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Laser-induced graphene on axially oriented alginate–tannic acid–clay aerogel for energy harvesting and storage applications
Get PDFIn this work, a bio-based aerogel with axially oriented morphology was used as a substrate for laser induced graphene (LIG) conversion to fabricate carbon-based electrodes for energy harvesting and storage devices. The substrate materials provide intrinsic flame-retardancy and, together with the anisotropic structure, enable facile and controlled laser writing. The resulting LIG preserves the directional anisotropy of the aerogel, guiding heat propagation. Dye-Sensitized Solar Cells (DSSCs) including the developed material, achieved a fill factor of 0.72 and photoconversion efficiency of 2.2 % under indoor conditions (1000 lux) using γ-valerolactone (GVL) as the electrolyte solvent. Supercapacitors (SCs) were also developed employing LIG electrodes, exhibiting typical electrical double layer capacitor (EDLC) behaviour and remarkable rate capabilities under both cyclic voltammetry and galvanostatic cycling tests, with negligible resistive losses. The areal capacitance reached 1.49 mF/cm2, and the Ragone plot showed areal energy and power densities comparable to other sustainable LIG-based SCs. Overall, the aerogel has proven to be a sustainable alternative compatible with laser conversion processes. Remarkably, the aerogel's initial structure is preserved after the laser conversion process, allowing for substrate morphology pre-design to control the final LIG structure. These aerogels' features allow for tailoring carbon-based electrode morphologies toward optimized energy harvesting and storage
Assessing Physical Ergonomics in Industry 5.0: A Preliminary Deep Learning-Based Approach
No full textAdjacent backbone interactions control self-sorting of chiral heteroleptic Pd3A2B4 isosceles triangles and Pd4A4C4 pseudo-tetrahedra
Get PDFTaking inspiration from biological structures, self-assembled nano-objects promise application potential, e.g., in selective catalysis, smart materials, and drug delivery. While integrating multiple different building blocks greatly enhances their versatility, it is a challenge to design low-symmetry multicomponent structures without generating statistical mixtures. Based on adjacent backbone interactions (ABIs), we introduce here a series of integratively self-sorted PdnL2n (n = 3, 4) assemblies, each comprising two distinct, easily accessible ligands. In these Pd3A2B4 isosceles triangles and Pd4A4C4 pseudo-tetrahedra, one ligand is always chiral. Noteworthy, the triangular structures contrast previously described heteroleptic Pd3L6 assemblies in that the two differentiable ligands are not contained in equal stoichiometries. The chiral assemblies bind and strongly discriminate chiral guests. They can coexist orthogonally, leading to cage populations under heteromeric narcissistic self-sorting. We further present a computational toy model toward structure prediction of such assemblies, opening avenues for the rational design of discrete metallosupramolecular three-dimensional architectures
Pure Copper Additive Manufacturing: Investigation of Processes and Environmental Applications
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Characterization of Communicating Turbulent Grazing Flows Through a Resolved Porous Medium
No full textPorous media are a promising technology to reduce turbulent boundary layer trailing edge noise. However, the fact that the porous material is grazed by turbulent flow on both sides makes its characterization not trivial. This paper describes the modifications resulting from the interaction between the grazing flows through the porous medium, defined as communication. To this end, lattice-Boltzmann simulations of two communicating turbulent channel flows separated by a fully resolved porous medium are carried out. The porous medium is realized as a 75% porous triply periodic minimal surface of type Schwarz’ P. Results are compared against the case with porous medium backed by a solid wall and the smooth wall channel flow. When communication between the two channel flows is allowed, spanwise coherent structures appear that are assimilated to a
shear instability at a non-dimensional frequency of Stt = 0.02. Instantaneous flow through the porous medium is observed and is driven by a time-dependent pressure differential between the channels (with a zero mean and 7.8 Pa standard deviation). This leads to a decrease in energy in turbulent scales smaller than 2.5δ and for bulk scaled frequencies greater than Stb = 0.41. These flow modifications are not observed in the non-communicating case, with the wall preventing flow through, where the topology of the fluctuating statistics is similar to the smooth wall case. Finally, the drag is found to increase by over 200%
with respect to the non-communicating case and 650% with respect to a smooth turbulent channel flow. The drag increase is found to be driven by the velocity fluctuations impinging on the porous topology. The communication does not fol-
low the asymptotic drag relation for the same equivalent roughness, thus entering a different drag regime
From augmentation to translation: Data generation by conditional hierarchical variational autoencoder, enhancing monitoring mooring systems in floating offshore wind turbines
Get PDFThe integrity of mooring systems in floating offshore wind turbines (FOWTs) is crucial, as their degradation alters the platform’s dynamic behavior. A robust machine learning-based health monitoring system that continuously monitors different mooring systems for FOWTs requires data under diverse health, operational, and metocean conditions. To this end, we propose a Conditional Hierarchical Variational Autoencoder (CHVAE) generative model designed for simultaneous data augmentation and domain translation to generate the required data. We train the model to learn the nonlinear relationships between healthy and minority-damaged fairlead tension records from the source mooring system across various sea states. CHVAE generates realistic damaged responses under diverse conditions by leveraging healthy data from the target mooring system. We first assess CHVAE’s ability to augment minority data based on majority distribution, validated on the Modified National Institute of Standards and Technology (MNIST) benchmark dataset. This experiment compares the performance of CHVAE variants with conventional and recent oversampling methods. Second, the open-source software OpenFast simulates the testing and training datasets for simultaneously data augmentation and domain translation on the Offshore Code Comparison Collaboration Continuation (OC4) semi-submersible platform (DeepCwind) FOWT benchmark. OpenFast and CHVAE records are compared through visual, statistical, and behavioral methodologies. Simulations utilize diverse wave seeds to represent excitation randomness and undetected damage severities, assessing CHVAE’s one-to-all capability. Generated records for unobserved sea states and damage severities closely mimic real behavior in downstream binary classification, illustrating the versatility of CHVAE for zero-shot, real-time damage identification
A self-contained proof of the alt-caffarelli-friedman monotonicity formula
Get PDFThe Alt-Caffarelli-Friedman monotonicity formula is a cornerstone
in the theory of free boundary problems. In this note we provide a selfcontained proof of this result. To prove the main stepping stone, namely the
Friedland-Hayman inequality, we exploit a useful convexity propert
Early detection of physical fatigue in industry using wearable sensors and contextual modeling
Get PDFPhysical fatigue in repetitive production lines contributes to musculoskeletal disorders and absenteeism. This study investigates a pharmaceutical packaging environment in Colombia with 43 operators (42 female; 19–53 years) performing repetitive inspection and packing. Smartwatches captured pulse rate, electrodermal activity, skin temperature, and motion, complemented by demographic (age, experience) and occupational factors (task load, line, shift, timing). Principal Component Analysis (PCA) reduced dimensionality, and a fuzzy logic–based labeling method—adapted from prior controlled experiments—generated binary and four-class fatigue labels without mid-shift self-reports. These labeled datasets were used to train multiple machine-learning classifiers. Integrating contextual features with biometrics substantially improved performance: in binary classification, F1 increased from 0.8848 (biometrics only) to 0.9375; in four-level classification, F1 rose from 0.8232 to 0.8793. Motion-related metrics emerged as the most informative predictors. Critically, feature integration improved reliability: accuracy for intermediate states (Higher Non-Fatigue and Higher Fatigue) rose by ∼10 percentage points, while false negatives in the Pure Fatigue class were eliminated—3% of cases previously misclassified as Higher Non-Fatigue were instead correctly mapped within the fatigue spectrum. This shift strengthens the system’s effectiveness for real-time safety interventions. The novelty of this work lies in combining biometric and contextual modeling to reduce false negatives in critical fatigue states, providing a scalable, non-intrusive, and human-centered early-warning system. By aligning with Industry 5.0, this approach demonstrates how wearable and contextual data can jointly support proactive and trustworthy safety interventions while maintaining operational flow
Achieving Machine Learning Dependability Through Model Switching and Compression
Get PDFMachine learning (ML) can be often distributed, owing to the need to harness more resources and/or to preserve privacy. Accordingly, distributed learning has received significant attention from the literature; however, most works focus on the expected learning quality (e.g., loss) attained and do not consider the distribution thereof. It follows that ML models are not dependable, and may fall short of the required performance in many real-world cases. In this work, we tackle this challenge and propose DepL, a framework attaining dependable learning orchestration. DepL efficiently makes joint, near-optimal decisions concerning (i) which data to use for learning, (ii) the ML models to use – chosen within a set of full-size models and compressed versions thereof – and when to switch from one model to another, and (iii) the clusters of physical nodes to use for the learning. DepL improves over previous works by guaranteeing that the learning quality target (e.g., a minimum loss) is achieved with a target probability, while minimizing the learning (e.g., energy) cost. DepL has provably low polynomial computational com- plexity and a constant competitive ratio. Further, experimental results using the CIFAR-10 and GTSRB datasets show that it consistently matches the optimum and outperforms state-of-the- art approaches (30% faster learning and 40–80% lower cost)
Current status and perspectives on shared moorings for offshore floating renewable energy systems: A review
Get PDFFloating renewable energy systems, including offshore wind turbines (FOWTs) and wave energy converters (WECs), are increasingly recognised as pivotal for achieving decarbonisation targets. However, the high cost of station-keeping systems remains a key barrier to large-scale deployment. Shared mooring strategies, comprising shared lines and shared anchors, offer promising pathways for reducing both capital and operational expenditures. This review presents a comprehensive analysis of the current state-of-the-art in shared mooring systems for floating renewable technologies. The manuscript is structured to (i) introduce the mooring design problem in the context of renewable energy applications; (ii) categorise and critically assess all peer-reviewed publications to date on shared mooring systems for both wind and wave energy; and (iii) identify key modelling approaches, limitations, and emerging trends. Emphasis is placed on dynamic simulation techniques, hydrodynamic loading, and the often-neglected role of anchors and soil-structure interaction. Furthermore, the review highlights the divergence in modelling fidelity across studies and the implications for reliability and cost. Particular attention is given to recent experimental efforts and the Hywind Tampen project, the first industrial-scale wind farm to implement shared anchors. The findings underscore the lack of standardised methodologies and the pressing need for integrated design frameworks that account for nonlinearities, multidirectional loading, and site-specific constraints. Ultimately, this work aims to bridge gaps in current practice, offering a technical foundation for future research and development in cost-effective and scalable mooring solutions for floating renewable energy systems