In this study, tin oxide (SnO₂)/polyaniline (PANI) composite nanowires (NWs) with varying amounts of PANI were synthesized for carbon dioxide (CO₂) gas sensing at room temperature (RT, 25 °C). SnO₂ NWs were fabricated via the vapor–liquid–solid (VLS) method, followed by coating with PANI. CO₂ sensing investigations revealed that the sensor with 186 μL PANI exhibited the highest response to CO₂ at RT. Additionally, the optimized sensor demonstrated excellent selectivity for CO₂, long-term stability, and reliable performance across different humidity levels. The enhanced sensing performance of the optimized sensor was attributed to the formation of SnO₂-PANI heterojunctions and the optimal PANI concentration. This study underscores the potential of SnO₂-PANI composites for CO₂ detection at RT.

Aiming at the problems of scarce datasets and the low identification accuracy faced in the field of weld-crack detection, this paper proposes an artificial-weld-crack preparation method based on the doping of dissimilar metal particles to augment the number of samples of weld-crack defects. Meanwhile, data augmentation methods such as random cropping, scaling and Mosaic are combined to further enhance the richness of the samples, so as to provide strong data support for the proposed weld-crack-defect detection model. Given the limitations of storage and computational resources in industrial application scenarios, this paper designs the lightweight detection network YOLOv6-NW. It is achieved by optimizing the YOLOv6-N model for width and depth compression to efficiently identify and locate weld-crack defects. The experimental results demonstrate that YOLOv6-NW significantly outperforms YOLOv5 in terms of both model detection performance and model size. Compared to YOLOv6-N, the number of model parameters in YOLOv6-NW is only 16% of that in YOLOv6-N, yet its model detection accuracy and recall rate remain on a comparable level with YOLOv6-N. Additionally, the precision of crack detection by YOLOv6-NW under low-resolution or low-illumination conditions remains above 0.9.

This work demonstrates the incorporation of gold nanoparticles (AuNPs) into crosslinker-free poly(N-isopropylacrylamide), PNIPAM, particles in situ and the examination of their structural and catalytic properties. The formation process of the AuNPs across the crosslinker-free PNIPAM particles are compared to that of crosslinked PNIPAM particles. Given the relatively larger free volume across the crosslinker-free polymer network, the AuNPs formed by the in situ reduction of gold ions are detectably larger and more polydisperse, but their overall integration efficiency is slightly inferior. The structural features and stability of these composite particles are also examined in basic and alcoholic solvent environments, where the crosslinker-free PNIPAM particles still offer comparable physicochemical properties to the crosslinked PNIPAM particles. Interestingly, the crosslinker-free composite particles as a colloidal catalyst display a higher reactivity toward the homocoupling of phenylboronic acid and reveal the importance of the polymer network density. As such, the capability to prepare composite particles in a controlled polymer network and reactive metal nanoparticles, as well as understanding the structure-dependent physicochemical properties, can allow for the development of highly practical catalytic systems.

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is ${\left[0.0245\mathrm{m}0.0224\mathrm{m}0.0039\mathrm{rad}\right]}^T$ and the average localization errors using the conventional EKF are ${\left[1.0405\mathrm{m}2.2700\mathrm{m}0.1782\mathrm{rad}\right]}^T$, ${\left[0.4963\mathrm{m}0.3482\mathrm{m}0.0254\mathrm{rad}\right]}^T$ and ${\left[0.2774\mathrm{m}0.3897\mathrm{m}0.0353\mathrm{rad}\right]}^T$, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

(AlCrMoNiTi)N high-entropy alloy nitride (HEAN) films were synthesized at various bias voltages using the co-filter cathodic vacuum arc (co-FCVA) deposition technique. This study systematically investigates the effect of bias voltage on the microstructure and performance of HEAN films. The results indicate that an increase in bias voltage enhances the energy of ions while concomitantly reducing the deposition rate. All synthesized (AlCrMoNiTi)N HEAN films demonstrated the composite structure composed of FCC phase and metallic Ni. The hardness of the (AlCrMoNiTi)N HEAN film synthesized at a bias voltage of −100 V attained a maximum value of 38.7 GPa. This high hardness is primarily attributed to the synergistic effects stemming from the formation of strong metal-nitrogen (Me-N) bonding formed between the target elements and the N element, the densification of the film structure, and the ion beam-assisted bombardment strengthening of the co-FCVA deposition technique. In addition, the corrosion current density of the film prepared at this bias voltage was measured at 4.9 × 10⁻⁷ A·cm⁻², significantly lower than that of 304 stainless steel, indicating excellent corrosion resistance.