Concentration Indicator Kit Based on Dandelion Propagation-Inspired SERS Strategy.

Surface-enhanced Raman scattering (SERS) technology has been extensively employed for the detection of liquid samples in the biomedical field due to high sensitivity and non-invasive characteristics. However, quantitative SERS detection is hindered by sophisticated instrumentation and complex calibration procedures, while qualitative analyses often provide insufficient concentration information for clinical diagnosis. Herein, a concentration indicator kit based on dandelion propagation-inspired SERS strategy is developed for the semi-quantitative detection of drug residues in urine.

Segment-Weighting Similarity-Based Fragment-Learning Model for Single-Cell Raman Spectral Analysis.

Raman spectroscopy provides intrinsic biochemical profiles of all cellular biomolecules in a segmented manner, promising nondestructive and label-free phenotyping at the single-cell level. However, current analytical methods rarely utilize spectral biological characteristics and their fusion with data characteristics, limiting the application of these methods to biological Raman spectroscopy. Herein, a segment-weighting similarity-based fragment-learning (SWS-FL) model, integrating SWS-based feature extraction and fusion learning, is proposed to fuse biological and data characteristics for single-cell spectral analysis, which segments spectra into fragments and differentiates their biological characteristics for fusing feature matrices.

Raman Peak Features Matching: Enhancing Spectral Analysis through Feature Augmentation.

Raman spectroscopy has emerged as a pivotal technology in modern scientific research and industrial applications, offering nondestructive, high-resolution analysis with robust molecular fingerprinting capabilities. The extraction of Raman spectral features is a critical step in spectral data analysis, directly influencing sample identification, classification, and quantitative outcomes. However, integrating important data features from machine learning models with context-specific biosignatures to derive meaningful insights into spectral analysis remains a significant challenge.

Dynamic Liquid Integrated Single-Cell SERS Platform Based on the Twisted Mixing Microfluidic Chip and Multi-Modified Nanoprobe for the Label-Free Detection of Cancer Cells.

Surface-enhanced Raman scattering (SERS) has emerged as a potent spectroscopic technique for the detection of single cells. However, it is difficult to achieve label-free detection at the single-cell level in dynamic liquids because nanoprobe aggregation in biological fluids and the low combination of nanoprobes and cells reduce the sensitivity of SERS detection. Herein, a dynamic liquid integrated single-cell SERS (DLISC-SERS) platform is developed for the label-free detection of single cancer cells.

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.

Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples.

Surface-enhanced Raman scattering (SERS) technology, as an important analytical tool, has been widely applied in the field of chemical and biomedical sensing. Automated testing is often combined with biochemical analysis technologies to shorten the detection time and minimize human error. The present SERS substrates for sample detection are time-consuming and subject to high human error, which are not conducive to the combination of SERS and automated testing.

Drug preconcentration and direct quantification in biofluids using 3D-Printed paper cartridge.

Drug detection in biofluids has always been great importance for clinical diagnosis. Many detection technologies such as chromatography-mass spectrometry, have been applied to the detection of drugs. However, these technologies required multi-step operations, including complicated and time-consuming pretreatment processes and operations of bulky detection instruments, significantly limiting development of drug detection in clinical diagnosis.

Non-powered capillary force-driven stamped approach for directly printing nanomaterials aqueous solution on paper substrate.

The recent boom of nanomaterials printing in the fields of biomedical engineering, bioanalysis and flexible electronics has greatly stimulated researchers' interest in printing technologies. However, specifically formulated nanomaterial inks have limited the types of printable nanomaterials. Here, a unique non-powered capillary force-driven stamped (CFDS) approach, combining a 3D-printed stamper with a paper substrate, is developed for directly printing patterned nanomaterials aqueous solution.

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.