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Design of multi-luminescent silica-based nanoparticles for the detection of liquid organic compounds
Tracer testing in reservoir formations is utilised to determine residual oil saturation as part of optimum hydrocarbon production. Here, we present a novel detection method of liquid organic compounds by monodisperse SiO2 nanoparticles (NPs) containing two luminophores, a EuIII:EDTA complex and a newly synthesised fluorophore based on the organic boron-dipyrromethene (BODIPY)-moiety. The particles exhibited stable EuIII PL emission intensity with a long lifetime in aqueous dispersion. The fluorescence of the BODIPY was also preserved in the aqueous environment. The ratiometric PL detection technique was demonstrated by using toluene and 1-octanol as model compounds of crude oil. The optimal synthesis conditions were found to give NPs with a diameter of ~100 nm, which is suitable for transport through porous oil reservoir structures. The cytotoxicity of the NPs was confirmed to be very low for human lung cell and fish cell lines. These findings demonstrate the potential of the NPs to replace the hazardous chemicals used to estimate the residual oil saturation. Moreover, the ratiometric PL detection technique is anticipated to be of benefit in other fields, such as biotechnology, medical diagnostics, and environmental monitoring, where a reliable and safe detection of a liquid organic phase is needed.
2024
2016
Abstract This study introduces a community-focused eCoach recommendation system aimed at enhancing physical activity by leveraging demographic data, wearable sensor inputs, and machine learning algorithms to generate both individual and community-based activity recommendations using advice-based collaborative filtering. Existing eCoaching systems largely focus on personalized feedback without incorporating social reinforcement or group-level motivation, creating a gap in leveraging community influence for sustained health behaviors. Our system combines real-time activity tracking through wearable sensors and advice-based collaborative filtering to deliver adaptive recommendations. We collected data from 31 participants (16 using MOX2-5 sensors and 15 from a public Fitbit-based dataset), targeting daily activity levels to generate actionable guidance. Through decision tree classification and SHAP-based interpretability, we achieved a model accuracy of 99.8%, with F1, precision, recall, and MCC metrics confirming robustness across both balanced and imbalanced datasets. Ethical considerations, including privacy, bias mitigation, and informed consent, were integral to our design and implementation. Limitations include potential biases due to sample size and data imbalances, suggesting the need for future validation on independent datasets. This system demonstrates the potential to integrate with real-world healthcare initiatives, offering trust, transparency, and user engagement opportunities to meet public health objectives.
2025
2023
2008
Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements.
2017
2010
2016
2010
Detection of Body Shaming in Social Media: Traditional Machine Learning vs. Transformer-based Models
2025
2005
Detection of ozone recovery in the Arctic from ground-based measurements
Contrary to the Antarctic, where ozone recovery has been observed for about a decade, the detection of positive ozone trends in the Arctic remains challenging due to higher natural variability of ozone in that region. Using a merging of long-term ozone data from Fourier transform infrared spectrometers, ozonesondes, and Dobson and Brewer spectrophotometers, we present regional long-term trends (2000–2024) for total, stratospheric and tropospheric ozone. First, ground-based measurements are cross-compared to two satellite data sets (MEGRIDOP and IASI-CDR). This enables the detection of drifts in ground-based data sets we further exclude from our study. We then use a representativeness study based on CAMS re-analysis data to define regions for which representative trends with reduced uncertainties are obtained by combining data sets from different instruments and stations. Annual and seasonal trends are calculated using a multiple linear regression technique involving a set of proxies that represent physical processes influencing the natural ozone variability. Annual trends indicate increasing total ozone over the Arctic, and are statistically significant over Canada and Reykjavik (+2.1 % per decade) and North-West Europe (Harestua and Lerwick, +0.7 % per decade). Ozone recovery is also observed over Canada in the mid-stratosphere (+2.0 % per decade) and over the North Pole region (Canada and Ny-Ålesund) in the upper stratosphere (+2.1 % per decade to +3.8 % per decade). By analyzing the sensitivity of the ozone trends to the proxies, we observe a slow down of the expected ozone recovery, especially in the lower stratosphere, due to stratospheric cooling (−0.6 % per decade) and to the increase of volume of polar stratospheric clouds (−0.8 % per decade).
2026
2010
2014