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HighlightsPVTMS@MWCNT nano-aerogel with nano-pore size and abundant heterogeneous interface was fabricated via radical polymerization, sol–gel transition and CO2 drying.The nano-aerogel shows superior electromagnetic wave absorption property (RLmin = −36.1 dB and cover all Ku-band) and thermal infrared stealth property (ΔT reached 60.7 °C).Layered nano-aerogel/graphene film with high EMI shielding and absorption properties was obtained;Pre-polymerized vinyl trimethoxy silane (PVTMS)@MWCNT nano-aerogel system was constructed via radical polymerization, sol–gel transition and supercritical CO2 drying. The fabricated organic–inorganic hybrid PVTMS@MWCNT aerogel structure shows nano-pore size (30–40 nm), high specific surface area (559 m2 g−1), high void fraction (91.7%) and enhanced mechanical property: (1) the nano-pore size is beneficial for efficiently blocking thermal conduction and thermal convection via Knudsen effect (beneficial for infrared (IR) stealth); (2) the heterogeneous interface was beneficial for IR reflection (beneficial for IR stealth) and MWCNT polarization loss (beneficial for electromagnetic wave (EMW) attenuation); (3) the high void fraction was beneficial for enhancing thermal insulation (beneficial for IR stealth) and EMW impedance match (beneficial for EMW attenuation). Guided by the above theoretical design strategy, PVTMS@MWCNT nano-aerogel shows superior EMW absorption property (cover all Ku-band) and thermal IR stealth property (ΔT reached 60.7 °C). Followed by a facial combination of the above nano-aerogel with graphene film of high electrical conductivity, an extremely high electromagnetic interference shielding material (66.5 dB, 2.06 mm thickness) with superior absorption performance of an average absorption-to-reflection (A/R) coefficient ratio of 25.4 and a low reflection bandwidth of 4.1 GHz (A/R ratio more than 10) was experimentally obtained in this work.

The electrical conductivity of conductive polymer composites (CPCs) with a classical double percolated structure is highly connected to the phase morphology. However, lots of conductive fillers, which are added to guarantee CPCs’ electrical conductivity, caused increased viscosity and limited phase coarsening in traditional atmosphere annealing. Herein, a novel processing method of supercritical carbon dioxide (scCO 2 ), assisted phase coarsening for polylactide acid (PLA)/polystyrene (PS)/multi-wall carbon nanotube (MWCNT) composites was explored. It was first proved that obviously coarsened conductive network of double percolation after scCO 2 annealing was achieved, which benefited from CO 2 plasticized polymer chains and thus decreased viscosity. Therefore, the electrical properties and electromagnetic interference (EMI) shielding performance of the composites were significantly improved. The percolation threshold of PLA/PS/MWCNT composites decreased from 0.31 wt.% to 0.16 wt.%, and EMI shielding effectiveness increased from 32.8 to 37.4 dB at 5 wt.% MWCNT loading. This work provides a simple, green, and effective way of post-processing to tailor the phase structure via varying conditions of annealing medium such as temperature, CO 2 pressure, and time, and gains CPCs with improved electrical conductivity and EMI shielding performance.

Accurate prediction of tool wear plays a vital role in improving machining quality in intelligent manufacturing. However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal-to-noise ratios. To address these challenges, this paper presents an innovative tool wear prediction method that integrates a nonlinear mean function and a multi-kernel function-optimized GPR model combined with an LSTM-enhanced particle filter algorithm. The approach incorporates the LSTM network into the state transition model, utilizing its strong time-series feature extraction capabilities to dynamically adjust particle weight distributions, significantly enhancing the accuracy of state estimation. Experimental results demonstrate that the proposed method reduces the mean absolute error (MAE) by 47.6% and improves the signal-to-noise ratio by 15.4% compared to traditional filtering approaches. By incorporating a nonlinear mean function based on machining parameters, the method effectively models the coupling relationships between cutting depth, spindle speed, feed rate, and wear, leading to a 31.09% reduction in MAE and a 42.61% reduction in RMSE compared to traditional linear models. The kernel function design employs a composite strategy using a Gaussian kernel and a 5/2 Matern kernel, achieving a balanced approach that captures both data smoothness and abrupt changes. This results in a 58.7% reduction in MAE and a 64.5% reduction in RMSE. This study successfully tackles key challenges in tool wear monitoring, such as noise suppression, nonlinear modeling, and non-stationary data handling, providing an efficient and stable solution for tool condition monitoring in complex manufacturing environments.

Flexible pressure sensors have gained attention for their comfort, portability, and potential in long‐term pulse monitoring and early cardiovascular disease diagnosis. However, stretching stress during daily activities affects sensor accuracy, causing motion artifacts (MAs) that hinder precise pulse signal detection. To address this challenge, the anti‐motion artifact iontronic pressure sensor (S‐smooth sensor), featuring a soft‐hard stretchable interface with energy dissipation properties is developed. By regulating the local modulus of the encapsulation layer, this structure dissipates stretching stress, achieving an MAs suppression rate of up to 90%, significantly improving pulse signal accuracy and reliability. Additionally, the sensor incorporates a dielectric layer and double electrode layer (EDL) sensing interface, with a low‐friction design that ensures high sensitivity (92.76 kPa−¹) and stability, maintaining performance over millions of cycles. The sensor accurately captures heart rate (HR) and pulse peak time differences (Δt) under various finger‐bending conditions. When integrated into a portable wireless pulse monitoring system, it shows a heart rate loss rate of only 2.9% during intense physical activity. This approach avoids complex chemical processes and material restrictions, offering a novel solution for motion artifact suppression in sensors, with significant potential for real‐time health monitoring and assisted diagnosis. Flexible pressure sensors are essential for pulse monitoring and cardiovascular disease diagnosis but are affected by motion artifacts (MAs) due to stretching stress. The S‐smooth sensor with a soft‐hard stretchable interface that dissipates energy, reducing MAs by 90% is developed. The sensor ensures high sensitivity (92.76 kPa−¹) and stability for one million cycles, providing a reliable solution for real‐time health monitoring, even during intense physical activity.

Tungsten carbide is currently the most widely used tool material for machining difficult-to-machine materials, such as titanium alloys and nickel-based super alloys. In order to improve the performance of tungsten carbide tools, surface microtexturing, a novel technology that can effectively reduce cutting forces and cutting temperatures and improve wear resistance, has been applied in metalworking processes. However, when fabricating the micro-textures such as micro-grooves or micro-holes on tool surfaces, the significant decrease in material removal rate is a major obstacle. In this study, a straight-groove-array microtexture was fabricated on the surface of tungsten carbide tools via a femtosecond laser with different machining parameters including laser power, laser frequency, and scanning speed. The material removal rate, surface roughness, and the laser-induced periodic surface structure were analyzed. It was found that the increase in the scanning speed decreased the material removal rate, whereas increasing the laser power and laser frequency had the opposite effects on the material removal rate. The laser-induced periodic surface structure was found to have a significant influence on the material removal rate, and the destruction of the laser-induced periodic surface structure was the reason for the reduction in the material removal rate. The results of the study revealed the fundamental mechanisms of the efficient machining method for the fabrication of microtextures on ultrahard materials with an ultrashort laser.

Laser–electrochemical hybrid machining (LECM) is promising in the processing of thin-wall parts, which avoids problems such as the weak stiffness of structures and thermal defects. However, while most studies focus on precision machining via LECM, few investigate the potential of this technique in macro-area processing. In this paper, the synergistic effects on the coupling of thermal field and electrochemical field on bulk material removal mechanisms in the LECM of additively manufactured Ti6Al4V are comprehensively analyzed experimentally and theoretically. According to the experimental results, LECM improved the material removal rate (MRR) by up to 28.6% compared to ECM. The induction of the laser increases local heating, accelerating the temperature rise of the electrolyte, eventually promoting the electrochemical reaction. The hydrogen bubble flow promotes overall heat convection between the electrode and workpiece, which facilitates the removal of the facial precipitates and increases the efficiency of electrochemical dissolution. Higher voltages and laser powers promote the formation of hydrogen bubble flow; meanwhile, they also aggravate laser energy scattering, limiting the overall machining efficiency. Additionally, laser irradiation causes the ablation and rupture of hydrogen bubbles, which weakens the bubble flow effect and ultimately decreases the material removal efficiency. This study reveals the underlying mechanisms of the joint effects of the laser field and electrical field in LECM, and the findings can provide valuable insights for the optimization of LECM parameters in industrial applications.

Titanium alloys are difficult to machine using conventional metal cutting methods due to their low thermal conductivity and high chemical reactivity. This study explores the new multi-channel discharge machining of Ti-6Al-4V using silicon electrodes, leveraging their internal resistivity to generate potential differences for multi-channel discharges. To investigate the underlying machining mechanism, the equivalent circuit model was developed and a theoretical simulation was carried out. Comparative experiments with silicon and conventional copper electrodes under identical parameters were also conducted to analyze discharge waveforms, material removal rate, surface quality, and heat-affected zones (HAZ). The results demonstrate that the bulk resistance of silicon is the main mechanism for generating multi-channel discharges. This process efficiently disperses the discharge energy of the single discharge pulse, resulting in smaller craters, smoother machined surfaces, and shallower recast layers and HAZ.

Gecko‐inspired adhesives, which are traditionally based on polydimethylsiloxane (PDMS), are suitable for the delicate handling of 3D objects under vacuum and microgravity conditions but lose almost all their adhesion strength in environments below their crystallization temperatures. To address this issue, poly(methyl‐phenyl‐vinyl)siloxane (PMPVS) is synthesized by the simultaneous introduction of phenyl and vinyl side groups to achieve a crystallization temperature of −93.0 °C. The developed PMPVS‐based adhesive exhibited adhesion strengths of 25.9 and 36.7 kPa at −70 and −80 °C, respectively, while a conventional PDMS‐based adhesive has an adhesion strength of only 0.7 kPa at −70 °C. Moreover, the PMPVS‐based adhesive retained an adhesion strength of 29.4 kPa at −80 °C even after 100 cycles. Robotic grippers equipped with this adhesive can softly grasp irregular, fragile, and heavy objects with a reduced gripping force of up to 90% compared to grippers without adhesive over a wide temperature range. Phenyl and vinyl groups are used to modify conventional polydimethylsiloxane (PDMS) to yield poly(methylphenylvinylsiloxane) (PMPVS). PMPVS‐based gecko‐inspired arrays with mushroom tips exhibit superior adhesion strength of 36.7 kPa even at −80 °C, which enhances by 5143% compared to PDMS‐based arrays (0.7 kPa at −70 °C). This work significantly broadens the applications of gecko‐inspired adhesives into an extremely low temperature range.

The online monitoring and prediction of tool wear are important to maintain the stability of machining processes. In most cases, the tool wear condition can be evaluated by signals such as force, sound, vibration, and temperature, which are often processed via Fourier-transform based methods, typically, the short-time Fourier transform (STFT). However, the fixed-width window function in STFT has many limitations. In this paper, a novel tool wear monitoring method based on variational mode decomposition (VMD) and Hilbert–Huang transformation (HHT) were developed to monitor the wear of carbide tools in machining stainless steel. In this method, the intrinsic mode function (IMF) was used as the fitness function, and the (K alpha) parameter sets for VMD were optimized by the gray wolf optimization (GWO). The results show that the characteristic frequency in the GWO-VMD-HHT method is more significant with no aliasing compared with the EMD-HHT method, and an obvious characteristic frequency shift phenomenon is present. By utilizing the energy value of IMF3 as the feature to classify the wear state of the cutting tool, the increase of energy reached 85.48% when 260–315 milling passes were in severe wear state. GWO, which can accurately find the best parameters for VMD, not only solves the problem that the Entropy Function is not suitable for force signals, but also provides reference for the selection of parameters of VMD.

Due to their excellent mechanical properties and intrinsic flexibility, polyimides (PIs) are promising candidates for optoelectrical applications under harsh conditions such as flexible organic solar cells as well as flexible smart windows, etc. Much progress has been made on their optical transmittance; however, there remain significant concerns about their environmental stability, particularly their UV resistance. Herein, 4 types of colorless polyimides (CPIs) with different molecular structures containing trifluoromethyl, ethers, or fluorenes are carefully designed, and the dependence of their UV resistance on structures is explored systematically. It is found that the introduction of isopropylidene, ethers and fluorenes effectively enhances the UV resistance of CPI and its initial performance (optical transparency, thermal stability, and toughness) simultaneously as a result of the subtle manipulation of the conjugation structures. Specifically, the optimized polyimide film shows decent optical properties ( T 550 nm  ~ 88%, yellowness index ~3.26), and thermal stability ( T 5 %  ~ 503 °C in N 2 atmosphere, T g  ~ 312 °C). Moreover, after high-intensity UV irradiation, CPI not only maintains over 90% of mechanical properties but also retains excellent optical properties ( T 550 nm  ~ 88%) and thermal stability ( T 5 %  ~ 506 °C). The design strategy paves the way for enhancing the durability of PIs for energy conversion and electronic applications resisting harsh conditions.