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DNA has shown great biocompatibility, programmable mechanical properties, and precise structural addressability at the nanometer scale, rendering it a material for constructing versatile nanorobots for biomedical applications. Here, we present the design principle, synthesis, and characterization of a DNA nanorobotic hand, called DNA NanoGripper, that contains a palm and four bendable fingers as inspired by naturally evolved human hands, bird claws, and bacteriophages. Each NanoGripper finger consists of three phalanges connected by three rotatable joints that are bendable in response to the binding of other entities. NanoGripper functions are enabled and driven by the interactions between moieties attached to the fingers and their binding partners. We demonstrate that the NanoGripper can be engineered to effectively interact with and capture nanometer-scale objects, including gold nanoparticles, gold NanoUrchins, and SARS-CoV-2 virions. With multiple DNA aptamer nanoswitches programmed to generate a fluorescent signal that is enhanced on a photonic crystal platform, the NanoGripper functions as a highly sensitive biosensor that selectively detects intact SARS-CoV-2 virions in human saliva with a limit of detection of ~100 copies per milliliter, providing a sensitivity equal to that of reverse transcription quantitative polymerase chain reaction (RT-qPCR). Quantified by flow cytometry assays, we demonstrated that the NanoGripper-aptamer complex can effectively block viral entry into the host cells, suggesting its potential for inhibiting virus infections. The design, synthesis, and characterization of a sophisticated nanomachine that can be tailored for specific applications highlight a promising pathway toward feasible and efficient solutions to the detection and potential inhibition of virus infections. 

Abstract Ferromanganese concretions commonly occur in shallow‐water coastal regions worldwide. In the Baltic Sea, they can record information about past and present underwater environments and could be a potential source for critical raw materials. We report on their microstructural characteristics and magnetic properties and link them to their formation mechanisms and environmental significance. Microstructural investigations from nano‐ and micro‐computed tomography, electron microscopy, and micro‐X‐ray fluorescence elemental mapping reveal diverse growth patterns within concretions of different morphologies. Alternating Fe‐ and Mn‐rich growth bands indicate fluctuating redox conditions during formation. Bullet‐shaped magnetofossils, produced by magnetotactic bacteria, are present, which suggests the influence of bacterial activity on concretion formation. Spheroidal concretions, which occur in deeper and more tranquil environments, have enhanced microbial biomineralization and magnetofossil preservation. Conversely, crusts and discoidal concretions from shallower and more energetic environments contain fewer magnetofossils and have a greater detrital content. Our results provide insights into concretion formation mechanisms and highlight the importance of diagenetic processes, oxygen availability, and bacterial activity in the Baltic Sea. 

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.