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Low-cost sensor arrays are an economical and efficient solution for large-scale networked monitoring of atmospheric pollutants. These sensors need to be calibrated in situ before use, and existing data-driven calibration models have been widely used, but require large amounts of co-location data with reference stations for training, while performing poorly across domains. To address this problem, a meta-learningbased calibration network for air sensors is proposed, which has been tested on ozone datasets. The tests have proved that it outperforms five other conventional methods in important metrics such as mean absolute error, root mean square error and correlation coefficient. Taking Manlleu and Tona as the source domain and Vic as the target domain, the proposed method reduces MAE and RMSE by 17.06% and 6.71% on average, and improves R2 by an average of 4.21%, compared with the suboptimal pre-trained multi-source transfer calibration. The method can provide a new idea and direction to solve the problem of cross-domain and reliance on a large amount of co-location data in the calibration of sensors.

The surface plasmon resonance (SPR) sensor was used for the detection of the PML/RARA fusion gene, and the sandwich structure as the amplification strategy was constructed with functionalized gold nanoparticles (AuNPs). Three different sizes of AuNPs were selected to investigate the role that size plays in genotypic SPR sensing. To enhance sensor sensitivity and ensure signal reliability, the effects of experimental conditions were examined, such as buffer, flow rate, and probe density, on the sensor performance. Experimental and simulation results demonstrate that buffers with lower dielectric constants exhibit superior sensitivity. While larger AuNPs initially improve the sensor performance, this advantage diminishes as the analyte concentration decreases. Furthermore, varying probe densities influence AuNP enhancement capabilities, with a larger probe spacing effectively regulating the hybridization efficiency. Finally, after considering the nonspecific binding of DNA-AuNPs, the spacer layer of the probe was increased to reduce the background signal. By increasing the spacer layer from polyT5 to polyT16, the limit of detection was reduced significantly from 9.03 pM to 5.53 fM, and the coefficient of variation for repeatability improved from 14.89 to 3.40%. Otherwise, the sensor performed exceptionally well in terms of specificity, resistance to fouling, and recovery from complex matrices. The above results establish a surface chemical treatment method for AuNP-enhanced SPR biosensors. [ABSTRACT FROM AUTHOR]

The proliferation of drug-resistant bacteria and infections such as methicillin-resistant Staphylococcus aureus (MRSA) pose a serious challenge to global healthcare. Photothermal therapy (PTT) is becoming a highly effective antimicrobial method. Compared with near infrared I (NIR-I), near infrared II (NIR-II) has deeper penetration depth, less light scattering and higher maximum permissible exposure (MPE). The selection of a photothermal agent (PTA) and laser parameters is important. Three kinds of MXenes, Nb2C, Ti3C2 and V2C, were prepared in this paper, and the extinction coefficient and photothermal conversion efficiency (PCE) of NIR-I and NIR-II were compared. The extinction coefficient of Ti3C2 (26.23 L g−1 cm−1) at 808 nm was greater than those of Nb2C (24.38 L g−1 cm−1) and V2C (19.43 L g−1 cm−1). However, V2C had a higher temperature than Nb2C at 808 nm due to its high PCE (49.26%). Nb2C had a higher extinction coefficient (24.99 L g−1 cm−1) and PCE (45.55%) at 1064 nm, resulting in the highest temperature change. High extinction coefficient and high PCE are the two important factors for an excellent PTA. To reduce laser power used in the treatment, NIR-II laser and a gelation strategy for MXenes were used. The prepared Nb2C/Gel could be rapidly heated to 62 °C at a laser power of 0.112 W (lower than MPE), and the sterilization rate for MRSA was 92.48%. A NIH-3T3 cell activity study showed that the prepared Nb2C had good biocompatibility. Nb2C/Gel has excellent antibacterial properties and it is expected that a rapid, efficient and safe biological antimicrobial strategy against bacterial infections can be established using Nb2C/Gel, especially for drug-resistant bacterial infections. [ABSTRACT FROM AUTHOR]

MicroRNA-21 is a potential cancer biomarker that is highly expressed in many cancer cells. Therefore, it is important to perform highly sensitive detection of miRNA-21. In this study, we designed a surface plasmon resonance imaging (SPRi) sensor based on an AuNPs/Ti3C2 composite for real-time and highly sensitive detection of miRNA-21. The fixation of the capture polyA-DNA probes was completed by the freezing method, which improved the detection efficiency. DNA−AuNPs/Ti3C2 conjugates were added to amplify the SPRi signal. The signal amplification combines the large specific surface area of Ti3C2 and the electronic coupling between the local surface plasmon resonance (LSPR) of AuNPs and the plasmon wave on the surface of the Au chip, thereby enhancing the SPRi response signal. Using this sensing strategy, the detection limit for miRNA-21 can reach 6.13 fM, with a wide dynamic range between 10 fM and 10 nM. In addition, the sensor has excellent selectivity for miRNA-21 and miRNAs with similar sequences, and receives minimal interference when applied to complex matrices. Based on these results, we believe that this study provides a simple and highly sensitive method for miRNA detection, which has great potential for the quantitative detection of miRNA in biomedical research and early clinical diagnosis. [ABSTRACT FROM AUTHOR]