Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air

Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy

Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.

Excitation of optically trapped single particles using femtosecond pulses

Excitation from optically trapped particles is examined through laser-induced breakdown spectroscopy following interactions with mJ-level fs pulses. Optical emissions from sub-ng ablation of precisely positioned cupric oxide microparticles are used as a method to spatially resolve laser-particle interactions resulting in excitation. External focusing lenses are often used to change the dynamics of nonlinear self-focusing of fs pulses to form laser filaments or, alternatively, to form very intense air plasmas. Given the significant implications external focusing has on laser propagation and plasma conditions, single-particle emissions are studied with focusing lenses ranging from 50 to 300 mm. It is shown that, while single particles are less excited at longer focal lengths due to limited energy transfer through laser-particle interactions, the cooler plasma results in a lower thermal background to reveal resolved single-shot emission peaks. By developing an understanding in the fundamental interaction that occurs between single particles and fs pulses and filaments, practical improvements can be made for atmospheric remote sensing of low-concentration aerosols.

Ultrasensitive Optical Detection and Elimination of Residual Microtumors with a Postoperative Implantable Hydrogel Sensor for Preventing Cancer Recurrence

In vivo optical imaging of trace biomarkers in residual microtumors holds significant promise for cancer prognosis but poses a formidable challenge. Here, a novel hydrogel sensor is designed for ultrasensitive and specific imaging of the elusive biomarker. This hydrogel sensor seamlessly integrates a molecular beacon nanoprobe with fibroblasts, offering both high tissue retention capability and an impressive signal-to-noise ratio for imaging. Signal amplification is accomplished through exonuclease I-mediated biomarker recycling. The resulting hydrogel sensor sensitively detects the biomarker carcinoembryonic antigen with a detection limit of 1.8 pg mL-1 in test tubes. Moreover, it successfully identifies residual cancer nodules with a median diameter of less than 2 mm in mice bearing partially removed primary triple-negative breast carcinomas (4T1). Notably, this hydrogel sensor is proven effective for the sensitive diagnosis of invasive tumors in post-surgical mice with infiltrating 4T1 cells, leveraging the role of fibroblasts in locally enriching tumor cells. Furthermore, the residual microtumor is rapidly photothermal ablation by polydopamine-based nanoprobe under the guidance of visualization, achieving ≈100% suppression of tumor recurrence and lung metastasis. This work offers a promising alternative strategy for visually detecting residual microtumors, potentially enhancing the prognosis of cancer patients following surgical interventions.

Detection of trace phosphorus in water by plasma amplification laser-induced breakdown spectroscopy

For monitoring the extent of eutrophication in water, phosphorus (P) was detected by laser-induced breakdown spectroscopy (LIBS). A plasma amplification method was proposed and the filtered aerosol was guided to interact with the collinear laser in conjunction with a nebulizer, cyclonic spray chamber, and quartz tube. With this method, the length of the plasma was amplified from 5.27∼8.73 to 17.58 mm. Moreover, the limit of detection (LoD) values of P in water improved from 6.13∼17.75 to 3.60 ppm. Furthermore, the average relative error (REAV) values reduced from 10.23∼23.84 to 6.17%. The root mean square error of cross-validation (RMSECV) values decreased from 16.68∼64.29 to 3.24 ppm. This demonstrated that plasma amplification LIBS could improve the quantitative analysis performance of LIBS detection of trace phosphorus in water.

Ultrafast Laser Excitation Improves LIBS Performance for the Analysis of Optically Trapped Single Nanoparticles Owing to Characteristic Interaction Mechanisms

Owing to the exceedingly small mass involved, complete elemental characterization of single nanoparticles demands a highly precise control of signal background and noise sources. LIBS has demonstrated remarkable merits for this task, providing a unique tool for the multielemental analysis of particles on the attogram-picogram mass scale. Despite this outstanding sensitivity, the air plasma acting as a heat source for particle dissociation and excitation is a meddling agent, often limiting the acquisition of an accurate sample signature. Although thermal effects associated with ultrashort laser pulses are known to be reduced when compared to the widely used nanosecond pulse duration regime, attempts to improve nanoinspection performance using ultrafast excitation have remained largely unexplored. Herein, picosecond laser pulses are used as a plasma excitation source for the elemental characterization of single nanoparticles isolated within optical traps in air at atmospheric pressure. Results for picosecond excitation of copper particles lead to a mass detection limit of 27 attogram, equivalent to single particles 18 nm in diameter. Temporally and wavelength-resolved plasma imaging reveals unique traits in the mechanism of atomic excitation in the picosecond regime, leading to a deeper understanding of the interactions occurring in single nanoparticle spectroscopy.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R²_P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.