Magnetic microbead-based upconversion immunoassay with laser-induced breakdown spectroscopy readout for the detection of prostate-specific antigen.

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the readout of immunochemical assays utilizing indirect detection of labels (Tag-LIBS), typically based on nanoparticles. We have previously demonstrated that Tag-LIBS immunoassay employing yttrium-based photon-upconversion nanoparticles (UCNPs) can reach sensitivity similar to commonly used enzyme and fluorescence immunoassays. In this study, we report on further increasing the sensitivity of UCNP-based Tag-LIBS immunoassay by employing magnetic microbeads (MBs) as the solid phase in the determination of cancer biomarker prostate-specific antigen. Due to the possibility of analyte preconcentration, MBs enabled achieving a limit of detection (LOD) of 4.0 pg·mL-1, representing two orders of magnitude improvement compared with equivalent microtiter plate-based assay (LOD of 460 pg·mL-1). In addition, utilizing MBs opens up the possibility of an internal standardization of the LIBS readout by employing iron spectral lines, which improves the assay robustness by compensating for LIBS signal fluctuations and bead-bound immunocomplexes lost throughout the washing steps. Finally, the practical applicability of the technique was confirmed by the successful analysis of clinical samples, showing a strong correlation with the standard electrochemiluminescence immunoassay. Overall, MB-based Tag-LIBS was confirmed as a promising immunoassay approach, combining fast readout, multiplexing possibilities, and high sensitivity approaching upconversion luminescence scanning while avoiding the requirement of luminescence properties of labels.

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.

Multi-spectroscopic characterization system for cultural heritage materials analysis (SYSPECTRAL): Conception and example.

A comprehensive understanding of chemical composition of cultural heritage materials usually requires several complementary analytical techniques. Given the fragility and value of artworks, minimizing or avoiding sampling and performing in situ analysis under ambient light is an important goal. This article outlines a novel prototype designed to merge LIBS, laser-induced fluorescence spectroscopy (LIF), Raman spectroscopy using a single pulsed laser, and reflectance spectroscopy in a multi-spectroscopic characterization system for cultural heritage analysis (SYSPECTRAL). The aim is to analyze cultural heritage materials in their original place, obtaining both elemental and molecular information at such same point that is not always insured with several separated experimental settings. The SYSPECTRAL system focuses on compactness, mobility, and ease of operation. Software designed for the prototype controls multi-spectroscopic measurements, allows for image capture, precise localization, and data acquisition. Reflectance spectra examined the material and colors at the surface, and the LIBS-LIF-Raman package examines the stratigraphic structure of a multi-layered painted sample.

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. Here, we demonstrate a bagging-voting fusion (BVF) algorithm for the detection and identification of multiple types of cancer. In the BVF model of this paper, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), and random forest (RF) models, which have relatively small homogeneity to obtain more comprehensive decision boundaries, are fused at both the training and decision levels. LIBS spectral data was collected from four types of serum samples, including liver cancer, lung cancer, esophageal cancer, and healthy control. LIBS detection was conducted on the samples, which were directly dropped onto ordered microarray silicon substrates and dried. The results showed that the BVF model achieved an accuracy of 92.53 % and a recall of 92.92 % across the four types of serum, outperforming the best single machine-learning model (SVM: accuracy 75.86 %, recall 77.50 %). Moreover, the BVF model with manual line selection feature extraction required only 140 s for a single detection and identification. In conclusion, the aforementioned results demonstrated that LIBS with BVF has excellent performance in detecting a multitude of cancers, and is expected to provide a new method for efficient and accurate cancer diagnosis.

Preparation of interconnected tin oxide nanoparticles on multi-layered MXene for lithium storage anodes.

MXenes, a novel class of two-dimensional (2D) materials known for their excellent electronic conductivity and hydrophilicity, have emerged as promising candidates for lithium-ion battery anodes. This study presents a simple wet-chemical method for depositing interconnected SnO2 nanoparticles (NPs) onto MXene sheets. The SnO2 NPs act as both a high-capacity energy source and a spacer to prevent MXene sheets from restacking. The highly conductive MXene facilitates rapid electron and lithium-ion transport and mitigates the volume changes of SnO2 during the lithiation/delithiation process by confining the SnO2 NPs between the MXene layers. This composite anode, SnO2@MXene, leverages the high capacity of SnO2 and the structural and mechanical stability MXene provides. The SnO2@MXene anode exhibits superior electrochemical performance, with a high specific capacity of 678 mAh g- 1 at a current rate of 2.0 A g- 1 over 500 cycles, outperforming pristine MXenes and SnO2 nanoparticles.

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.

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.

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.

Assessing spatial distribution of bioindicator elements in various cutaneous tumors using correlative imaging with laser-ablation-based analytical methods.

Correlative imaging of cutaneous tumors provides additional information to the standard histopathologic examination. However, the joint progress in the establishment of analytical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in clinical practice is still limited. Their combination provides complementary information as it is also shown in our study in terms of major biotic (Ca, Mg, and P) and trace (Cu and Zn) elements. To elucidate changes in the elemental composition in tumors, we have compiled a set of malignant tumors (Squamous Cell Carcinoma, Basal Cell Carcinoma, Malignant Melanoma, and Epithelioid Angiosarcoma), one benign tumor (Pigmented Nevus) and one healthy-skin sample. The data processing was based on a methodological pipeline involving binary image registration and affine transformation. Thus, our paper brings a feasibility study of a practical methodological concept that enables us to compare LIBS and LA-ICP-MS results despite the mutual spatial distortion of original elemental images. Moreover, we also show that LIBS could be a sufficient pre-screening method even for a larger number of samples according to the speed and reproducibility of the analyses. Whereas LA-ICP-MS could serve as a ground truth and reference technique for preselected samples.

Fungal elemental profiling unleashed through rapid laser-induced breakdown spectroscopy (LIBS).

Elemental profiling of fungal species as a phenotyping tool is an understudied topic and is typically performed to examine plant tissue or non-biological materials. Traditional analytical techniques such as inductively coupled plasma-optical emission spectroscopy (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS) have been used to identify elemental profiles of fungi; however, these techniques can be cumbersome due to the difficulty of preparing samples. Additionally, the instruments used for these techniques can be expensive to procure and operate. Laser-induced breakdown spectroscopy (LIBS) is an alternative elemental analytical technique-one that is sensitive across the periodic table, easy to use on various sample types, and is cost-effective in both procurement and operation. LIBS has not been used on axenic filamentous fungal isolates grown in substrate media. In this work, as a proof of concept, we used LIBS on two genetically distinct fungal species grown on a nutrient-rich and nutrient-poor substrate media to determine whether robust elemental profiles can be detected and whether differences between the fungal isolates can be identified. Our results demonstrate a distinct correlation between fungal species and their elemental profile, regardless of the substrate media, as the same strains shared a similar uptake of carbon, zinc, phosphorus, manganese, and magnesium, which could play a vital role in their survival and propagation. Independently, each fungal species exhibited a unique elemental profile. This work demonstrates a unique and valuable approach to rapidly phenotype fungi through optical spectroscopy, and this approach can be critical in understanding these fungi's behavior and interactions with the environment. IMPORTANCE: Historically, ionomics, the elemental profiling of an organism or materials, has been used to understand the elemental composition in waste materials to identify and recycle heavy metals or rare earth elements, identify the soil composition in space exploration on the moon or Mars, or understand human disorders or disease. To our knowledge, ionomic profiling of microbes, particularly fungi, has not been investigated to answer applied and fundamental biological questions. The reason is that current ionomic analytical techniques can be laborious in sample preparation, fail to measure all potential elements accurately, are cost-prohibitive, or provide inconsistent results across replications. In our previous efforts, we explored whether laser-induced breakdown spectroscopy (LIBS) could be used in determining the elemental profiles of poplar tissue, which was successful. In this proof-of-concept endeavor, we undertook a transdisciplinary effort between applied and fundamental mycology and elemental analytical techniques to address the biological question of how LIBS can used for fungi grown axenically in a nutrient-rich and nutrient-poor environment.

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.

Highly efficient selective extraction of Li from spent LiNixCoyMnzO2 assisted with activated pyrite in a subcritical water system.

As the strategic importance of Li in the energy sector increases, selective Li extraction technology from spent lithium-ion batteries (LIBs) is attracting increasing attention. Current Li extraction processes typically suffer from lengthy procedures, high costs, and low efficiency. To improve the efficiency of Li extraction, a novel approach to achieve efficient Li recovery is proposed in this study, namely, reacting pyrite (FeS2) with LiNixCoyMnzO2 (NCM) powder in a subcritical water reduction (SWR) system. The reducing solvent environment created by the enhanced reaction of FeS2 with subcritical water converts the high-valent metals in NCM to a low-valent state, causing the collapse of the stable laminar structure and allowing Li+ to be released smoothly. After dual activation through mechanochemical and roasting processes, more than 99 % of Li is preferentially extracted under optimal conditions. Furthermore, Li+ in solution is converted into highly pure Li2CO3, while other metallic elements remain in the residue. Using inexpensive FeS2 for efficient Li extraction without adding additional chemical reagents is a promising approach for recovering spent LIBs.

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.

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%.

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. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model's robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.

The influence of chlorination additives on metal separation during the pyrometallurgical recovery of spent lithium-ion batteries.

The difficulty of separating Li during pyrometallurgical smelting of spent lithium-ion batteries (LIBs) has limited the development of pyrometallurgical processes. Chlorination enables the conversion of Li from spent LIBs to the gas phase during the smelting process. In this paper, the effects of four solid chlorinating agents (KCl, NaCl, CaCl2 and MgCl2) on Li volatilization and metal (Co, Cu, Ni and Fe) recovery were investigated. The four solid chlorinating agents were systematically compared in terms of the direct chlorination capacities, indirect chlorination capacities, alloy physical losses and chemical losses in the slag. CaCl2 was better suited for use as a solid chlorinating agent to promote Li volatilization due to its excellent results in these indexes. The temperature required for the release of HCl from MgCl2, facilitated by CO2 and SiO2, was lower than 500 °C. The prematurely released HCl failed to participate in the chlorination reaction. This resulted in approximately 12 % less Li volatilization when MgCl2 was used as a chlorinating agent compared to when CaCl2 was used. In addition, the use of KCl as a chlorinating agent decreased the chemical dissolution loss of alloys in the slag. The performance of NaCl was mediocre. Finally, based on evaluations of the four indexes, recommendations for the selection and optimization of solid chlorinating agents were provided.

Conversion of cobalt from spent LIBs to Co3O4 electrode material for application in supercapacitors.

The cathode material of lithium-ion batteries (LIBs) is endowed with valuable metals, such as cobalt. The improper treatment of these batteries pollutes the environment and causes enormous resource waste. Therefore, the recovery of valuable metals from spent LIBs has attracted widespread attention. In this study, Co3O4 electrode materials were prepared by a simple homogeneous precipitation method and heat treatment using a leaching solution of spent LIBs-positive electrode material as the cobalt source. The crystal structure and morphology of the products were examined at different annealing temperatures, and their electrochemical performance was analyzed. The results show that low-temperature annealing contributes to grain refinement. The Co3O4 material prepared at 300°C annealing temperature has a rod-like structure with distinct pores and a specific surface area of 58.98 m2 g-1. Furthermore, electrochemical performance testing reveals that Co3O4 prepared at 300°C displays the best electrochemical performance as an electrode material, with a specific capacitance of 97.93 F g-1 and a cycle retention rate of 79.12% after 500 charge-discharge cycles. These findings demonstrate the feasibility of recycling valuable metal cobalt from spent LIBs cathode materials to produce Co3O4 materials for use as supercapacitor electrode materials, opening up new avenues for the recycling and utilisation of spent LIBs.