Dynamic Raman imaging system with high spatial and temporal resolution

A Wide-Field Imaging Approach for Simultaneous Super-Resolution Surface-Enhanced Raman Scattering Bioimaging and Spectroscopy

High spatial resolution imaging and chemical-specific detection in living organisms is important in a wide range of fields from medicine to catalysis. In this work, we characterize a wide-field surface-enhanced Raman scattering (SERS) imaging approach capable of simultaneously capturing images and SERS spectra from nanoparticle SERS tags in cancer cells. By passing the image through a transmission diffraction grating before it reaches an array detector, we record the image and wavelength dispersed signal simultaneously on the camera sensor. Optimization of the experiment provides an approach with better spectral resolution and more rapid acquisition than liquid crystal tunable filters commonly used for wide-field SERS imaging. Intensity fluctuations inherent to SERS enabled localization algorithms to be applied to both the spatial and spectral domain, providing super-resolution SERS images that are correlated with improved peak positions identified in the spectrum of the SERS tag. The detected Raman signal is shown to be sensitive to the focal plane, providing three-dimensional (3D) sectioning abilities for the detected nanoparticles. Our work demonstrates spectrally resolved super-resolution SERS imaging that has the potential to be applied to complex physical and biological imaging applications.

Spatiotemporal-Resolved Hyperspectral Raman Imaging of Plasmon-Assisted Reactions at Single Hotspots

Raman spectroscopy facilitates the study of reacting molecules on single nanomaterials. In recent years, the temporal resolution of Raman spectral measurement has been remarkably reduced to the millisecond level. However, the classic scan-based imaging mode limits the application in the dynamical study of reactions at multiple nanostructures. In this paper, we propose a spatiotemporal-resolved Raman spectroscopy (STRS) technology to achieve fast (∼40 ms) and high spatial resolution (∼300 nm) hyperspectral Raman imaging of single nanostructures. With benefits of the outstanding electromagnetic field enhancement factor by surface plasmon resonance (∼10¹²) and the snapshot hyperspectral imaging strategy, we demonstrate the observation of stepwise Raman signals from single-particle plasmon-assisted reactions. Results reveal that the reaction kinetics is strongly affected by not only the surface plasmon-polariton generation but also the density of Raman molecules. In consideration of the spatiotemporal resolving capability of STRS, we anticipate that it provides a potential platform for further extending the application of Raman spectroscopy methods in the dynamic study of 1D or 2D nanostructures.

Surface-enhanced Raman spectroscopy: benefits, trade-offs and future developments

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique with sensitivity down to the single molecule level that provides fine molecular fingerprints, allowing for direct identification of target analytes. Extensive theoretical and experimental research, together with continuous development of nanotechnology, has significantly broadened the scope of SERS and made it a hot research field in chemistry, physics, materials, biomedicine, and so on. However, SERS has not been developed into a routine analytical technique, and continuous efforts have been made to address the problems preventing its real-world application. The present minireview focuses on analyzing current and potential strategies to tackle problems and realize the SERS performance necessary for translation to practical applications.

Speeding Up the Line-Scan Raman Imaging of Living Cells by Deep Convolutional Neural Network

Raman imaging is a promising technique that allows the spatial distribution of different components in the sample to be obtained using the molecular fingerprint information on individual species. However, the imaging speed is the bottleneck for the current Raman imaging methods to monitor the dynamic process of living cells. In this paper, we developed an artificial intelligence assisted fast Raman imaging method over the already fast line scan Raman imaging method. The reduced imaging time is realized by widening the slit and laser beam, and scanning the sample with a large scan step. The imaging quality is improved by a data-driven approach to train a deep convolutional neural network, which statistically learns to transform low-resolution images acquired at a high speed into high-resolution ones that previously were only possible with a low imaging speed. Accompanied with the improvement of the image resolution, the deteriorated spectral resolution as a consequence of a wide slit is also restored, thereby the fidelity of the spectral information is retained. The imaging time can be reduced to within 1 min, which is about five times faster than the state-of-the-art line scan Raman imaging techniques without sacrificing spectral and spatial resolution. We then demonstrated the reliability of the current method using fixed cells. We finally used the method to monitor the dynamic evolution process of living cells. Such an imaging speed opens a door to the label-free observation of cellular events with conventional Raman microscopy.

A practical wide-field Raman imaging method with high spectral and spatial resolution

Raman imaging has a great advantage in characterizing inhomogeneous systems. A practical wide-field Raman imaging platform is developed that shows major improvements on imaging speed, sensitivity, and resolution. Different from the traditional Raman imaging systems using the wavelength-fixed lasers and the chromatic dispersion devices, this system adopts an inverted architecture, integrated with a tunable laser and the wavelength-fixed filters. Owing to the high transmission of the fixed filters, the imaging sensitivity can be improved 5-10 times in comparison to the present wide-field Raman imaging setups using liquid-crystal tunable filters. Via combining with the high-power tunable laser, Raman images could be obtained in minutes and the Raman shift of the images could be tuned easily and accurately. The resolution of this system can reach 1.5 cm^-1 in the spectrum and 490 nm in space, which could provide more fingerprint details of the analytes. This effective Raman imaging method allowing us to see chemical spatial variations on microscale is anticipated to be widely applied in scientific research fields.