A novel surface plasmon resonance (SPR) sensor with graphene and subwavelength gratings is proposed to improve the sensing performance. A series of numerical analyses were performed to investigate the effect of structural parameters on the sensing performance, such as minimum reflectance at resonance (MRR), full width at half maximum (FWHM), and resonance angle. The results indicated that near-zero MRR (2.9 × 10⁻⁶) and narrow FWHM (about 3.5 deg) could be obtained by optimizing the geometrical parameters. Moreover, the influence of the number of graphene layers on sensitivity was also studied. The maximum sensitivity of the designed sensor could reach 192 deg/refractive index unit (RIU), which is a great enhancement compared to the silver-only SPR sensor. In addition, ethylene glycol solutions with different refractive indices were detected. The results showed that the sensitivity of the sensor could reach 220.67 deg/RIU, and the proposed sensor had excellent linearity between the resonance angle and refractive index, enabling extensive potential practical sensing applications.

Semiconductor gas sensors utilizing cyclic temperature operation (CTO) have been extensively employed in the gas detection and sample collection. However, a drift-like effect is always present in CTO, observed as an increasement in response amplitude over time during consecutive short-term measurements. This effect can significantly impact sensor calibration and field identification accuracy. To address this issue, a cost-effective drift compensation method based on the temporal data decomposition is developed in this article. The raw response data is decomposed into trend, periodic, and residual components using an additive model of temporal data. The drift-compensated data is then obtained by removing the trend component from the raw response data. The application of this drift compensation method can greatly enhance the accuracy of concentration prediction models, reducing the mean absolute error by 40.21% on average and increasing the R² value by 0.0179 averagely compared to the scenarios where no drift compensation was implemented for benzene concentration prediction in this study. In summary, this article presents a cost-effective compensate strategy for drift-like effect in the CTO by performing adaptive correction.

Super-resolution optical imaging is a consistent research hotspot for promoting studies in nanotechnology and biotechnology due to its capability of overcoming the diffraction limit, which is an intrinsic obstacle in pursuing higher resolution for conventional microscopy techniques. In the past few decades, a great number of techniques in this research domain have been theoretically proposed and experimentally demonstrated. Graphene, a special two-dimensional material, has become the most meritorious candidate and attracted incredible attention in high-resolution imaging domain due to its distinctive properties. In this article, the working principle of graphene-assisted imaging devices is summarized, and recent advances of super-resolution optical imaging based on graphene are reviewed for both near-field and far-field applications.

The precursor chemical (PC) plays a crucial role in the drug manufacture. In recent years, PC intelligent recognition technology based on the analysis of semiconductor gas sensor signals has become a research hotspot due to PC’s volatility. However, in practice, the weak discrimination for commercial semiconductor gas sensor signals of PC gases is frequent, making it difficult to accurately recognize PC gases using the existing intelligent recognition methods. To tackle this problem, this article proposes a data-driven model based on ensemble empirical mode decomposition (EEMD) and gate recurrent unit (GRU) union convolutional neural network XGBoost (GUCNN-XGBoost) methods for PC gases recognition. First, the signals are collected by the commercial semiconductor gas sensor (MQ2) under the square wave temperature modulation, and then processed by the EEMD to obtain the intrinsic mode functions (IMF1–IMF7). Second, the GUCNN-XGBoost model is constructed to automatically extracts the binary morphological characteristics (BMC) and the time sequence characteristics (TSC) of the IMF1–IMF6; the XGBoost is utilized as the final classifier instead of common softmax to realize the high precision of 97.54%. Subsequently, we employ the IMF7-XGBoost regression model to achieve the accurate estimation of PC gases concentration, which can result in an average mean absolute error (MAE) of 4.98. Finally, a typical comparative experimental analysis validates the effectiveness of the proposed approach.

Acetic anhydride is a crucial chemical composition for heroin synthesis and is also a hazardous volatile organic compound (VOC). Hence, the development of a reliable and sensitive chemical sensor for its detection is essential. In this study, ZnO/La2O3 nanoparticles were successfully synthesized via a simple one-step hydrothermal process to detect acetic anhydride. The surface morphology, composition, and microstructure of particles were thoroughly analyzed. The 0.2 at% La-ZnO-based gas sensor exhibits a maximum of 422 toward 100-ppm acetic anhydride at the optimal operating temperature of 300 °C. The synergistic catalysis between ZnO and La2O3 heterojunction solves the problem of low sensitivity of pure ZnO to acetic anhydride. The unique selectivity and the high sensitivity of sensors to acetic anhydride are associated with the Lewis acid–base interaction because of the enhanced Lewis base sites on the surface of sensing materials. Significantly, the gas-sensitive mechanism is elaborated, including the effect of heterojunction and oxygen vacancies, and the catalysis of La2O3. The most important thing is that the rapid detection of acetic anhydride has been realized, which is significant in curbing the spread of drugs.