Broad-bandwidth and accurate optical vibration sensing by using FDM Φ-OTDR with linear regression analysis of multi-frequency phase responses.

We demonstrate Φ-OTDR distributed acoustic sensing (DAS) that realizes both a broad bandwidth for the vibration frequency and wide dynamic range for the vibration amplitude based on optical frequency-division-multiplexing (FDM). We enhance the sampling rate of DAS by using FDM while suppressing waveform distortion in time domain (spurious components in spectral domain) caused by sensor nonlinearity inherent in Φ-OTDR, thus improving dynamic range, with linear regression analysis of multi-frequency phase responses. The proposed method compares the phase offsets and responses of each frequency to those of a common reference frequency and uses the information to calibrate each of the different responses. We clarify the physical origin of the problem and the validity of the proposed method in both simulations and experiments. Experimental results show an improvement in dynamic range by above 8 dB on average for vibration waveforms with nɛ-order amplitudes and kHz-order frequencies over 10-km single-mode fiber.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Probing the metabolic heterogeneity of live Euglena gracilis with stimulated Raman scattering microscopy.

Understanding metabolism in live microalgae is crucial for efficient biomaterial engineering, but conventional methods fail to evaluate heterogeneous populations of motile microalgae due to the labelling requirements and limited imaging speeds. Here, we demonstrate label-free video-rate metabolite imaging of live Euglena gracilis and statistical analysis of intracellular metabolite distributions under different culture conditions. Our approach provides further insights into understanding microalgal heterogeneity, optimizing culture methods and screening mutant microalgae.