Ultra-sensitive detection of heavy metal aerosols: A hybrid LIBS-LIF and gated PMT enhancement approach.

Laser-induced breakdown spectroscopy (LIBS) has great potential for rapid and in-situ detection in various applications, including environmental monitoring, mining, and metallurgy. However, its application is constrained by low sensitivity, particularly in the field of heavy metal aerosol detection due to little mass ablation and significant spectral fluctuations. For the first time, we achieved an outstanding limit of detection (LOD) of 0.

Cancer diagnosis based on laser-induced breakdown spectroscopy with bagging-voting fusion model.

Advances in cancer diagnostics play a pivotal role in increasing early detection of cancer. Integrating laser-induced breakdown spectroscopy (LIBS) with machine learning algorithms has attracted wide interest in cancer diagnosis. However, using a single model`s efficacy is limited by algorithm principles, making it challenging to meet the comprehensive needs of cancer diagnosis.

A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving the performance of LIBS.

Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters.

Laser-Induced Breakdown Spectroscopy-Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma.

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique.

Background: Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem.

Transient charge-driven 3D conformal printing via pulsed-plasma impingement.

Seamless integration of microstructures and circuits on three-dimensional (3D) complex surfaces is of significance and is catalyzing the emergence of many innovative 3D curvy electronic devices. However, patterning fine features on arbitrary 3D targets remains challenging. Here, we propose a facile charge-driven electrohydrodynamic 3D microprinting technique that allows micron- and even submicron-scale patterning of functional inks on a couple of 3D-shaped dielectrics via an atmospheric-pressure cold plasma jet.

Sensitivity and stability improvement on slippery surface-aggregated substrate for trace heavy metals detection using NELIBS.

The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS.

Plasma parameters correction method based on plasma image-spectrum fusion for matrix effect elimination in LIBS.

Matrix effect is one of the obstacles that hinders the rapid development of laser-induced breakdown spectroscopy (LIBS), and it is currently a hot, challenging, and focal point in research. To eliminate the matrix effect, this study proposed a plasma parameters correction method based on plasma image-spectrum fusion (PPC-PISF). This method corrects the total number density, plasma temperature, and electron number density variations caused by matrix effect using effective features in plasma images and spectra.

Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy.

The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions.

All laser direct writing process for temperature sensor based on graphene and silver.

A highly sensitive temperature sensing array is prepared by all laser direct writing (LDW) method, using laser induced silver (LIS) as electrodes and laser induced graphene (LIG) as temperature sensing layer. A finite element analysis (FEA) photothermal model incorporating a phase transition mechanism is developed to investigate the relationship between laser parameters and LIG properties, providing guidance for laser processing parameters selection with laser power of 1-5 W and laser scanning speed (greater than 50 mm/s). The deviation of simulation and experimental data for widths and thickness of LIG are less than 5% and 9%, respectively.

Staging classification of omicron variant SARS-CoV-2 infection based on dual-spectrometer LIBS (DS-LIBS) combined with machine learning.

Effective differentiation of the infection stages of omicron can provide significant assistance in transmission control and treatment strategies. The combination of LIBS serum detection and machine learning methods, as a novel disease auxiliary diagnostic approach, has a high potential for rapid and accurate staging classification of Omicron infection. However, conventional single-spectrometer LIBS serum detection methods focus on detecting the spectra of major elements, while trace elements are more closely related to the progression of COVID-19.

Heavy metals high-sensitive detection by laser-induced breakdown spectroscopy based on radial electroosmotic flow-driven enrichment.

Heavy metal detection is imperative for human health and environmental sustainability. However, the commonly used liquid sample pretreatment, drying liquid droplet to solid, encounters solute diffusion and nonuniform distribution, thus causing unpromising detection results. Here, we developed a radial electroosmotic flow-driven (REOF) platform to enrich heavy metals in water for high-sensitive detection using laser-induced breakdown spectroscopy (LIBS).

Visualization of laser-induced breakdown spectroscopy data of mouse organs based on the feature extraction method.

At present, there is no comprehensive and systematic research on laser-induced breakdown spectroscopy (LIBS) data visualization. In particular, the LIBS spectra of biological samples have large noise and weak signals, which seriously affect the feature visualization. Here, three commonly used sample visualization methods were compared, and a new method was applied for tissue sample visualization.

Drying Acoustically Levitated Droplets as Signal-Amplifying Platforms for Ultrasensitive and Multimode Laser Sensing.

Ultrasensitive sensing to trace atomic and molecular analytes has gained interest for its intimate relation to industrial sectors and human lives. One of the keys to ultrasensitive sensing for many analytical techniques lies in enriching trace analytes onto well-designed substrates. However, the coffee ring effect, nonuniform distribution of analytes onto substrates, in the droplet drying process hinders the ultrasensitive and stable sensing onto the substrates.

Line intensity calculation of laser-induced breakdown spectroscopy during plasma expansion in nonlocal thermodynamic equilibrium.

We present a new, to the best of our knowledge, simulation method for laser-induced breakdown spectroscopy during the plasma expansion phase in nonlocal thermodynamic equilibrium. Our method uses the particle-in-cell/Monte Carlo collision model to calculate dynamic processes and line intensity of nonequilibrium laser-induced plasma (LIP) in the afterglow phase. The effects of ambient gas pressure and type on LIP evolution are investigated.

Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning.

Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model.

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

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol.

Stable sensing platform for diagnosing electrolyte disturbance using laser-induced breakdown spectroscopy.

Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem.

A plasma image-spectrum fusion correction strategy for improving spectral stability based on radiation model in laser induced breakdown spectroscopy.

Spectral fluctuation is one of the main obstacles affecting the further development of LIBS, and it is also the current research hotspot and difficulty. To meet the requirements of industrial monitoring, a novel method named plasma image-spectrum fusion laser induced breakdown spectroscopy (PISF-LIBS) was proposed to correct the spectral fluctuation and improve the quantitative accuracy. In this method, by systematically analyzing the spectral radiation model, six main factors affecting the spectral stability were obtained.

Time-Resolved Laser-Induced Breakdown Spectroscopy for Accurate Qualitative and Quantitative Analysis of Brown Rice Flour Adulteration.

To solve the adulteration problem of brown rice flour in the commodity market, a novel, accurate, and stable detection method based on time-resolved laser-induced breakdown spectroscopy (TR-LIBS) is proposed. Qualitative and quantitative analysis was used to detect five adulterants and seven different adulterant ratios in brown rice flour. Being able to excavate more information from plasma by obtaining time-resolved spectra, TR-LIBS has a stronger performance, which has been further verified by experiments.

Laser ultrasonic improvement and its application in defect detection based on the composite coating method.

Herein, we studied the increasing tendency of photoacoustic (PA) conversion efficiency of the Au/polydimethylsiloxane (PDMS) composite. The thickness of the Au layer was optimized by modeling the PA process based on the Drude-Lorentz model and finite element analysis method, and corresponding results were verified. The results showed that the optimal Au thickness of the Au/PDMS composite was 35 nm.

Laser-Induced Fast Assembly of Wettability-Finely-Tunable Superhydrophobic Surfaces for Lossless Droplet Transfer.

Rose-petal-like superhydrophobic surfaces with strong water adhesion are promising for microdroplet manipulation and lossless droplet transfer. Assembly of self-grown micropillars on shape-memory polymer sheets with their surface adhesion finely tunable was enabled using a picosecond laser microprocessing system in a simple, fast, and large-scale manner. The processing speed of the wettability-finely-tunable superhydrophobic surfaces is up to 0.

Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy.

As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration.