Fire safe and sustainable lightweight materials based on Layer-by-Layer coated keratin fibers from tannery wastes

The increasing consciousness about the depletion of natural resources and the sustainability agenda are the major driving forces to try to reuse and recycle organic materials such as agri-food and industrial wastes. In this context, keratin fibers, as a waste from the tannery industry, represent a great opportunity for the development of green functional materials. In this paper, keratin fibers were surface functionalized using the Layer-by-Layer (LbL) deposition technique and then freeze-dried in order to obtain a lightweight, fire-resistant, and sustainable material. The LbL coating, made with chitosan and carboxymethylated cellulose nanofibers, is fundamental in enabling the formation of a self-sustained structure after freeze-drying. The prepared porous fiber networks (density 100 kg m–3) display a keratin fiber content greater than 95 wt% and can easily self-extinguish the flame during a flammability test in a vertical configuration. In addition, during forced combustion tests (50 kW m–2) the samples exhibited a reduction of 37 % in heat release rate and a reduction of 75 % in smoke production if compared with a commercial polyurethane foam. The results obtained represent an excellent opportunity for the development of fire-safe sustainable materials based on fiber wastes

An implementation detail about the scaling of monomial bases in polytopal finite element methods

The usual definition of scaled monomials found in polytopal finite elements literature leads to elemental matrices with an unnecessarily high condition number. A trivial but apparently overlooked rescaling significantly improves the situation. The extent of the improvement is demonstrated numerically

Evaluating the impact of hurdle rates on the Italian energy transition through TEMOA

Technology-specific hurdle rates significantly influence capital expenditures for deploying new technologies in the energy system, yet their definition in energy system optimization models often lacks a solid evaluation basis. This is crucial for providing relevant policy insights on clean finance investments. To address this gap, this paper introduces a framework for evaluating the impact of green finance measures on the future evolution of energy systems. Using the weighted average cost of capital methodology and recent literature, we robustly evaluate hurdle rates and explore their sensitivity by assessing the impact of reduced hurdle rates for green technologies on the cost of the energy transition through TEMOA-Italy. We differentiate hurdle rates for green and brown technologies to measure their potential to encourage low-carbon investments. The findings indicate that reducing hurdle rates for green technologies results in relatively low potential savings for the energy transition cost. Additionally, a 2-3% difference in hurdle rates is required to shift competitiveness from brown to green technologies, exceeding the realistic impact of green finance measures like the EU Taxonomy for Sustainable Activities (estimated at around 1%). Therefore, green finance schemes should be combined with other strategic measures to fully support the energy transition

Efficient recovery of lithium from spent lithium-ion battery raffinate by Mn and Al-based adsorbents: pretreatment, adsorption mechanism, and performance comparison

As a strong wave of retired lithium-ion battery approaches, lithium extraction from spent lithium-ion battery raffinate (SLR) becomes increasingly critical for environmental protection and for sustainable lithium supply. To understand the factors that affect maximum recovery of lithium from SLR, the organic and inorganic components of SLR were initially determined. The organic matter content (up to 760.5 mg/L) seriously impacted the recovery rate of lithium. Therefore, SLR was managed with a series of pretreatment techniques, including coagulation, biochar aerogel adsorption, and ultrafiltration, achieving more than 84.3% removal of organic substances. H1.33Mn1.67O4 and Li/Al layered double hydroxides adsorbents were then synthesized by solid state reaction method and hydrothermal method, respectively, granulated into spheres with a PVC skeleton, and applied to recycle lithium from pretreated SLR in a fixed bed adsorption column. The results indicated that both Mn and Albased adsorbents exhibited rapid adsorption kinetics, reaching saturation within 2 h. The Mn-based adsorbent exhibited superior adsorption selectivity for Li+ and higher Li+/Na+ separation factor (αLNia) compared to Albased adsorbent, with partition coefficients and αLNia values equal to 6.62 mL/g, 8.79 for the former material, and 4.92 mL/g, 8.17 for the latter. On the other hand, the Al-based adsorbent displayed better stability with negligible Al loss, while Mn loss from the related adsorbent was less than 0.2% in every adsorption–desorption cycle. Notably, both adsorbents demonstrated excellent reusability with their adsorption capacity maintained after twenty adsorption–desorption cycles

Understanding calendar aging degradation in cylindrical lithium-ion cell: A novel pseudo-4-dimensional electrochemical-thermal model

This study presents a comprehensive investigation of calendar aging degradation in commercial 21,700 cylin- drical lithium-ion cells with a LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode and a silicon- graphite composite anode. The cells underwent accelerated aging at 60 ◦ C for 63 days at various states of charge to assess the impact of high-temperature calendar aging. Experimental analysis was performed using non-destructive electrochemical techniques, and a novel pseudo-4D electrochemical-thermal model was developed using COMSOL Multiphysics to provide insights into the degradation processes. This model extends the traditional 1D geometry of a pseudo- 2D model into a 3D framework to simulate the local heterogeneity of the real electrochemical and thermal processes in commercial cells with jellyroll configurations, providing detailed insights into the behavior of the cell. The model incorporates various degradation mechanisms while considering the interaction between the cathode aging products and the solid electrolyte interphase growth at the anode. Experimental validation was performed using charge/discharge tests and calendar aging results, emphasizing the complex interplay between degradation mechanisms

Evaluating the sustainability of Artificial Intelligence applications across the product lifecycle

Due to the emergence of global challenges, the European policy called Industry 5.0 demands technologies to become resilient, sustainable and human-centric; the integration of Artificial Intelligence (AI) and Knowledge Management (KM) (i.e., AI-based KM) represents an opportunity to accomplish this ambitious goal thanks to the possibility of combining humans and computers’ unique skills. While the compliance of AI-based KM with human-centric requirements is ap-parent, its long-term sustainability is still an object of discussion. In response to I5.0’s request for metrics of sustainability, this paper presents a lifecycle analysis of AI-based KM’s applications retrieved from literature to establish whether they can be deemed socially, environmentally, and economically sustainable. A list of sources was collected by means of a literature search, to be later evaluated through sustainability criteria specific for the design and use phases of life. It was found that AI-based KM applications supporting fractions of the knowledge lifecycle may become fragile in the long-term, because potentially leading to re-positories’ indefinite growth and information overload in humans and computers; furthermore, it emerged that not enough attention was paid to AI’s accountability and data governance. The mentioned pain points, complemented in this article with practical examples of the counter-measures adopted by virtuous sources, provide scholars with practical indications to lead future research efforts, and contribute to the validation of a framework to assess the long-term sustainability of AI solutions

Laser fabrication: a flexible nano-engineering approach towards plasmonics, anticancer, and sensing applications

The present book chapter delves into the field of nanoscale engineering for fabricating functional interfaces, exploring its implications across diverse fields such as electrochemistry, photoplasmonics, antimicrobial agents, and anticancer applications. It begins with recalling the optical properties of bulk metallic materials, providing a comprehensive foundation for understanding their behavior when scaled down to nanometric dimensions. Therefore, the focus shifts to the description of laser-matter interaction and on the more recent techniques to obtain nano-engineered functional surfaces. Those techniques include both lithography- and nonlithography-based processes employed in synthesizing metal nanoparticles, unveiling the precise control and manipulation achievable. To conclude, a review of the main applications of the nanoengineered surfaces obtained with the described methods is presented and covers different fields, from chemical sensors to antimicrobial and anticancer applications

On the number of solutions to a random instance of the permuted kernel problem

The Permuted Kernel Problem (PKP) is a problem in linear algebra that was first introduced by Shamir in 1989. Roughly speaking, given an × m matrix A and an m × 1 vector b over a finite field of q elements Fq, the PKP asks to find an m × m permutation matrix π such that πb belongs to the kernel of A. In recent years, several post-quantum digital signature schemes whose security can be provably reduced to the hardness of solving random instances of the PKP have been proposed. In this regard, it is important to know the expected number of solutions to a random instance of the PKP in terms of the parameters q, ,m. Previous works have heuristically estimated the expected number of solutions to be m!/q. We provide, and rigorously prove, exact formulas for the expected number of solutions to a random instance of the PKP and the related Inhomogeneous Permuted Kernel Problem (IPKP), considering two natural ways of generating random instance

Mammography classification with multi-view deep learning techniques: investigating graph and transformer-based architectures

The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-resolution images, and the need to combine information from multiple views and scales still pose technical challenges. Multi-view architectures that combine information from the four mammographic views to produce an exam-level classification score, are a promising approach to the automated processing of screening mammography. However, training such architectures from exam-level labels, without relying on pixel-level supervision, requires very large datasets and may result in suboptimal accuracy. Emerging architectures such as Visual Transformers (ViT) and graph-based architectures can potentially integrate ipsi-lateral and contra-lateral breast views better than traditional convolutional neural networks, thanks to their stronger ability of modelling long-range dependencies. In this paper, we extensively novel transformer-based and graph-based architectures against state-of-the-art multi-view convolutional neural networks, trained in a weakly-supervised setting on a middle-scale dataset, both in terms of performance and interpretability. Extensive experiments on the CSAW dataset suggest that, while transformer-based architecture outperform other architectures, different inductive biases lead to complementary strengths and weaknesses, as each architecture is sensitive to different signs and mammographic features. Hence, an ensemble of different architectures should be preferred over a winner-takes-all approach to achieve more accurate and robust results. Overall, the findings highlight the potential of a wide range of multi-view architectures for breast cancer classification, even in datasets of relatively modest size, although the detection of small lesions remains challenging without pixel-wise supervision or ad-hoc networks