Nanozymes have drawn considerable attention because of their lower cost, higher stability and convenient preparation compared to protein enzymes. In the present work, Mo, S co-doped carbon quantum dots (Mo-CQDs) as a peroxidase mimic were used to fabricate a cascade colorimetric biosensor to detect cholesterol. The Mo-CQDs possess a robust peroxidase-like activity, and they can easily catalyze 3,3,5,5-tetramethylbenzidine (TMB) to produce an oxidized TMB in the presence of H₂O₂. The Mo, S doping in the carbon quantum dots (CQDs) notably boosts the yield of CQDs and may facilitate the electron transfer between TMB and H₂O₂, which further enhances the catalytic activity of CQDs. The colorimetric biosensor based on Mo-CQDs and cholesterol oxidase exhibited excellent selectivity and high sensitivity for cholesterol in the range of 0.01–1.0 mM along with a detection limit as low as 7 μM. The total cholesterol concentration in the serum sample was measured with satisfactory results and read out by the naked eye, indicating the potential application in clinical diagnosis and portable test kits.

The fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers, and a variety of gas sensors have been simultaneously developed. However, these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture, resulting in less accuracy in the analysis of gas compositions in real mining conditions. To address these challenges, we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH₃ and NO₂ gas detections under real mining conditions. A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions. Further introduction of backpropagation neural network (BP‐NN) and partial least squares (PLS) algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture, realizing over 99% theoretical prediction level on NH₃ and NO₂ concentrations within a wide relative humidity range, showing great promise in real mining detection. As proof of concept, a wireless wearable bracelet, integrated with sensing arrays and machine‐learning algorithms, is developed for wireless real‐time warning of hazardous gases in mines under different humidity conditions. image

Environmentally friendly degradable sensors with both hazardous gases and pressure efficient sensing capabilities are highly desired for various promising applications, including environmental pollution monitoring/prevention, wisdom medical, wearable smart devices, and artificial intelligence. However, the transient gas and pressure sensors based on only identical sensing material that concurrently meets the above detection needs have not been reported. Here, we present transient all-MXene NO₂ and pressure sensors employing three-dimensional porous crumpled MXene spheres prepared by ultrasonic spray pyrolysis technology as the sensing layer, accompanied with water-soluble polyvinyl alcohol substrates embedded with patterned MXene electrodes. The gas sensor achieves a ppb-level of highly selective NO₂ sensing, with a response of up to 12.11% at 5 ppm NO₂ and a detection range of 50 ppb–5 ppm, while the pressure sensor has an extremely wide linear pressure detection range of 0.14–22.22 kPa and fast response time of 34 ms. In parallel, all-MXene NO₂ and pressure sensors can be rapidly degraded in medical H₂O₂ within 6 h. This work provides a new avenue toward environmental monitoring, human physiological signal monitoring, and recyclable transient electronics.