This study reports the effective adsorptive removal of amoxicillin from aqueous solutions using halloysite nanotubes as adsorbents. Pharmaceuticals are identified as well-known emerging pollutants which can pose risks to aquatic life and other organisms due to their toxicity. Toxicity may cause long-term effects even if they are present in trace levels. Among them, amoxicillin is the most widely used drug for the treatment of bacterial infections. As they are used extensively, they can be found in different environmental compartments. Studies have shown that drugs cannot be absorbed and digested well by living organisms. In this study, halloysite nanotubes were selected due to their high adsorption capacity, wider porous structure and cost effectiveness. In the study, Langmuir, Freundlich and Temkin isotherm models were used to investigate adsorption isotherms. Also, kinetic studies were evaluated using pseudo-first-order, pseudo-second-order, Elovich and intra-particle diffusion modules. Langmuir isotherm was found to be the best fitted isotherm for the experimental data, which indicated a monolayer adsorption. Kinetic data were also fitted to pseudo-second-order and Elovich modules. The structural and surface properties of halloysite nanocomposites were characterized by X- Ray diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR). The maximum adsorption capacity was 118.75 mg/g and a 70.03% removal efficiency was observed. Unlike other tubular materials, halloysite is an abundantly available natural nanomaterial, which makes it attractive and convenient for technological applications.

Plastics are widely used across various industries due to their light weight, durability, and low cost. However, their extensive use has led to environmental contamination, with microplastics and nanoplastics (NPs) becoming emerging pollutants of global concern. These particles can enter the food chain and pose significant threats to food safety and human health. While numerous studies have examined the accumulation, translocation, and toxicity of nanoplastics in herbaceous crops, their potential impact on woody nut-producing species remains largely unexplored. In this study, we investigated the effects of NPs on the nuts of Torreya grandis, a commercially and nutritionally important tree species. Our preliminary results demonstrated that exogenously applied NPs could penetrate the nut tissue and accumulate internally. This accumulation significantly reduced nut size and led to a notable change in key nutritional components, including sucrose and oils. Transcriptome analysis revealed that nut size reduction was associated with alterations in multiple metabolic and signaling pathways. In particular, genes involved in sucrose biosynthesis were significantly downregulated following NP exposure, suggesting a direct link between nanoplastic stress and impaired carbon metabolism. In summary, this study provides novel insights into the adverse effects of NPs on nut development and nutritional quality in T. grandis, and highlights the potential ecological and food safety risks posed by nanoplastic contamination in perennial nut crops.

The hydrothermal process assisted by microwaves offers several advantages for processing various materials, including nanoparticles. In this study, we synthesized pure SnO₂ nanoparticles (NPs) and Cu-SnO₂ and Zn-SnO₂ nanocomposites using the hydrothermal synthesis method. Characterization techniques such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) were employed to confirm the successful synthesis. The XRD analysis revealed that all NPs had a tetragonal rutile crystal structure. FTIR identified a range of functional groups, while SEM images showed that pure SnO₂ NPs formed small clusters. In contrast, the Cu-SnO₂ NPs displayed a semi-spherical shape, and the Zn-SnO₂ NPs exhibited larger spherical structures and interconnected clusters. Energy-dispersive X-ray (EDX) spectroscopy confirmed the presence of tin (Sn), oxygen (O), copper (Cu), and zinc (Zn). The formed nanocomposites were then tested for degradation of indigo caramine and para-nitrophenol. Photocatalytic activity tests demonstrated that Cu-SnO₂ NPs were less effective in degrading toxic indigo carmine dye and para-nitrophenol samples. However, Zn-SnO₂ NPs achieved complete degradation of these compounds within six hours. The findings indicate that the synthesized nanoparticles can effectively degrade the toxicity of harmful substances, such as para-nitrophenol, found in wastewater. This efficacy can be attributed to the efficient separation of electron–hole pairs made possible by surface modification. The unique properties of the prepared nanoparticles makes them valuable in tackling environmental pollution challenges.

Water contamination by heavy metals, especially nickel ions (Ni²⁺), poses serious risks to public health and the environment due to their toxicity and persistence in aquatic systems. Traditional detection methods often struggle to monitor Ni²⁺ efficiently at trace levels. This study develops a sustainable, cost-effective fluorescence-based sensor using carbon nanostructures (CNs) derived from coffee waste for selective Ni²⁺ detection. CNs were synthesized by green pyrolysis of coffee waste at 600 °C for one hour and characterized by FTIR spectroscopy, zeta potential, and particle size analysis. Fluorescence spectroscopy evaluated CNs interactions with Co²⁺, Cu²⁺, Cd²⁺, and Ni²⁺ ions in ultrapure water. CNs displayed distinct fluorescence responses to various metal ions, with Cu²⁺ causing quenching, Co²⁺ enhancing fluorescence, Cd²⁺ having minimal impact, and Ni²⁺ inducing pronounced fluorescence quenching. Ni²⁺ detection was further tested in ultrapure, tap, and mineral water across concentrations from 10⁻⁸ to 10⁻³ M. Stability tests over six hours identified optimal sensing conditions. Fluorescence intensity decreased progressively with increasing Ni²⁺ concentration, indicating high sensitivity and selectivity. Detection limits reached 10⁻⁴ M. Stability studies revealed an initial drop in fluorescence, a recovery peak at one hour, followed by a gradual decline, establishing one hour as the ideal detection time. Tap water exhibited some variability due to matrix effects, while mineral water showed consistent quenching, confirming the CNs sensor's robustness across water types. This work highlights coffee waste–derived CNs as effective, eco-friendly fluorescent probes for Ni²⁺ detection. The platform enables sensitive, selective monitoring of nickel ions in diverse water matrices, combining heavy metal detection with sustainable waste valorization and supporting circular economy goals. Acknowledgment: This project was conducted with the support of S.D. through a Doctoral Fellowship funded by the National Operational Programme Research and Innovation 2014-2020 (grant CCI2014IT16M2OP005).

Abstract:

In recent years, the importance of rare earth elements (REEs) has become increasingly important due to their unique properties, limited availability, and numerous applications, especially in modern technologies. Therefore, alternative sources of these elements are sought, especially among industrial wastes, such as coal fly ash, which can become their excellent secondary source [1, 2]. In this work, the use of modern hybrid adsorption membranes for rare earth metal ion recovery was proposed [3]. The adsorption membranes were produced by the method of magnetic casting from a 2-component solution of polymers imprinted with the appropriate REE ion with the appropriate addition of functionalized MWCNT-ILs (0.5 - 5% wt.). The formed membranes were tested for their application in the recovery of selected REE ions from synthetic solutions corresponding to the composition of real extracts obtained during the leaching of REE ions from coal fly ash. Characteristic separation parameters were determined, such as selectivity, REE recovery, and adsorption capacity.

References

1. Balaram V., Geosci. Front. 10:1285-1303, 2019.
2. Rybak A., Rybak Au., Metals 11 (1), 142, 2021.
3. Rybak A., Rybak Au., Boncel S., Kolanowska A., Jakobik-Kolon A., Bok-Badura J., Kaszuwara W., Materials 17, 3087, 2024.

Acknowledgements

This research was funded by Norway Grants 2014–2021 via the National Centre for Research and Development, grant number NOR/SGS/MOHMARER/0284/2020-00.

In this study, optimized polymeric functional coatings were successfully developed using advanced manufacturing techniques, specifically electrospinning. An exhaustive optimization process was carried out to eliminate defects in surface morphology by precisely controlling parameters such as the flow rate, applied voltage, and the ratio of solvent to precursors. Small variations in these parameters significantly influenced the final quality of the coatings.

The main precursors used were polyaniline (PANI), polyethylene oxide (PEO), and camphorsulfonic acid (CSA), with chloroform serving as the solvent. Additionally, titanium dioxide (TiO₂) was incorporated as a functional additive to enable photocatalytic activity.

To evaluate the photocatalytic performance, a solution containing methylene blue (a model organic pollutant), distilled water, and the functionalized coating was prepared. This solution was exposed to visible light for one hour. Absorbance measurements were taken before and after exposure using UV-Vis spectroscopy, enabling quantification of methylene blue degradation. The observed decrease in absorbance confirmed the photocatalytic degradation of the organic compound.

This research highlights the potential of electrospun polymeric coatings as effective materials for environmental applications, particularly with regard to reducing the quantity of organic pollutants in water. The combination of conductive polymers and photocatalytic additives offers a promising route for the development of smart coatings aimed at environmental remediation. These materials contribute to achieving several United Nations Sustainable Development Goals: Goal 6 (Clean Water and Sanitation), Goal 13 (Climate Action), Goal 14 (Life Below Water), and Goal 15 (Life on Land).

Reliable baseline correction is a cornerstone of spectroscopic analysis, underpinning critical tasks such as peak identification and performance of machine learning classifiers. This process is particularly crucial in Surface-Enhanced Raman Spectroscopy (SERS), where subtle spectral features carry vital chemical signatures. Traditional baseline correction techniques often struggle with artifact introduction and extensive manual parameter adjustments. The adaptive iteratively reweighted penalized least squares (airPLS) algorithm, though widely appreciated for its speed, has notable drawbacks: its piecewise linear baseline fails to capture smooth backgrounds, it overestimates baselines by linking adjacent peak "feet," and yields significant mean absolute errors (MAE) in high-intensity regions. To address these limitations, we developed an innovative machine learning approach that predicts optimal airPLS parameters tailored to any input spectrum, eliminating the need for prior baseline knowledge. We fixed the smoothness parameter at 2 and systematically adjusted the penalizing and tolerance parameters. Using three peak types with four baseline profiles, we generated 6,000 simulated spectra with known true baselines. An iterative grid search optimization was used to identify optimal parameter sets for each spectrum, reducing average MAE by 96% compared to default airPLS. For practical deployment, we trained a machine learning model integrating principal component analysis with random forest, achieving direct parameter prediction from spectra while retaining 90% of the MAE reduction. We expanded the training dataset to 12,000 spectra, incorporating diverse peak characteristics guided by statistical distributions of optimal parameters, enhancing adaptability to real-world spectral variability. Importantly, we demonstrated that for both synthetic and experimental noisy spectra, our model successfully predicts parameters and baselines after simple denoising. Future work will focus on identifying optimal denoising strategies to further enhance results. By automating baseline correction, our approach enhances analytical precision with applications spanning virus detection, environmental monitoring, and beyond, making SERS more reliable and accessible.

Ethylene di-amine (EDA) acts as a surface modification as well as reducing agent. It becomes covalently attached to the surface of GO by means of nucleophilic substitution reaction. In this work, a series of experiments were performed in which GO was treated with different concentrations of ethylene di-amine (EDA) to study the simultaneous functionalization and reduction of GO by EDA. The morphology, composition, and surface functionalization of EDA-GO were studied using Scanning Electron Microscopy (SEM), X-Ray Diffraction, Fourier Transform Infrared Spectroscopy (FTIR), and Raman and UV-Vis Spectroscopies. It was observed that EDA does not completely eliminate the oxygen functionalities from the surface of GO. Hence, all EDA-GOs were also reduced to EDA-rGO at a constant temperature (200°C) under an inert atmosphere of argon (Ar) gas. The photo response behavior of the EDA-rGO-based optical sensor was studied under different wavelengths of source illumination (635 and 1064nm). The GO treated with 100 ml of EDA delivered an enhanced photo response characteristic in comparison to the other samples prepared with different concentrations of EDA. Generating energy in a shorter time and storing it for longer periods is a crucial part of energy science. Storage systems play a major role in storing energy to dissipate it for longer periods of time. Supercapacitors and solid-state batteries are the two most used sources for storing energy in chemical form through electrochemical reaction, where supercapacitors store high-density energy quickly and dissipate it at the same rate, whereas solid-state batteries store energy for a longer duration and dissipate it slower than the supercapacitors depending upon the mechanics and material used in the cathode and anode.

Plastic materials made from a wide range of polymers are used in almost every aspect of our day-to-day lives. However, the linear synthetic strategy used for preparing common polymers only involves ease of production, high stability, and processability, with no circularity or sustainability. Large amounts of different polymers are produced and processed annually; t however, used polymer-based objects are difficult to recycle or reuse for multiple practical reasons. As a result, significant amounts of plastic materials end up in the environment. Such plastic wastes in the environment undergo slow degradation and release small particles called micro- and nanoplastic particles. Although many studies have highlighted the toxicity of additive chemicals such as phthalates and bisphenol A, present in plastics, only limited knowledge exists on the adverse impact of plastic particles on the health of terrestrial or aquatic animals. To address the plastic waste issues, several groups are working on developing sustainable polymers to replace synthetic ones. We focus on understanding the biological impact of plastic particles using animal and human cellular models. Our results indicate that the toxicity or adverse health impact of nanoparticles made from common polymers depends on the choice of animal models and the chemical nature of the polymers used. In a comparative study, fluorescent polymethylmethacrylate (PMMA) and polyvinylchloride (PVC) nanoparticles were used to investigate the uptake, translocation, and toxicity in various biological models. PVC nanoparticles caused high mortalities in different animal and cellular models. A few sustainable polymers are also being developed to mitigate the risks associated with synthetic plastics. This talk will provide some of our recent results and challenges in this area.

Capturing CO₂ from the atmosphere represents a key challenge, since CO₂ has been recognized as the primary anthropogenic greenhouse contributor to the increase in Earth’s average temperature. Their high porosity, tunable pore size, and large surface area make Metal–Organic Frameworks (MOFs) promising candidates to uptake and separate CO₂ from gaseous mixtures. In particular, the ultramicroporosity (pore size < 0,7 nm) and the presence of nitrogen atoms are crucial requirements in the design of MOFs for CO₂ separation. In 2021, some of us synthesized a new microporous MOF, formulated as [Co(trz₂An)]n·3H₂O (CAMOF1), by combining 3, 6-N-ditriazolyl-2,5-dihydroxy-1,4-benzoquinone (trz₂An), as an organic linker, with Co^II meta nodes in a 1:1 stoichiometric ratio. This MOF showed a high capability to separate CO₂ from natural gas. On this basis, since cobalt is classified as a critical raw material, by using Cu^II metal ions, a new isomorphous, robust, chemical, and thermally stable MOF, formulated as [Cu(trz₂An)]· 2.5H₂O (CAMOF2), was obtained. The synthesis performed by the hydrothermal approach was optimized in order to scale-up the reaction mixture by ten times. CAMOF2 is formed by cubic cavities with a void volume of 30 % due to the coordination to the N atom in the 4-position of the triazole ring, which induces an alternate orientation of Cu-anilate chains. Static and dynamic adsorption measurements revealed i) a remarkable carbon dioxide uptake, ii) a high selectivity in CO₂ separation in CO₂:N₂ gas mixtures, and iii) easy regeneration in mild conditions. Furthermore, preliminary CO₂ electroreduction studies show a good CAMOF2 capability to convert carbon dioxide to ethylene. In conclusion, by replacing the metal node in the MOF structure, a new microporous biocompatible multifunctional MOF has been obtained, with CO₂ adsorption and reduction capabilities.