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In order to detect early tumor markers and gain valuable time for treatment, there is an urgent need to develop a fast, cheap, and ultrasensitive multi-reading sensing platform. Herein, a solid/liquid two-phase dual-output biosensor was explored based on a sensitized sonochemiluminescence (SCL) strategy and a multifunctional carbon nano-onion (CNO) probe. It is clear that ultrasonic radiation caused the formation of hydroxyl radicals (˙OH), triggering the SCL signal of the emitter lucigenin (Luc2+). Meanwhile, titanium carbide nanodots and ethanol were used to enhance the SCL signal, and an astonishingly linear enhancement of the SCL intensity was produced with increasing ethanol concentration. More importantly, the CNOs, with their excellent photothermal properties and adsorption capacity, can output both the temperature signal and an enhanced SCL strength from the solid–liquid phase. Through inter-calibration of the signals from the two-phases, this biosensor shows excellent analytical performance for the detection of the ovarian cancer biomarker, human epididymis-specific protein 4, from 10−5 to 10 ng mL−1 with a low detection limit of 3.3 fg mL−1. This work not only provides a novel two-phase signal-output mode that broadens the scope of multiperformance joint applications of CNOs, but also enriches the quantitative detection of point-of-care testing. [ABSTRACT FROM AUTHOR]

Recent advancements in transmission electron microscopy (TEM) have enabled the study of atomic structures of materials at unprecedented scales as small as tens of picometers (pm). However, accurately detecting atomic positions from TEM images remains a challenging task. Traditional Gaussian fitting and peak‐finding algorithms are effective under ideal conditions but perform poorly on images with strong background noise or contamination areas (shown as ultrabright or ultradark contrasts). Moreover, these traditional algorithms require parameter tuning for different magnifications. To overcome these challenges, AtomID‐Net is presented, a deep neural network model for atomic detection from multiscale low‐SNR experimental images of scanning TEM (scanning transmission electron microscopy (STEM)). The model is trained on real images, which allows the robust and efficient detection of atomic positions, even in the presence of background noise and contamination. The evaluation on a test set of 50 images with a resolution of 800 × 800 yields an average F1‐Score of 0.964, which demonstrates significant improvements over existing peak‐finding algorithms. [ABSTRACT FROM AUTHOR]

In order to explore the long-term variation law of methane (CH4) and nitrous oxide (N2O) emissions from rice fields in cold regions under different straw-returning and irrigation methods, this study set up two irrigation methods, namely, conventional flooding and controlled irrigation, and two straw-returning quantities (0 t·hm−2 and 6 t·hm−2). Based on the field in situ test data, a sensitivity analysis of the main factors of the DNDC model affecting the emissions of CH4 and N2O from rice fields was conducted, and the emission fluxes of CH4 and N2O were calibrated and validated. Under different future climate scenarios (RCP4.5 and RCP8.5), greenhouse gas emissions from rice fields were simulated on a 60-year scale under different straw-returning and irrigation methods using the DNDC model. The results indicate that the DNDC model can effectively simulate the seasonal emission laws of CH4 and N2O from rice fields in cold regions under different straw-returning and irrigation methods. The simulated values have a significant correlation with the measured values (R2 ≥ 0.794, p < 0.05), and the consistency is controlled within 30%. The soil texture, soil organic carbon (SOC) content, annual average temperature, and straw-returning amount are sensitive factors for CH4 emissions from rice fields. The total nitrogen fertilizer application amount and SOC content are sensitive factors for N2O emissions from rice fields. Over the next 60 years, under the two different emission scenarios of RCP4.5 and RCP8.5, straw returning combined with control irrigation has a good coupling effect on the GWP of rice fields, and compared with conventional flooding without straw returning, the GWP of rice fields is reduced by 31.41% and 34.13%, respectively, and the SOC content in 0–20 cm soil layer is increased by 54.69% and 52.80%, respectively. Thus, it can be used as a long-term carbon sequestration and emission reduction tillage model for rice fields in Northeast China. The results of this study can provide a reference for a further regional estimation of greenhouse gas emissions from rice fields using models. [ABSTRACT FROM AUTHOR]