Identification Of Novel Materials Research Articles

Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy

The compilation and preprocessing of electromagnetic shielding material databases using machine learning techniques are pivotal in contemporary materials science and engineering. These materials play a crucial role in diverse applications such as electronics, telecommunications, aerospace, and automotive industries, necessitating effective attenuation of electromagnetic interference (EMI). This paper underscores the significance of comprehensive databases in organizing material properties systematically, facilitating the identification of novel materials with enhanced shielding effectiveness. It explores the role of machine learning algorithms in predictive modeling and data analysis, expediting the screening process of candidate materials and uncovering hidden correlations within complex datasets. However, the efficacy of machine learning techniques relies heavily on the quality of input data and preprocessing steps. Thus, this paper discusses methodologies for collecting material data, challenges in data curation and integration, and common preprocessing techniques such as data cleaning, feature extraction, and normalization. The integration of electromagnetic shielding material databases with machine learning preprocessing holds great promise for FeCo-based alloy laser cladding manufacturing and design, leading to innovative solutions for electromagnetic interference mitigation across various technological domains.

Phenotypic characterization and quality perception of unsulfurated dried apricots in a panel of European accessions and breeding selections

Dried apricots are a nutrient-rich food increasingly attracting consumer's interest, especially when processed without preservative substances. Despite a certain increase of market demand, dried apricot production still relies on a few varieties with poorly differentiated taste and aroma. This study evaluates several quality-related features in fresh apricots and their impact on taste after drying in a set of fourteen accessions and breeding selections and includes three commercial standards to corroborate the results. The characterization of European apricot germplasm could allow the identification of novel materials that possess superior organoleptic characteristics after hot-air drying in order to renew the current varietal landscape.Apricot fruits were collected at full-physiological ripening stage in two harvesting seasons (2021 and 2022) in Italy and were characterized for several physical (fruit weight, flesh firmness and dry matter) and chemical (content of total polyphenols, soluble solids, titratable acidity and ten organic acids) attributes. The whole apricot accessions showed a phenotypic variation extremely valuable in ongoing breeding programmes that try to enhance fruit quality. Among the totality of apricot advanced selections, ‘BO02611038’, ‘BO10619011’, ‘BO11618023’ and ‘BO12607228’ were the most ranked for their high aroma, medium sourness and low bitterness. Citrate and malate were the most abundant organic acids found in fruit flesh and skin but only quinate seemed to enhance the sour taste and impact the sweetness. Interestingly, no differences were perceived by panelists in sweet taste perception leading to consider it an already-achieved breeding target in the European accessions included in our study. In addition, fruit dry matter content seemed to affect both textural properties and taste advancing it as a reliable selection criterion in novel apricot accessions meant for drying.The majority of the dried selections analyzed in this study revealed a superior organoleptic profile compared to commercial standards. A detailed characterization of fresh fruit quality attributes indicated that some parameters might be used to predict the sensory potential of dried apricots, while the aromatic profile seemed the next breeding target to improve the overall taste and further increase the degree of liking in consumers.

Elucidating the Mode of Action of Hybrid Nanoparticles of Cu/Zn Against Copper-Tolerant Xanthomonas euvesicatoria.

The widespread presence of tolerance to copper in Xanthomonas species has resulted in the need to develop alternative approaches to control plant diseases caused by xanthomonads. In recent years, nanotechnological approaches have resulted in the identification of novel materials to control plant pathogens. With many metal-based nanomaterials having shown promise for disease control, an important question relates to the mode of action of these new materials. In this study, we used several approaches, such as scanning electron microscopy, propidium monoazide quantitative polymerase chain reaction, epifluorescence microscopy, and RNA sequencing to elucidate the mode of action of a Cu/Zn hybrid nanoparticle against copper-tolerant strains of Xanthomonas euvesicatoria. We demonstrate that Cu/Zn did not activate copper resistance genes (i.e., copA and copB) in the copper-tolerant bacterium but functioned by disrupting the bacterial cell structure and perturbing important biological processes such as cell respiration and chemical homeostasis.

In-orbit aerodynamic coefficient measurements using SOAR (Satellite for Orbital Aerodynamics Research)

The Satellite for Orbital Aerodynamics Research (SOAR) is a CubeSat mission, due to be launched in 2021, to investigate the interaction between different materials and the atmospheric flow regime in very low Earth orbits (VLEO). Improving knowledge of the gas–surface interactions at these altitudes and identification of novel materials that can minimise drag or improve aerodynamic control are important for the design of future spacecraft that can operate in lower altitude orbits. Such satellites may be smaller and cheaper to develop or can provide improved Earth observation data or communications link-budgets and latency. In order to achieve these objectives, SOAR features two payloads: (i) a set of steerable fins which provide the ability to expose different materials or surface finishes to the oncoming flow with varying angle of incidence whilst also providing variable geometry to investigate aerostability and aerodynamic control; and (ii) an ion and neutral mass spectrometer with time-of-flight capability which enables accurate measurement of the in-situ flow composition, density, velocity. Using precise orbit and attitude determination information and the measured atmospheric flow characteristics the forces and torques experienced by the satellite in orbit can be studied and estimates of the aerodynamic coefficients calculated. This paper presents the scientific concept and design of the SOAR mission. The methodology for recovery of the aerodynamic coefficients from the measured orbit, attitude, and in-situ atmospheric data using a least-squares orbit determination and free-parameter fitting process is described and the experimental uncertainty of the resolved aerodynamic coefficients is estimated. The presented results indicate that the combination of the satellite design and experimental methodology are capable of clearly illustrating the variation of drag and lift coefficient for differing surface incidence angle. The lowest uncertainties for the drag coefficient measurement are found at approximately 300 km, whilst the measurement of lift coefficient improves for reducing orbital altitude to 200 km.

Ultralow Thermal Conductivity and Large Figure of Merit in Low-Cost and Nontoxic Core-Shell Cu@Cu2O Nanocomposites.

Identification of novel materials with enhanced thermoelectric (TE) performance is critical for advancing TE research. In this direction, this is the first report on TE properties of low-cost, nontoxic, and abundant core-shell Cu@Cu2O nanocomposites synthesized using a facile and cheap solution-phase method. They show ultralow thermal conductivity of nearly 10-3 of the copper bulk value, large thermopower of ∼373 μVK-1, and, consequently, a TE figure of merit of 0.16 at 320 K which is larger than those of many of the potential TE materials such as PbTe, SnSe, and SiGe, showing its potential for TE applications. The ultralow thermal conductivity is mainly attributed to the multiscale phonon scattering from intrinsic defects in Cu2O, grain boundaries, lattice-mismatched interface, as well as dissimilar vibrational properties. The large thermopower is associated with a sharp modulation in carrier density of states due to charge transfer between Cu and Cu2O nanoparticles and carrier energy filtering. They are tuned by varying the trioctylphosphine concentration.

Toward Design of Novel Materials for Organic Electronics.

Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials.

High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior.

The identification of biomaterials that modulate cell responses is a crucial task for tissue engineering and cell therapy. The identification of novel materials is complicated by the immense number of synthesizable polymers and the time required for testing each material experimentally. In the current study, polymeric biomaterial-cell interactions were assessed rapidly using a microarray format. The attachment, proliferation, and differentiation of human dental pulp stem cells (hDPSCs) were investigated on 141 homopolymers and 400 diverse copolymers. The copolymer of isooctyl acrylate and 2-(methacryloyloxy)ethyl acetoacetate achieved the highest attachment and proliferation of hDPSC, whereas high cell attachment and differentiation of hDPSC were observed on the copolymer of isooctyl acrylate and trimethylolpropane ethoxylate triacrylate. Computational models were generated, relating polymer properties to cellular responses. These models could accurately predict cell behavior for up to 95% of materials within a test set. The models identified several functional groups as being important for supporting specific cell responses. In particular, oxygen-containing chemical moieties, including fragments from the acrylate/acrylamide backbone of the polymers, promoted cell attachment. Small hydrocarbon fragments originating from polymer pendant groups promoted cell proliferation and differentiation. These computational models constitute a key tool to direct the discovery of novel materials within the enormous chemical space available to researchers.

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

Materials informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. Despite significant experimental effort, the development of highly efficient, stable, and cost-effective photovoltaic materials remains a challenging scientific problem. The quest for precisely defined semiconductor properties revolves around an immensely broad landscape of structural parameters. Here, we have resolved this challenge by applying material informatics to design a novel photocathode material for dye-sensitized solar cells (DSSCs). By conducting a virtual screening of 50,000 known inorganic compounds, we have identified lead titanate (PbTiO3), a perovskite, as the most promising photocathode material. Notably, lead titanate is significantly different from the traditional base elements or crystal structures used for photocathodes. The fabricated PbTiO3 DSSC devices exhibited the best performance in aqueous solution, showing remarkably high fill factors compared to typical photocathode systems. The results highlight the pivotal role materials informatics can play in streamlining the experimental development of materials with the desired properties.

High-Throughput Screening of Metal–Organic Frameworks for CO2 Separation

A parallel high-throughput sorption methodology is described for screening CO(2) and N(2) adsorption and diffusion selectivity in metal organic frameworks, before and after exposure to water vapor and acid gases. We illustrate this approach by simultaneously investigating 8 candidate Metal-Organic Framework (MOF) materials, of which the best material was found to have a CO(2)/N(2) membrane selectivity of 152 and a CO(2) permeability of 60 barrer for Co-NIC. This approach provides a significant increase in efficiency of obtaining the separation properties of MOFs. While we describe here the identification of novel materials for CO(2) capture, the methodology enables exploration of the performance and stability of novel porous materials for a wide range of applications.