An innovative, fast and facile soft-template approach for the fabrication of porous PDMS for oil-water separation

Oil wastewater and spilled oil caused serious environmental pollution and damage to public health in the last years. Therefore, considerable efforts are made to develop sorbent materials able to separate oil from water with high selectivity and sorption capacity. However most of them are low reusable, with low volume absorption capacity and poor mechanical properties. Moreover, the synthesis is time-consuming, complex and expensive limiting its practical application in case of emergency. Here we propose an innovative approach for the fabrication of porous PDMS starting from an inverse water-in-silicone procedure able to selectively collect oil from water in few seconds. The synthesis is dramatically faster than previous approaches, permitting the fabrication of the material in few minutes independently from the dimension of the sponges. The porous material evidenced a higher volume sorption capacity with respect to other materials already proposed for oil sorption from water and excellent mechanical and reusability properties.This innovative fast and simple approach can be successful in case of emergency, as oil spill accidents, permitting in situ fabrication of porous absorbents

Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation

Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years. By representing the user-item matrix as a bipartite and undirected graph, GNNs have demonstrated their potential to capture short- and long-distance user-item interactions, thereby learning more accurate preference patterns than traditional recommendation approaches. In contrast to previous tutorials on the same topic, this tutorial aims to present and examine three key aspects that characterize GNNs for recommendation: (i) the reproducibility of state-of-the-art approaches, (ii) the potential impact of graph topological characteristics on the performance of these models, and (iii) strategies for learning node representations when training features from scratch or utilizing pre-trained embeddings as additional item information (e.g., multimodal features). The goal is to provide three novel theoretical and practical perspectives on the field, currently subject to debate in graph learning but long been overlooked in the context of recommendation systems

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

: Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors

On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorization models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be affected by popularity bias, meaning that it inherently tends to boost the recommendation of popular (i.e., short-head) items at the detriment of niche (i.e., long-tail) items from the catalog. Motivated by this assumption, in this work, we provide one of the first analyses on how multimodality in recommendation could further amplify popularity bias. Concretely, we evaluate the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN, GRCN, LATTICE) on three datasets from Amazon by assessing, along with recommendation accuracy metrics, performance measures accounting for the diversity of recommended items and the portion of retrieved niche items. To better investigate this aspect, we decide to study the separate influence of each modality (i.e., visual and textual) on popularity bias in different evaluation dimensions. Results, which demonstrate how the single modality may augment the negative effect of popularity bias, shed light on the importance to provide a more rigorous analysis of the performance of such models

Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation

In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework implements its multimodal extraction procedures with specific strategies and tools. This is limiting for two reasons: (i) different extraction strategies do not ease the interdependence among multimodal recommendation frameworks; thus, they cannot be efficiently and fairly compared; (ii) given the large plethora of pre-trained deep learning models made available by different open source tools, model designers do not have access to shared interfaces to extract features. Motivated by the outlined aspects, we propose Ducho, a unified framework for the extraction of multimodal features in recommendation. By integrating three widely-adopted deep learning libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we provide a shared interface to extract and process features where each backend's specific methods are abstracted to the end user. Noteworthy, the extraction pipeline is easily configurable with a YAML-based file where the user can specify, for each modality, the list of models (and their specific backends/parameters) to perform the extraction. Finally, to make Ducho accessible to the community, we build a public Docker image equipped with a ready-to-use CUDA environment and propose three demos to test its functionalities for different scenarios and tasks. The GitHub repository and the documentation is accessible at this link: https://github.com/sisinflab/Ducho

Vapor‐Phase Synthesis of Molecularly Imprinted Polymers on Nanostructured Materials at Room‐Temperature

Molecularly imprinted polymers (MIPs) have recently emerged as robust and versatile artificial receptors. MIP synthesis is carried out in liquid phase and optimized on planar surfaces. Application of MIPs to nanostructured materials is challenging due to diffusion-limited transport of monomers within the nanomaterial recesses, especially when the aspect ratio is >10. Here, the room temperature vapor-phase synthesis of MIPs in nanostructured materials is reported. The vapor phase synthesis leverages a >1000-fold increase in the diffusion coefficient of monomers in vapor phase, compared to liquid phase, to relax diffusion-limited transport and enable the controlled synthesis of MIPs also in nanostructures with high aspect ratio. As proof-of-concept application, pyrrole is used as the functional monomer thanks to its large exploitation in MIP preparation; nanostructured porous silicon oxide (PSiO2) is chosen to assess the vapor-phase deposition of PPy-based MIP in nanostructures with aspect ratio >100; human hemoglobin (HHb) is selected as the target molecule for the preparation of a MIP-based PSiO2 optical sensor. High sensitivity and selectivity, low detection limit, high stability and reusability are achieved in label-free optical detection of HHb, also in human plasma and artificial serum. The proposed vapor-phase synthesis of MIPs is immediately transferable to other nanomaterials, transducers, and proteins

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation