Editor's Note: Since October last year, our newspaper's special column "Guo Yuansheng Explains Sensors" has published nearly 30 professional articles (with plans to further expand the scope of scenarios in the future). The articles focus on national strategic industries and eight key application scenarios, including intelligent manufacturing, smart agriculture, smart home appliances and consumer electronics, and low-altitude economy. Using accessible and popular language, the column systematically and clearly elaborates on the technological applications, demand space, industrial scale, and development trends of sensors in different scenarios. With clear viewpoints, ideas, and concepts, the articles have inspired innovative thinking, broadened horizons, and pointed out the direction for industrial innovation and development. They have attracted widespread attention from readers across various sectors, gained industry recognition and discussion, and generated a positive impact.
If these articles represent the "Application Chapter" of this column, starting from this issue, the column will launch a brand-new series—the "Technology Chapter." As an extension and deepening of the previous series, Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society and Executive Vice Chairman of the China Sensor and IoT Industry Alliance, shifts the focus from "application scenarios" to the "technology itself." He focuses on the ten mainstream sensor types: quantum, acoustic, force, optical, gas, magnetic, temperature, humidity, radio frequency (RFID), and biosensors. He systematically and popularly dissects each type from dimensions such as sensing mechanisms and materials, characteristics and performance indicators, industrial chain and capabilities, technological application status, domestic and international industrial landscapes, and future technological development trends. The aim is to provide industry practitioners, technology managers, investment institutions, and cross-disciplinary researchers with a technical map that combines professional depth and quick accessibility, helping readers establish a clear knowledge framework and industrial coordinates during the critical period of rapid sensor technology iteration and track switching.
We welcome readers to continue following the "Application Chapter" while paying closer attention to the series content of "Guo Yuansheng Explains Sensors: Technology Chapter." This will allow you to gain a more detailed understanding of the technical systems and underlying logic of sensing mechanisms and materials, as well as sensing devices and processes. Starting from the most fundamental technologies, you will comprehend the evolutionary logic and innovative development opportunities of sensing technology.
For decades, relying on basic sensing elements formed by sensing mechanisms and materials such as silicon-based, optical fiber, and metal oxide, sensors have achieved the conversion of physical, chemical, and biological quantities, completing the acquisition and measurement of basic signals like temperature, pressure, gas, and optics. However, these materials and mechanisms face issues such as increasing R&D costs, growing difficulties in performance improvement, and continuously low marginal benefits. Therefore, the global sensing industry is facing the situation of breaking out of inherent models and industrial involution. Industry development is gradually bidding farewell to mere hardware parameter iteration and moving towards the multiple integration of "material innovation + mechanism innovation + AI empowerment + system integration." It integrates perception, storage, cognition, analysis, decision-making, and self-adaptation, evolving into a systematic, integrated, and autonomous super-intelligent sensing unit system (i.e., Intelligent Super-Sensing Unit), stepping into a new track of generational leap.
This article conducts analysis based on information from top international journals, official white papers, and publicly available corporate technical reports. Combined with the latest frontier achievements from top global research institutions and hard-core measured data from the industry in 2025, it systematically and comprehensively dissects the structural breakthroughs and transformations in the global sensor industry from multiple dimensions. It presents a panoramic view of the technological reshaping and application paradigms of "Intelligent Super-Sensing Units," and deduces the industry development trends and new landscape.
I. Technical Characteristics and Development Limitations of Traditional Sensing Elements
The technical system, product forms, and application models of traditional sensing elements have obvious technical shortcomings and development bottlenecks. The specific characteristics and limitations are as follows:
Core Technical Characteristics: Singularization, Passivity, and Discretization
From the perspective of functional attributes, traditional sensing elements only undertake the task of raw signal acquisition. They lack data parsing, logical judgment, and environmental self-adaptation capabilities. They are typical passive discrete independent devices with single, closed hardware and software, lacking interconnection capabilities. Operating mainly in an isolated mode, they can only achieve basic, single monitoring functions and cannot meet the collaborative sensing needs of modern industries for intelligence, systematization, and networking.
Industrial Development Limitations: Increased Innovation Difficulty and Shrinking Incremental Space
After decades of technological iteration and industrial competition in cost and scale, the potential in the three dimensions of materials, mechanisms, and functions has been fully released.
First, the limitations of sensing materials. Traditional materials have approached the limit of process optimization in terms of sensitivity, temperature range tolerance, anti-interference, long-term reliability, and stability. To achieve a significant improvement in performance, it is necessary to rely on ultra-high precision process iteration and complex structural improvement, which directly leads to a substantial increase in R&D and mass production costs.
Second, the limitations of sensing mechanisms. Previous sensors generally followed the solidified logic of "one device, one mechanism; one device, one parameter." There is an inherent shortcoming that their fixed mechanisms can only correspond to a single detection dimension, making it impossible to achieve simultaneous acquisition and fusion analysis of multiple physical quantities and chemical parameters.
Third, the limitations of intelligent functions. Most of them mainly output analog signals and are not equipped with edge computing power. All signal noise reduction, data analysis, fault judgment, and decision control rely on secondary processing by backend devices or cloud platforms, making them unable to directly support high-end needs such as real-time early warning, autonomous regulation, and intelligent decision-making at the terminal.
Rigid Market Demand and Raised Technical Thresholds
Given the growth of the Internet of Everything and intelligent upgrades, new and stringent access requirements have been proposed for the technical indicators, functional attributes, integration capabilities, intelligence levels, and reliability standards of sensors. Traditional sensor products are difficult to match the rigid digital demand. Meanwhile, market access conditions such as industry standardization, localized development, and high-reliability certification continue to tighten, significantly raising the market entry threshold. The intelligent extension of sensors has become an inevitable trend for industrial development and technological innovation.
II. Disruptive Industry Transformation: Four Leaps from Basic Elements to Intelligent Super-Sensing Units
The core definition of an Intelligent Super-Sensing Unit is: a new generation of intelligent sensing system that integrates multiple disruptive new materials, innovative intelligent sensing mechanisms, multi-dimensional data fusion and storage, edge AI computing power and optimization, and networked collaborative architecture. It possesses capabilities of active perception, data caching, autonomous cognition, intelligent decision-making, and environmental self-adaptation, thoroughly subverting the underlying logic and functional boundaries of traditional sensors.
Material Transformation: From Single Systems like Silicon-Based to Diverse Super-Sensing Material Systems
1. Two-dimensional materials: Atomic-level thickness two-dimensional materials such as graphene, MoS₂, and MXene possess ultra-large specific surface areas and ultra-high carrier mobility. Their sensitivity is improved by more than 1000 times compared to traditional silicon-based materials, achieving precise detection of micro and trace substances. Among them, the graphene formaldehyde sensor developed by the University of Cambridge in the UK has a measured lowest limit of detection (LOD) of 8.02 ppb; the MXene sensing material developed by ETH Zurich in Switzerland can simultaneously and accurately identify 3-4 harmful gases (such as NH₃/SO₂/NO₂/acetone). Single-molecule level measurement mostly adopts nanopore/biosensing and non-gas sensing methods, with trace detection capabilities reaching 0.1 ppb. In high-precision environmental detection scenarios such as atmospheric environment tracing, indoor air treatment, and monitoring of trace pollutants in industrial exhaust, it can accurately capture ultra-low concentration harmful substances that traditional sensors cannot identify, supporting refined management of the ecological environment.
2. Nanocomposites: This composite material combines the advantages of quantum confinement detection with the flexible mechanical characteristics of carbon nanotubes. It not only achieves precise detection of ultra-trace gases but also possesses excellent deformation adaptation capabilities, making it suitable for both high-precision chemical monitoring and flexible wearable scenarios. Research teams in the US and South Korea have developed a carbon nanotube-quantum dot composite sensing layer. Relying on unique quantum confinement effects, and with ultra-high detection accuracy and excellent flexible mechanical properties, the gas detection limit is reduced to 0.1 ppb (i.e., 10⁻⁹), far exceeding traditional sensing accuracy. Meanwhile, when the flexible conductive film of carbon nanotubes is stretched to the limit of 100%–300% deformation, the attenuation of conductive performance after repeated bending and stretching is less than 10%–30%, perfectly adapting to unstructured application scenarios such as curved surfaces, deformation, and wearables. In scenarios such as intelligent wearable health monitoring, deformation detection of flexible industrial equipment, and ultra-trace gas leak investigation in precision chemicals, it can meet the long-term stable monitoring needs of complex curved surface equipment and dynamic deformation conditions.
3. Quantum functional materials: Frontier quantum materials such as diamond NV centers and superconducting quantum interference devices break through the limits of classical physical sensing accuracy, opening a new era of quantum super-sensing. The Romalis team at Princeton University in the US has achieved ultra-high precision magnetic field detection at the level of 0.16 fT/√Hz, capable of accurately capturing the weak discharge signals of single neurons. It has currently been successfully applied in animal experiments for brain-computer interfaces, providing core support for precise neural perception and medical brain science research. In frontier fields such as brain-computer interaction, precision medical diagnosis, geological geomagnetic exploration, and weak magnetic field anomaly detection in high-end precision equipment, it fills the technical gap in the precise perception of ultra-weak signals.
4. Flexible bionic materials: Organic semiconductors and bionic polymer materials break the constraints of the rigid structure of traditional sensors. They possess flexible conformal characteristics and can simulate the sensory functions of human skin. Imperial College London has developed a flexible electronic skin with unit density at the international top level, deploying 200-500 sensitive units per square centimeter to form a biological nerve-adapted sensing array. It can simultaneously identify four types of signals: pressure, temperature, texture, and deformation, approaching the omnidirectional perception level of the human body. This material is suitable for scenarios such as embodied intelligent arms, bionic prosthetics, human body surfaces, and special-shaped industrial curved surfaces. It can be applied to robot tactile recognition, rehabilitation wearable medical equipment, and industrial omnidirectional conformal monitoring, achieving omnidirectional multi-dimensional perception without dead angles. In 2025, the University of Cambridge, in collaboration with UCL, developed a gelatin-based hydrogel electronic skin, achieving the integrated integration of sensing arrays and neural networks. The device is equipped with 32 microelectrodes to build massive sensing pathways. Relying on AI algorithms to analyze signals, it can distinguish different mechanical stimuli such as light touch, pressing, and damage. In early 2026, human amputee adaptation trials were conducted. The electronic skin can establish signal linkage with the remaining nerves of the human body, allowing users to perceive the direction of external touch.
Mechanism Transformation: From Physical Signal Conversion to Intelligent Perception Leap
Material innovation is the foundation of performance upgrade, and sensing mechanism innovation is the core soul of intelligent super-sensing. Modern sensors have thoroughly subverted the traditional single physical and chemical signal conversion mechanisms, achieving a fundamental leap from "passive mechanical detection" to "active intelligent perception."
1. Multi-mechanism fusion detection: The international community has currently entered the frontier technology R&D stage of multi-mechanism coupling and multi-parameter synchronous perception, forming three representative technical routes:
① Optical-electrical-thermal tri-modal fusion: In 2023, China's Zhijiang Laboratory published results in "Advanced Materials," integrating optical waveguides, thermal resistance, and electric field effects. A single device can simultaneously measure pressure, temperature, and proximity distance, truly achieving no crosstalk among the three parameters. It has been demonstrated on a small scale in scenarios such as industrial precision detection, smart warehousing, and robot electronic skin.
② Force-optical-electrical fusion sensing: Led by Stanford University in the US, based on micro-nano optical fiber/waveguide structures, pressure is first converted into an optical signal and then into an electrical signal, forming a pressure→optical→electrical chain amplification. The sensitivity is improved by about 5 times compared to traditional piezoelectric sensing. It mainly targets robot tactile sensing, bionic prosthetics, and flexible electronic skin. Grasping and texture recognition verification have been completed in the laboratory, and it is in the prototype stage.
③ Magnetic-optical-thermal fusion regulation: Pioneered by the Technical University of Munich in Germany, it uses magnetic fields to precisely regulate photothermal conversion efficiency, achieving temperature control accuracy at the ±0.5℃ level. It is corely used for tumor-targeted photothermal therapy and minimally invasive interventional surgery. Animal experiments have been completed, and preclinical research is being advanced.
Overall, all three types of technologies have broken through the limitations of traditional single parameters. China has formed a clear advantage in the integrated fusion of optical-electrical-thermal, while the US and Germany have formed barriers in the directions of force-optical-electrical and magnetic-optical-thermal, respectively. The three parties are rapidly moving from the laboratory to industrial, medical, and robotic fields.
2. Biological bionic perception mechanism: In the field of bionic olfaction, the Massachusetts Institute of Technology in the US published results in "Nature Biomedical Engineering" in 2026: a bio-electronic fusion bionic nose. Relying on gene-edited olfactory receptors and nano-optoelectrode structures, the detection limit for trimethylamine, a marker of food spoilage, reaches 0.01 ppb. The sensitivity is 10 times that of police dogs, and it can respond within 10 seconds. Field detection verification in food factories has been completed in collaboration with Nestlé. In the field of bionic hearing, ETH Zurich published owl-like auditory localization technology in "Science Robotics" in 2025. Based on MEMS sensors and multi-modal signal coupling algorithms, it replicates the biological binaural phase difference and intensity difference resolution mechanism. The sound source localization error in noisy environments is only ±0.5°, far superior to the ±5° accuracy of traditional hearing aids. Verified by 20 preclinical tests, it can accurately lock onto target sound sources, possessing extremely high application value in intelligent hearing aids and high-precision robot perception.
3. AI fusion cognitive mechanism: Imperial College London deeply integrates lightweight TinyML AI algorithms with sensing mechanisms. It can complete data cleaning, feature recognition, pattern learning, and error self-calibration at the far end without relying on backend cloud computing power, achieving the integration of "perception + interpretation + cognition + judgment," significantly reducing sensor data transmission delay and cloud computing pressure. It can be used in scenarios with extremely high requirements for response speed, autonomy, and stability, such as real-time perception of intelligent terminals, edge intelligent early warning of industrial equipment, and real-time assessment of in-vehicle environments, achieving localized intelligent decision-making.
Architecture Transformation: From Discrete Components to Integrated Intelligent Microsystems
1. Digital upgrade: Adopting a fully digital acquisition, transmission, and encoding scheme, the signal anti-interference capability is significantly improved, and the detection accuracy is improved by more than 10 times compared to traditional devices. Data stability and consistency are significantly optimized. It fundamentally avoids the defects of analog signal transmission loss and interference.
2. Integration upgrade: Through advanced micro-nano processing and packaging processes, sensitive functional materials, MEMS microstructures, ASIC dedicated chips, microprocessors, and storage units are highly integrated. A single chip can constitute a complete intelligent sensing unit. It greatly adapts to the mounting needs of miniaturized, lightweight, and low-power terminal equipment, reducing equipment assembly costs and space occupation.
3. Network upgrade: Natively adapting to mainstream communication methods such as 5G, LoRa, industrial buses, and IoT (Internet of Things) protocols, a single sensor node no longer works in isolation. It can quickly form a network, collaboratively perceive, and exchange data, forming a global distributed intelligent super-sensing network, thoroughly changing the drawbacks of single-point independent monitoring and data fragmentation of traditional sensors.
4. Intelligent upgrade: Built with lightweight edge computing power, it possesses capabilities of autonomous self-learning, fault self-diagnosis, environmental self-adaptation, data self-calibration, and real-time self-decision-making. It realizes the identity transformation from passive data acquisition to active intelligent judgment, significantly reducing the computing pressure and O&M costs of the backend platform, and improving autonomous adaptation capabilities and emergency response efficiency.
Capability Transformation: From Passive Data Acquisition to Active Intelligent Decision-Making
The new working link of the Intelligent Super-Sensing Unit is: active global perception → local AI intelligent analysis → real-time situation assessment → autonomous early warning and adaptation decision-making. It allows sensors to thoroughly get rid of the positioning of "data supporting roles" in industry, upgrading to the core sensory nerves and edge intelligent decision-making terminals of intelligent society and digital industry, becoming the core hub for the interaction between the physical world and the digital world.
III. Global Frontier Technological Achievements and Productized Practical Applications
Currently, global Intelligent Super-Sensing Units have moved from "mechanism exploration" to the stage of "integrated hardware and software modules and productized implementation." Top universities, national laboratories, and leading enterprise systems in various countries have formed full-chain integrated products of "materials—mechanisms—chips—algorithms—structures."
United States: Quantum Super-Sensing + AI Perception
The Defense Advanced Research Projects Agency (DARPA) of the US continues to deploy special projects in flexible electronics, neuromorphic sensing, and quantum super-sensing. It has formed verifiable and purchasable physical units in three major directions: quantum magnetic field sensing, non-invasive physiological monitoring, and implantable multi-parameter monitoring.
1. The basic research results on flexible sweat sensing from Northwestern University were published in "Science Advances." The research core: Relying on flexible microfluidic structures and electrochemical sensing technologies, a skin-attached non-invasive sweat detection unit was developed, which can achieve non-invasive monitoring of physiological indicators such as blood glucose through sweat metabolites. This flexible sensing system has the technical characteristics of being thin, skin-friendly, and capable of real-time collection of sweat biochemical signals, providing frontier technical ideas for continuous non-invasive blood glucose monitoring.
2. Medtronic's new generation of continuous glucose monitoring sensing unit in 2025 relies on mature electrochemical blood glucose detection and low-power wireless transmission technologies. It adapts to the insulin closed-loop regulation system, enabling continuous dynamic monitoring of blood glucose indicators and assisting in intelligent blood glucose management for diabetes. The device received official approval from the US FDA in 2025 and is mainly applied in home blood glucose regulation scenarios for diabetes.
Europe: Bionic Super-Sensing + Trace Detection Technology
Relying on the "Quantum Flagship" and "Super-Sensing Earth" programs, the EU has achieved the leap from the laboratory to industrial-grade modules in the fields of single-molecule gas detection, bionic hearing/olfaction, and industrial multi-modal diagnosis.
1. University of Cambridge (Cambridge) single-molecule level formaldehyde intelligent detection unit (2025). Research core: Surface-enhanced Raman scattering (SERS) + on-chip AI spectral interpretation. Volume is 1.8 cm³, capable of real-time identification of ppb-level VOCs. Key indicators: Formaldehyde detection limit of 0.3 ppb, response time <1s, anti-humidity interference. Published in "Nature" 2025, "Single-molecule detection of formaldehyde using a SERS-based intelligent sensor." In collaboration with the UK Environment Agency, it is used for indoor air monitoring and industrial emission tracing; it has been mass-produced by Cambridge spin-off Owlstone Medical.
2. ETH Zurich (ETH Zurich) BionicEar Pro bionic auditory super-sensing unit (2025). Research core: MEMS micro-microphone array + embedded multi-modal AI (beamforming + sound source separation). Diameter 12 mm, thickness 3 mm. Signal-to-noise ratio improved by 18 dB, supporting 360° omnidirectional sound pickup within 10 meters. Published on the cover of "Science Robotics" 2025, "A bionic auditory sensor with embedded AI for robust sound localization." 200 units were purchased by the Swiss Hearing Impaired Center for cochlear implant assistance; Swiss Railways uses it for tunnel abnormal noise monitoring.
Japan: Known for Optical Computing Sensing, Miniaturized Multi-modal Integration, and Industrial-Grade Reliability, Emphasizing "Small Size, Low Power Consumption, and High Stability."
1. University of Tokyo optical computing super-sensing module (2025). Research core: Photonic sensing + optical computing AI (no electronic delay). Volume 4 cm³, integrating photodetectors, optical waveguides, and optical neural networks. Key indicators: Industrial robot sorting accuracy of 99.98%, processing speed 10 times faster than electronic solutions, power consumption reduced by 70%. Published in "IEEE Journal of Microelectromechanical Systems" 2025, "An optical computing sensor for high-speed industrial sorting." Used for Toyota auto parts sorting, Sony electronic component inspection, and FANUC humanoid robot vision-tactile fusion.
2. Sony's intelligent visual super-sensing pre-research unit relies on a stacked CMOS photosensitive architecture and embedded AI ISP intelligent image processing technology. Through the deep integration of hardware architecture optimization and intelligent algorithms, it significantly improves imaging frame rate, dynamic imaging range, and anti-interference capability in complex environments. It can effectively adapt to harsh conditions such as backlighting, rain, and fog, accurately locking onto target features. Meanwhile, relying on edge intelligent computing power to compress redundant image data, it reduces transmission bandwidth and computing power consumption. This technology can be widely adapted to multiple frontier scenarios such as autonomous driving perception, UAV high-definition imaging, industrial high-speed quality inspection, and humanoid robot environmental visual recognition. Related results were published in the international top computer vision conference "CVPR" and included in Sony's 2025 official white paper.
China: Optical Field/Force/Skin Multi-modal Super-Sensing Units. Breakthroughs in Four Directions: Sub-Angstrom Spectral Imaging, Micro-Scale Six-Dimensional Force Sensing, High-Density Electronic Skin, and Brain-Like Visual Chips.
1. Tsinghua University "Yuheng" sub-angstrom level spectral imaging unit (2025). Research core: Snapshot spectral chip + integrated AI calculation. Volume 2 cm × 2 cm × 0.5 cm, covering 400–1000 nm broad spectrum. Key indicators: Sub-angstrom spectral resolution (0.1 nm), tens of millions of pixels spatial resolution, single snapshot synchronous acquisition of full spectrum + full spatial information. Published in "Nature" 2025, "A sub-angstrom snapshot spectral imaging chip for intelligent photonics." Used for lithium battery electrode micro-crack detection (accuracy 0.2 mm), agricultural UAV remote sensing, and non-destructive appraisal of cultural relics.
2. Tsinghua Shenzhen International Graduate School SuperTac super optoelectronic skin unit (2026). Research core: Multi-spectral imaging + triboelectric sensing + tactile language model (DOVE). Thickness 1 mm, area customizable (1–100 cm²). Key indicators: 10 sensing capabilities (pressure, texture, temperature, humidity, hardness, etc.), material recognition accuracy >94%, spatial resolution 0.1 mm. Published in "Nature Sensors" 2026, "A super multimodal optoelectronic skin with tactile language understanding." Can be used for medical assistance robots (grasping tofu without breaking it), industrial precision assembly robots, and prosthetic tactile feedback.
3. Peking University F-TAC Hand full-hand high-resolution tactile robotic hand (2025). Research core: Integration of 17 high-resolution tactile sensors + AI real-time contact state calculation, tactile coverage of 70% of the palm area. Key indicators: Spatial resolution 0.1 mm (≈10,000 tactile pixels/cm²), grasping success rate increased from 53.5% to 100%. Published in "Science Robotics" 2025, "A fully tactile robotic hand with high-resolution contact sensing." Can be used for precision assembly, fragile goods handling, and remote surgical robots.
South Korea: Korea Advanced Institute of Science and Technology (KAIST) neuromorphic artificial tactile sensing unit (2025). Research core: Memristor array + neuromorphic computing, mimicking human skin nerves, capable of simultaneously perceiving pressure, vibration, and temperature. Key indicators: Power consumption is only 1/100 of traditional electronic skin, response time <1 ms, possessing "pain" intensity perception and self-healing capabilities. Published in "Nature Electronics" 2025, "A neuromorphic tactile sensor with pain perception and self-healing." Can be used for human-robot safe interaction in service robots, prosthetic perception, and wearable medical monitoring.
IV. Full-Domain Implementation of Intelligent Super-Sensing Unit Technology, Reshaping Industrial Application Paradigms
Medical Health Field: Non-invasive, Implantable Precision Super-Sensing for Full-Cycle Health Dynamic Monitoring
Traditional medical detection mostly relies on invasive methods such as large precision instruments and puncture blood collection, which have pain points such as low detection frequency, poor timeliness, and inability to monitor continuously, making it difficult to capture instantaneous fluctuations and precursors of lesions in human physiological indicators. The new generation of medical-grade Intelligent Super-Sensing Units, based on the ultra-high sensitive perception characteristics of two-dimensional materials and flexible polymer materials, combined with the electrochemical-optical fusion perception mechanism, can achieve non-invasive on the body surface and implantable high-precision, continuous physiological signal acquisition inside the body.
In non-invasive health monitoring scenarios, flexible Intelligent Super-Sensing Units can conform to the curved surface of human skin, synchronously collecting multi-dimensional physiological parameters such as ECG, blood oxygen, pulse, body temperature, sweat glucose, and electrolytes. Relying on local TinyML intelligent algorithms, it completes data noise reduction, feature extraction, and error self-calibration. The detection accuracy approaches medical equipment standards. Without the need for frequent blood collection and offline testing, it can achieve normalized chronic disease screening and health status assessment at home. In high-end clinical medical scenarios, miniaturized implantable super-sensing units, with the advantages of low power consumption, high compatibility, and long battery life, can be implanted inside the human body for a long time, dynamically monitoring core pathological indicators such as blood glucose, lactic acid, inflammatory factors, and organ pressure in real time. It accurately captures weak physiological signal changes in the early stage of lesions, providing all-weather, high-density data support for severe disease early warning, postoperative rehabilitation tracking, and refined management of chronic diseases. Meanwhile, relying on the networked architecture, monitoring data can be uploaded in real time, and collaborative judgment between doctors and patients can be achieved, effectively making up for the shortcomings of intermittent detection in traditional medicine and significantly improving the accuracy of early disease screening and clinical diagnosis and treatment efficiency.
Industrial Manufacturing Field: Multi-modal Collaborative Super-Sensing to Build a Predictive Intelligent O&M System
Traditional industrial monitoring mostly uses single-function sensors, which can only independently collect data such as temperature or vibration. They cannot associate multi-dimensional equipment operating condition parameters, making it difficult to identify hidden precursors of complex faults, which easily leads to problems such as sudden equipment shutdown, production interruption, and high O&M costs.
In intelligent manufacturing scenarios, industrial Intelligent Super-Sensing Units rely on the "optical-electrical-thermal-mechanical" multi-modal fusion perception mechanism, combined with a high-precision integrated design of triaxial vibration, temperature, acoustics, and pressure. They can synchronously collect full-dimensional operating condition data such as vibration spectrum, operating temperature, working noise, load pressure, and current harmonics of core equipment such as industrial motors, fans, machine tools, and transmission bearings. The sampling frequency can accurately match the equipment fault characteristic frequency, comprehensively covering the core status indicators of equipment operation. Meanwhile, with the help of built-in edge computing power, real-time data cleaning, fault feature recognition, operating condition pattern classification, and health assessment are completed locally on the equipment. Without relying on cloud computing power, it can achieve advance prediction of hidden faults such as equipment wear, looseness, overheating, and insulation aging, with a maximum of 72 hours of advance warning for equipment hazards. Relying on the networking capabilities of 5G and industrial buses, globally distributed sensor nodes can form a collaborative perception network, achieving global linked monitoring and data exchange of equipment status in workshops, production lines, and factory areas. It builds standardized equipment health ledgers, continuously optimizing the accuracy of early warning models through self-learning iteration, effectively reducing equipment failure rates, unplanned downtime, and manual O&M costs, and comprehensively improving the stability, safety, and production efficiency of intelligent manufacturing production lines.
Environmental Monitoring Field: Distributed Self-Powered Super-Sensing to Build a Full-Domain Refined Monitoring Network
Traditional environmental monitoring equipment is mostly centrally deployed and samples at fixed points, relying on external power sources or high-frequency battery replacement. It is difficult to deploy and has high O&M costs in complex field scenarios such as mountains, rivers, lakes, wilderness, and offshore areas. Moreover, it can only detect conventional pollutants and cannot identify trace and ultra-low concentration pollutants, making it difficult to meet the industry's rigid demand for refined management of the ecological environment.
The new generation of environment-specific Intelligent Super-Sensing Units, relying on the triboelectric-piezoelectric coupled self-powered mechanism, can autonomously collect wind energy, water flow mechanical energy, and deformation potential energy in the field environment and convert them into working electrical energy, achieving energy self-sufficiency. It thoroughly gets rid of the limitations of traditional power supply and battery O&M, increasing the field battery life of the equipment by more than 10 times, perfectly adapting to various complex field unattended scenarios. Meanwhile, based on the ultra-high sensitive detection characteristics of two-dimensional super-sensing materials such as graphene and MXene, a single device can synchronously complete multi-dimensional parameter detection such as PM2.5, greenhouse gases, volatile organic compounds, heavy metal ions, and trace water pollutants. It accurately captures ppb and ppq level ultra-low concentration pollutants, achieving pollution tracing, concentration tracking, and trend prediction. Through LoRa and IoT wireless networking technologies, a large number of distributed sensor nodes can quickly build a full-domain three-dimensional monitoring network, achieving dead-angle-free normalized monitoring of urban and rural atmosphere, watershed water quality, mining area ecology, and extreme climate regions. Combined with local AI algorithms, it completes intelligent assessment of pollution situations and abnormal tracing, providing accurate, continuous, and full-domain data support for ecological governance, environmental emergency response, and climate change research.
Consumer Electronics and Automotive Fields: Miniature Intelligent Super-Sensing Empowering Intelligent Upgrades of Terminal Products
Traditional consumer electronics and vehicle-mounted sensors are mostly discrete single-function devices with large size, high power consumption, and weak intelligence. They can only complete basic posture and environmental parameter acquisition, and cannot achieve autonomous calibration, intelligent adaptation, and scenario adaptive regulation.
In home scenarios, highly integrated miniature Intelligent Super-Sensing Units, with the advantages of flexible conformal, multi-dimensional perception, and local intelligent decision-making, integrate multi-data collaboration such as sound, light, electricity, and magnetism. They can accurately capture status monitoring of large and small home appliances and wearable devices, realizing the upgrade of intelligent terminals from "passive response" to "active adaptation," significantly improving human-computer interaction accuracy and intelligent experience. In the NEV (New Energy Vehicle) and intelligent driving scenarios, vehicle-mounted Intelligent Super-Sensing Units rely on multi-modal fusion perception and real-time cognitive decision-making capabilities to synchronously monitor core parameters such as vehicle body posture, battery temperature, cabin environment, and road conditions. They can autonomously complete data self-calibration, abnormal fault judgment, and safety risk early warning, assisting in core functions such as battery thermal management, vehicle body adaptive regulation, and driving environment intelligent perception. This effectively improves the driving safety, operating condition adaptability, and autonomous driving perception accuracy of intelligent vehicles, becoming the core sensing infrastructure for the safe operation and intelligent upgrade of intelligent vehicles.
Conclusion
Track Completely Switched: Intelligent Super-Sensing Units Open a New Era of Sensing Technology
Currently, the global sensing industry has officially bid farewell to the traditional development model of "silicon-based and other material improvement, single signal detection," and fully entered the new era of intelligent super-sensing with diverse new materials, cognitive new mechanisms, microsystem integration, and active intelligent decision-making.
This transformation is by no means a minor iteration and parameter optimization of traditional sensing technology, but a thorough breaking of the performance boundaries and application limitations of sensing technology, covering the full dimensions of materials, mechanisms, architecture, and functions, reshaping the industrial technology system and market competition landscape. It needs to be clarified that traditional sensing technology still possesses irreplaceable industrial value in general scenarios, but limited by the bottleneck of performance iteration, it is no longer the focus of competition among countries and the mainstream R&D direction at the high-end frontier.
As the core infrastructure of intelligent manufacturing, smart healthcare, smart cities, and the Internet of Everything, this cross-generational industry revolution sees Intelligent Super-Sensing Units continuously empowering thousands of industries, drawing a brand-new innovation direction for the future development of the global sensing industry, and opening a brand-new era of full-domain intelligent perception.
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Central Committee of Jiusan Society, Executive Vice Chairman of the China Sensor and IoT Industry Alliance
Editor | Yang Pengyue Art Editor | Malia Supervisor | Zhao Chen