Dynamic Temperature Changes Research Articles

Dual-Mode cellulose acetate@Al2O3/MWCNTs Janus fabric with radiative cooling and solar heating for personal thermal management

Personal thermal management (PTM) textiles with heating and cooling functionalities are highly appealing due to their potential to offer personalized comfort while also contributing to decrease energy consumption. However, most PTM fabrics are static and unable to meet the human body’s thermal comfort needs during seasonal and ambient temperature fluctuations. Herein, this work presents a dual-mode Janus Metafabric to provide radiation cooling and solar heating. The cooling side of Metafabric (cellulose acetate@Al2O3 porous coating) offers high solar reflectance (92.12 %), infrared emissivity (83.22 %), and net cooling power of 55.85 W·m−2. The heating side (multi-walled carbon nanotubes) exhibits a high solar absorption rate (88.4 %). Compared with raw fabric, it can achieve an additional cooling of 4.58 °C and heating of 38.02 °C. In addition, Metafabric also have excellent ultraviolet resistance, wear resistance, and washing properties, making it suitable for practical wearing. By flipping the Janus fabric, it is easy to switch between the cooling and heating modes to adapt to dynamic ambient temperature changes, which have great potential for maintaining human thermal comfort and alleviating the energy crisis.

Experimental study on internal heat transfer among adjacent rooms for building energy efficiency with housing vacancy consideration

With the expansion of the real estate sector and the rise of tourist resorts, many individuals now own multiple properties, resulting in a substantial number of homes remaining vacant for extended periods or being used seasonally. Despite being unheated, rooms in these vacant properties continue to transfer heat to adjacent heated rooms. While previous research has explored the heat transfer between non-heated and heated rooms based on their relative positioning, few studies have considered the dynamic aspects of this process. To fill this gap, we developed a scaled-down model to replicate real-world conditions and conducted experiments to track the dynamic temperature changes between non-heated and heated rooms in different configurations. The heat transfer mechanism was analyzed, and a theoretical model was constructed to quantify the dynamic heat exchange. This model accounts for variations in both convective and radiative heat transfer between rooms. Initial findings suggest that heat transfer to a non-heated room below a heated space can reach 28 % of the total heat supplied, while heat transfer to an upper adjacent room can account for 16 %. The peak heat transfer is observed after 5–10 h of heating. These results provide valuable insights for optimizing space heating, promoting energy-saving practices, and improving indoor heating efficiency, alongside offering actionable recommendations for behavioral energy conservation.

Huddling substates in mice facilitate dynamic changes in body temperature and are modulated by Shank3b and Trpm8 mutation

Social thermoregulation is a means of maintaining homeostatic body temperature. While adult mice are a model organism for studying both social behavior and energy regulation, the relationship between huddling and core body temperature (Tb) is poorly understood. Here, we develop a behavioral paradigm and computational tools to identify active-huddling and quiescent-huddling as distinct thermal substates. We find that huddling is an effective thermoregulatory strategy in female but not male groups. At 23 °C (room temperature), but not 30 °C (near thermoneutrality), huddling facilitates large reductions in Tb and Tb-variance. Notably, active-huddling is associated with bidirectional changes in Tb, depending on its proximity to bouts of quiescent-huddling. Further, group-housed animals lacking the synaptic scaffolding gene Shank3b have hyperthermic Tb and spend less time huddling. In contrast, individuals lacking the cold-sensing gene Trpm8 have hypothermic Tb – a deficit that is rescued by increased huddling time. These results reveal how huddling behavior facilitates acute adjustments of Tb in a state-dependent manner.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

Numerical investigation of photovoltaic performance improvement using Al2O3 nanofluid

To further elucidate the impact of fluctuations in environmental temperature and radiation intensity within a day on the temperature and performance of photovoltaic systems with and without nanofluid cooling, this study established a photovoltaic panel temperature and efficiency calculation model based on a nanofluid cooling system that considers the dynamic changes of photovoltaic panel temperature and air temperature over time. An empirical formula for predicting photovoltaic panel efficiency has been derived based on experimental data. Then a PV panel temperature and efficiency calculation model is established and validated. The simulation results show that using nanofluids to cool the PV panel can significantly reduce the temperature and improve the PV efficiency. Compared to the bare PV panel, the average PV panel temperature decreases by 7.99 ℃, 8.48 ℃, and 8.92 ℃ respectively when nanofluid volume fraction is 1 vol%, 3 vol%, and 5 vol%, and it decreases by 8.48 ℃, 9.27 ℃, and 9.94 ℃ respectively when nanofluid mass flow rate is 0.08 m3/h, 0.10 m3/h, and 0.12 m3/h. As the nanofluids' concentration increases, nanofluids' cooling ability in the high temperature range also increases.

Investigation of temperature field variations induced by the air thermodynamic behavior when trains pass through a high-speed railway tunnel in cold regions

High-speed railway tunnels in the cold region, whose numbers are increasing, are widely exposed to the threat of frost damage caused by extremely negative temperatures. The operation of high-speed trains has made this problem even more pronounced. But there are fewer studies focusing on the dynamic changes in temperature during train operation. In this paper, the air thermodynamic behavior of trains passing through a tunnel and its consequences were investigated with the help of the CFD method. In numerical simulations, conditions of different natural airflows, different train speeds, different blockage ratios, as well as two trains intersecting in the tunnel were involved. The following valuable results were obtained. In cold environments, train-induced pressure wave effects will affect the air thermodynamic behavior, but the overall trend in air temperature is mainly influenced by the heat transfer between cold and warm air. The high-speed train will lead to massive cold air being carried into the tunnel and some warm air inside being pushed out. Heat transfer between cold and warm air will cause a significant decrease in tunnel air temperature. This air thermodynamic behavior is particularly prominent in the case of large blockage ratios. When there are same-direction natural airflows, they will be coupled with train-induced airflows, causing a greater decrease in tunnel air temperature. In contrast, opposite-direction natural airflows can reduce the negative effects of the train. Taking the case of opposite-direction natural airflow velocity of 5 m/s as an example, the temperature changes of the three cross-sections are only 16.7 %, 14.8 %, and 42.1 % of those in the case of no natural airflow. Whatever the direction of natural airflows, coupling effects between them and train-induced airflows will be more significant as the natural airflow speed increases. Greater train speed means more cold air entering the tunnel and stronger heat transfer between cold and warm air, causing a more significant temperature decrease. When two trains intersect through the tunnel, airflows caused by them will be coupled. As a result, only a little air enters the tunnel from the entrance and exit, and the heat transfer between air with different temperatures is weak. In this case, the air temperature doesn't decrease but slightly increases as the air is compressed.

Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

A utility and easily fabricated dual-mode fiber film for efficient and comfortable thermal management

Nowadays, the global climate is constantly being destroyed and the fluctuations in ambient temperature are becoming more frequent. However, conventional single-mode thermal management strategies (heating or cooling) failed to resolve such dynamic temperature changes. Moreover, developing thermal management devices capable of accommodating these temperature variations while remaining simple to fabricate and durable has remained a formidable obstacle. To address these bottlenecks, we design and successfully fabricate a novel dual-mode hierarchical (DMH) composite film featuring a micro-nanofiber network structure, achieved through a straightforward two-step continuous electrospinning process. In cooling mode, it presents a high solar reflectivity of up to 97.7% and an excellent atmospheric transparent window (ATW) infrared emissivity of up to 98.9%. Noted that this DMH film could realize a cooling of 8.1 °C compared to the ambient temperature outdoors. In heating mode, it also exhibits a high solar absorptivity of 94.7% and heats up to 11.9 °C higher than black cotton fabric when utilized by individuals. In practical application scenarios, a seamless transition between efficient cooling and heating is achieved by simply flipping the film. More importantly, the DMH film combining the benefits of composites demonstrates portability, durability, and easy-cleaning, promising to achieve large-scale production and use of thermally managed textiles in the future. The energy savings offered by film applications provide a viable solution for the early realization of carbon neutrality.

Gas desorption characteristics of coal particles under dynamic temperature changes: Implication for the lost gas estimation during borehole coal sampling

The estimation of lost gas during borehole coal sampling determines the accuracy of coalbed methane (CBM) content determination. The isothermal desorption data is used frequently for analyzing the estimation error of the lost gas, which is not consistent with the engineering practice that the gas desorption during borehole coal sampling is in dynamic temperature changing condition (i.e., DTC), thus resulting in the result of the lost gas estimation is not accurate. In this study, a DTC adsorption/desorption experimental system was set up to study the gas desorption characteristics of coal particles during borehole coal sampling, and the differences between isothermal desorption and DTC desorption were analyzed in detail. Furthermore, the isothermal desorption data and DTC desorption data were fitted using the unipore diffusion model (UDM) to discuss the influence of DTC desorption on the lost gas estimation. Finally, the fractional diffusion model (FDM) was applied to estimate the lost gas. The results showed that there is a significant difference between isothermal desorption and DTC desorption. For the case of DTC desorption, more gas will be lost at the same exposure time, resulting in the lost gas being underestimated in the CBM content determination. With the exposure time of 12 min, the average fitting errors of lost gas using the FDM with different coal particle sizes and gas pressures are less than 17.9%, while that using the UDM are greater than 57.5%, suggesting that the FDM could improve the fitting accuracy of lost gas estimation in the CBM content determination.

Huddling substates in mice facilitate dynamic changes in body temperature and are modulated by Shank3b and Trpm8 mutation.

Social thermoregulation is a means of maintaining homeostatic body temperature. While adult mice are a model organism for studying both social behavior and energy regulation, the relationship between huddling and core body temperature (Tb) is poorly understood. Here, we develop a behavioral paradigm and computational tools to identify active-huddling and quiescent-huddling as distinct thermal substates. We find that huddling is an effective thermoregulatory strategy in female but not male groups. At 23°C (room temperature), but not 30°C (near thermoneutrality), huddling facilitates large reductions in Tb and Tb-variance. Notably, active-huddling is associated with bidirectional changes in Tb, depending on its proximity to bouts of quiescent-huddling. Further, group-housed animals lacking the synaptic scaffolding gene Shank3b have hyperthermic Tb and spend less time huddling. In contrast, individuals lacking the cold-sensing gene Trpm8 have hypothermic Tb - a deficit that is rescued by increased huddling time. These results reveal how huddling behavior facilitates acute adjustments of Tb in a state-dependent manner.

Operational strategy optimization of an existing ground source heat pump (GSHP) system using an XGBoost surrogate model

Ground source heat pumps (GSHPs) are experiencing a surge in popularity due to their efficient use of geothermal energy for building heating and cooling. Optimizing GSHP systems is crucial for maximizing heat extraction efficiency. However, existing research primarily focuses on design-stage optimization, neglecting the potential of improving operational strategies in already deployed systems. This study proposes a novel method for optimizing the operational strategy of existing GSHPs with machine-learning-based surrogate models. Our approach considers both demanded heat extraction and the variability of GSHP operation, including running and idle times. First, a numerical model is established with a closed-loop procedure replicating real-world GSHP operation, capturing dynamic inlet temperature changes. The Latin hypercube sampling is subsequently employed to generate sufficient data for XGBoost model development, where a designed disassembly method is proposed to shed light on the problem of a fixed number of input parameters. The operational strategy of the GSHP system is then implemented to offer solutions for both regular and irregular modes. The effectiveness of the method is validated using 40 days of real-case monitoring data. The results demonstrate that the surrogate model achieves high accuracy, with an absolute temperature difference error below 0.1 K and a heat extraction absolute percentage error (APE) less than 2.0 %. Additionally, the optimized operational strategy reduces total operation time by 2–9 % solely through scheduling adjustments, showcasing its significant practical potential. This study presents an efficient method for optimizing the operational strategies of existing GSHPs while highlighting the value of process-accelerated optimization using numerical simulation-based surrogate models.

Ultrafast Opto-Electronic and Thermal Tuning of Third-Harmonic Generation in a Graphene Field Effect Transistor.

Graphene is a unique platform for tunable opto-electronic applications thanks to its linear band dispersion, which allows electrical control of resonant light-matter interactions. Tuning the nonlinear optical response of graphene is possible both electrically and in an all-optical fashion, but each approach involves a trade-off between speed and modulation depth. Here, lattice temperature, electron doping, and all-optical tuning of third-harmonic generation are combined in a hexagonal boron nitride-encapsulated graphene opto-electronic device and demonstrate up to 85% modulation depth along with gate-tunable ultrafast dynamics. These results arise from the dynamic changes in the transient electronic temperature combined with Pauli blocking induced by the out-of-equilibrium chemical potential. The work provides a detailed description of the transient nonlinear optical and electronic response of graphene, which is crucial for the design of nanoscale and ultrafast optical modulators, detectors, and frequency converters.

A Multiplexing Optical Temperature Sensing System for Induction Motors Using Few-Mode Fiber Spatial Mode Diversity

Induction motors are widely applied in motor drive systems. Effective temperature monitoring is one of the keys to ensuring the reliability and optimal performance of the motors. Therefore, this paper introduces a multiplexed optical temperature sensing system for induction motors based on few-mode fiber (FMF) spatial mode diversity. By using the spatial mode dimension of FMF, fiber Bragg grating (FBG) carried by different spatial modes of optical paths is embedded in different positions of the motor to realize multipoint synchronous multiplexing temperature monitoring. The paper establishes and demonstrates a photonic lantern-based mode division sensing system for motor temperature monitoring. As a proof of concept, the system demonstrates experiments in multiplexed temperature sensing for motor stators using the fundamental mode LP01 and high-order spatial modes LP11, LP21, and LP02. The FBG sensitivity carried by the above mode is 0.0107 nm/°C, 0.0106 nm/°C, 0.0097 nm/°C, and 0.0116 nm/°C, respectively. The dynamic temperature changes in the stator at different positions of the motor under speeds of 1k rpm, 1.5k rpm, 2k rpm with no load, 3 kg load, and 5 kg load, as well as at three specific speed–load combinations of 1.5k rpm_3 kg, 1k rpm_0kg, 2k rpm_5 kg and so on are measured, and the measured results of different spatial modes are compared and analyzed. The findings indicate that different spatial modes can accurately reflect temperature variations at various positions in motor stator winding.

Heat transfer study of metal foam with partial filling method to strengthen phase change material

The advancement of ice-ball thermal energy storage systems is limited by the poor thermal conductivity of phase change materials(PCM). This paper presents a numerical investigation into enhancing heat transfer in ice balls by partially filling them with metal foam. Dynamic temperature changes, solid phase fraction, and cold storage capacity are analyzed for various filling radius ratios (2/13, 4/13, 6/13, 8/13, and 13/13). We quantitatively assess the specific impact of metal foam filling on heat transfer by calculating changes in the comprehensive thermal conductivity coefficient. Our findings reveal that the comprehensive thermal conductivity coefficient increases nonlinearly with the growing metal foam filling radius ratio, indicating that full filling may not the most optimal configuration. Furthermore, the energy storage capacity per unit time, per unit weight and per unit cost of the ice ball filled with metal foam under different radius ratios was evaluated by comprehensive evaluation criteria, and the optimal filling radius ratio was determined to be 6/13. Contrary to prior findings, this research highlights the efficacy of partial filling strategies, offering valuable insights for optimizing ice ball performance in thermal energy storage applications.

Experimental study on the water and heat dynamic characteristics of silty clay at different depths under temperature gradient

Soil properties play a crucial role in the stability of engineering foundations, particularly in cold regions where temperature gradients impact water migration and soil conditions. This study utilized MRI-Cryogenic Soil-Moisture Analyzer (MRI-CSMA) to conduct indoor freeze-thaw experiments on silty clay specimens with varying initial water contents (12.0 %, 16.8 %, and 21.1 %). Dynamic changes in water content and temperature at different soil depths were analyzed. The results revealed distinct stage decrease characteristics in both temperature and water content as freezing progressed, with more pronounced changes closer to the cold end. Conversely, during thawing, temperatures and water content gradually increased as the boundary temperature rose. Notably, after the freeze-thaw cycle, the frozen zone consistently retained higher water content than the unfrozen zone, indicating water redistribution, regardless of initial water content levels. Furthermore, in three soil samples with different initial water contents, the water migration flux in the unfrozen soil layer decreased exponentially as freezing time increased. This phenomenon can be attributed to the combined effects of water boundary conditions, temperature gradients, and soil structure changes during soil freezing. These findings are likely to have significant implications for understanding water-heat dynamics in cold region soils as well as ensuring the stability of engineering projects.

Well Shut-In Pressure Determination Method for Deepwater Drilling Considering Fluid-Solid-Heat Coupling

Blowout is one of the most serious safety threats in deepwater drilling. Considering the characteristics of gas invasion in complex formations, gas migration and distribution, and dynamic changes in temperature inside a wellbore, a deepwater well-closing pressure determination method considering thermal-fluid-solid coupling was proposed. The model was verified using actual data, and the average error in the increase in casing pressure during the closing process was found to be 5.42%. The shut-in pressure of oil and gas wells under a transient shut-in process was analyzed. The results showed that the fluid thermal expansion caused by temperature recovery had a significant impact on the change in wellhead backpressure after well closure. Furthermore, the time required for the wellbore pressure to recover to the formation pressure varies nonlinearly with factors such as geothermal gradients, pit gains, bottom-hole pressure, and gas production indices. A pressure calculation chart was developed for deepwater drilling. The shut-in time for deepwater drilling should not be less than 15 min, and in cases where the gas production index is small and the bottomhole pressure difference is large, the shut-in time should exceed 90 min. The results can provide theoretical guidance and technical support for well shut-in processes during deepwater drilling.

Dual-parameter stretchable, transferable mesh piezoresistive sensor for electronic skin detection of strain and temperature changes

Abstract Wearable sensors integrating multiple functions have great potential in artificial intelligence and flexible electronics at this stage and can perceive various external stimuli with high sensitivity and accuracy, such as strain, stress, and temperature. However, because multiple parameters do affect each other and reduce the sensing performance, making a single device that can detect multiple functions simultaneously is a huge challenge. In this paper, a strain-temperature dual-parameter sensor is developed with a planar structure design and used poly(3,4-ethylenedioxythiophene): poly(styrenesulfonic acid) and multi-walled carbon nanotubes polymerization materials to prepare a micron-sized film. The influence of two-dimensional structures on sensing performance is explored through simulation, and a structure with large deformation is selected to improve the strain detection range. The sensor can detect static and dynamic strain signals, and can maintain good linearity and response speed below 100 ms within a large strain range of 20%. In addition, the sensor also exhibits good temperature detection capability, with a temperature sensitivity of 18.2 μV K−1 and the ability to detect static and dynamic temperature changes with long-term stability. Finally, the sensor is tested in some actual scenarios, reflecting that the sensor manufactured has the dual-detection ability, showing sensitive strain monitoring and temperature perception decoupled between the dual signals. The sensor is realized with circuit board acquisition and wireless communication, combining multi-channel applications. Our research provides a feasible method for constructing multi-parameter human-computer interaction sensors.

Parameter extraction approaches for compact modeling of thermoelectric modules

Thermoelectric (TE) cooling has experienced rapid advancements with the foundational understanding of TE materials. TE modules, compact and lightweight devices, have become the prevalent approach for implementing TE technologies. Accurately quantifying TE physical parameters (Seebeck coefficient α, thermal conductivity κ, and thermal resistance ρ) is challenging due to the dynamic temperature changes in operation. Furthermore, extracting lumped property parameters is crucial for designing energy systems using TE modules. Existing research has several limitations, such as lack of comparative analysis between prevalent formulae, reliance on potentially inaccurate vendor-supplied data, disregard for fundamental assumptions, and absence of empirical measurements. This study addresses these gaps by conducting TE material characterization, comparing three existing formulae using vendor datasheets, designing a laboratory test facility for model validation and refinement, and outlining a structured data extraction procedure. The study's novelty lies in multiple key contributions: (1) a detailed comparative analysis of existing formulae for extracting TE property parameter; (2) executing experimental work in a laboratory setting to validate the model and elucidate its limitations; (3) highlighting potential risks; (4) clarifying possible assumptions from both material and engineering perspectives; and (5) considering temperature differential impacts. This comprehensive approach addresses the current research gaps and provides valuable insights into the design and application of TE modules in various energy systems.

Exploring on the prediction model of human skin temperature and rectal temperature under heat stress

Hot environments cast direct influence on people's well-beings and health besides working productivity during the accelerating global warming. The systematic mechanism is not entirely clear due to the complex human physiological response which also handled human heat strain protection. This study carries out mathematical derivation on the rectal temperature and skin temperature under human heat strain based on the PHS model of ISO 7933 standard. The experimental validation was conducted in a climate chamber with continuously monitoring the typical physiological parameters of 10 subjects in 9 hot working environments. Physiological parameters of subjects were analyzed on average difference (AV), coefficient of variation (CV) and skewness (SKEW) to explore their mutual linkages. Both experimental and theoretical result shows that certain relationship existed between the rectal temperature and skin temperature. The Poisson's ratio on their average value, coefficient of variation, average difference and skew were 0.957, 0.991, 0.990 and 0.941 respectively. Linear relation with coefficient of 0.73 was found between rectal temperature and skin temperature coupling with the constant of 12.09. Comparison on the dynamic changes of rectal temperature and skin temperature was carried out and verified under PHS model. The results showed that rectal temperature was adjusted earlier and more forcefully than skin temperature indicating that human core temperature was better protected under heat stress. The findings contribute to a convenient method of human heat strain prediction when taking rectal temperature as the best physiological threshold with fast human skin temperature and environment parameters monitoring in complex high temperature working environments.

3D Printed Electronic Skin for Strain, Pressure and Temperature Sensing

AbstractElectronic skin (E‐skin) that can mimic the flexibility and stretchability of human skin with sensing capabilities, holds transformative potential in robotics, wearable technology, and healthcare. However, developing E‐skin poses significant challenges such as creating durable materials with skin‐like flexibility, integrating biosensing abilities, and using advanced fabrication techniques for wearable or implantable applications. To overcome these hurdles, a 3D‐printed electronic skin utilizing a novel class of nanoengineered hydrogels with tunable electronic and thermal biosensing capabilities is fabricated. This methodology takes advantage of the shear–thinning behavior in hydrogel precursors, allowing to construct intricate 2D and 3D electronic structures. The elasticity of skin using triple crosslinking in a robust fungal exopolysaccharide, and pullulan is simulated, while defect‐rich 2D molybdenum disulfide (MoS2) nanoassemblies ensure high electrical conductivity. The addition of polydopamine nanoparticles enhances adhesion to wet tissue. The hydrogel exhibits outstanding flexibility, stretchability, adhesion, moldability, and electrical conductivity. A distinctive feature of this technology is the precise detection of dynamic changes in strain, pressure, and temperature. As a human motion tracker, phonatory‐recognition platform, flexible touchpad, and thermometer, this technology represents a breakthrough in flexible wearable skins and holds transformative potential for the future of robotics and human‐machine interfaces.