Laser-Induced Breakdown Spectroscopy as an Accurate Forensic Tool for Bone Classification and Individual Reassignment.

This article provides a detailed discussion of the evidence available to date on the application of laser-induced breakdown spectroscopy (LIBS) and supervised classification methods for the individual reassignment of commingled bone remains. Specialized bone chemistry studies have demonstrated the suitability of bone elemental composition as a distinct individual identifier. Given the widely documented ability of the LIBS technique to provide elemental emission spectra that are considered elemental fingerprints of the samples analyzed, the analytical potential of this technique has been assessed for the investigation of the contexts of commingled bone remains for their individual reassignment. The LIBS bone analysis consists of the direct ablation of micrometric portions of bone samples, either on their surface or within their internal structure. To produce reliable, accurate, and robust bone classifications, however, the available evidence suggests that LIBS spectral information must be processed by appropriate methods. When comparing the performance of seven different supervised classification methods using spectrochemical LIBS data for individual reassociation, those employing artificial intelligence-based algorithms produce analytically conclusive results, concretely individual reassociations with 100% accuracy, sensitivity, and robustness. Compared to LIBS, other techniques used for the purpose of interest exhibit limited performance in terms of robustness, sensitivity, and accuracy, as well as variations in these results depending on the type of bones used in the classification. The available literature supports the suitability of the LIBS technique for reliable individual reassociation of bone remains in a fast, simple, and cost-effective manner without the need for complicated sample processing.

In Situ Construction of Crumpled Ti3C2Tx Nanosheets Confined S-Doping Red Phosphorus by Ti-O-P Bonds for LIBs Anode with Enhanced Electrochemical Performance.

Red phosphorus (RP) with a high theoretical specific capacity (2596 mA h g-1) and a moderate lithiation potential (∼0.7 V vs Li+/Li) holds promise as an anode material for lithium-ion batteries (LIBs), which still confronts discernible challenges, including low electrical conductivity, substantial volumetric expansion of 300%, and the shuttle effect induced by soluble lithium polyphosphide (LixPPs). Here, S-NRP@Ti3C2Tx composites were in situ prepared through a phosphorus-amine-based method, wherein S-doped red phosphorus nanoparticles (S-NRP) grew and anchored on the crumpled Ti3C2Tx nanosheets via Ti-O-P bonds, constructing a three-dimensional porous structure which provides fast channels for ion and electron transport and effectively buffers the volume expansion of RP. Interestingly, based on the results of adsorption experiments of polyphosphate and DFT calculation, Ti3C2Tx with abundant oxygen functional groups delivers a strong chemical adsorption effect on LixPPs, thus suppressing the shuttle effect and reducing irreversible capacity loss. Furthermore, S-doping improved the conductivity of red phosphorus nanoparticles, facilitating Li-P redox kinetics. Hence, the S-NRP@Ti3C2Tx anode demonstrates outstanding rate performance (1824 and 1090 mA h g-1 at 0.2 and 4.0 A g-1, respectively) and superior cycling performance (1401 mAh g-1 after 500 cycles at 2.0 A g-1).

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.

Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus through nanosecond pulsed laser-induced breakdown spectroscopy (LIBS) by integrating a state-of-the-art machine learning approach. LIBS spectra were collected from urine samples of diabetic and healthy individuals. Principal component analysis and an ensemble learning classification model were used to identify significant changes in LIBS peak intensity between the diseased and normal urine samples. The model, integrating six distinct classifiers and cross-validation techniques, exhibited high accuracy (96.5%) in predicting diabetes mellitus. Our findings emphasize the potential of LIBS for diabetes mellitus identification in urine samples. This technique may hold potential for future applications in diagnosing other health conditions.

A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System for Remote Bacteria Identification.

Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.

Raman microspectroscopy and laser-induced breakdown spectroscopy for the analysis of polyethylene microplastics in human soft tissues.

People are exposed to microplastics (MPs) on a large scale in everyday life. However, it is not clear whether MPs can also be distributed and retained in certain tissues. Therefore, the development of analytical methods capable of detecting MPs in specific human organs/tissues is of utmost importance. In this study, the use and combination of spectroscopic techniques, namely Raman microspectroscopy and laser-induced breakdown spectroscopy (LIBS), was tested for the detection of polyethylene (PE) MPs in human tonsils. Preliminary results showed that Raman microspectroscopy was able to detect MPs down to 1 µm in size and LIBS down to 10 µm. In the next step, human tonsils were spiked with PE MPs, and digested. The filtered particles were analyzed using Raman microspectroscopy and LIBS, and complemented by X-ray fluorescence (XRF). The results showed that Raman microspectroscopy could reliably detect PE MPs in spiked human tonsils, while LIBS and XRF served as a reference analytical method to characterize particles that could not be classified by Raman microspectroscopy for their non-organic origin. The results of this study, supported by a current feasibility study conducted on clinical samples, demonstrated the reliability and feasibility of this approach for monitoring MPs in biotic samples.

Enhanced reducing capacity of citric acid for lithium-ion battery recycling under microwave-assisted leaching.

The management and sustainable recycling of spent lithium-ion batteries (LIBs) holds critical importance from both economic and environmental standpoints. H2O2 and ascorbic acid are widely used inorganic and organic reductants in the hydrometallurgical process for battery recycling. In this study, citric acid, as a reductant, was found to have superior metal leaching efficiencies under microwave-assisted leaching than H2O2 and ascorbic acid. The enhanced performance was attributed not only to the inherent reducing property of citric acid but also to the chelation of citric acid with Cu and Fe, resulting in the formation of reductive radicals under microwave. The effect of acid type, H2SO4 concentration, citric acid concentration, solid-liquid (S/L) ratio, reaction time, and temperature were investigated. 99.5 % of Li, 99.7 % of Mn, 99.5 % of Co, and 99.3 % of Ni were leached from spent lithium nickel manganese cobalt oxides (NCM) battery black mass using 0.2 mol/L H2SO4 and 0.05 mol/L citric acid at 120 °C for 20 min with a fixed S/L ratio of 10 g/L in the microwave-assisted leaching process. Leaching kinetic results were best fitted with the Avrami model, suggesting that the microwave-assisted leaching process was controlled by diffusion. The leaching activation energies of Li, Mn, Co, and Ni were 30.11 kJ/mol, 27.48 kJ/mol, 21.32 kJ/mol, and 33.29 kJ/mol, respectively, providing additional evidence that supports the proposed diffusion-controlled microwave-assisted leaching mechanism. This method provided a green and efficient solution for spent LIBs recycling.

Recycling Spent Ternary Cathodes to Oxygen Evolution Catalysts for Pure Water Anion-Exchange Membrane Electrolysis.

Recycling spent lithium-ion batteries (LIBs) to efficient water-splitting electrocatalysts is a promising and sustainable technology route for green hydrogen production by renewables. In this work, a fluorinated ternary metal oxide (F-TMO) derived from spent LIBs was successfully converted to a robust water oxidation catalyst for pure water electrolysis by utilizing an anion-exchange membrane. The optimized catalyst delivered a high current density of 3.0 A cm-2 at only 2.56 V and a durability of >300 h at 0.5 A cm-2, surpassing the noble-metal IrO2 catalyst. Such excellent performance benefits from an artificially endowed interface layer on the F-TMO, which renders the exposure of active metal (oxy)hydroxide sites with a stabilized configuration during pure water operation. Compared to other metal oxides (i.e., NiO, Co3O4, MnO2), F-TMO possesses a higher stability number of 2.4 × 106, indicating its strong potential for industrial applications. This work provides a feasible way of recycling waste LIBs to valuable electrocatalysts.

Analysis of Lithium Aging Using Machine Learning-Enhanced Spectroscopy Techniques.

Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt% and a detection limit of 6.3 wt%.

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Monitoring of nanoplasmonics-assisted deuterium production in a polymer seeded with resonant Au nanorods using in situ femtosecond laser induced breakdown spectroscopy.

In this brief report, we present laser induced breakdown spectroscopy (LIBS) evidence of deuterium (D) production in a 3:1 urethane dimethacrylate (UDMA) and triethylene glycol dimethacrylate (TEGDMA) polymer doped with resonant gold nanorods, induced by intense, 40 fs laser pulses. The in situ recorded LIBS spectra revealed that the D/(2D + H) increased to 4-8% in the polymer samples in selected events. The extent of transmutation was found to linearly increase with the laser pulse energy (intensity) between 2 and 25 mJ (up to 3 × 1017W/cm2). The observed effect is attributed only to the field enhancing effects due to excited localized surface plasmons on the gold nanoparticles.