Chemical conjugation to differentiate monosaccharides by Raman and surface enhanced Raman spectroscopy

An Autonomously Liquefied Hydrogel Adhesive for Programmable Bioelectronic Interface

Hydrogel adhesives have many important applications in the fields of drug delivery, regenerative medicine, and bioelectronics. The detachment of hydrogel adhesives under the benign conditions is vital to the definitive surgical repair and implanted devices. Although stimuli-mediated detachment of hydrogel adhesives has been achieved, it is still a grand challenge to develop a transient adhesive with programmable adhesion and autonomous detachment from the substrate, especially the hairy skins. Here, we report a transient hydrogel adhesive driven by antagonistic enzyme reaction networks for programmable bioelectronic interface. The transient hydrogel shows tunable mechanical properties, adjustable adhesive strength, and autonomous sol-gel-sol transition with a programmable lifetime. Moreover, the transient hydrogel adhesive enables conformable and stable adhesion to various materials. In particular, the bioelectrode coated by the transient hydrogel adhesive allows to record stable and high-quality electromyogram, electrocardiogram, and electroencephalogram signals directly on the hairy skins without hair shaving. Notably, the autonomous liquefication of the hydrogel adhesives enables the easy removal of bioelectrode from hairy skins after usage without any noticeable damages to the hairy skins and electrode. This work paves a new avenue in the innovative development of hydrogel adhesives for the conformable and detachable bioelectronic interface.

A Chiral Sensing System Based on Single Au Nanowire: A General SERS Strategy for Identification of the Enantiomers and Mechanism for Chiral Interaction

Enantiomers are a widespread phenomenon in chemistry, and the identification, separation, and synthesis of enantiomers were important in the fields of drug screening, disease diagnosis, and environmental monitoring. Herein, a new strategy was presented to recognize enantiomers based on the surface-enhanced Raman scattering (SERS) technique using a single Au nanowire (Au NW) decorated by Ag core-Au satellite nanocomposites as a new platform. The SERS discrimination detection of enantiomers is based on molecular interactions between chiral targets and this chiral Au NW-based nanosensor: the SERS signals of l-nanosensor can be enhanced by d-targets but decreased by l-targets. In contrast, the SERS signals of the d-nanosensor can be enhanced by l-targets but decreased by d-targets. Therefore, a new SERS method can be established for the detection of different enantiomers with high selectivity and sensitivity. A variety of chiral molecules, including glucose, threonine, mandelic acid, phenylalanine, sorbitol, tartaric acid and tryptophan, were successfully identified using this system, and the chiral recognition mechanism of this nanosensor was also explored in depth via the density functional theory (DFT) calculation. Due to the small overall dimension of single Au NW-based nanosensor, this method provides a facile and universal solution for online analysis and noninvasive diagnosis, especially for some confined environment and single-cell analysis.

Identification of structural stability and fragility of mouse liver glycogen via label-free Raman spectroscopy coupled with convolutional neural network algorithm

Glycogen structure is closely associated with its physiological functions. Previous studies confirmed that liver glycogen structure had two dominant states: mainly stable during the day and largely fragile at night. However, the diurnal change of glycogen structure is impaired, with dominant fragility in diseased conditions such as diabetes mellitus and liver fibrosis. Therefore, the persistent structural fragility of glycogen particles could be a potential molecular-level pathological biomarker for early screening of certain liver diseases. However, the current method for identifying glycogen structural stability and fragility suffers from sophisticated procedures and reliance on expensive instruments, which demands developing novel methods for rapidly discriminating the two types of glycogen particles. This study applied surface-enhanced Raman spectroscopy (SERS) to generate SERS spectra of glycogen samples, revealing distinct structural differences between fragile and stable glycogen particles. Machine learning models were then constructed to predict the structural states of unknown glycogen samples via SERS spectra, according to which the convolutional neural network (CNN) model achieved the best discrimination capacity. Taken together, the SERS technique coupled with the CNN model can identify stable and fragile liver glycogen samples, facilitating the application of glycogen structural fragility as a biomarker in diagnosing liver diseases.

Digital surface enhanced Raman spectroscopy for quantifiable single molecule detection in flow

Surface enhanced Raman scattering (SERS) provides a label free method of analyzing molecules from diverse and complex signals, potentially with single molecule sensitivity. The chemical specificity inherent in the SERS spectrum can identify molecules; however signal variability arising from the diversity of plasmonic environments can limit quantification, particularly at low concentrations. Here we show that digitizing, or counting SERS events, can decrease the limit of detection in flowing solutions enabling quantification of single molecules. By using multivariate curve resolution and establishing a score threshold, each individual spectrum can be classified as containing an event or not. This binary "yes/no" can then be quantified, and a linear region can be established. This method was shown to lower the limit of detection to the lowest physical limit, and lowered the limit of detection by an order of magnitude from the traditional, intensity based LOD calculations.

A 3D printed sheath flow interface for surface enhanced Raman spectroscopy (SERS) detection in flow

Surface enhanced Raman spectroscopy (SERS) is an effective technique for detecting molecules in aqueous solutions due to its insensitivity to water, which makes it especially useful for biological samples. Utilizing SERS in flow can aid in a variety of applications such as metabolomics, pharmaceuticals, and diagnostics. The ability to 3D print complex objects enables rapid dissemination of prototypes. A 3D printed flow cell for sheath flow SERS detection has been developed that can incorporate a variety of planar substrates. The 3D printed flow cell incorporates hydrodynamic focusing, a sheath flow, that confines the analyte near the SERS substrate. Since the SERS signal obtained relies on the interaction between analyte molecules and nanostructures, sheath flow increases the detection efficiency and eliminates many issues associated with SERS detection in solution. This device was optimized by analyzing both molecules and particles with and without using sheath flow for SERS detection. Our results show that the flow rates can be optimized to increase the SERS signal obtained from a variety of analytes, and that the signal was increased when using sheath flow. This 3D printed flow cell offers a straightforward method to disseminate this technology and to facilitate online SERS detection.