ADVANCED MATERIALS
·
2025
·
IF 26.8
Chao Xue,Yanran Zhao,Yuantai Liao,Hongyu Zhang
Conductive hydrogels have attracted significant attention due to exceptional flexibility, electrochemical property, and biocompatibility. However, the low mechanical strength can compromise their stability under high stress, making the material susceptible to fracture in complex or harsh environments. Achieving a balance between conductivity and mechanical robustness remains a critical challenge. In this study, super-robust conductive hydrogels were designed and developed with highly oriented structures and densified networks, by employing techniques such as stretch-drying-induced directional assembly, salting-out, and ionic crosslinking. The hydrogels showed remarkable mechanical property (tensile strength: 17.13–142.1 MPa; toughness: 50 MJ m − 3 ), high conductivity (30.1 S m −1 ), and reliable strain sensing performance. Additionally, it applied this hydrogel material to fabricate biomimetic electronic skin device, significantly improving signal quality and device stability. By integrating the device with 1D convolutional neural network algorithm, it further developed a real-time material recognition system based on triboelectric and piezoresistive signal collection, achieving a classification accuracy of up to 99.79% across eight materials. This study predicted the potential of the high-performance conductive hydrogels for various applications in flexible smart wearables, the Internet of Things, bioelectronics, and bionic robotics.
研究领域:
材料科学
·
生物医学工程
·
人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
ACS Nano
·
2025
·
IF 16
Xiangyu Duan,Yongzhen Mi,Tingyu Lei,Xiu Yun Daphne Ma,Zhong Chen,Junhua Kong,Xuehong Lu
Hydrogel-based sensors have been widely studied for perceiving the environment. However, the simplest type of resistive sensors still lacks sensitivity to localized strain and other extractable data. Enhancing their sensitivity and expanding their functionality to perceive multiple stimuli simultaneously are highly beneficial yet require optimal material design and proper testing methods. Herein, we report a highly elastic, sponge-like hydrogel and its derived multimodal iontronic sensor. By unidirectional freeze casting of poly(vinyl alcohol) (PVA) with electrospun cellulose nanofibers (CNF), a hierarchical structure with aligned PVA channels supported by interlaced CNF tangles is created. The structure ensures both efficient mass transport and good elasticity, enhancing reversible compressibility and ionic conductivity. Combining this sponge hydrogel with impedance-based measurement methods allows the development of multimodal sensors capable of detecting local strain, position, and material type of object-in-contact. Integrating these sensing capabilities, a two-dimensional small motion monitor, a 3D input interface, and a material identification gripper are demonstrated. This study provides a simple approach to versatile multimodal sensors.
研究领域:
材料科学
·
传感器技术
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Nano Energy
·
2024
·
IF 16.8
Bingkun Cheng,Chenyu Li,Teng Ji,Yuda Zhao,Kaige Gao,Jianhua Hao,Wei Xu
To develop skin-like tactile sensor systems with both self-healing ability of repairing mechanical damage and perception ability of identifying materials and objects is significantly valuable for smart and high-quality interaction with environment. It is noticed that the sensing array comprised of distinct triboelectric nanogenerator (TENG) units shows great advantages in material recognition. However, those attempts of integrating self-healing ability into above triboelectric sensing array are challenged by the lack of self-healing TENG constituent units with differentiated electron affinity . Herein, we exploit a self-healing polyamide (SPA) with high mechanical strength as the base material for constructing a novel self-healing TENG unit with distinctive electron affinity. The healing of the prepared SPA-based TENG (SPA-TENG) can be autonomously conducted at room temperature without external intervention required. Our results reveal that SPA-TENG possesses stronger electron affinity compared to existing self-healing TENGs, which enables it to be an ideal constituent unit to construct self-healing triboelectric sensing array for material identification. After combining SPA-TENG with other previously reported self-healing TENGs, we firstly demonstrate a fully self-healing triboelectric sensing array capable of distinguishing various commercial plastic products based on their fingerprint waveform combinations, which shows promising application in fields of intelligent robots, artificial prosthesis , and automatic production etc.
研究领域:
材料科学
·
传感器技术
·
智能系统
✓ 已验证
产品关联
DOI
高影响因子
Nano Energy
·
2025
·
IF 17.1
Jiabin Wang,Suwen Xu,Weiqi Qian,Md Al Mahadi Hasan,Zilin Ren,Ya Yang
As the human body's most vital environmental interface, the skin integrates diverse sensory receptors enabling simultaneous perception of multiple stimuli through multisensory processing. However, the realization of multifunctional sensing system on robots or prosthetics remains a huge challenge. Here we report a multifunctional tactile sensor fabricated from ionic thermoelectric gelatin that achieves simultaneous pressure and temperature detection. The thermoelectric effect of this ionic thermoelectric gelatin is mainly achieved through the synergy of the thermodiffusion effects and the thermogalvanic effect of ions. In addition, by applying pressure, the thermoelectric effect of this ionic thermoelectric gelatin can be changed (the thermopower of the ionic thermoelectric gelatin increases from 1.21 mV/K to 1.67 mV/K, representing a 38 % increase), so as to enable the sensor to sense external forces. By comparing the thermoelectric effect driven by temperature difference and the thermoelectric effect driven by pressure-temperature difference, there is a difference in the voltage response time. This allows for the decoupling of signals at the backend with only a simple algorithm. Using this sensor, we achieved a 95.31 % accuracy rate in identifying different materials. The multifunctional tactile sensor can promote the development of robots and better assist the disabled in restoring their sensory abilities.
研究领域:
材料科学
·
生物医学工程
✓ 已验证
产品关联
DOI
高影响因子
CHEMICAL ENGINEERING JOURNAL
·
2025
·
IF 13.2
Bingyu Huang,Kailong Xu,Xiaofei Zhang,Yongcheng Zhang,Yun-Ze Long,Shuzhi Sam Ge,Xiangxia Wei
Triboelectric nanogenerators (TENGs) have attracted global attention for energy conversion from various types of mechanical energy into electricity. Traditional approaches for improving triboelectric conversion efficiency generally involved complicated preparation methods and strict structural design, which cannot fulfill the stringent requirements of material adaptability and application scenarios. In this work, a flexible TENG was developed based on zirconium-doped barium titanate (BaZr x Ti 1−x O 3 , BZT) particles in polydimethylsiloxane (PDMS) matrix. Subsequently, BZT/PDMS composites with patterned structures were fabricated by direct ink writing (DIW) based three-dimensional (3D) printing to significantly enhance the electrical performance. It was demonstrated that the TENG could deliver a maximum open-circuit voltage (V OC ) of 350 V and a short-circuit current (I SC ) of 4.9 µA with a maximum power density of 2.4 W m −2 . Moreover, an effective utilization of the TENG was designed to detect the presence of humans, saving a huge amount of electrical energy. Finally, a TENG-based tactile sensor was integrated with machine learning to construct a material identification system, which was capable of recognizing different materials with a high accuracy of 98.54 %. The TENG-based tactile sensor with machine learning demonstrated a tremendous application in material perception, promoting the development of versatile sensors in the field of machine intelligence.
研究领域:
材料科学
·
能源技术
·
人工智能
✓ 已验证
产品关联
DOI
高影响因子
Advanced Fiber Materials
·
2025
·
IF 21.3
Zhu Mengjia,Li Shuo,Bi Peng,Liang Huarun,Wu Xun-En,Zhang Chi,Song Xian,Yu Aifang,Xu Jingtao,Lu Haojie,Wang Haomin,Zhai Junyi,Li Yi,Zheng Zijian,Zhang Yingying
Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation. Nonetheless, these devices face challenges in practical applications, including high susceptibility to the operating environments, such as variations in pressure, humidity, and touch speed, as well as concerns regarding wearability and comfort. In this work, we developed an augmented-tactility superskin, termed AtSkin, which integrates a skin-compatible nanofiber sensor array and deep learning algorithms to enhance material recognition regardless of the ambient environment. We fabricated a lightweight and breathable triboelectric sensor array with multilayer nanofiber architectures through electrospinning and hot pressing. The carefully selected combination of sensing layers can capture the electrical characteristics of different materials, thus enabling their distinction. Combined with deep learning algorithms, AtSkin achieved an accuracy of 97.9% in distinguishing visually similar resin and fabric materials, even under varying environmental pressures and humidities. As a proof of concept, we constructed an intelligent augmented-tactility system capable of identifying fabrics with similar textures and hand feel, demonstrating the potential of the superskin to expand human tactile capabilities, enhance augmented reality experiences, and revolutionize intelligent healthcare solutions. Graphical
研究领域:
生物医学工程
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人工智能
·
材料科学
✓ 已验证
产品关联
DOI
高影响因子
ACS Nano
·
2022
·
IF 18.027
Chao Ye,Shuo Yang,Jing Ren,Shaojun Dong,Leitao Cao,Ying Pei,Shengjie Ling
IntelliSense fabrics that can sense transient mechanical stimuli are widely anticipated in flexible and wearable electronics. However, most IntelliSense fabrics developed so far are only sensitive to quasi-static forces, such as stretching, bending, or twisting. In this work, a sheath-core triboelectric nanogenerator (SC-TENG) yarn was developed via a rational design, electroassisted core spinning technique, that consisted of a rough nanoscale dielectric surface and mechanically strong and electrically conductive core yarns. The resulting system was used to sense and distinguish the instantaneous mechanical stimuli generated by different materials. To further improve the sensing accuracy, a machine learning model, based on a classification coding and recurrent neural network, was built to predict the type of contact materials from the peak profiles of output voltages. With these experimental and algorithmic optimizations, we finally used SC-TENG yarn to identify the type of materials in real-time. Moreover, by applying Internet of Things techniques, we investigated that SC-TENG yarn could be integrated into an IntelliSense system to recognize and control various electronic and electrical systems, demonstrating promising applications in wearable energy supply, IntelliSense fabrics, and human–machine interactions.
研究领域:
材料科学
·
电子工程
·
人工智能
·
物联网
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Journal of Materials Chemistry A
·
2023
·Vol. 11
·期 34
·18441-18453
·
IF 11.9
Junjun Huang,Wenqing Zhang,Xin Chen,Sanlong Wang,Zhenming Chen,Peng Li,Honglin Li,Chengmei Gui
Textile-based triboelectric nanogenerators (T-TENGs) have great potential in new-generation tactile sensors because their output signal is determined by the specific charge interaction between the target and triboelectric material. However, gesture recognition by existing T-TENG tactile sensors is very challenging because the electron affinity and polarity of the volunteer skin are similar. This study proposes a new structural sensor that integrates a Ni particle-encapsulated carbon tube (Ni@C tube) with polydimethylsiloxane (PDMS) by means of multiple strain mechanisms for improving recognition accuracy using a signal superposition technique. Interestingly, dynamic strain from multiple sources, i.e., PDMS layer strain (1.24–2.47 kPa), Ni@C tube crack (2.47–5.56 kPa), PDMS layer further strain (5.56–52.97 kPa), and Ni@C tube slip laterally (52.97–139.05 kPa), was established in the PDMS-coated Ni@C tube. In addition, the output signal of the as-fabricated T-TENG device was a combination of output signals from the PDMS/Ni@C tube and PDMS/contact object devices, which could fully reflect the stress field caused by the contact material pressing the surface and the surface characteristics, promoting the deep learning algorithm to be able to extract more hidden features of the identified materials. By integrating convolutional neural network (CNN) analysis based on the voltage signals of the T-TENG device, we explored a strategy that could enable the as-fabricated device to recognize 6 gestures with 99.3% recognition accuracy and 8 different materials with 99.8% recognition accuracy in the natural environment.
研究领域:
纳米技术
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传感器技术
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人工智能
·
材料科学
✓ 已验证
DOI
高影响因子
SusMat
·
2024
·Vol. 5
·期 1
·e254
·
IF 18.7
Shengjie Yin,Hongyu Li,Yun Ji,Yuke Li,Chris R. Bowen,Ya Yang
Tactile sensors are a potential solution for material identification. However, current potential tactile sensors for material identification are pressed, expensive, and applications-confined. Here we report a clamped-on pyroelectric tactile sensor on the basis of a ferroelectric Bi 0.5 Na 0.5 TiO 3 material to identify different film materials. The fabricated device exhibits different heat absorption capacities while in contact with different materials, leading to a different temperature change in the ferroelectric material under the same illumination. As a result, the device can recognize different materials by comparing the pyroelectric charge via integrating the obtained current under the same irradiation of 365 nm light-emitting diode. The clamped-on pyroelectric tactile sensor can identify six individual materials with a high accuracy of 98.8% and a fast response of 40 ms. All of the above processes can be accomplished with an intelligent material identification system. The device provides a new solution for material identification and lays a foundation for smart factories and laboratories.
研究领域:
传感器技术
·
功能材料
·
智能感知
·
自动化检测
·
工业智能化
✓ 已验证
DOI
高影响因子
Small
·
2024
·
IF 13
Yunsheng Li,Wenxu Wang,Xiaojing Cui,Ning Li,Xueliang Ma,Zhaosu Wang,Yuyou Nie,Zhiquan Huang,Hulin Zhang
Wearable devices equipped with high-performance flexible sensors that can identify diverse physical information free from batteries are playing an indispensable role in various fields. However, previous studies on flexible sensors have primarily focused on their elasticity and temperature-sensing capability, with few reports on material identification. In this paper, a thermogalvanic dual-network hydrogel is fabricated with [Fe(CN) 6 ] 3-/4− as a redox couple and lithium magnesium silicate, Gdm + and lithium bromide as key electrolytes to optimize the interconnected porous structure of the gel, which shows excellent mechanical and thermoelectric properties with a thermopower as high as 4.01 mV K −1 . A self-powered material identification ring is developed based on the temperature-triggered thermoelectric response of the gel in conjunction with machine learning, which can actively infer materials without an external power connection by analyzing the voltage signals correlated with interfacial heat transfer produced upon contact with different materials. The proposed gel ring has important applications for future areas such as human–computer interaction and haptic-associated artificial intelligence.
研究领域:
材料科学
·
人工智能
·
生物医学工程
✓ 已验证
产品关联
DOI
PubMed
高影响因子
ACS Applied Materials & Interfaces
·
2024
·
IF 8.3
Lu Peng,Jingyi Xu,Shen Yuan,Shengzhao Li,Shihao Deng,Hao Zhu,Lei Fu,Ting Zhang,Tie Li
Humans possess the remarkable ability to perceive the intricate world by integrating multiple senses. However, the challenge of enabling humanoid robots to achieve multimodal sensing and fine recognition of metallic materials persists. In this study, we propose a flexible tactile sensor that mimics the sensory patterns of human skin, which is assembled by a flexible electromagnetic coil that is engraved on the surface of a polyimide substrate and porous MXene/CNT aerogel. This sensor is capable of detecting pressure, temperature, and inductive signals with minimal interference via three disparate response mechanisms of the piezoresistive sensing, the thermoelectric principle, and the electromagnetic induction effect, allowing the device with the abilities of sensing grasp forces and selectively identifying ferromagnetic and nonferromagnetic metals, which has a high accuracy rate of 99.2% in distinguishing mixed metals with varying ratios based on the fusion algorithm of multimodal sensory data. Further, the sensor was integrated on a humanoid robotic hand to demonstrate its recognition capacity of objects used in a kitchen setting and a simulated scenario of mineral exploration, achieving a remarkable ultrafine accuracy of 100% in distinguishing 16 common metal products. These findings will pave the way for humanoid robots to attain heightened levels of perception and recognition.
研究领域:
机器人技术
·
材料科学
·
传感器技术
✓ 已验证
产品关联
DOI
PubMed
高影响因子
ADVANCED MATERIALS
·
2022
·Vol. 34
·期 29
·2203073
·
IF 32.086
Xuelian Wei,Baocheng Wang,Zhiyi Wu,Zhong Lin Wang
Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.
研究领域:
机器人学
·
传感器技术
·
人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）