Advances of Electrochemical and Electrochemiluminescent Sensors Based on Covalent Organic Frameworks

Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore microenvironments.Since the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy storage.Among them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive interest.In this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and aptasensors.Notably,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary applications.Additionally,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.

Mechanoluminescent-Triboelectric Bimodal Sensors for Self-Powered Sensing and Intelligent Control

Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices;however,it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control.Here,we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micronanostructured mechanoluminescent elastomer,which can patterned-display the force trajectories.The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence(with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility).Moreover,a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances(voltage increases from 8 to 24 V).Based on the excellent bimodal sensing performances and durability of the obtained composite,a highly reliable intelligent control system by machine learning has been developed for controlling trolley,providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

MXene-Based Elastomer Mimetic StretchableSensors: Design, Properties, and Applications

Flexible sensors based on MXene-polymer composites are highly prospective for next-generation wearable electronics used in human-machine interfaces.One of the motivating factors behind the progress of flexible sensors is the steady arrival of new conductive materials.MXenes,a new family of 2D nanomaterials,have been draw-ing attention since the last decade due to their high electronic conduc-tivity,processability,mechanical robustness and chemical tunability.In this review,we encompass the fabrication of MXene-based polymeric nanocomposites,their structure-property relationship,and applications in the flexible sensor domain.Moreover,our discussion is not only lim-ited to sensor design,their mechanism,and various modes of sensing platform,but also their future perspective and market throughout the world.With our article,we intend to fortify the bond between flexible matrices and MXenes thus promoting the swift advancement of flexible MXene-sensors for wearable technologies.

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Flexible piezoresistive pressure sensor based on a graphene-carbon nanotube-polydimethylsiloxane composite

Flexible sensors are used widely in wearable devices, specifically flexible piezoresistive sensors, which are common and easy to manipulate.However, fabricating such sensors is expensive and complex, so proposed here is a simple fabrication approach involving a sensor containing microstructures replicated from a sandpaper template onto which polydimethylsiloxane containing a mixture of graphene and carbon nanotubes is spin coated. The surface morphologies of three versions of the sensor made using different grades of sandpaper are observed, and the corresponding pressure sensitivities and linearity and hysteresis characteristics are assessed and analyzed. The results show that the sensor made using 80-mesh sandpaper has the best sensing performance. Its sensitivity is 0.341 kPa-1in the loading range of 0–1.6 kPa, it responds to small external loading of 100 Pa with a resistance change of 10%, its loading and unloading response times are 0.126 and 0.2 s, respectively,and its hysteresis characteristic is ~7%, indicating that the sensor has high sensitivity, fast response, and good stability. Thus, the presented piezoresistive sensor is promising for practical applications in flexible wearable electronics.

Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Progress of Proximity Sensors for Potential Applications in Electronic Skins

Recently,electronic skins and fl exible wearable devices have been developed for widespread applications in medical monitoring,artifi cial intelligence,human–machine interaction,and artifi cial prosthetics.Flexible proximity sensors can accurately perceive external objects without contact,introducing a new way to achieve an ultrasensitive perception of objects.This article reviews the progress of fl exible capacitive proximity sensors,fl exible triboelectric proximity sensors,and fl exible gate-enhanced proximity sensors,focusing on their applications in the electronic skin fi eld.Herein,their working mechanism,materials,preparation methods,and research progress are discussed in detail.Finally,we summarize the future challenges in developing fl exible proximity sensors.

Recent progress on nanomaterial-based electrochemical dissolved oxygen sensors

Dissolved oxygen(DO)usually refers to the amount of oxygen dissolved in water.In the environment,medicine,and fermentation industries,the DO level needs to be accurate and capable of online monitoring to guide the precise control of water quality,clinical treatment,and microbial metabolism.Compared with other analytical methods,the electrochemical strategy is superior in its fast response,low cost,high sensitivity,and portable device.However,an electrochemical DO sensor faces a trade-off between sensitivity and long-term stability,which strongly limits its practical applications.To solve this problem,various advanced nanomaterials have been proposed to promote detection performance owing to their excellent electrocatalysis,conductivity,and chemical stability.Therefore,in this review,we focus on the recent progress of advanced nanomaterial-based electrochemical DO sensors.Through the comparison of the working principles on the main analysis techniques toward DO,the advantages of the electrochemical method are discussed.Emphasis is placed on recently developed nanomaterials that exhibit special characteristics,including nanostructures and preparation routes,to benefit DO determination.Specifically,we also introduce some interesting research on the configuration design of the electrode and device,which is rarely introduced.Then,the different requirements of the electrochemical DO sensors in different application fields are included to provide brief guidance on the selection of appropriate nanomaterials.Finally,the main challenges are evaluated to propose future development prospects and detection strategies for nanomaterial-based electrochemical sensors.

Low palladium content CeO_(2)/ZnO composite for acetone sensor with sub-second response prepared by ultrasonic method

In practical applications,noble metal doping is often used to prepare high performance gas sensors,but more noble metal doping will lead to higher preparation costs.In this study,CeO_(2)/ZnO-Pd with low palladium content was prepared by ultrasonic method with fast response and high selectivity for acetone sensing.With the same amount of palladium added,the selectivity coefficient of CeO_(2)/ZnO-Pd is 1.88 times higher than that of the stirred sensor.Compared with the pure PdO-doped CeO_(2)/ZnO-PdO material,the content of Pd in CeO_(2)/ZnO-PdO is about 30%of that in CeO_(2)/ZnO-PdO,but the selectivity coefficient for acetone is 2.56 times higher.The CeO_(2)/ZnO-Pd sensor has a higher response(22.54)to 50×10^(−6) acetone at 300℃and the selectivity coefficient is 2.57 times that of the CeO_(2)/ZnO sensor.The sensor has a sub-second response time(0.6 s)and still has a 2.36 response to 330×10^(−9) of acetone.Ultrasonic doping makes Pd particles smaller and increases the contact area with gas.Meanwhile,the composition of n-p-n heterojunction and the synergistic effect of Pd/PdO improve the sensor performance.It shows that ultrasonic Pd doping provides a way to improve the utilization rate of doped metals and prepare highly selective gas sensors.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Ultra-broadband microwave absorber and high-performance pressure sensor based on aramid nanofiber,polypyrrole and nickel porous aerogel

Electronic devices have become ubiquitous in our daily lives,leading to a surge in the use of microwave absorbers and wearable sensor devices across various sectors.A prime example of this trend is the aramid nanofibers/polypyrrole/nickel(APN)aerogels,which serve dual roles as both microwave absorbers and pressure sensors.In this work,we focused on the preparation of aramid nanofibers/polypyrrole(AP15)aerogels,where the mass ratio of aramid nanofibers to pyrrole was 1:5.We employed the oxidative polymerization method for the preparation process.Following this,nickel was thermally evaporated onto the surface of the AP15 aerogels,resulting in the creation of an ultralight(9.35 mg·cm^(-3)).This aerogel exhibited a porous structure.The introduction of nickel into the aerogel aimed to enhance magnetic loss and adjust impedance matching,thereby improving electromagnetic wave absorption performance.The minimum reflection loss value achieved was-48.7 dB,and the maximum effective absorption bandwidth spanned 8.42 GHz with a thickness of 2.9 mm.These impressive metrics can be attributed to the three-dimensional network porous structure of the aerogel and perfect impedance matching.Moreover,the use of aramid nanofibers and a three-dimensional hole structure endowed the APN aerogels with good insulation,flame-retardant properties,and compression resilience.Even under a compression strain of 50%,the aerogel maintained its resilience over 500 cycles.The incorporation of polypyrrole and nickel particles further enhanced the conductivity of the aerogel.Consequently,the final APN aerogel sensor demonstrated high sensitivity(10.78 kPa-1)and thermal stability.In conclusion,the APN aerogels hold significant promise as ultra-broadband microwave absorbers and pressure sensors.

Highly Sensitive Ammonia Gas Sensors at Room Temperature Based on the Catalytic Mechanism of N,C Coordinated Ni Single-Atom Active Center

Significant challenges are posed by the limitations of gas sensing mechanisms for trace-level detection of ammonia(NH3).In this study,we propose to exploit single-atom catalytic activation and targeted adsorption properties to achieve highly sensitive and selective NH3 gas detection.Specifically,Ni singleatom active sites based on N,C coordination(Ni-N-C)were interfacially confined on the surface of two-dimensional(2D)MXene nanosheets(Ni-N-C/Ti_(3)C_(2)Tx),and a fully flexible gas sensor(MNPE-Ni-N-C/Ti_(3)C_(2)Tx)was integrated.The sensor demonstrates a remarkable response value to 5 ppm NH3(27.3%),excellent selectivity for NH3,and a low theoretical detection limit of 12.1 ppb.Simulation analysis by density functional calculation reveals that the Ni single-atom center with N,C coordination exhibits specific targeted adsorption properties for NH3.Additionally,its catalytic activation effect effectively reduces the Gibbs free energy of the sensing elemental reaction,while its electronic structure promotes the spill-over effect of reactive oxygen species at the gas-solid interface.The sensor has a dual-channel sensing mechanism of both chemical and electronic sensitization,which facilitates efficient electron transfer to the 2D MXene conductive network,resulting in the formation of the NH3 gas molecule sensing signal.Furthermore,the passivation of MXene edge defects by a conjugated hydrogen bond network enhances the long-term stability of MXene-based electrodes under high humidity conditions.This work achieves highly sensitive room-temperature NH3 gas detection based on the catalytic mechanism of Ni single-atom active center with N,C coordination,which provides a novel gas sensing mechanism for room-temperature trace gas detection research.

Electromagnetic responses on microstructures of duplex stainless steels based on 3D cellular and electromagnetic sensor finite element models

Microstructures determine mechanical properties of steels,but in actual steel product process it is difficult to accurately control the microstructure to meet the requirements.General microstructure characterization methods are time consuming and results are not rep-resentative for overall quality level as only a fraction of steel sample was selected to be examined.In this paper,a macro and micro coupled 3D model was developed for nondestructively characterization of steel microstructures.For electromagnetic signals analysis,the relative permeability value computed by the micro cellular model can be used in the macro electromagnetic sensor model.The effects of different microstructure components on the relative permeability of duplex stainless steel(grain size,phase fraction,and phase distribu-tion)were discussed.The output inductance of an electromagnetic sensor was determined by relative permeability values and can be val-idated experimentally.The findings indicate that the inductance value of an electromagnetic sensor at low frequency can distinguish dif-ferent microstructures.This method can be applied to real-time on-line characterize steel microstructures in process of steel rolling.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.