• Handheld Raman spectrometer is able to generate SERS spectra with sufficient quality for Mycobacterium tuberculosis detection. • It is feasible to accurately discriminate Mtb-positive sputum from Mtb-negative sputum through SERS spectrometry. • Pulmonary and extra-pulmonary Mtb strains were able to be accurately distinguished via SERS spectral analysis. • Profiling of antibiotic resistance of Mtb strains was successfully achieved through machine learning analysis of SERS spectra.

Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

The phonon dispersion and optical properties of mechanically exfoliated SnSSe were investigated with the aid of high-resolution Raman scattering and photoluminescence (PL) spectroscopies along with first-principles calculations. The Raman modes indicate two-fold symmetry for in-plane vibrations and four-fold symmetry for out-of-plane vibrations. Consequently, the different polarization properties of the phonon modes demonstrate the anisotropic nature of SnSSe. The softening of Raman modes with the increase in temperature is described in terms of thermal expansion and anharmonic scattering, which contributes to phonon vibration, whereas a weak interlayer interaction in the SnSSe layers is observed. The significant suppression of PL signals at a higher temperature is related to the increase in the non-radiative recombination of electron–hole pairs as a result of the increment in the number of phonons and in the thermal excitation of carriers. In addition, the fabricated electrical device on few-layer SnSSe shows a significant I_ON/I_OFF ratio and good electron mobility for optoelectronic applications. These results further improve the understanding of the microscopic details of SnSSe as an important material, providing better insights to control phonon propagation in thermoelectric, photovoltaic, and other materials to induce thermal management.