Sewage sludge ash (SSA), derived from the incineration of wastewater treatment sludge, typically contains phosphorus concentrations ranging from 4 % to 12 % by weight. This significant P content makes SSA a promising secondary resource, particularly for applications such as fertilizer production. In this study, we explore the feasibility of using handheld Laser-Induced Breakdown Spectroscopy (LIBS) for the rapid and direct determination of phosphorus content in SSA samples. The proposed method enables rapid and accurate quantification of phosphorus with minimal sample preparation and demonstrates strong resilience to matrix effects, which often compromise the reliability of conventional LIBS analysis. The approach is based on a Convolutional Neural Network (CNN), designed to produce a single calibration model capable of addressing the wide variability in phosphorus concentration typically observed in SSA samples. The innovative aspect of this work is the complete separation of the training stage of the CNN, which is done using simple synthetic reference samples, from the validation, which involves actual SSA samples collected from a waste-to-energy power plant, previously characterized using standard laboratory methods. This procedure allows to select, among the many spectral features that can be used for modelling the training set, only the ones that are proven to effectively work for the determination of phosphorous concentration in the SSA samples, which have a much more complex composition with respect to the synthetic training samples. In addition to presenting this novel methodology, the study also includes a discussion of alternative approaches reported in the literature for matrix-independent quantitative LIBS analysis of phosphorus. This comparative overview highlights the advantages of the proposed method for in-situ analysis of SSA.

This work investigates the volatile fraction released from black mass (BM) obtained from spent lithium-ion batteries subjected to microwave (MW) thermal treatment. MW processing is emerging as an alternative to conventional pyrometallurgy for improving energy efficiency and recovery of critical metals such as lithium, yet the associated emission profile remains poorly characterized. However, the studies of the emissions associated with these treatments are quite limited. Here, a multilevel full factorial Design of Experiments is applied for the first time to evaluate the influence of MW power, exposure time, and BM mass on heating dynamics and lithium extraction efficiency. Volatile organic compounds generated during MW processing are identified by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), showing a complex mixture of aliphatic and aromatic hydrocarbons, carbonate esters, and phosphorus- and fluorine-containing species. Multinuclear NMR spectroscopy (H-1, Li-7, F-1(9), P-3(1)) confirms the presence of electrolyte-derived residues such as Li+, PF6-, and phosphate esters. The combined analytical approach clarifies degradation pathways during MW heating and highlights the need to monitor and mitigate the formation of potentially hazardous volatile species in future MW-assisted recycling processes. Statistical models reveal that the time to reach 600 degrees C and the maximum temperature depend primarily on power and exposure time, while Li recovery is governed by BM mass and its interaction with power.

Edible mushroom cultivation generates a large quantity of agricultural by-products that are often underutilized, leading to environmental and financial problems. This waste, known as spent mushroom substrate (SMS), consists of exhausted lignocellulosic materials and residual mycelium, the vegetative network of the fungus. This research explores the potential of repurposing SMS through different methods for producing mycelium-based composites (MBCs). MBCs have gained significant academic and commercial interest due to their unique capacity to upcycle agricultural and industrial waste into low-cost, environmentally sustainable composite materials. To evaluate the feasibility of using SMS for MBCs production, twelve distinct growing Treatments were compared. Treatments achieving over 50

The detection and quantification of microplastics (MPs) in environmental samples remain a significant analytical challenge due to the heterogeneity of polymer mixtures and the presence of organic and inorganic interferents. While Near-infrared (NIR) spectroscopy has emerged as a rapid, cost-effective alternative, most studies have focused on qualitative detection or simplified systems, leaving the influence of environmental interferents largely unexplored. This study proposes a quantitative analytical strategy using a portable NIR spectrometer combined with multivariate regression for the determination of four target polymers (polypropylene, PP, polyethylene, PE, polystyrene, PS, and polyethylene terephthalate, PET) in complex mixtures. MPs were generated through a trueto-life protocol, ensuring realistic particle morphologies and surface conditions. Model robustness was systematically assessed against a wide range of environmental interferents, including non-target polymers (polyvinyl chloride, polylactic acid, and polyamide), natural fibres (cotton, silk), vegetal material, and mineral particles (CaCO3). Polymer quantification was performed through Partial Least Squares (PLS) regression, with each polymer modelled independently. The proposed modelling approach was subjected to a double cross-validation procedure, and their predictive ability was further estimated by external validation procedure. In particular, when external validation samples were spiked with interferents, prediction errors increased moderately due to added spectral complexity; however, the models maintained satisfactory performance, with PE and PET demonstrating the greatest resilience to matrix effects. Finally, the models were successfully applied for the quantification in real environmental samples, with a satisfactory accuracy considering the inherent complexity of "unknown" environmental matrices. These results demonstrate the potential of portable NIR spectroscopy and robust chemometric modelling for quantitative MP analysis in heterogeneous, environmentally realistic scenarios.

LIBS is emerging as a powerful technique for phosphorus analysis in agricultural, environmental and industrial applications. This review critically discusses recent advances, current limitations and future strategies for robust quantitative analysis.

Lithium has emerged as a pivotal material for the global energy transition, yet its supply security is challenged by limited geographical availability and growing demand. These constraints highlight the need for analytical methods that are not only accurate but also sustainable and deployable across the entire lithium value chain. In this context, Laser-Induced Breakdown Spectroscopy (LIBS) offers distinctive advantages, including minimal sample preparation, real-time and in situ analysis and the potential for portable and automated implementation. Such features make LIBS a valuable tool for monitoring and optimizing lithium extraction, refining and recycling processes. This review critically examines the recent progress in the use of LIBS for lithium detection and quantification in geological, industrial, biological and extraterrestrial matrices. It also discusses emerging applications in closed-loop recycling and highlights future prospects related to the integration of LIBS with artificial intelligence and machine learning to enhance analytical accuracy and material classification.

This work presents the results of enforced carbonation experiments realised on steel slags, sourced from various European plants and proposes carbonation as a viable valorization method. All samples demonstrated rapid carbonation reactions and effectively sequestered substantial amounts of CO2. Calcite and silica gel were identified as the main reaction products. The mineralogical and chemical compositions of the slags played a crucial role in influencing the carbonation process. This transformation altered the slags' properties, converting them from latent hydraulic materials into pozzolanic supplementary cementitious materials. However, carbonation alone did not significantly enhance composite cement performance. To optimize the cement composition, the addition of calcium sulphate, hydration accelerators, or a combination of both, depending on the characteristics of the slag used, was necessary.

The development of next-generation catalysts is crucial for advancing sustainable CO2 conversion technologies and addressing pressing environmental challenges. This work integrates green chemistry principles by combining CO2 valorization, waste recovery, and renewable energy use, demonstrating a sustainable and circular approach for catalyst discovery and application. This study investigates the functional and structural properties of a novel malate-based catalyst synthesized starting from spent lithium-ion battery waste, developed after lithium recovery. Under solar photothermo-catalytic conditions, the catalyst showed excellent CO2-to-solar fuel conversion (CO and CH4) at low temperature, with a higher CH4 selectivity (>80%) compared to classical catalysts based on critical raw materials. X-ray pair distribution function analysis was used for the first time to reveal a significant structural transformation: the catalyst undergoes a transition from a crystalline resting state to an amorphous, catalytically active shell during the reaction, significantly enhancing the material efficiency. A preliminary sustainability analysis shows that the embodied energy and carbon footprint values associated with the synthesis of the new malate are comparable with those of the classical catalysts used for this application, based on ceria, titania, and bismuth.

Microplastics are small plastic particles found widely in the environment, posing significant challenges as diverse environmental contaminants. Their pervasive presence and potential impacts on ecosystems and human health underscore the importance of research in this field. However, working with microplastics in the laboratory and field can be challenging due to the difficulty in creating particles that are similar to those found in the environment. The advancement of research in this area is, therefore, dependent on the availability of reference materials or representative test materials that can simulate real-world conditions. One of the biggest challenges in creating more relevant test microplastics is investigating processes that can mimic as close as possible the environmental counterpart. To tackle this challenge, we have explored three distinct cryogenic grinding techniques for generating microplastics on a laboratory scale (ultracentrifugal mill, immersion blender, mixer mill). The resulting products were examined, and the advantages and limitations of the technologies were analyzed to gain deeper insights into the correlation between the various techniques utilized and the distinctive characteristics of the "true-to-life" microplastics produced. This allows us to tailor the production of test materials to the specific research questions they are intended to address. Furthermore, by understanding the characteristics of true-to-life microplastics, we can gain insights into their behavior under various environmental conditions. This knowledge can help in developing better methods for detecting and monitoring microplastics in the environment, as well as developing more effective mitigation strategies to reduce their impact.

Lithium has become one of the most strategic materials in the industry, given its wide use for the realization of efficient energy storage devices and for improving the chemical and physical characteristics of advanced ceramic and glass materials. Its widespread use in the last decades has also posed problems for its recovery and recycling, mostly from exhaust Li-ion batteries, which are mechanically treated to obtain the black mass. The black mass is a carbon-based material derived from the cathodic and anodic components of the batteries, containing several metals, as for example cobalt and nickel, along with lithium in varying quantities. Therefore, it is important to develop fast and accurate techniques for determining the black mass composition. This information is also essential for optimizing the extraction processes of lithium or other metals from the black mass. In this paper, we employ automated data processing using an Artificial Neural Network (ANN) to analyze spectra acquired from black mass equivalent materials with a commercial hand-held Laser-Induced Breakdown Spectroscopy (LIBS) instrument, enabling the determination of lithium content from a minimal set of spectral features. Additionally, we compare the results obtained for real black mass with reference values from other elemental analysis techniques. The combination of the ANN algorithm speed and robustness with the reliability of the LIBS instrument demonstrates the feasibility of accurate determination of the lithium content in black mass with a minimum treatment of the samples and on very different matrices.

This research explores the valorization of ladle furnace (LF) slag as a functional filler in recycled nitrile butadiene rubber (NBR) industrial scrap, addressing waste management challenges in both the steelmaking and rubber industries. A comprehensive characterization of the LF slag was conducted, including its leaching behavior, chemical composition and mineralogy. Notably, the slag exhibited self-pulverizing properties, with 60 % of particles measuring below 90 mu m. XRD and SEM analyses revealed the presence of gamma-dicalcium silicate, beta-dicalcium silicate (larnite), gehlenite, and MgAl2O4 spinel, with their distribution varying according to particle size. Comparative evaluations were performed on virgin NBR, recycled NBR, and recycled NBR reinforced with 10 % v/v LF slag in two distinct particle size ranges (0-50 mu m and 50-100 mu m). The key properties evaluated included crosslink density, hardness, tensile strength, and dynamic mechanical behavior. The incorporation of LF slag promoted devulcanization during calendering, increased hardness and elastic modulus, and enhanced dynamic mechanical performance. Furthermore, leaching tests demonstrated that the NBR matrix significantly reduced the release of hazardous elements from LF slag, bringing leachate concentrations well below regulatory thresholds. These findings highlight the potential to develop sustainable rubber composites entirely from recycled materials, exemplifying the principles of the circular economy.

The integration of Artificial Intelligence (AI) into the discovery of new materials offers significant potential for advancing sustainable technologies. This paper presents a novel approach leveraging AI-driven methodologies to identify a new malate structure derived from the treatment of spent lithium-ion batteries. By analysing bibliographic data and incorporating domain-specific knowledge, AI facilitated the identification and structure refinement of a new malate complex containing different metals (Ni, Mn, Co, and Cu). The synthesized compound was investigated through chemical and physical analyses, confirming its unique structure and composition. The present work proposes a significant difference from the classical use of AI in materials science, typically rooted in data-driven approaches relying on extensive datasets. This hybrid approach, combining AI's computational power with human expertise, not only expedited the structure determination process but also ensured the reliability and accuracy of the results. Finally, AI-driven material discovery highlights that waste materials can be transformed into valuable chemical products, suggesting their possible reuse, with several expected benefits, emphasising the role of AI in fostering not only innovation but also sustainability in material science.

Microplastics (MPs), defined as plastic fragments smaller than 1 mm, are pervasive pollutants posing considerable ecological and health hazards owing to their durability and potential to cause adverse environmental effects. These particles originate mainly from the breakdown of bigger plastic debris by mechanisms such as UV-induced photodegradation, resulting in fragmentation into micro- and nanoplastics. Appropriate laboratory test materials that simulate naturally degraded plastics are essential for evaluating the environmental impact of MPs, enhancing analytical methods, and assessing remediation pathways. In this study we generated "true-to-life" MPs from commonly utilized plastic products through controlled photodegradation processes designed to accelerate polymer aging. Two aging protocols were developed: (i) UV irradiation of macroplastic fragments for up to eight weeks followed by mechanical milling, and (ii) UV exposure of pre-fragmented MPs over the same period. Five polymers, namely polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE), polyvinyl chloride (PVC), and polyethylene terephthalate (PET), were chosen for analysis, with PET investigated separately due to the presence of the carbonyl group, which complicates carbonyl index (CI) calculations used as a quantitative index to monitor the photo-oxidation. The surface morphology of aged MPs was examined using Scanning Electron Microscopy (SEM), their chemical composition was investigated by Near-Infrared (NIR) and Fourier-transform Infrared (FTIR) spectroscopy, thermal properties were also evaluated by Thermogravimetric Analysis (TGA). PET degradation was further analyzed using supplementary techniques such as X-Ray Diffraction (XRD) and Differential Scanning Calorimetry (DSC) to assess structural and thermal alterations. These findings demonstrate that the proposed protocols generate MPs with consistent physicochemical properties, providing a model system suitable for studying MP degradation and behavior in laboratory studies, ultimately supporting environmental risk assessment and mitigation strategies.

Plastic and microplastics, including polyethylene (PE), polypropylene (PP), and polystyrene (PS), are major contributors to environmental pollution. However, there is a growing recognition of the need to investigate a wider range of plastic polymers to fully understand the extent and impacts of plastic pollution. This study focuses on the comprehensive characterization of true-to-life nanoplastics (T2LNPs) derived from polyethylene terephthalate (PET) and polyamide (PA) to enhance our understanding of environmental nanoplastics pollution. T2LNPs were produced through cryogenic mechanical fragmentation of everyday items made from these polymers. A solid methodological framework incorporating various characterization techniques was established. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and thermogravimetric analysis (TGA) were employed to study the chemical composition and confirm the absence of chemical modifications possibly occurring during fragmentation. Atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were used to analyze the morphology of the T2LNPs. Additionally, AFM image analysis compared to dynamic light scattering (DLS) measurements provided insights into the size distribution and the stability of the T2LNP suspensions. The results revealed the heterogeneity of T2LNPs derived from PET and PA, emphasizing the importance of studying different plastic compositions to comprehensively understand nanoplastics pollution. Lastly, the distinctive characteristics and morphology of T2LNPs were translated into the realm of biological interactions, offering initial insights into the influence of these disparities on the formation of the protein corona on the surface of T2LNPs. By proposing T2LNPs as test materials and establishing a comprehensive characterization approach, this study aims to bridge the knowledge gap regarding the behavior and toxicity of nanoplastics. Furthermore, it highlights the need for a reliable and transferable analytical package for nanoplastic characterization to facilitate future studies on the environmental impact of nanoplastics.

Responsible sourcing of minerals and metals is critical for the transition to a low-carbon energy system. The increased demand for these materials requires a shift towards more efficient and environmentally friendly mining practices, as well as better recycling and reuse of these materials. Based on a transdisciplinary approach, in this work, the power law, normally used to model biological systems, is applied for the first time to model the relationship between the abundance of strategic elements and their extraction, at different times (in 1999 and 2023). An increase in the exponent of the power law when analyzing materials production data in 1999 and 2023 (the data can be fitted by two power laws with an exponent of 0.61 and 0.74, respectively) suggests changes in production dynamics, which could be driven by factors such as increased demand, improved efficiency, resource diversification, regulatory changes, market dynamics, or environmental considerations. Understanding these changes is crucial for assessing the sustainability and long-term viability of material production systems. Overall, the proposed power law offers a valuable framework for analyzing the dynamic relationship between material abundance and production over time, providing insights that can inform decision-making and support efforts toward sustainable resource management and economic development. This is particularly important with the recently established policies about the increase of mining activities in the EU countries. Based on these findings, this study suggests several priority actions, to achieve the objectives of the Critical Raw Materials Act, thereby enhancing the effectiveness of the proposed European plan to increase the availability of strategic materials. This involves diversifying resource inputs, promoting renewable and alternative materials, and adopting practices that prioritize conservation and responsible stewardship of natural resources.

The need for elemental analysis in quality control of food, pharmaceuticals and cosmetics, taking into account human safety, has become an essential requirement for any product destinated for commercialization and targets in particular potentially toxic elements. The main objective is to obtain reliable data on elemental content that enables end-users to make informed decisions and to address problems effectively. To align with the principles of green and sustainable analytical chemistry, recent efforts have focused on incorporating new, low-cost screening and quantitative analytical tools such as total reflection X-ray fluorescence spectrometry (TXRF). This paper provides an overview of recent TXRF applications in the elemental analysis of food, pharmaceuticals, and cosmetic samples. The state-of-the-art procedures for sample preparation with details on the sample type and amount, dilution steps, mixing agents, analysed elements, and addition of internal calibration standards are presented together with analytical parameters such as limits of detection and quantification. The current challenges for applying TXRF to each of these research fields are discussed. Ensuring reliable elemental analysis in food, cosmetic and pharmaceutical research is a prerequisite to human safety. Here we report on the use of total reflection X-ray fluorescence spectrometry, its state-of the art and challenges in those fields.

This work is connected with the increasing demand for fertilizers and the associated supply risks and market volatility in the European Union (EU). It highlights the reliance on phosphorus from Moroccan phosphate rocks and the environmental issues stemming from the overuse of phosphate-based fertilizers. The paper suggests a shift in perspective, viewing waste as a valuable resource. Specifically, it explores the potential of recycling phosphorus from sewage sludge ash, which contains a high phosphorus concentration. The focus is on comparing two methods of phosphorus extraction from sewage sludge ashes: classical wet-chemical extraction and a new microwave-based technology. There is an increase in phosphorus bioavailability with the microwave treatment, making it a promising approach for waste management. An initial economic evaluation suggests that the microwave method is highly competitive, especially when compared with wet-chemical extraction technologies. The resulting material from the microwave treatment is classified as a straight solid inorganic macronutrient fertilizer according to EU regulations.

The amount of rubber scraps derived from rubber goods production consists of about 20–30

The ubiquitous distribution of plastics and microplastics (MPs) and their resistance to biological and chemical decay is adversely affecting the environment. MPs are considered as emerging contaminants of concern in all the compartments, including terrestrial, aquatic, and atmospheric environments. Efficient monitoring, detection, and removal technologies require reliable methods for a qualitative and quantitative analysis of MPs, considering point-of-need testing a new evolution and a great trend at the market level.In the last years, portable spectrometers have gained popularity thanks to the excellent capability for fast and on-site measurements. Ultra-compact spectrometers coupled with chemometric tools have shown great potential in the polymer analysis, showing promising applications in the environmental field. Nevertheless, systematic studies are still required, in particular for the identification and quantification of fragments at the microscale. This study demonstrates the proof-of-concept of a Miniaturized Near-Infrared (MicroNIR) spectrometer coupled with chemometrics for the quantitative analysis of ternary mixtures of MPs. Polymers were chosen representing the three most common polymers found in the environment (polypropylene, polyethene, and polystyrene). Daily used plastic items were mechanically fragmented at laboratory scale mimicking the environmental breakdown process and creating "true-to-life" MPs for the assessment of analytical methods for MPs identification and quantification. The chemical nature of samples before and after fragmentation was checked by Raman spec-troscopy. Sixty three different mixtures were prepared: 42 for the training set and 21 for the test set. Blends were investigated by the MicroNIR spectrometer, and the dataset was analysed using Principal Component Analysis (PCA) and Partial Least Square (PLS) Regression. PCA score plot showed a samples distribution consistent with their composition. Quantitative analysis by PLS showed the great capability prediction of the polymer's per-centage in the mixtures, with R2 greater than 0.9 for the three analytes and a low and comparable Root-Mean Square Error. In addition, the developed model was challenged with environmental weathered materials to validate the system with real plastic pollution. The findings show the feasibility of employing a portable tool in conjunction with chemometrics to quantify the most abundant forms of MPs found in the environment.

The carbonation of alkaline industrial wastes is a pressing issue that is aimed at reducing CO2 emissions while promoting a circular economy. In this study, we explored the direct aqueous carbonation of steel slag and cement kiln dust in a newly developed pressurized reactor that operated at 15 bar. The goal was to identify the optimal reaction conditions and the most promising by-products that can be reused in their carbonated form, particularly in the construction industry. We proposed a novel, synergistic strategy for managing industrial waste and reducing the use of virgin raw materials among industries located in Lombardy, Italy, specifically Bergamo–Brescia. Our initial findings are highly promising, with argon oxygen decarburization (AOD) slag and black slag (sample 3) producing the best results (70 g CO2/kg slag and 76 g CO2/kg slag, respectively) compared with the other samples. Cement kiln dust (CKD) yielded 48 g CO2/kg CKD. We showed that the high concentration of CaO in the waste facilitated carbonation, while the presence of Fe compounds in large amounts caused the material to be less soluble in water, affecting the homogeneity of the slurry.