Alessandra Sutti

The widespread release of microplastics (MPs), especially fibrous microplastics (FMPs) originating from synthetic textiles, poses a growing threat to environmental systems due to their persistence, mobility, and potential for bioaccumulation in aquatic and terrestrial ecosystems. Conventional gravimetric methods (GMs) remain the primary approach for assessing FMP shedding, yet they are hindered by moisture-sensitive filters, false positives from detergents and minerals, environmental contamination, and the labor-intensive manual measurement of individual fibers. To address these limitations, we developed an automated image analysis (AIA) framework that integrates an attention-based U-Net architecture with meta-learning modules to quantify FMP number, length, diameter, and mass from stitched microscopic images of entire filter membranes. This approach enables detection of fibers down to 28 μm in diameter with the spatial resolution of 2.17 µm/pixel, supports both target-color and multi-color analysis, and eliminates the need for manual characterization or extrapolation from partial membrane segments. The method achieved the highest accuracy of approximately 98% in color-specific fiber detection, correctly identifying 257 of 263 white fibers, and demonstrated similarly robust performance for black, red, and green fibers, while minimizing interference from non-target colors, even when their fibers overlapped. Multi-color detection was further validated using effluent water samples containing mixed-color fibers. Overall, the developed system enhances the accuracy, efficiency, and reproducibility of FMP analysis, offering a standardized and scalable approach for environmental monitoring of MP pollution.

Dimensionless groups quantify the balance among key forces governing a system’s physical behaviour and are foundational in engineering for describing, comparing, and scaling processes. By condensing complex system interactions into single values, they provide a powerful means of abstraction. Yet, their potential to actively guide process optimisation remains largely untapped. This study presents a framework that integrates dimensionless analysis with Bayesian optimisation to enhance both process performance and interpretability. Using this combined approach, we demonstrate that optimisation conducted in the dimensionless space not only accelerates convergence towards optimal process conditions but also reveals the underlying physical balances driving system behaviour. The method thus bridges data-driven optimisation with physically grounded understanding, enabling more efficient and explainable control of complex manufacturing processes.

Literature provides only a few examples of contamination of groundwater with microplastics, mainly investigated using a chemical approach. Little importance is given to the hydrogeological processes able to affect the contamination, such as river–groundwater interactions. This study was carried out with two aims. The first aim is the formulation of a method with a high result-to-cost ratio, based on the hydrogeological aspects of the investigated area. Microplastics were extracted from samples through filtration and successively counted and characterized morphologically through analysis of optical microscopy images. The second aim is to evaluate the presence of microplastics in some portions of an alluvial aquifer using this methodology. Microplastics in groundwater showed a higher circularity and Feret diameter than those found in surface waters, indicating that in porous aquifers the transport is likely more influenced by the microplastics’ shape than by their size. The aquifer recharge did not modify the microplastics’ characteristics in groundwater, whereas in surface water the flood wave promoted the resuspension of microplastics with lower circularity. These findings provide new pieces of evidence on the presence and transport of microplastics in both groundwater and surface waters, underlining how the hydrogeological characteristics of the area can be one of the main drivers of microplastics’ contamination.

Wet spinning of silkworm silk has the potential to overcome the limitations of the natural spinning process, producing fibers with exceptional mechanical properties. However, the complexity of the extraction and spinning processes have meant that this potential has so far not been realized. The choice of silk processing parameters including fiber degumming, dissolving and concentration, are critical in producing a sufficiently viscous dope, while avoiding silk’s natural tendency to gel via self-assembly. This study utilized recently developed rapid Bayesian optimization to explore the impact of these variables on dope viscosity. By following the dope preparation conditions recommended by the algorithm, a 13% (w/v) silk dope was produced with a viscosity of 0.46 Pa.s, approximately five times higher than the dope obtained using traditional experimental design. The tensile strength, modulus and toughness of fibers spun from this dope also improved by a factor of 2.20x, 2.16x and 2.75x respectively. These results represent the outcome of just five sets of experimental trials focusing on just dope preparation. Given the number of parameters in the spinning and post spinning processes, the use of Bayesian optimization represents an exciting opportunity to explore the multivariate wet spinning process to unlock the potential to produce wet spun fibers with truly exceptional mechanical properties.

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for such functional optimisation of expensive black-box processes. We represent the control function using Bernstein polynomial basis and optimise in the coefficient space. We derive the theory and practice required to dynamically adjust the order of the polynomial degree, and show how prior information about shape can be integrated. We demonstrate the effectiveness of our approach for short polymer fibre design and optimising learning rate schedules for deep networks.

The scale-up of laboratory procedures to industrial production is the main challenge standing between ideation and the successful introduction of novel materials into commercial products. Retaining quality while ensuring high per-batch production yields is the main challenge. Batch processing and other dynamic strategies that preserve product quality can be applied, but they typically involve a variety of experimental parameters and functions that are difficult to optimize because of interdependencies that are often antagonistic. Adaptive Bayesian optimization is demonstrated here as a valuable support tool in increasing both the per-batch yield and quality of short polymer fibers, produced by wet spinning and shear dispersion methods. Through this approach, it is shown that short fiber dispersions with high yield and a specified, targeted fiber length distribution can be obtained with minimal cost of optimization, starting from sub-optimal processing conditions and minimal prior knowledge. The Bayesian function optimization demonstrated here for batch processing could be applied to other dynamic scale-up methods as well as to cases presenting higher dimensional challenges such as shape and structure optimization. This work shows the great potential of synergies between industrial processing, material engineering, and machine learning perspectives.

The recent progress in healable and reprocessable bulk network polymers with dynamic B–O bonds is reviewed.

In materials science, the investigation of a large and complex experimental space is time-consuming and thus may induce bias to exclude potential solutions where little to no knowledge is available. This work presents the development of a highly hydrophobic material from an amphiphilic polymer through a novel, adaptive artificial intelligence approach. The hydrophobicity arises from the random packing of short polymer fibers into paper, a highly entropic, multistep process. Using Bayesian optimization, the algorithm is able to efficiently navigate the parameter space without bias, including areas which a human experimenter would not address. This resulted in additional knowledge gain, which can then be applied to the fabrication process, resulting in a highly hydrophobic material (static water contact angle 135°) from an amphiphilic polymer (contact angle of 90°) through a simple and scalable filtration-based method. This presents a potential pathway for surface modification using the short polymer fibers to create fluorine-free hydrophobic surfaces on a larger scale.

Phenylalanine functionalised norbornene (9:Na) functions as a potent, low molecular-mass (M_W = 333 Da) ionic organogelator with a minimum gelating concentration of 0.5 wt% in THF, i-PrOH, 1,4-dioxane and n-BuOH. Fibrous crystals form in the gel and X-ray crystallography identified a cation mediated helical assembly process controlled by the chirality of the phenylalanine. In addition to excellent gelating properties 9:Na readily forms aqueous biphasic and triphasic systems.