular algorithm based on Iterative Thresholding Shrinkage Algorithm (ISTA) for fast MR image reconstruction. However TwIST algorithms cannot dynamically adjust shrinkage factor according to the degree of convergence. So it is difficult to balance speed and efficiency. In this paper, we proposed an algorithm which can dynamically adjust the shrinkage factor to rebalance the fidelity item and regular item during TwIST iterative process. The shrinkage factor adjusting is judged by the previous reconstructed results throughout the iteration cycle. It can greatly accelerate the iterative convergence while ensuring convergence accuracy. We used MR images with 2 body parts and different sampling rates to simulate, the results proved that the proposed algorithm have a faster convergence rate and better reconstruction performance. We also used 60 MR images of different body parts for further simulation, and the results proved the universal superiority of the proposed algorithm.

Compressively sampled magnetic resonance imaging reconstruction based on split Bregman iteration with general non-uniform threshold shrinkage.

PURPOSE: K-space under-sampling reconstruction technology is an effective means to improve the speed of magnetic resonance imaging. Among its many reconstruction algorithms, split Bregman iteration is an effective method to solve multi-constrained models. This model often contains TV variational regularization terms, the generalized threshold shrinkage operator often used to solve TV constraints subproblem. However, when the generalized threshold shrinkage operator is performing the shrinking operation, it does not consider the inconsistency of the elements in the image matrix, which will cause the loss of image details. METHODS: In response to this problem, in this paper, a non-uniform threshold shrinkage operator was proposed to solve above TV constraints subproblem, which can dynamically adjust the shrinkage threshold by the residuals of each image element. And introduce this operator when performing Split Bregman iteration to improve the performance of generalized threshold shrinkage. RESULTS: After qualitative and quantitative analysis during the experiments, it can be concluded that compared with the other three methods, the proposed method has better performance in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Transferred Edge Information(TEI) and Normalized Mutual Information(NMI), and the visual perception is better. Then we also did denoising performance analysis at different noise levels, this method also showed good robustness. CONCLUSIONS: The proposed method can improve the reconstruction performance of TV constrained subproblem in split Bregman iteration, and then improve the overall performance of reconstruction algorithm. Moreover, this method also shows good denoising performance at different noise levels.

Self-Powered Triboelectric Nanosensor for Microfluidics and Cavity-Confined Solution Chemistry.

Micro total analysis system (µTAS) is one of the important tools for modern analytical sciences. In this paper, we not only propose the concept of integrating the self-powered triboelectric microfluidic nanosensor (TMN) with µTAS, but also demonstrate that the developed system can be used as an in situ tool to quantify the flowing liquid for microfluidics and solution chemistry. The TMN automatically generates electric outputs when the fluid passing through it and the outputs are affected by the solution temperature, polarity, ionic concentration, and fluid flow velocity. The self-powered TMN can detect the flowing water velocity, position, reaction temperature, ethanol, and salt concentrations. We also integrate the TMNs in a µTAS platform to directly characterize the synthesis of Au nanoparticles by a chemical reduction method.

A Flexible Fiber-Based Supercapacitor-Triboelectric-Nanogenerator Power System for Wearable Electronics.

A flexible self-charging power system is built by integrating a fiber-based supercapacitor with a fiber-based triboelectric nanogenerator for harvesting mechanical energy from human motion. The fiber-based supercapacitor exhibits outstanding electrochemical properties, owing to the excellent pseudocapacitance of well-prepared RuO2 ·xH2 O by a vapor-phase hydrothermal method as the active material. The approach is a step forward toward self-powered wearable electronics.

A flexible, stretchable and shape-adaptive approach for versatile energy conversion and self-powered biomedical monitoring.

A flexible triboelectric nanogenerator (FTENG) based on wavy-structured Kapton film and a serpentine electrode on stretchable substrates is presented. The as-fabricated FTENG is capable of harvesting ambient mechanical energy via both compressive and stretching modes. Moreover, the FTENG can be a bendable power source to work on curved surfaces; it can also be adaptively attached onto human skin for monitoring gentle body motions.

Paper-based origami triboelectric nanogenerators and self-powered pressure sensors.

Discovering renewable and sustainable power sources is indispensable for the development of green electronics and sensor networks. In this paper, we present origami triboelectric nanogenerators (TENGs) using paper as the starting material, with a high degree of flexibility, light weight, low cost, and recyclability. Slinky- and doodlebug-shaped TENGs can be easily fabricated by properly folding printer papers. The as-fabricated TENGs are capable of harvesting ambient mechanical energy from various kinds of human motions, such as stretching, lifting, and twisting. The generated electric outputs have been used to directly light-up commercial LEDs. In addition, the as-fabricated TENGs can also serve as self-powered pressure sensors.

3D fiber-based hybrid nanogenerator for energy harvesting and as a self-powered pressure sensor.

In the past years, scientists have shown that development of a power suit is no longer a dream by integrating the piezoelectric nanogenerator (PENG) or triboelectric nanogenerator (TENG) with commercial carbon fiber cloth. However, there is still no design applying those two kinds of NG together to collect the mechanical energy more efficiently. In this paper, we demonstrate a fiber-based hybrid nanogenerator (FBHNG) composed of TENG and PENG to collect the mechanical energy in the environment. The FBHNG is three-dimensional and can harvest the energy from all directions. The TENG is positioned in the core and covered with PENG as a coaxial core/shell structure. The PENG design here not only enhances the collection efficiency of mechanical energy by a single carbon fiber but also generates electric output when the TENG is not working. We also show the potential that the FBHNG can be weaved into a smart cloth to harvest the mechanical energy from human motions and act as a self-powered strain sensor. The instantaneous output power density of TENG and PENG can achieve 42.6 and 10.2 mW/m(2), respectively. And the rectified output of FBHNG has been applied to charge the commercial capacitor and drive light-emitting diodes, which are also designed as a self-powered alert system.

A wireless magnetic resonance energy transfer system for micro implantable medical sensors.

Based on the magnetic resonance coupling principle, in this paper a wireless energy transfer system is designed and implemented for the power supply of micro-implantable medical sensors. The entire system is composed of the in vitro part, including the energy transmitting circuit and resonant transmitter coils, and in vivo part, including the micro resonant receiver coils and signal shaping chip which includes the rectifier module and LDO voltage regulator module. Transmitter and receiver coils are wound by Litz wire, and the diameter of the receiver coils is just 1.9 cm. The energy transfer efficiency of the four-coil system is greatly improved compared to the conventional two-coil system. When the distance between the transmitter coils and the receiver coils is 1.5 cm, the transfer efficiency is 85% at the frequency of 742 kHz. The power transfer efficiency can be optimized by adding magnetic enhanced resonators. The receiving voltage signal is converted to a stable output voltage of 3.3 V and a current of 10 mA at the distance of 2 cm. In addition, the output current varies with changes in the distance. The whole implanted part is packaged with PDMS of excellent biocompatibility and the volume of it is about 1 cm(3).