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Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring. 

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy. 

Cobalt sulfide nanomaterials are among the most active and stable catalysts for the electrocatalytic oxygen reduction reaction in pH 7 electrolyte. However, due to the complexity and dynamism of the catalytic surfaces in cobalt sulfide bulk materials, it is challenging to identify and tune the active site structure in order to achieve low overpotential oxygen reduction reactivity. In this work, we synthesize isolated Co sites supported on colloidal WS 2 nanosheets and develop a synthetic strategy to rationally control the first-shell coordination environment surrounding the adsorbed Co active sites. By studying Co–WS 2 materials with a range of Co–S coordination numbers, we are able to identify the optimal active site for pH 7 oxygen reduction catalysis, which comprises cobalt atoms bound to the WS 2 support with a Co–S coordination number of 3–4. The optimized Co–WS 2 material exhibits an oxygen reduction onset potential of 0.798 V vs. RHE, which is comparable to the most active bulk phases of cobalt sulfide in neutral electrolyte conditions. 

Abstract Comprehensive comparison of paleoclimate change based on records constrained by precise chronology and high-resolution is essential to explore the correlation and interaction within earth climate systems. Here, we propose a new stalagmite-based multidecadal resolved Asian summer monsoon (ASM) record spanning the past thirty-seven thousand years (ka BP, before ad 1950) from Furong Cave, southwestern China. This record is consistent with the published Chinese stalagmite sequences and shows that the dominant controls of the ASM dynamics include not only insolation and solar activity but also suborbital-scale hydroclimate events in the high latitudes of the northern hemisphere, such as the Heinrich events, Bølling-Allerød (BA), and Younger Dryas (YD). Benefit from the unprecedented accurate chronology, the timings of these events are precisely dated, with uncertainties of, at most, 40 years (2σ). The onset of the weak ASM during the YD began at 12.92 ka BP and lasted for 430 years. The occurrence of the 200-yr Older Dryas during the BA period was dated from 13.87 to 14.06 ka BP. The durations of the three Heinrich (H) events, H1, H2, and H3, are 14.33–18.29, 23.77–24.48, and 28.98–30.46 ka BP, respectively. Furong record shows surprisingly variable onset transitions of 980, 210, and 40 years for the corresponding weak ASM events. These discrepancies suggest different influences of the H events on ASM dynamics. During the periods of H 1–3, the obvious difference between our Furong record and NGRIP δ 18 O record indicated the decoupling correlation between the mid-low latitudes and high latitudes. On the other hand, synchronous climate change in high and low latitudes suggests another possibility which different to the dominant role of Northern high latitudes in triggering global climate change. Our high quality records also indicate a plausible different correlation between the high and mid-low latitudes under glacial and inter-glacial background, especially for the ASM regimes.