Experimental Efforts Research Articles

An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences

N4-methylcytosine (4mC) is a chemical modification that occurs on one of the four nucleotide bases in DNA and plays a vital role in DNA expression, repair, and replication. It also actively participates in the regulation of cell differentiation and gene expression. Consequently, it is important to comprehend the role of 4mC in the epigenetic regulation for revealing the complications of the gene expression and their associated governing cellular operations. However, the inherent resource requirements and time constraints of the experimental procedure, present challenges to the cellular culture process. While data-driven methodologies present promising solutions to mitigate the demand for extensive experimental efforts, their performance relies on the suitability and existence of high-quality data. This study presents a multi-model framework that integrates convolutional neural network (CNN) with the distributed k-mer and embedding feature extraction techniques to enhance the identification of 4mC sites in DNA sequences. The integration of k-mers ensures the effective representation of the local sequence patterns, while the utilization of embedding enables a more holistic encoding by considering the broader context and semantics of the sequence data. Following the initial step, the obtained distributed representation of the DNA sequence seamlessly enters the CNN, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. The proposed integrated multi-model framework was applied to six publicly available datasets and evaluated against the cutting-edge 4mCPred, 4mCCNN, iDNA4mC, Meta-4mCpred, DeepTorrent, 4mCPred-SVM, and DMKL-HFIS methods. The evaluation was based on accuracy, specificity, sensitivity, and Matthews Correlation Coefficient. The results demonstrated that the proposed multi-model framework outperformed the state-of-the-art methods, as well as one-hot encoding and the hybrid of one-hot & TNC features, in accurately identifying 4mC sites.

Hydroxy-Carboxylic Acids as Green and Abundant Ligands for Sustainable Recovery of Copper from a Multimetallic Powder: A Proof of Concept.

The recovery of copper and other valuable metals had become increasingly strategic for the future of the global economy, particularly in regions lacking abundant mineral resources, such as most European countries. In this study, we investigated the viability of utilizing environmentally friendly, cost-effective, abundant and bio-based ligands, specifically carboxylic acids and their derivatives, for copper leaching in a low-temperature hydrometallurgical process. Our investigation focused on elucidating the impact of substituents in the α position of hydroxy-carboxylic acids on copper solubilization efficacy. Notably, hydroxy-carboxylic acids, like malic acid and lactic acid, were evidenced as particularly promising ligands for leaching copper from a custom-made multimetallic powder. By thoroughly characterizing the obtained complexes (Raman, UV-Vis) and by supporting the experimental efforts by a Design of Experiment (DoE) approach, we optimized the leaching process. The influence of experimental parameters such as pH, temperature, leaching time, and Cu/ligand molar ratio on process yield (determined through Inductively Coupled Plasma - Optical Emission Spectroscopy, ICP-OES, analysis) was thoroughly investigated. Additionally, we developed a subsequent copper recovery step by precipitating copper (II) hydroxide in an alkaline environment, guided by speciation diagrams tailored for each copper-ligand system.

Test-and-FE-based method for obtaining complete stress-strain curves of structural steels including fracture

Ductile fracture is a common limit state of frameworks of steel structures and can be predicted by incorporating models for fracture initiation and propagation in finite element (FE) analyses. However, accurate prediction of fracture relies on unique fracture parameters for a given material, requiring precise predictions of fracture strain and stress states obtained through coupon tests and accompanying FE data analysis. In recent decades, various methods have been proposed to predict ductile fracture initiation, including various design geometries of coupons, test setups and FE analyses, which may lead to inconsistent fracture strains and stress states. Hence, there is a need for proposing a standard procedure for the design, test and accompanying FE-based calibration of fracture parameters, as pursued in this paper. Given that ductile fracture initiates under significant plastic strain, a constitutive model for the full strain range is required, as also proposed in this paper. Moreover, to reduce the experimental and data analytical efforts, an empirical method is proposed that uses only a small number of coupon tests to calibrate the fracture parameters. The method encompasses three levels wherein one, two, or three coupon tests are required. All in all, the paper presents a methodology for determining the full-range true stress-strain curve and the initiation of fracture, including a new post-necking constitutive model and methods for determining the free parameters of the post-necking and fracture initiation models. While applied to steel in this paper, the proposed methodology is potentially also applicable to other metals.

Optimization of Reactive Ink Formulation for Controlled Additive Manufacturing of Copolymer Membrane Functionalization.

Multifunctional, nanostructured membranes hold immense promise for overcoming permeability-selectivity trade-offs and enhancing membrane durability in challenging molecule separations. Following the fabrication of copolymer membranes, additive manufacturing technologies can introduce reactive inks onto substrates to modify pore wall chemistries. However, large-scale implementation is hindered by a lack of systematic optimization. This study addresses this challenge by elucidating the membrane functionalization mechanisms and optimal manufacturing conditions using a copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) "click" reaction. Leveraging a data science toolkit (e.g., nonlinear regression, uncertainty quantification, identifiability analyses, model selection, and design of experiments), we developed two mathematical models: (1) algebraic equations to predict equilibrium concentrations after preparing reactive inks by mixing copper sulfate, ascorbic acid (AA), and an alkynyl-terminated reactant; and (2) reaction-diffusion partial differential equations (PDEs) to describe the functionalization process. The ink preparation chemistry with side reactions was validated through pH and UV-vis measurements, while the diffusion and kinetic parameters in the PDE model were calibrated using time-series conversion of the azide moieties inferred from Fourier-transform infrared spectroscopy. This modeling framework avoids redundant experimental efforts and offers a functionalization protocol for scaling up designs. Ink optimization problems were proposed to reduce the use of expensive and environmentally insulting ink materials, i.e., Cu(II), while ensuring the desired chemical distributions. With optimal ink formulation Cu(II)/AA/alkyne = 1:1:2 identified, we uncovered trade-offs between Cu(II) usage and functionalization time; for example, in continuous roll-to-roll manufacturing with a conserved functionalization bath setup, our optimal operational conditions to achieve ≥90% functionalization enable at least a 20% reduction in total copper investment compared to previous experimental results. The data science-enabled ink optimization framework is extendable for on-demand multifunctional membranes in numerous future applications such as metal recovery from wastewater and brine.

Enhancement of second-harmonic generation in a lithium niobate metasurface by exploring the bound states in the continuum

The nonlinear lithium niobate (LiNbO3), characterized by transparency across a broad spectral range from ultraviolet to mid-infrared, stands out as an ideal material for second-harmonic generation (SHG). The concept of bound states in the continuum (BIC) represents a non-radiative mode embedded within the radiation continuum, offering the capability to confine the electromagnetic field within the nanostructure. Here, we propose the design of a LiNbO3 metasurface utilizing the BIC mechanism to enhance SHG at fundamental wavelengths above 2 µm, which injects new thoughts into the field of integrated optics and on-chip photonics. Notably, we rigorously accounted for the influence of the side-wall angle θ and the corner rounding radius R of the LiNbO3 metasurface, which arises from fabrication tolerances. Considering those influences in the simulation, we achieved a quasi-BIC (q-BIC) with a quality factor (Q-factor) up to 1.44 × 105. Moreover, by considering the depleted pump model, the absolute efficiency of SHG reached 4.09% with a corresponding normalized efficiency of 8.19 × 10−10m2/W under a radiation intensity of 5 kW/cm2. Our research aims to establish a predictive framework through numerical simulations, specifically addressing realistic fabrication problems. This approach is intended to optimize the parameters for LiNbO3 metasurface fabrication, ensuring that the subsequent experiment efforts are efficient. Our findings provide an approach to predicting the optical response in actual structures and inspire new applications in photonics.

AI-Guided Design of MALDI Matrices: Exploring the Electron Transfer Chemical Space for Mass Spectrometric Analysis of Low-Molecular-Weight Compounds.

The development of matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) has traditionally relied on experimental efforts. Here, we propose a Goal-Directed artificial intelligence generative model, fueled by computational chemistry calculated data, to construct a chemical space optimized for Electron Transfer (ET) processes in MALDI analysis. We utilized a group of 30 reported ET matrices, subjected to structural enumeration and molecular properties prediction using semiempirical and ab initio calculations, to establish a comprehensive database comprising diverse structural and property data. Subsequently, employing a protocol of structural enumeration with 68 canonical SMILES of Bemis-Murcko (BM) fragments, we expanded the structural complexity of the initial library. This process generated 82753 compounds organized into 10 scaffold levels, with a p50 index from the Cyclic System Retrieval (CSR) curve of scaffolds of 50%. From the resulting enumerated library, a diverse subset of structures was selected by using the Jarvis-Patrick clustering method. These structures, along with their associated properties measured from quantum mechanics and experimental data, were used to train a Machine Learning (ML) model to predict ionization energy (Ei) values. Subsequently, a Scoring Neural Network (SNN), coupled with our Goal-Directed generative model using a Recurrent Neural Network (RNN) with Deep Learning (DL) architectures, was trained. The generative model was guided using a prior network within a Reinforcement/Transfer Learning environment. The final AI-generative model learned that structures with high unsaturation, H/C ratios under 1, and molecular weights between 100 and 300 u are favorable for ET MALDI matrices, as well as those with few aromatic rings and zero aliphatic rings. Other molecular features were also favored. The resulting AI-generated library exhibits Ei values over 8.0 eV, akin to those of reported "good" ET MALDI matrices, indicating successful design with high synthesis accessibility scores. In conclusion, our generative model provided valuable insights into the molecular features ideal for ET MALDI compounds while generating a wide range of structurally diverse molecules within a similar molecular property space. The next critical step in this process is to synthesize a selection of these generated compounds for the experimental validation and further characterization.

Mechanisms of Controllable Growth and Ohmic Contact of Two-Dimensional Molybdenum Disulfide: Insight from Atomistic Simulations.

ConspectusTwo-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs), in particular molybdenum disulfide (MoS2), have recently attracted huge interest due to their proper bandgap, high mobility at 2D limit, and easy-to-integrate planar structure, which are very promising for extending Moore's law in postsilicon electronics technology. Great effort has been devoted toward such a goal since the demonstration of protype MoS2 devices with high room-temperature on/off current ratios, ultralow standby power consumption, and atomic level scaling capacity down to sub-1-nm technology node. However, there are still several key challenges that need to be addressed prior to the real application of MoS2-based electronics technology. The controllable growth of wafer-scale single-crystal MoS2 on industry-compatible insulating substrates is the prerequisite of application while the currently synthesized MoS2 films mostly are polycrystalline with limited sizes of single-crystal domains and may involve metal substrates. The precise layer-control is also very important for MoS2 growth since its electronic properties are layer-dependent, whereas the layer-by-layer growth of multilayer MoS2 dominated by the van der Waals (vdW) epitaxy leads to poor thickness uniformity and noncontinuously distributed domains. High density up to 1013 cm-2 of sulfur vacancies (SVs) in grown MoS2 can cause unfavorable carrier scatting and electronic properties variations and will inevitably disturb the device performance. The dangling-bond-free surface of MoS2 gives rise to an inherent vdW gap at metal-semiconductor (M-S) contact, which leads to high electrical resistance and poor current-delivery capability at the contact interface and thereby substantially limits the performances of MoS2 devices.In this Account, we briefly review recent experimental and theoretical attempts for addressing the aforementioned challenges and present our own insights from atomistic simulations. We theoretically revealed the vital role of substrate steps for guiding unidirectional nucleation of monolayer MoS2 and uniform nucleation and edge-aligned growth of bilayer MoS2 by advanced simulations. The established thermodynamic mechanisms have successfully directed the experimental works on the controllable growth of 2 in. single-crystal monolayer and centimeter-scale uniform bilayer MoS2. The postgrowth repair mechanism of SV defect in MoS2 via thiol chemistry treatment has been theoretically explored with the consideration of side reaction of surface functionalization to help experimentally reduce SV defect density by 75%. Beyond the atomic level understanding, theoretical simulations proposed the electronic states hybridization mechanism across the semimetal-MoS2 vdW interface, thereby guiding experimental effort for realizing Ohmic contact at the MoS2-Sb(0112) vdW interface with record-low contact resistance.These advances provide a sound basis with an atomic-level understanding for addressing the related issues. However, there are still notable gaps in terms of system size and time scale of dynamics between atomistic simulations and experimental observations for the studies of MoS2 growth and interfaces. The combination of multiscale simulations and artificial intelligence technology is expected to narrow these gaps and provide a more insightful understanding of the controllable growth and interfacial properties modulation of MoS2. We conclude the Account with the standing challenges and outlook on future research directions from the theoretical perspective.

Pivotal Role of Surface Terminations in MXene Thermodynamic Stability.

MXenes, i.e., two-dimensional transition metal carbides and nitrides, have been reported as promising materials for various applications, including energy storage, biomedicine, and electronics. The family of MXenes has proliferated, and the chemical space of synthesized MXenes has expanded to 13 transition metals and a dozen elements in surface terminations. The diverse chemistry of MXenes enables systematical tuning of MXene properties to meet the needs of target applications. However, synthesizing new MXene compositions largely relies on a trial-and-error approach. To overcome it, computational predictions of MXene compositions that are thermodynamically stable are crucial to rationalize experimental efforts. Here, we report a comprehensive computational screening for thermodynamically stable MXenes across 29 transition metals and 11 surface terminations. Density functional theory calculations are employed to compute the energy above the convex energy hull as a descriptor of thermodynamic stability. The results are analyzed to explore factors crucial for determining the thermodynamic stability of MXenes, by which the chemistry of surface terminations is found to play a crucial role. The insights on the chemistry of 998 MXene compositions predicted to be (meta)stable are given to systematically guide further research on MXene synthesis and application.

Specifics of Al substitution into boron carbide: A DFT study

Boron carbide (B4C), known for its hardness and low density, is prone to local amorphization under high non-hydrostatic loads, limiting its applications. Aluminum doping is promising due to aluminum's atomic size, fitting into the B4C crystal lattice, particularly the intericosahedra chain, potentially reducing amorphization. This work uses first-principles calculations to study aluminum placement in B4C. We examine the potential for Al substitution in the icosahedron and intericosahedra chain, identifying possible locations. Results show that aluminum can't replace atoms in the icosahedron, but substituting one central atom in the intericosahedra chain with aluminum is energetically favorable and likely changes the chain configuration to an angular one. Additionally, aluminum can substitute one carbon in the (C-B-C) chain of B12(C-B-C) boron carbide. Comparative analysis suggests these configurations may coexist. Our study offers a theoretical model that can guide future experimental efforts and provides valuable insights into the structural specifics of aluminum-doped boron carbide.

Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices.

Efficient electrocatalytic reduction of nitric oxide (NO) to ammonia (NH3) on metal-free B4@g-C3N4 nanosheet

The combustion of fossil fuels releases substantial amounts of nitric oxide (NO), posing serious environmental and health risks. Addressing NO pollution effectively is, therefore, a pressing need. Conversion of NO into valuable chemicals is an appealing strategy with dual benefits, including waste utilization and sustainable energy transformation. In this study, we investigate the metal-free electrocatalytic reduction of NO (NORR) to NH3 on a B4 atomic cluster supported by a g-C3N4 nanosheet (B4@g-C3N4) using density functional theory (DFT). Our findings reveal that NO is efficiently activated on the B4@g-C3N4 catalyst, favoring side-on and N end-on adsorption configurations. Significant electron transfer and strong adsorption energies confirm the chemisorption of NO. The NORR exhibits an impressively low limiting potential (UL) of −0.29 V in the gas phase, which decreases further under solvent effects and varying charge states (−2e-, −1e-, +1e-, +2e-). Additionally, the high negative UL values for competing side reactions such as H2 (−0.42 V), N2 (−0.29 V), and N2O (−0.72 V) underscore the selectivity of B4@g-C3N4 for ammonia synthesis. These results highlight the promising potential of B4@g-C3N4 nanosheets for eco-friendly ammonia production from NO, providing a theoretical basis for future experimental efforts to combat NO pollution.

Robust Edge Flows in Swarming Bacterial Colonies

Understanding if and how the chirality of biomolecules is transferred across scales into larger components and active processes remains elusive. For instance, flagellated bacteria swim in helical trajectories but chirality seems absent from the active turbulence dynamics they display in dense suspensions. We address this question by examining multiscale dynamics in colonies. We find active turbulence without manifest chirality in the bulk, but wide, clockwise (viewed from the air side) circulation all along the tortuous centimeter-scale external boundary, while similar but counterclockwise flows follow internal boundaries. We trace the origin of these robust edge flows to an unexpected asymmetry at the individual level that is amplified by local interactions. We rationalize our findings with a model of noisy self-propelled particles immersed in a Stokes fluid that accounts faithfully for our observations. Our modeling and experimental efforts reveal that local nematic alignment and hydrodynamic interactions amplify the weak chiral bias in individual motion, promoting the formation of strong edge flows. The likely topological protection of these flows provides robust transport mechanism over large scales. Such robust boundary phenomena in weakly chiral active fluids may inspire new control strategies for active and biological matter. Published by the American Physical Society 2024

An investigation of the performances of asynchronous measurements methods

In acoustical imaging and source localization, several methods for the exploitation of asynchronous array measurements have been proposed, based on covariance matrix completion, Bayesian estimation, fusion of beamforming maps, etc. The goal of asynchronous measurements is to reach the performance of large and dense arrays, with reduced experimental effort, by moving an array of limited size between experiments, and fuse the obtained data to produce an estimation of the distribution of acoustical sources. In this study, we consider the performances that one can expect from asynchronous measurements, and investigate the actual performances of several methods from the state of the art. In particular, the mean squared errors of the estimation of the position and power of an acoustical source are estimated using simulations and experimental measurements.

Through-thickness Work Hardening Variation in Thick High Strength Steel Plates: A Novel Inverse Characterization Method

Due to the production process, thick high strength steel plates potentially exhibit inhomogeneous plastic material behavior through the thickness. For example, the initial yield stress and work hardening behavior might vary through the plate thickness. In order to establish a reliable plasticity model that can be used in finite element simulations to optimize forming processes or to investigate the structural integrity of large structures, this material behavior needs to be characterized. A straightforward characterization method consists of slicing the thick high strength steel plate and conducting standard tensile tests. However, this approach comes with a large experimental effort. A novel specimen is proposed enabling to inversely identify the work hardening behavior at distinct locations through the thickness of a thick steel plate. To this end, a tensile specimen with circular pockets at different depths is used. The strain fields within the pockets are captured using digital image correlation (DIC). finite element model updating (FEMU) is used to inversely identify a predefined strain hardening law for each pocket. First, the procedure is optimized and numerically verified using virtual experiments generated by a finite element model with a known variation of the work hardening behavior. Finally, the procedure is experimentally validated by characterizing the strain hardening behavior of a 10 mm thick S690QL grade.

An Attempt for Numerical Optimisation of a Micro-Groove Geometry at The Rake Face When Turning Ti6Al4V Alloy with Indexable Inserts

Machining of Ti6Al4V is considered difficult because the material removal rates are relatively small if the tool wear shall be low. In recent years the reduction of process forces as well as tool wear have been investigated by introducing textures (pockets) into the tool surface. To advance the understanding how those textured tools function and to reduce the experimental effort, a smoothed particle hydrodynamics (SPH) model of the orthogonal cutting process with a parametrised tool containing a single pocket on the rake face with variable position and depth is presented. This simulation model is used to enhance the understanding of rake face textures in order to design optimum cutting tools for given process parameters. Using an optimisation algorithm, an optimum texture geometry is determined numerically and is then experimentally validated, followed by a discussion, why process force reductions are lower than predicted.

Stable Isotope Labelling Reveals Water and Carbon Fluxes in Temperate Tree Saplings Before Budbreak.

Despite considerable experimental effort, the physiological mechanisms governing temperate tree species' water and carbon dynamics before the onset of the growing period remain poorly understood. We applied 2H-enriched water during winter dormancy to the soil of four potted European tree species. After 8 weeks of chilling, hydrogen isotopes in stem, twig and bud water were measured six times during 2 consecutive weeks of forcing conditions (Experiment 1). Additionally, we pulse-labelled above-ground plant tissues using 2H-enriched water vapour and 13C-enriched CO2 7 days after exposure to forcing conditions to trace atmospheric water and carbon uptake (Experiment 2). Experiment 1 revealed soil water incorporation into the above-ground organs of all species during the chilling phase and significant species-specific differences in water allocation during the forcing conditions, which we attributed to differences in structural traits. Experiment 2 illustrated water vapour incorporation into all above-ground tissue of all species. However, the incorporation of carbon was found for evergreen saplings only. Our results suggest that temperate trees take up and reallocate soil water and absorb atmospheric water to maintain sufficient above-ground tissue hydration during winter. Therefore, our findings provide new insights into the water allocation dynamics of temperate trees during early spring.

Floquet engineering of a dynamical Z2 lattice gauge field with ultracold atoms

Abstract Gauge field theory is a fundamental concept in modern physics, attracting many theoretical and experimental efforts towards its simulation. In this paper we propose that a simple model, in which fermions coupled to a dynamical lattice gauge field, can be engineered via the Floquet approach. The model possesses both an independent Maxwell term and local Z 2 gauge symmetry. Our proposal relies on a species-dependent optical lattice, and can be achieved in one, two or three dimensions. By a unitary transformation, this model can be mapped into a non-interacting composite fermion system with fluctuating background charge. With the help of this composite fermion picture, two characteristic observations are predicted. One is radio-frequency spectroscopy, which exhibits no dispersion in all parameter regimes. The second is dynamical localization, which depends on the structure of the initial states.

Experimental study on residual compressive bearing capacity of cracked reinforced concrete columns by SCDA

Abstract The static demolition of structures via soundless chemical demolition agent (SCDA) is attracting increasing attention because of its environmental friendliness and safety, especially in the dense urban areas. However, the research on the application of SCDA in demolishing the reinforced concrete is still scarce, seriously limiting the development of static demolition. In this work, with the combined efforts of experiments and theoretical analysis, we investigate the critical role of borehole layouts in the static demolition of reinforced concrete (RC) by determining the occurrence and expansion of cracks during static demolition, and the resulting residual mechanical properties of RC. The results show that the growth of cracks is significantly dependent on the reaction of SCDA. Additionally, the initiation time of cracking exhibits minimal sensitivity to variations in the layouts pattern of boreholes, while the progression of cracking is predominantly governed by the layouts of boreholes. We demonstrate that cracks formed in static demolition process can significantly reduce the strength of RC with about the remaining bearing capacity is only 10% to 33% of the original and change its failure mode. Further, it is point out that the key factor controlling the failure of RC under axial compression (i.e., the integrity of structures), and the RC with larger crack size and less structural integrity is more prone to fail. The results and findings in this work further clarify the role of borehole layouts and are of great significance in improving the efficiency of static demolition.

Minimizing the cobalt content in black ceramic pigments by Design of Experiments

This paper presents a comprehensive optimization strategy for the synthesis of new black ceramic pigments with low cobalt content while maintaining a single-phase spinel structure. The aim is to achieve comparable hues to those of an industrial benchmark containing five transition metals while minimizing the environmental impact. Exploring all possible compositions deriving from a five–component system (Cr, Mn, Fe, Co and Ni), through traditional methods would be extremely time consuming to guarantee an efficient sampling, requiring high experimental efforts. Hence, to identify the best black compositions, we employed a chemometric approach, the Design of Experiments, aiming to investigate the compositional domain derived by varying the metals stoichiometry within fixed boundaries to identify optimal pigment compositions. The resulting pigments (comprising Cr, Fe and Co), despite the lower cobalt content, exhibited optimal colorimetric properties comparable to the standard benchmark, with experimental ΔE values comparable to the ones predicted by the model. In particular, the pure spinels Cr1.05Fe1.05Co0.9O4 and Cr1.2Fe1.05Co0.75O4 displayed low lightness and chroma (L∗ = 2.4; C∗ = 5 and L∗ = 1.12; C∗ = 1.1, respectively), providing deep and dark black tonality. These compositions exhibit promising colorimetric performance and chemical stability, offering potential benefits for industrial applications.

Vortex-Induced Vibration of Flexible Cylinders in Cross-Flow

This review provides a comprehensive analysis of the literature on vortex-induced vibration (VIV) of flexible circular cylinders in cross-flow. It delves into the details of the underlying physics governing the VIV dynamics of cylinders characterized by low mass damping and high aspect ratio, subject to both uniform and shear flows. It compiles decades of experimental investigations, modeling efforts, and numerical simulations and describes the fundamental findings in the field. Key focal points include but are not limited to amplitude–frequency response behavior, the relationship between the distributed loading acting on the cylinder and the trajectories and the near wake structures around the cylinder, the existence of traveling waves, the identification of power-in/power-out regions, and the modal overlapping and mode competition phenomena.