Search for: All records

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 As 3+ and 6.8 pM for Cr 6+ , 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. 

Design of the molecular environment of single atom catalysts (SAC) is promising for achieving high catalytic activity without expensive and scarce platinum-group metals (PGM). We utilize a first principles approach to examine how the spin state of the SAC and reactants can affect catalytic energy barriers of V, Fe, Mo, and Ta on two different graphene defects with differing magnetic moments. Spin polarized projected density of states and climbing image nudged elastic band calculations demonstrate relatively lower activation energy barriers for systems with higher spin state asymmetry near the Fermi energy; CO oxidation on Ta and V SAC have decreases in activation barrier energies of 27% and 44%, respectively.

Graphic Abstract