LIBS as an alternative method to control an industrial hydrometallurgical process for the recovery of Cu in waste from electro-electronic equipment (WEEE)

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Electronic waste analysis using laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF): Critical evaluation of data fusion for the determination of Al, Cu and Fe

BACKGROUND

Electronic waste (e-waste) proliferation and its implications underscore the imperative for advanced analytical methods to mitigate its environmental impact. It is estimated that e-waste production stands at a staggering 20-50 million tons yearly, of which merely 20-25% undergo formal recycling. The e-waste samples evaluated contain computers, laptops, smartphones, and tablets.

RESULTS

Forty-one samples were processed, involving the disassembly and separation of components. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF), were applied to quantify aluminum (Al), copper (Cu), and iron (Fe) in the e-waste samples. The samples were then analyzed after acid mineralization with 50% v v-1 aqua regia in a digester block and finally by ICP OES. A solid residue composed of Si and Ti was observed after the digestion of the samples. Multivariate calibration strategies such as partial least-squares regression (PLS), principal component regression (PCR), maximum likelihood principal component regression (MLPCR), and error covariance penalized regression (ECPR) were used for calibration. Finally, the figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, varying from 8.98 to 35.04 Signal (a.u.)(% w w-1) -1, low Limits of Detection (LoD) within the range of 0.001-0.2 % w w-1, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe.

SIGNIFICANCE

Notably, the models for Al faced inherent challenges, thus highlighting the complexities associated with its quantification in e-waste samples. In conclusion, this research represents an important step toward a more sustainable and efficient future for electronic waste recycling, signifying its relevance to global environmental welfare and resource conservation.

Selective recovery of nickel from stainless steel pickling sludge with NH3-(NH4)2CO3 leaching system

The recovery of valuable metals from stainless steel pickling sludge(SSPS) has great economic and environmental benefits. In this study, a new method is proposed for selective recovery of nickel from SSPS by NH3-(NH4)2CO3 ammonia leaching system. The Eh-pH diagram was used to analyze Ni, Fe, Cr leaching behavior during the ammonia leaching process. Nickel can be leached as the complex [Ni(NH3)n]2+, whereas Fe and Cr remain as precipitates in the leaching slag. The effects of NH3·H2O concentration, liquid-solid ratio, reaction temperature, and reaction time on the leaching efficiency of nickel in the ammonia leaching system were analyzed and optimized by single-factor study and response surface analysis, and the kinetics were analyzed. The optimal conditions for Ni leaching were found to be 28.28 min, 54.07 °C, a liquid-solid ratio of 23.7:1, and NH3·H2O concentration of 5.10 mol/L. Each factor had a greater effect on the rate of Ni leaching in the following order: liquid-solid ratio > NH3·H2O concentration > leaching time > leaching temperature. The ammonia leaching recovery system was controlled by chemical reaction and the activation energy was 58.17 KJ/mol. The results of scanning electron microscopy-energy dispersion spectrum (SEM-EDS), x-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) show that the leaching slag was in granular form with agglomerated particles and particle size of approximately 2.8 μm The major components of the leaching slag were Fe(OH)3, Fe2O3, Fe(OH)2, Cr(OH)3, and Cr2O3. Therefore, this study provides a new and effective way of using the resources of SSPS.

Quantitative analysis of fertilizer using laser-induced breakdown spectroscopy combined with random forest algorithm

Chemical fertilizers are important for effectively improving soil fertility, promoting crop growth, and increasing grain yield. Therefore, methods that can quickly and accurately measure the amount of fertilizer in the soil should be developed. In this study, 20 groups of soil samples were analyzed using laser-induced breakdown spectroscopy, and partial least squares (PLS) and random forest (RF) models were established. The prediction performances of the models for the chemical fertilizer content and pH were analyzed as well. The experimental results showed that the R ² and root mean square error (RMSE) of the chemical fertilizer content in the soil obtained using the full-spectrum PLS model were .7852 and 2.2700 respectively. The predicted R ² for soil pH was .7290, and RMSE was .2364. At the same time, the full-spectrum RF model showed R ² of .9471 (an increase of 21%) and RMSE of .3021 (a decrease of 87%) for fertilizer content. R ² for the soil pH under the RF model was .9517 (an increase of 31%), whereas RMSE was .0298 (a decrease of 87%). Therefore, the RF model showed better prediction performance than the PLS model. The results of this study show that the combination of laser-induced breakdown spectroscopy with RF algorithm is a feasible method for rapid determination of soil fertilizer content.

Analytical and reclamation technologies for identification and recycling of precious materials from waste computer and mobile phones

Waste electrical and electronic equipment (WEEE) is one of the world's fastest-growing class of waste. WEEE contain a large amount of precious materials that have aroused the interest to develop new recycling technologies. Hence, effective recycling strategies are extremely necessary to promote the proper handling of these materials as well as for environmentally sound recovery of secondary raw resource. This paper reviews important existing methods and emerging technologies in WEEE management, with special emphasis in characterization, extraction and reclamation of precious materials from waste computer and mobile phones. Traditional pyrometallurgical and hydrometallurgical technologies still play a central role in the recovery of metals. More recently, emerging greener recycling technologies using microorganisms (i.e. biometallurgical), plasma arc fusion method and pretreatments (i.e. ultrasound and mechanochemical technologies) combined with other recycling methods (e.g. hydrometallurgical), and using less toxic solvents such as ionic liquids (ILs) and deep eutectic solvents (DESs) have also been attempted to recycle metals from computer and mobile phone scrap. The role of analytical method development, especially using spectroanalytical methods for chemical inspection and e-waste sorting process at industrial applications is also discussed. This confirmed that most direct sampling techniques such as laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XFR) have several advantages over traditional sorting methods including rapid analytical response, without use of chemical reagents or waste generation, and greater reclamation of precious and critical materials in the WEEE stream.

Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a rapidly developing technique for chemical materials analysis. LIBS is applied for fundamental investigations, e.g., the laser plasma matter interaction, for element, molecule, and isotope analysis, and for various technical applications, e.g., minimal destructive materials inspection, the monitoring of production processes, and remote analysis of materials in hostile environment. In this review, we focus on the element analysis of industrial materials and the in-line chemical sensing in industrial production. After a brief introduction we discuss the optical emission of chemical elements in laser-induced plasma and the capability of LIBS for multi-element detection. An overview of the various classes of industrial materials analyzed by LIBS is given. This includes so-called Technology materials that are essential for the functionality of modern high-tech devices (smartphones, computers, cars, etc.). The LIBS technique enables unique applications for rapid element analysis under harsh conditions where other techniques are not available. We present several examples of LIBS-based sensors that are applied in-line and at-line of industrial production processes.