Micro-macro SERS strategy for highly sensitive paper cartridge with trace-level molecular detection.

Surface-enhanced Raman Scattering (SERS) has become a powerful spectroscopic technology for highly sensitive detection. However, SERS is still limited in the lab because it either requires complicated preparation or is limited to specific compounds, causing poor applicability for practical applications. Herein, a micro-macro SERS strategy, synergizing polymer-assisted printed process with paper-tip enrichment process, is proposed to fabricate highly sensitive paper cartridges for sensitive practical applications.

Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis.

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features.

Highly accurate and label-free discrimination of single cancer cell using a plasmonic oxide-based nanoprobe.

The detection of cancer cells at the single-cell level enables many novel functionalities such as next-generation cancer prognosis and accurate cellular analysis. While surface-enhanced Raman spectroscopy (SERS) has been widely considered as an effective tool in a low-cost and label-free manner, however, it is challenging to discriminate single cancer cells with an accuracy above 90% mainly due to the poor biocompatibility of the noble-metal-based SERS agents. Here, we report a dual-functional nanoprobe based on dopant-driven plasmonic oxides, demonstrating a maximum accuracy above 90% in distinguishing single THP-1 cell from peripheral blood mononuclear cell (PBMC) and human embryonic kidney (HEK) 293 from human macrophage cell line U937 based on their SERS patterns.

Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm.

Extracellular vesicles and particles (EVPs), which contain the same surface proteins as their mother cells, are promising biomarkers for cancer liquid biopsy. However, most of the isolation methods of EVPs are time-consuming and complicated, and hence, sensitive detection and classification methods are required for EVPs. Here, we report a facile polyethylene glycol (PEG)-based method for isolating and classifying EVPs with label-free surface-enhanced Raman scattering (SERS) and pattern recognition algorithm.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers.

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers.

Engineering of Removing Sacrificial Materials in 3D-Printed Microfluidics.

Three-dimensional (3D) printing will create a revolution in the field of microfluidics due to fabricating truly three-dimensional channels in a single step. During the 3D-printing process, sacrificial materials are usually needed to fulfill channels inside and support the printed chip outside. Removing sacrificial materials after printing is obviously crucial for applying these 3D printed chips to microfluidics.