Engineered nanomaterials with precisely controlled size, morphology, and surface functionality are redefining the performance limits of analytical sensing platforms. Their tunable plasmonic, catalytic, and electronic properties enable the construction of multimodal nanosensors capable of delivering high sensitivity, selectivity, and stability across diagnostic and environmental applications. This presentation focuses on the design and synthesis of responsive nanostructured materials and the development of signal-detection platforms based on material properties such as colorimetric, fluorescent, electrochemical, and surface-enhanced Raman scattering (SERS) sensing.

In this work, gold-based nanostructures—ranging from gold nanoclusters and spherical gold nanoparticles to anisotropic gold nanostars—are highlighted for their strong localized surface plasmon resonance (LSPR), enhanced electromagnetic fields, and quantum-size-dependent redox behavior. These properties are exploited to amplify optical contrast in lateral flow assays (LFA and LFA-SERS) for biological detection through plasmonic coupling and scattering intensification and to improve electrochemical detection (EChem) via accelerated heterogeneous electron-transfer kinetics and catalytic enhancement at the electrode–nanoparticle interface. For example, these mechanisms significantly lower the detection limits for electrochemical sensing of toxic heavy metals (Pb²⁺, Hg²⁺, Cd²⁺, As³⁺) in complex matrices. Beyond gold nanostructures, complementary sensing modalities are also achieved using magneto-fluorescent hybrid nanomaterials—silica-coated carbon dot–ferrite nanocomposites—which enable magnetic preconcentration, reduced matrix interference, and fluorescent sensor readout. These multifunctional platforms support not only fluorescent sensing but also magnetic hyperthermia applications.

The integration of these nanomaterials into devices such as paper-based platforms, microelectrodes, and miniaturized analytical modules is a crucial step that will enable the next generation of nanoparticle-enabled multimodal sensors for diagnostics and environmental monitoring to achieve real-world implementation.

Wide-bandgap mixed-oxide nanoparticles are attractive materials for light emission, sensing, and energy applications. The balance between radiative (light-emitting) and non-radiative (heat-dissipating) processes is key for making efficient luminescent materials. Lanthanum aluminate perovskite LaAlO₃ is a promising host among such materials because it has a wide band gap, high thermal and chemical stability, and a flexible perovskite structure. Luminescence properties of undoped LaAlO₃ sol–gel nanoparticles as well as those of nanoparticles doped with Gd and Tm ions were investigated using synchrotron light at the DESY PETRA III Beamline P66. Emission spectra of the matrix revealed a wide band in the 350–650 nm spectral range. The peak of these bands is located around 420 nm at room temperature. Decreasing the temperature leads to the appearance of an additional long wavelength component at 460 nm. The maxima of excitation spectra of the matrix emission is located at 220 nm, which is near the bandgap edge. These measurements were interpreted in regard to reflection and transmission measurements of a commercial LaAlO₃ single crystal. It was found that the matrix emission is very weak for the single crystal, whereas for nanoparticles, it is much more intense. Analysis of luminescence decay curves showed that emission lifetimes of the matrix emission are typical for excitonic emission, in good agreement with the location of its excitation band being near the fundamental absorption edge. Further, the 420 and 460 nm emission bands are characterized by different temperature behaviors that can be used for luminescence thermometry. The obtained experimental results allowed us to show that mixed oxide perovskites are good hosts for rare-earth doping, their matrix emission has an excitonic nature, and to discover that LaAlO₃ nanoparticles have potential for optical sensing applications.

Monitoring emerging contaminants (ECs) e.g. pharmaceuticals, pesticides, microplastics and PFAs is a rapidly growing concern for environmental health and safety due to their persistence, bioaccumulation, and potential adverse effects on human health and the environment. Reducing the release of emerging contaminants from materials at source is crucial. One way is to minimize their human exposure by finding a better substitute through a toxicity assessment and an understanding of their mechanism of action. This contributes not only towards evaluation of their potential long term health hazards but also allows us to tune the material formulation with a less hazardous alternative, based on structure activity relationship (SAR) data, following the SSbD framework.

This presentation highlights the use of an electrochemical membrane-on-chip coupled with the innovative mini release accelerator (MRA) for screening biomembrane affinity and disruption of release products from plastic and PFAs-free coatings following the safe and sustainable by design (SSbD) approach. Leachates from these plastic coatings and other coating materials are subjected to membrane-on-chip screening to investigate the biomembrane affinity disruption properties of these material leachates The biomembrane sensor utilizes a layer of dioleoylphosphatidylcholine (DOPC) on a fabricated Hg-on-Pt chip electrode to generate characteristic rapid cyclic voltammograms (RCV). These RCVs contain current peaks due to underlying phase transitions in response to applied electric field. Changes in the RCV scan and associated capacitance peaks such as peak suppression in the presence of (bio)membrane active substances are related to membrane affinity and disruption detailing the nature and extent of the interactions. This unique advanced material screening technology, and the results from the screening of plastic and other coatings and emerging contaminants/pharmaceuticals and, their analysis will be presented at this conference.

Acknowledgements: UKRI Horizon Europe Guarantee Fund 10056199 and HE Bio-SUSHY programme GA number 101091464.

Chronic Kidney Disease (CKD) is a complex condition, representing the third fastest-growing cause of death globally. Clinical diagnosis depends on glomerular filtration rate (GFR), which reflect the kidney’s filtering efficiency. GFR is estimated by monitoring creatinine, cystatin C, and urea levels in blood. The development of a sweat-based sensing platform would allow for a minimally invasive strategy for frequent therapy assessment, improving patient management and reducing hospitalization rates.

To establish a sensing platform for remote patients monitoring, we developed three sensors for simultaneous detection of creatinine, cystatin C, and urea in human sweat. The cystatin C device was an aptasensor built on a titanium carbide (Ti₃C₂) MXene-based substrate, integrating the high specificity of the aptamer with the superior conductive property of MXenes. The creatinine sensor depended on the electro-oxidation of the copper–creatinine complex exploiting titanium carbide–copper quantum dots (Ti₃C₂-Cu QDs). The urea sensor consisted of a composite of metal hydroxide nanoparticles tailored for selective interaction with urea. Electrochemical impedance spectroscopy was used to evaluate the response of all three sensors to the specific analyte.

The cystatin C aptasensor achieved a limit of detection (LOD) of 3.1 ng/mL with an RMSE of 8% over the clinically relevant range of 2–18.5 ng/mL. In the 10–100 µM range, the creatinine biosensor had an LOD of 1 µM and an RMSE of 10%. The urea sensor had an LOD of 36 mM and an RMSE of 15% over the range of 50–200 mM. These results agreed with the clinical requirements for CKD assessment. Ongoing advancements are underway to integrate these sensors into a wearable platform for remote kidney function monitoring.

This work was supported by The European Union through the Horizon Europe EIC programme (Grant Agreement project 101115504).

The effective ocular delivery of therapeutic agents is hindered by the eye's natural barriers, necessitating advanced drug delivery systems. Novel drug-delivery technologies such as liposomes are increasingly studied as potential ophthalmic drug delivery systems able to encapsulate and efficiently deliver also highly lipophilic drugs. In this regard, the present study aims to develop and produce liposomes formulation able to encapsulate and allow the ocular delivery of Nutlin-3a, a small non-genotoxic inhibitor of the MDM2/p53 interaction, that shows interesting therapeutic potential against retinal disease. Liposomes were produced via a microfluidic approach and their size distribution was evaluated by photon correlation spectroscopy, and centrifugal field flow fractionation. Nutlin-3a entrapment capacity was evaluated via ultrafiltration and HPLC. Moreover, morphological, and structural characterization were conducted using transmission electron microscopy and Fourier-transform infrared spectroscopy, respectively. The microfluidic formulative study enabled the selection of LIPO constituted of phosphatidylcholine at concentrations of 5.4 and ethanol 10% ethanol, exhibiting a roundish vesicular structure with mean diameters around 150 nm, polydispersity index values always below 0.2, as well as high Nutlin-3a entrapment capacity [1]. Viability, proliferation, apoptosis, and migration assays were conducted to evaluate the biological effectiveness of Nutlin-3a. Nutlin-3a loaded in liposomes was able to induce a significant reduction of viability and migration in RPE cell models. This work paves the way for future applications of liposomes in the ocular delivery of Nutlin-3a.

[1] E. Esposito, E. Pozza, C. Contado, W. Pula, O. Bortolini, D. Ragno, … E. Melloni. Microfluidic Fabricated Liposomes for Nutlin-3a Ocular Delivery as Potential Candidate for Proliferative Vitreoretinal Diseases Treatment. International Journal of Nanomedicine, 2024, 19, 3513–3536.

The human central nervous system (CNS) is a highly complex, selective, and fragile environment. Under physiological conditions, the blood–brain barrier (BBB) maintains an immune-privileged state, allowing only specific biomolecules, nutrients, and cofactors to enter the CNS. However, this selectivity is compromised during neuroinflammation, where ferroptosis—a regulated, non-apoptotic cell death pathway driven by iron-dependent lipid peroxidation—plays a central role. In dopaminergic neurons of the substantia nigra pars compacta (SNpc), ferrous iron required for dopamine synthesis can react with hydrogen peroxide through the Fenton reaction, generating hydroxyl radicals and promoting mitochondrial dysfunction and oxidative damage. These processes are highly relevant to Parkinson’s disease, the second most prevalent neurodegenerative disorder, although the mechanisms underlying iron accumulation remain unclear. Understanding ferroptosis is therefore essential to elucidate the neurochemical progression of the disease.

In this work, we developed an in vitro model of progressive iron accumulation using human neuroblastoma SH-SY5Y cells differentiated into mature neurons with retinoic acid. Mesoporous SiO₂ nanoparticles synthesized using a modified Stöber method were employed as nanocarriers to deliver iron in a controlled manner. A modified MTT viability assay was implemented to avoid optical interference caused by the nanoparticles. Additionally, intracellular ferrous iron was quantified using the FerroOrange probe, and lipid peroxidation was assessed via 4-hydroxynonenal (4-HNE) detection, confirming the activation of ferroptosis. This approach enables a controlled and reproducible platform to study iron-induced neuronal vulnerability and provides insights into ferroptotic mechanisms relevant to Parkinson’s disease

In this work, we present a green synthesis approach for obtaining silver nanoparticles (AgNPs) using peel extract from the autochthonous Mollar de Elche pomegranate variety. The process was optimized using a Box–Behnken design (BBD) implemented in Python to evaluate the influence of silver nitrate concentration, extract concentration and temperature. Pomegranate bioactive compounds, especially punicalagin, acted as natural reducing and stabilizing agents. BBD enabled the identification of key factor interactions while reducing the number of required experiments. The optimal synthesis conditions predicted by the model were experimentally validated, showing strong agreement with theoretical values. The resulting AgNPs were characterized by UV–Vis spectroscopy, FTIR, XRD, and field-emission microscopy, confirming their successful formation and stability. These AgNPs demonstrated substantial antibacterial activity against Escherichia coli and Staphylococcus aureus. Furthermore, the AgNPs were incorporated into nanofibrous scaffolds as a proof of concept for potential biomedical applications, where their antibacterial activity was partially retained postincorporation. This study highlights the potential of pomegranate extract as a sustainable medium for AgNP synthesis with promising antibacterial applications and the ability of the BBD as a useful tool for efficient optimization of multivariable processes, including the synthesis of nanomaterials.

The development of new strategies to treat neurodegenerative diseases is among the most challenging and expensive tasks for pharma. Various types of nanoparticles (NPs) are considered as versatile drug delivery systems to the brain. The “Fourth Industrial Revolution” brought novel digital solutions for drug discovery based on artificial intelligence (AI), including machine learning (ML). Unfortunately, the application of in silico methods in nanomedicine remains uncommon.

This presentation is aimed at highlighting the most recent achievements of NanoCARRIERS project aimed at integrating ML and physics-based molecular modeling to support designing brain drug delivery nanocarriers. We will demonstrate, how to use a combination of physics-based modeling (Density Functional Theory at the level of B3LYP) and ML for increasing the efficiency of predicting the energy of interaction between the surface of gold nanoparticles with proteins that can be used as targeting ligands. Moreover, will show a model that estimates stability of the considered structures in a physiological condition. Finally, we will report Quantitative Structure-Activity Relationships models for screening nanoparticles and ligands, based on their cytotoxicity and oxidative stress generation.

In addition to that, we will draw a perspective for further development of the field. The most important challenges related to the use of advanced deep learning and fundamental AI models will be discussed. Special attention will be put on the availability of training data and regulatory expectation for such models to be wider accepted as stand-alone or parts of New Approach Methodologies (NAMs).

Metal oxide nanoparticles (MONPs) are increasingly used in biomedical applications due to their small size, high surface area, and tunable chemistries, yet these same characteristics raise safety concerns. MONPs can interact intimately with cells, causing toxicity through mechanisms like ion release, reactive oxygen species (ROS) generation, membrane damage, and cellular dysfunction. To enable safer development of MONPs, we created interpretable predictive models linking their physicochemical and exposure properties to cytotoxic outcomes. Utilizing the “Data Curation to Develop Machine Learning Models for Assessing the Toxicity of Nanoparticles” dataset curated from around 140 peer-reviewed publications we engineered a robust feature set and trained classification and regression models. Our two-stage workflow classifies materials into toxicity classes based on their features, which then inform regression models predicting biological outcomes. Using LIME plots for model interpretation, we found that core size was the strongest negative contributor to toxicity, followed by exposure dosage and time, while zeta potential, ion release, and aggregation state had lesser impacts. These results align with known mechanisms of MONP toxicity and highlight the importance of exposure parameters. Our study illustrates how interpretable machine learning can accelerate the design of safer MONPs, reducing reliance on extensive in-vitro screening while improving mechanistic understanding.

We apply our recently developed mathematical model to study the instability and breakup of metal filaments exposed to heating by laser pulses and placed on thermally conductive substrates. One notable aspect of this setup is that the heating is volumetric since the absorption length of the laser pulse is comparable to the typical filament thickness. In such a setup, the absorption of thermal energy and the filament's evolution are coupled and must be considered self-consistently. Our model enables significant simplification, which is crucial for understanding the main physical effects—including the relevance of the Marangoni effect and the temperature dependence of fluid viscosity and thermal conductivity — and for developing efficient simulations of filament evolution and subsequent nanoparticle formation. We focus particularly on the influence of thermal crowding, meaning that the evolution of the filaments depends on their size and number. This discovery opens the door to considerations of self- and directed-assembly of metal nanoparticles through a suitable choice of the initial metal geometry on the nanoscale. We illustrate some possibilities by arranging nanoscale structures to achieve controlled breakup of metal filaments at desired locations. More details can be found in recent research papers, including Phys. Rev. Lett. vol. 133 (214003), Phys. Rev. Fluids vol. 7 064001 (2002), and J. Fluid Mech. vol. 915, A133 (2021).