Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-...Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-sensor predictive accuracy,ensuring interpretability and reliable performance across varying industrial operating conditions remains a challenge[1]–[4].This is precisely what Industry 5.0,proposed by the European Commission in 2021,advocates[5],[6].It integrates various cutting-edge technologies,such as human-machine interaction,digital twins,cybersecurity and artificial intelligence,to facilitate the development of better soft sensors.展开更多
Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling...Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.展开更多
Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and relia...Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and reliable connectivity.This study proposes SAC-HTC(Soft Actor-Critic-based High-performance Topology Control),a deep reinforcement learning(DRL)method based on the Actor-Critic framework,implemented within a Software Defined Wireless Sensor Network(SDWSN)architecture.In this approach,sensor nodes periodically transmit state information,including coordinates,node degree,transmission power,and neighbor lists,to a centralized controller.The controller acts as the reinforcement learning(RL)agent,with the Actor generating decisions to adjust transmission ranges,while the Critic evaluates action values to reflect the overall network performance.The bidirectional Node-Controller feedback mechanism enables the controller to issue appropriate control commands to each node,ensuring the maintenance of the desired node degree,reducing energy consumption,and preserving network connectivity.The algorithmfurther incorporates soft entropy adjustment to balance exploration and exploitation,alongwith an off-policy mechanism for efficient data reuse,making it well-suited to the resource-constrained conditions ofWSNs.Simulation results demonstrate that SAC-HTC not only outperforms traditional methods and several existing RL algorithms but also achieves faster convergence,optimized communication range control,global connectivity maintenance,and extended network lifetime.The key novelty of this research lies in the integration of the SAC method with the SDWSN architecture forWSNs topology control,providing an adaptive,efficient,and highly promisingmechanism for large-scale,dynamic,and high-performance sensor networks.展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Un...Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Unit(FCCU).The FCCU operates through interconnected subsystems,generating high-dimensional,nonlinear,and non-stationary data characterized by spatiotemporally correlated.The decant oil solid content is the crucial indicator for monitoring catalyst loss from the reactor-regenerator system and coking risk tendency at the fractionating tower bottom that relies on sampling and laboratory testing,which is lagging responsiveness and labor-intensive.Developing the online decant oil solid content soft sensor using industrial data to support operators in conducting predictive maintenance is essential.Therefore,this paper proposes a hybrid deep learning framework for soft sensor development that combines spatiotemporal pattern extraction with interpretability,enabling accurate risk identification in dynamic operational conditions.This framework employs a Filter-Wrapper method for dimensionality reduction,followed by a 2D Convolutional Neural Network(2DCNN)for extracting spatial patterns,and a Bidirectional Gated Recurrent Unit(BiGRU)for capturing long-term temporal dependencies,with an Attention Mechanism(AM)to highlight critical features adaptively.The integration of SHapley Additive exPlanations(SHAP),Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),2DCNN,and expert knowledge precisely quantifies feature contributions and decomposes signals,significantly enhancing the practicality of risk identification.Applied to a China refinery with processing capacity of 2.80×10^(6) t/a,the soft sensor achieved the R^(2) value of 0.93 and five-level risk identification accuracy of 96.42%.These results demonstrate the framework's accuracy,robustness,and suitability for complex industrial scenarios,advancing risk visualization and management.展开更多
Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article intro...Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article introduces a novel GNN framework,termed entropy-regularized ensemble adaptive graph(E^(2)AG),aimed at enhancing the predictive accuracy of AGNNs.Specifically,this work pioneers a novel AGNN learning approach based on mirror descent,which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs.Subsequently,motivated by multi-head self-attention mechanism,the training of ensembled AGNNs is rigorously examined within this framework,incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph.After that,the architecture and training algorithm of the model are then concisely summarized.Finally,to ascertain the efficacy of the proposed E^(2)AG model,extensive experiments are conducted on real-world industrial datasets.The evaluation focuses on prediction accuracy,model efficacy,and sensitivity analysis,demonstrating the superiority of E^(2)AG in industrial soft sensing applications.展开更多
To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and ...To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.展开更多
Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movemen...Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movement assistance,the field has shifted towards developing prosthetics that function as seamless extensions of the human body.During this progress,a key challenge remains the reduction of interface artifacts between prosthetic components and biological tissues.Soft electronics offer a promising solution due to their structural flexibility and enhanced tissue adaptability.However,achieving full integration of prosthetics with the human body requires both artificial perception and efficient transmission of physical signals.In this context,synaptic devices have garnered attention as next-generation neuromorphic computing elements because of their low power consumption,ability to enable hardware-based learning,and high compatibility with sensing units.These devices have the potential to create artificial pathways for sensory recognition and motor responses,forming a“sensory-neuromorphic system”that emulates synaptic junctions in biological neurons,thereby connecting with impaired biological tissues.Here,we discuss recent developments in prosthetic components and neuromorphic applications with a focus on sensory perception and sensorimotor actuation.Initially,we explore a prosthetic system with advanced sensory units,mechanical softness,and artificial intelligence,followed by the hardware implementation of memory devices that combine calculation and learning functions.We then highlight the importance and mechanisms of soft-form synaptic devices that are compatible with sensing units.Furthermore,we review an artificial sensory-neuromorphic perception system that replicates various biological senses and facilitates sensorimotor loops from sensory receptors,the spinal cord,and motor neurons.Finally,we propose insights into the future of closed-loop neuroprosthetics through the technical integration of soft electronics,including bio-integrated sensors and synaptic devices,into prosthetic systems.展开更多
Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applicati...Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.展开更多
Monitoring biogenic amines,which are metabolic byproducts of shrimp spoilage,is crucial for assessing food quality.Currently,most detection methods for biogenic amines suffer from limitations such as time-consuming pr...Monitoring biogenic amines,which are metabolic byproducts of shrimp spoilage,is crucial for assessing food quality.Currently,most detection methods for biogenic amines suffer from limitations such as time-consuming procedures,complex operations,and delayed results.Colorimetric analysis techniques have gained attention in recent years due to their advantages of short analysis time,simple operation,and suitability for on-site testing.This study successfully developed a series of colorimetric sensor platforms for biogenic amines by loading the natural active ingredient curcumin(CUR)and its derivative of Boron complex BFCUR onto filter paper and electrospun nanofibre films(ENFs),respectively.By analyzing the color response differences of these sensors upon contact with biogenic amines,the colorimetric sensors with superior detection performance were selected and further applied to the visual monitoring and indication of shrimp spoilage processes.展开更多
Flexible fiber sensors,However,traditional methods face challenges in fabricating low-cost,large-scale fiber sensors.In recent years,the thermal drawing process has rapidly advanced,offering a novel approach to flexib...Flexible fiber sensors,However,traditional methods face challenges in fabricating low-cost,large-scale fiber sensors.In recent years,the thermal drawing process has rapidly advanced,offering a novel approach to flexible fiber sensors.Through the preform-tofiber manufacturing technique,a variety of fiber sensors with complex functionalities spanning from the nanoscale to kilometer scale can be automated in a short time.Examples include temperature,acoustic,mechanical,chemical,biological,optoelectronic,and multifunctional sensors,which operate on diverse sensing principles such as resistance,capacitance,piezoelectricity,triboelectricity,photoelectricity,and thermoelectricity.This review outlines the principles of the thermal drawing process and provides a detailed overview of the latest advancements in various thermally drawn fiber sensors.Finally,the future developments of thermally drawn fiber sensors are discussed.展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
Tactile sensing of subcutaneous organ vibrations provides a promising route toward human-machine interfaces and wear-able diagnostics,particularly for voice rehabilitation and silent-speech communication.Here,we prese...Tactile sensing of subcutaneous organ vibrations provides a promising route toward human-machine interfaces and wear-able diagnostics,particularly for voice rehabilitation and silent-speech communication.Here,we present a bioinspired piezoelectric vibration sensor that mimics the graded stiffness and stress-based transduction mechanism of otolithic cilia in the human vestibular system.The device consists of a trapezoidal cantilever array with tip inertial masses,fabricated through a hybrid stereolithography 3D printing and laser micromachining process for rapid prototyping without cleanroom facilities.Finite-element modeling and experimental measurements demonstrate a fundamental resonance near 1.2 kHz,a 5%flat-bandwidth of 350 Hz,and an in-band charge sensitivity of 3.17 pC/g.A wearable proof-of-concept test further verifies the sensor's ability to reproducibly distinguish phoneme-specific vibration patterns in both time and frequency domains.This work establishes a foundation for bioinspired tactile sensing front-ends in wearable voice interfaces and other intelligent diagnostic systems integrated with machine-learning algorithms.展开更多
Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve inte...Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve intelligent functions like autonomous decisions and mechanical computation.However,current soft switches suffer from complex fabrication processes,limited performance,and a lack of multimodal control,which hinder their practical application and the realization of machine intelligence.Herein,by harnessing the unique self-pinch and self-healing effects of the gallium-based liquid metals(LMs),we describe a soft high-performance electric switch composed of an LM line encapsulated within an elastomer.Applying pressure to deform the LM switch can increase local current density,leading to the electromagnetic self-pinch effect for switching off.After releasing pressure,the LM can spontaneously heal with the elastic recovery of the elastomer for switching on.This LM switch shows comprehensive advantages,including a compact design(0.5 mm×1.5 mm×10 mm),good stretchability(100%),high on/off ratio(~10^(9)),rapid response time(<100 ms),and excellent durability(>12000 cycles).Moreover,the LM switches enable multiple control modes,including magnetic and optical stimulation,through the integration of responsive materials.We demonstrate various LM switch-enabled functional soft machines,such as an interactive flexible gripper,a self-oscillating soft crawler,and wearable logic gates.This work will open new avenues for the application of LM in intelligent soft machines and advanced wearable electronics.展开更多
Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th...Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.展开更多
Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations...Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.展开更多
This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the ...In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.展开更多
Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ion...Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ionic liquid(IL),using 1-ethyl-3-methylimidazolium bromide([EMIM]Br)IL as solvent.Benefiting from the compatibility of ILs and alkene-PR,the cross-linked network slide-ring gel not only maintains excellent conductivity(1.52×10^(−2) S/m),but also has effectively improved mechanical properties(513%fracture strain,0.713 MPa fracture stress,211 kPa elastic modulus and 1366 kJ/m^(3) toughness)and adhesive properties(472.3±25.9 kPa).The supramolecular gel can be used as a strain sensor to efficiently monitor deformation signals in real-time at least 200 times.Especially,the slide-ring gel can self-power generated by triboelectric effect and electrostatic induction between the skin layer and the polydimethylsiloxane(PDMS)layer that encapsulates the gel,achieving reversible and durable motion sensing,which provides a convenient pathway for constructing supramolecular self-powered flexible electronic materials.展开更多
文摘Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-sensor predictive accuracy,ensuring interpretability and reliable performance across varying industrial operating conditions remains a challenge[1]–[4].This is precisely what Industry 5.0,proposed by the European Commission in 2021,advocates[5],[6].It integrates various cutting-edge technologies,such as human-machine interaction,digital twins,cybersecurity and artificial intelligence,to facilitate the development of better soft sensors.
基金the financial support provided by the Special Foundation for State Major Basic Research Program of China(2021YFD2101005)National Natural Science Foundation of China(22478057,22178045).
文摘Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.
文摘Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and reliable connectivity.This study proposes SAC-HTC(Soft Actor-Critic-based High-performance Topology Control),a deep reinforcement learning(DRL)method based on the Actor-Critic framework,implemented within a Software Defined Wireless Sensor Network(SDWSN)architecture.In this approach,sensor nodes periodically transmit state information,including coordinates,node degree,transmission power,and neighbor lists,to a centralized controller.The controller acts as the reinforcement learning(RL)agent,with the Actor generating decisions to adjust transmission ranges,while the Critic evaluates action values to reflect the overall network performance.The bidirectional Node-Controller feedback mechanism enables the controller to issue appropriate control commands to each node,ensuring the maintenance of the desired node degree,reducing energy consumption,and preserving network connectivity.The algorithmfurther incorporates soft entropy adjustment to balance exploration and exploitation,alongwith an off-policy mechanism for efficient data reuse,making it well-suited to the resource-constrained conditions ofWSNs.Simulation results demonstrate that SAC-HTC not only outperforms traditional methods and several existing RL algorithms but also achieves faster convergence,optimized communication range control,global connectivity maintenance,and extended network lifetime.The key novelty of this research lies in the integration of the SAC method with the SDWSN architecture forWSNs topology control,providing an adaptive,efficient,and highly promisingmechanism for large-scale,dynamic,and high-performance sensor networks.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(22021004)Sinopec Major Science and Technology Projects(321123-1)。
文摘Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Unit(FCCU).The FCCU operates through interconnected subsystems,generating high-dimensional,nonlinear,and non-stationary data characterized by spatiotemporally correlated.The decant oil solid content is the crucial indicator for monitoring catalyst loss from the reactor-regenerator system and coking risk tendency at the fractionating tower bottom that relies on sampling and laboratory testing,which is lagging responsiveness and labor-intensive.Developing the online decant oil solid content soft sensor using industrial data to support operators in conducting predictive maintenance is essential.Therefore,this paper proposes a hybrid deep learning framework for soft sensor development that combines spatiotemporal pattern extraction with interpretability,enabling accurate risk identification in dynamic operational conditions.This framework employs a Filter-Wrapper method for dimensionality reduction,followed by a 2D Convolutional Neural Network(2DCNN)for extracting spatial patterns,and a Bidirectional Gated Recurrent Unit(BiGRU)for capturing long-term temporal dependencies,with an Attention Mechanism(AM)to highlight critical features adaptively.The integration of SHapley Additive exPlanations(SHAP),Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),2DCNN,and expert knowledge precisely quantifies feature contributions and decomposes signals,significantly enhancing the practicality of risk identification.Applied to a China refinery with processing capacity of 2.80×10^(6) t/a,the soft sensor achieved the R^(2) value of 0.93 and five-level risk identification accuracy of 96.42%.These results demonstrate the framework's accuracy,robustness,and suitability for complex industrial scenarios,advancing risk visualization and management.
基金supported in part by the National Natural Science Foundation of China(NSFC)(62473103,62203169,62473121)the Postdoctoral Science Foundation of Zhejiang Province(ZJ2023011).
文摘Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article introduces a novel GNN framework,termed entropy-regularized ensemble adaptive graph(E^(2)AG),aimed at enhancing the predictive accuracy of AGNNs.Specifically,this work pioneers a novel AGNN learning approach based on mirror descent,which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs.Subsequently,motivated by multi-head self-attention mechanism,the training of ensembled AGNNs is rigorously examined within this framework,incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph.After that,the architecture and training algorithm of the model are then concisely summarized.Finally,to ascertain the efficacy of the proposed E^(2)AG model,extensive experiments are conducted on real-world industrial datasets.The evaluation focuses on prediction accuracy,model efficacy,and sensitivity analysis,demonstrating the superiority of E^(2)AG in industrial soft sensing applications.
基金supported by National Natural Science Foundation of China(No.52266012)Gansu Province College Industry Support Plan Project(No.2022CYZC-34)+1 种基金Gansu Province Major Science and Technology Special Project(Nos.20ZD7GF011,22ZD6GA063)Jiuquan City Science and Technology Programme Project(No.2023CA3058).
文摘To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2020R1C1C1005567)supported by the NAVER Digital Bio Innovation Research Fund,funded by NAVER Corporation(Grant No.[37-2023-0040])+3 种基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-00261,Development of low power/low delay/self-power suppliable RF simultaneous information and power transfer system and stretchable electronic epineurium for wireless nerve bypass implementation)supported by Institute for Basic Science(IBS-R015-D1,IBSR015-D2)supported by a grant of the Korea-US Collaborative Research Fund(KUCRF)funded by the Ministry of Science and ICT and Ministry of Health&Welfare,Republic of Korea(Grant Number.RS-2024-00467213)。
文摘Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movement assistance,the field has shifted towards developing prosthetics that function as seamless extensions of the human body.During this progress,a key challenge remains the reduction of interface artifacts between prosthetic components and biological tissues.Soft electronics offer a promising solution due to their structural flexibility and enhanced tissue adaptability.However,achieving full integration of prosthetics with the human body requires both artificial perception and efficient transmission of physical signals.In this context,synaptic devices have garnered attention as next-generation neuromorphic computing elements because of their low power consumption,ability to enable hardware-based learning,and high compatibility with sensing units.These devices have the potential to create artificial pathways for sensory recognition and motor responses,forming a“sensory-neuromorphic system”that emulates synaptic junctions in biological neurons,thereby connecting with impaired biological tissues.Here,we discuss recent developments in prosthetic components and neuromorphic applications with a focus on sensory perception and sensorimotor actuation.Initially,we explore a prosthetic system with advanced sensory units,mechanical softness,and artificial intelligence,followed by the hardware implementation of memory devices that combine calculation and learning functions.We then highlight the importance and mechanisms of soft-form synaptic devices that are compatible with sensing units.Furthermore,we review an artificial sensory-neuromorphic perception system that replicates various biological senses and facilitates sensorimotor loops from sensory receptors,the spinal cord,and motor neurons.Finally,we propose insights into the future of closed-loop neuroprosthetics through the technical integration of soft electronics,including bio-integrated sensors and synaptic devices,into prosthetic systems.
基金National Natural Science Foundation of China(52171191,52371198)Project of Constructing National Independent Innovation Demonstration Zones(XM2024XTGXQ05)。
文摘Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.
基金Supported by the Guangdong-Hong Kong-Macao Joint Laboratory on Micro-Nano Manufacturing Technology,China(No.2021LSYS004)Guangdong Provincial Key Laboratory of Sustainable Biomimetic Materials and Green Energy,China(No.2024B1212010003)。
文摘Monitoring biogenic amines,which are metabolic byproducts of shrimp spoilage,is crucial for assessing food quality.Currently,most detection methods for biogenic amines suffer from limitations such as time-consuming procedures,complex operations,and delayed results.Colorimetric analysis techniques have gained attention in recent years due to their advantages of short analysis time,simple operation,and suitability for on-site testing.This study successfully developed a series of colorimetric sensor platforms for biogenic amines by loading the natural active ingredient curcumin(CUR)and its derivative of Boron complex BFCUR onto filter paper and electrospun nanofibre films(ENFs),respectively.By analyzing the color response differences of these sensors upon contact with biogenic amines,the colorimetric sensors with superior detection performance were selected and further applied to the visual monitoring and indication of shrimp spoilage processes.
基金supported by the National Key Research and Development Program of China(2023YFB3809800)the National Natural Science Foundation of China(52172249,52525601)+2 种基金the Chinese Academy of Sciences Talents Program(E2290701)the Jiangsu Province Talents Program(JSSCRC2023545)the Special Fund Project of Carbon Peaking Carbon Neutrality Science and Technology Innovation of Jiangsu Province(BE2022011).
文摘Flexible fiber sensors,However,traditional methods face challenges in fabricating low-cost,large-scale fiber sensors.In recent years,the thermal drawing process has rapidly advanced,offering a novel approach to flexible fiber sensors.Through the preform-tofiber manufacturing technique,a variety of fiber sensors with complex functionalities spanning from the nanoscale to kilometer scale can be automated in a short time.Examples include temperature,acoustic,mechanical,chemical,biological,optoelectronic,and multifunctional sensors,which operate on diverse sensing principles such as resistance,capacitance,piezoelectricity,triboelectricity,photoelectricity,and thermoelectricity.This review outlines the principles of the thermal drawing process and provides a detailed overview of the latest advancements in various thermally drawn fiber sensors.Finally,the future developments of thermally drawn fiber sensors are discussed.
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
文摘Tactile sensing of subcutaneous organ vibrations provides a promising route toward human-machine interfaces and wear-able diagnostics,particularly for voice rehabilitation and silent-speech communication.Here,we present a bioinspired piezoelectric vibration sensor that mimics the graded stiffness and stress-based transduction mechanism of otolithic cilia in the human vestibular system.The device consists of a trapezoidal cantilever array with tip inertial masses,fabricated through a hybrid stereolithography 3D printing and laser micromachining process for rapid prototyping without cleanroom facilities.Finite-element modeling and experimental measurements demonstrate a fundamental resonance near 1.2 kHz,a 5%flat-bandwidth of 350 Hz,and an in-band charge sensitivity of 3.17 pC/g.A wearable proof-of-concept test further verifies the sensor's ability to reproducibly distinguish phoneme-specific vibration patterns in both time and frequency domains.This work establishes a foundation for bioinspired tactile sensing front-ends in wearable voice interfaces and other intelligent diagnostic systems integrated with machine-learning algorithms.
基金financial support from the Natural Science Foundation of Jiangsu Province(BK20220859)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0473)the SEU Innovation Capability Enhancement Plan for Doctoral Students(CXJH_SEU 24144)supported by Open Research Fund of State Key Laboratory of Analytical Chemistry for Life Science,School of Chemistry and Chemical Engineering,Nanjing University。
文摘Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve intelligent functions like autonomous decisions and mechanical computation.However,current soft switches suffer from complex fabrication processes,limited performance,and a lack of multimodal control,which hinder their practical application and the realization of machine intelligence.Herein,by harnessing the unique self-pinch and self-healing effects of the gallium-based liquid metals(LMs),we describe a soft high-performance electric switch composed of an LM line encapsulated within an elastomer.Applying pressure to deform the LM switch can increase local current density,leading to the electromagnetic self-pinch effect for switching off.After releasing pressure,the LM can spontaneously heal with the elastic recovery of the elastomer for switching on.This LM switch shows comprehensive advantages,including a compact design(0.5 mm×1.5 mm×10 mm),good stretchability(100%),high on/off ratio(~10^(9)),rapid response time(<100 ms),and excellent durability(>12000 cycles).Moreover,the LM switches enable multiple control modes,including magnetic and optical stimulation,through the integration of responsive materials.We demonstrate various LM switch-enabled functional soft machines,such as an interactive flexible gripper,a self-oscillating soft crawler,and wearable logic gates.This work will open new avenues for the application of LM in intelligent soft machines and advanced wearable electronics.
文摘Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.
基金supported by the National Natural Science Foundation of China(General Program)under Grant 52571385National Key R&D Program of China(Grant No.2024YFC2815000 and No.2024YFB3816000)+12 种基金Open Fund of State Key Laboratory of Deep-sea Manned Vehicles(Grant No.2025SKLDMV07)Shenzhen Science and Technology Program(WDZC20231128114452001,JCYJ20240813112107010 and JCYJ20240813111910014)the Tsinghua SIGS Scientific Research Startup Fund(QD2022021C)the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM 01 Z006)the Ocean Decade International Cooperation Center(ODCC)(GHZZ3702840002024020000026)Shenzhen Key Laboratory of Advanced Technology for Marine Ecology(ZDSYS20230626091459009)Shenzhen Science and Technology Program(No.KJZD20240903100905008)the National Natural Science Foundation of China(No.22305141)Pearl River Talent Program(No.2023QN10C114)General Program of Guangdong Province(No.2025A1515011700)the Guangdong Innovative and Entrepreneurial Research Team Program(2023ZT10C040)Scientific Research Foundation from Shenzhen Finance Bureau(No.GJHZ20240218113600002)Tsinghua University(JC2023001).
文摘Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
基金financially supported by the Natural Science Foundation of Jiangsu Province,China(No.BK 20221502)the National Natural Science Foundation of China(No.42477147)。
文摘In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.
基金National Natural Science Foundation of China(NSFC,No.22131008)Natural Science Foundation of Tianjin(No.22JCYBJC00500)the Haihe Laboratory of Sustainable Chemical Transformations for financial support.
文摘Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ionic liquid(IL),using 1-ethyl-3-methylimidazolium bromide([EMIM]Br)IL as solvent.Benefiting from the compatibility of ILs and alkene-PR,the cross-linked network slide-ring gel not only maintains excellent conductivity(1.52×10^(−2) S/m),but also has effectively improved mechanical properties(513%fracture strain,0.713 MPa fracture stress,211 kPa elastic modulus and 1366 kJ/m^(3) toughness)and adhesive properties(472.3±25.9 kPa).The supramolecular gel can be used as a strain sensor to efficiently monitor deformation signals in real-time at least 200 times.Especially,the slide-ring gel can self-power generated by triboelectric effect and electrostatic induction between the skin layer and the polydimethylsiloxane(PDMS)layer that encapsulates the gel,achieving reversible and durable motion sensing,which provides a convenient pathway for constructing supramolecular self-powered flexible electronic materials.
