jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: João Tedim | Tiago L. P. Galvão | K.A. Yasakau | A.C. Bastos | José R. B. Gomes | M.G.S. Ferreira
Source: Frontiers in Chemistry (review, 2022-11-18)
Field: Materials Science — Materials Chemistry

Layered double hydroxides for corrosion-related applications—Main developments from 20 years of research at CICECO This work describes the main advances carried out in the field of corrosion protection using layered double hydroxides (LDH), both as additive/pigment-based systems in organic coatings and as conversion films/pre-treatments. In the context of the research topic "Celebrating 20 years of CICECO", the main works reported herein are based on SECOP's group (CICECO) main advances over the years. More specifically, this review describes structure and properties of LDH, delving into the corrosion field with description of pioneering works, use of LDH as additives to organic coatings, conversion layers, application in reinforced concrete and corrosion detection, and environmental impact of these materials. Moreover, the use of computational tools for the design of LDH materials and understanding of ion-exchange reactions is also presented. The review ends with a critical analysis of the field and future perspectives on the use of LDH for corrosion protection. From the work carried out LDH seem very tenable, versatile, and advantageous for corrosion protection applications, although several obstacles will have to be overcome before their use become commonplace. Corrosion ; Layered double hydroxides ; Context (archaeology) ; Materials science ; Nanotechnology ; Metallurgy ; Engineering ; Construction engineering ; Biochemical engineering ; Chemical engineering ; Geology Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Corrosion Behavior and Inhibition
Authors: Marta Bordonhos | Tiago L. P. Galvão | José R. B. Gomes | José D. Gouveia | Miguel Jorge | Mirtha A. O. Lourenço | José Marcelo Rodrigues Pereira | Germán Pérez‐Sánchez | Moisés L. Pinto | Carlos M. Silva | João Tedim | Bruno Zêzere
Source: Advanced Theory and Simulations (article, 2022-11-27)
Field: Chemistry — Inorganic Chemistry

Multiscale Computational Approaches toward the Understanding of Materials Abstract Herewith, an overview of the group's collaborative research efforts on the development and deployment of computational approaches to understand materials and tools at different length and time scales is presented. The techniques employed range from quantum mechanical approaches based on the density functional theory to classical atomistic and coarse‐grained force field methods, targeting molecular systems composed of a few to several million atoms at different levels of detail. These new tools and molecular models are presented to the computational materials science community so they can be used in more realistic molecular modelling studies of the properties of materials and their dependence on subtle modifications of their structures. The review concludes by presenting a selection of recent computational case‐studies oriented toward the understanding of the synthesis of materials, the interpretation of unexpected experimental results, the prediction of material properties, and the materials selection based on their characteristics for applications in areas such as gas adsorption/separation, corrosion protection, and catalysis. Computer science ; Nanotechnology ; Selection (genetic algorithm) ; Biochemical engineering ; Software deployment ; Computational model ; Management science ; Systems engineering ; Materials science ; Artificial intelligence ; Engineering ; Software engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Shuo Liu | Chaochao Dun | Jilun Wei | Lu An | Shenqiang Ren | Jeffrey J. Urban | Mark T. Swihart
Source: Chemical Engineering Journal (article, 2022-11-01)
Field: Chemistry — Spectroscopy

Creation of hollow silica-fiberglass soft ceramics for thermal insulation Ceramic ; Materials science ; Composite material ; Thermal insulation ; Thermal ; Physics Aerogels and thermal insulation ; Glass properties and applications ; Mesoporous Materials and Catalysis
Authors: Atike İnce Yardımcı | Nesli Yağmurcukardeş | M. Yagmurcukardes | İnci Çapan | Matem Erdoğan | Rıfat Çapan | Özgür Tarhan | Yaser Açıkbaş
Source: Applied Physics A (article, 2022-02-03)
Field: Materials Science — Polymers and Plastics

Electrospun polyacrylonitrile (PAN) nanofiber: preparation, experimental characterization, organic vapor sensing ability and theoretical simulations of binding energies Polyacrylonitrile ; Nanofiber ; Electrospinning ; Quartz crystal microbalance ; Dichloromethane ; Chloroform ; Raman spectroscopy ; Chemical engineering ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Organic chemistry ; Nanotechnology ; Polymer ; Solvent ; Adsorption Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors ; Electrospun Nanofibers in Biomedical Applications
Authors: Mengke Li | Guohua Cao | Yufeng Luo | Caiyu Sheng | Huijun Liu
Source: npj Computational Materials (article, 2022-04-21)
Field: Materials Science — Materials Chemistry

Predicting the lattice thermal conductivity of alloyed compounds from the perspective of configurational entropy Abstract Accurate evaluation of lattice thermal conductivity is usually a tough task from the theoretical side, especially for alloyed systems with fractional stoichiometry. Using the tetradymite family as a prototypical class of examples, we propose a reliable approach for rapid prediction on the lattice thermal conductivity at arbitrary composition by utilizing the concept of configurational entropy. Instead of performing time-consuming first-principles calculations, the lattice thermal conductivities of any alloyed tetradymites can be readily obtained from a few samples with integer stoichiometry. The strong predictive power is demonstrated by good agreement between our results and those reported experimentally. In principle, such an effective method can be applicable to any other material families, which is very beneficial for high-throughput design of systems with desired thermal conductivity. Thermal conductivity ; Lattice (music) ; Stoichiometry ; Thermodynamics ; Materials science ; Entropy (arrow of time) ; Thermal ; Statistical physics ; Condensed matter physics ; Chemistry ; Physical chemistry ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Jin‐Cheng Zheng
Source: Research (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Asymmetrical Transport Distribution Function: Skewness as a Key to Enhance Thermoelectric Performance How to achieve high thermoelectric figure of merit is still a scientific challenge. By solving the Boltzmann transport equation, thermoelectric properties can be written as integrals of a single function, the transport distribution function (TDF). In this work, the shape effects of transport distribution function in various typical functional forms on thermoelectric properties of materials are systematically investigated. It is found that the asymmetry of TDF, characterized by skewness, can be used to describe universally the trend of thermoelectric properties. By defining symmetric and asymmetric TDF functions, a novel skewness is then constructed for thermoelectric applications. It is demonstrated, by comparison with ab initio calculations and experiments, that the proposed thermoelectric skewness not only perfectly captures the main feature of conventional skewness but also is able to predict the thermoelectric power accurately. This comparison confirms the unique feature of our proposed thermoelectric skewness, as well as its special role of connection between the statistics of TDF and thermoelectric properties of materials. It is also found that the thermoelectric performance can be enhanced by increasing the asymmetry of TDF. Finally, it is also interesting to find that the thermoelectric transport properties based on typical quantum statistics (Fermi-Dirac distributions) can be well described by typical shape parameter (skewness) for classical statistics. Skewness ; Thermoelectric effect ; Thermoelectric materials ; Seebeck coefficient ; Asymmetry ; Statistical physics ; Boltzmann equation ; Figure of merit ; Distribution function ; Condensed matter physics ; Materials science ; Computer science ; Physics ; Statistics ; Mathematics ; Quantum mechanics ; Optoelectronics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Heusler alloys: electronic and magnetic properties
Authors: M. M. Shahzamanian | Syed Sohail Akhtar | Abul Fazal M. Arif | Wan Jeffrey Basirun | Khaled S. Al-Athel | Matti Schneider | Neha Shakelly | Abbas Saeed Hakeem | Abba Abdulhamid Abubakar | Pengfei Wu
Source: Scientific Reports (article, 2022-06-30)
Field: Materials Science — Ceramics and Composites

Thermo-mechanical properties prediction of Ni-reinforced Al2O3 composites using micro-mechanics based representative volume elements For effective cutting tool inserts that absorb thermal shock at varying temperature gradients, improved thermal conductivity and toughness are required. In addition, parameters such as the coefficient of thermal expansion must be kept within a reasonable range. This work presents a novel material design framework based on a multi-scale modeling approach that proposes nickel (Ni)-reinforced alumina (Al<sub>2</sub>O<sub>3</sub>) composites to tailor the mechanical and thermal properties required for ceramic cutting tools by considering numerous composite parameters. The representative volume elements (RVEs) are generated using the DREAM.3D software program and the output is imported into a commercial finite element software ABAQUS. The RVEs which contain multiple Ni particles with varying porosity and volume fractions are used to predict the effective thermal and mechanical properties using the computational homogenization methods under appropriate boundary conditions (BCs). The RVE framework is validated by the sintering of Al<sub>2</sub>O<sub>3</sub>-Ni composites in various compositions. The predicted numerical results agree well with the measured thermal and structural properties. The properties predicted by the numerical model are comparable with those obtained using the rules of mixtures and SwiftComp, as well as the Fast Fourier Transform (FFT) based computational homogenization method. The results show that the ABAQUS, SwiftComp and FFT results are fairly close to each other. The effects of porosity and Ni volume fraction on the mechanical and thermal properties are also investigated. It is observed that the mechanical properties and thermal conductivities decrease with the porosity, while the thermal expansion remains unaffected. The proposed integrated modeling and empirical approach could facilitate the development of unique Al<sub>2</sub>O<sub>3</sub>-metal composites with the desired thermal and mechanical properties for ceramic cutting inserts. Composite material ; Volume (thermodynamics) ; Materials science ; Thermodynamics ; Physics Advanced ceramic materials synthesis ; Composite Material Mechanics ; Aluminum Alloys Composites Properties
Authors: Xiang Zhang | Teng Zhou | Kai Sundmacher
Source: AIChE Journal (article, 2022-05-27)
Field: Chemistry — Inorganic Chemistry

Integrated metal‐organic framework (<scp>MOF</scp>) and pressure/vacuum swing adsorption process design: <scp>MOF</scp> matching Abstract A two‐step integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design has been recently established for gas separation. In the first step, selected MOF descriptors and process operating conditions are simultaneously optimized to maximize the process performance. Based on the obtained results, the second step (i.e., MOF matching) is addressed and exemplified by propene/propane separation in this work. Computational MOF synthesis and screening are explicitly carried out to find new advanced material candidates for enhancing the separation process efficiency. First, model‐based property‐performance relationships are developed for fast MOF screening. Then, MOF building blocks are extracted from 471 MOFs contained in the Computation‐Ready Experimental (CoRE) MOF database. With these building blocks, 45,472 hypothetical MOFs are created. After model‐based and molecular simulation‐based screening, six candidates are left and sent to P/VSA process optimization. Finally, three candidates are found to meet the predefined separation specifications and one candidate shows a better process performance than the best out of the 471 CoRE MOFs. Metal-organic framework ; Propane ; Process (computing) ; Pressure swing adsorption ; Adsorption ; Process engineering ; Vacuum swing adsorption ; Gas separation ; Separation process ; Matching (statistics) ; Computer science ; Propene ; Process design ; Swing ; Materials science ; Process integration ; Chemistry ; Engineering ; Mechanical engineering ; Chromatography ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Dong Hyun Lee | Taehyun Park | Hocheon Yoo
Source: Polymers (review, 2022-07-15)
Field: Materials Science — Polymers and Plastics

Biodegradable Polymer Composites for Electrophysiological Signal Sensing Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited. Electrophysiology ; Biocompatible material ; SIGNAL (programming language) ; Materials science ; Electrode ; Biomedical engineering ; Nanotechnology ; Computer science ; Neuroscience ; Medicine ; Chemistry ; Biology Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Yuxiao Zhu | Daniel W. Newbrook | Peng Dai | Jian Liu | C.H. de Groot | Ruomeng Huang
Source: Energy and AI (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training Renewable energy technologies are central to emissions reduction and essential to achieve net-zero emission. Segmented thermoelectric generators (STEG) facilitate more efficient thermal energy recovery over a large temperature gradient. However, the additional design complexity has introduced challenges in the modelling and optimization of its performance. In this work, an artificial neural network (ANN) has been applied to build accurate and fast forward modelling of the STEG. More importantly, we adopt an iterative method in the ANN training process to improve accuracy without increasing the dataset size. This approach strengthens the proportion of the high-power performance in the STEG training dataset. Without increasing the size of the training dataset, the relative prediction error over high-power STEG designs decreases from 0.06 to 0.02, representing a threefold improvement. Coupling with a genetic algorithm, the trained artificial neural networks can perform design optimization within 10 s for each operating condition. It is over 5,000 times faster than the optimization performed by the conventional finite element method. Such an accurate and fast modeller also allows mapping of the STEG power against different parameters. The modelling approach demonstrated in this work indicates its future application in designing and optimizing complex energy harvesting technologies. Artificial neural network ; Computer science ; Generator (circuit theory) ; Process (computing) ; Genetic algorithm ; Renewable energy ; Thermoelectric generator ; Power (physics) ; Artificial intelligence ; Machine learning ; Thermoelectric effect ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Giovanni Di Liberto | Ángel Morales‐García | Stefan T. Bromley
Source: Nature Communications (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

An unconstrained approach to systematic structural and energetic screening of materials interfaces From grain boundaries and heterojunctions to manipulating 2D materials, solid-solid interfaces play a key role in many technological applications. Understanding and predicting properties of these complex systems present an ongoing and increasingly important challenge. Over the last few decades computer simulation of interfaces has become vastly more powerful and sophisticated. However, theoretical interface screening remains based on largely heuristic methods and is strongly biased to systems that are amenable to modelling within constrained periodic cell approaches. Here we present an unconstrained and generally applicable non-periodic screening approach for systematic exploration of material's interfaces based on extracting and aligning disks from periodic reference slabs. Our disk interface method directly and accurately describes how interface structure and energetic stability depends on arbitrary relative displacements and twist angles of two interacting surfaces. The resultant detailed and comprehensive energetic stability maps provide a global perspective for understanding and designing interfaces. We confirm the power and utility of our method with respect to the catalytically important TiO<sub>2</sub> anatase (101)/(001) and TiO<sub>2</sub> anatase (101)/rutile (110) interfaces. Computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; MXene and MAX Phase Materials
Authors: Xinchen Shen | Haoyuan Shi | Hongda Wei | Boxuan Wu | Qingyuan Xia | Jingjie Yeo | Wenwen Huang
Source: Frontiers in Chemistry (review, 2022-05-05)
Field: Materials Science — Biomaterials

Engineering Natural and Recombinant Silks for Sustainable Biodevices Silk fibroin (SF) is a structural protein derived from natural silkworm silks. Materials fabricated based on SF usually inherit extraordinary physical and biological properties, including high mechanical strength, toughness, optical transparency, tailorable biodegradability, and biocompatibility. Therefore, SF has attracted interest in the development of sustainable biodevices, especially for emergent bio-electronic technologies. To expand the function of current silk devices, the SF characteristic sequence has been used to synthesize recombinant silk proteins that benefit from SF and other functional peptides, such as stimuli-responsive elastin peptides. In addition to genetic engineering methods, innovated chemistry modification approaches and improved material processing techniques have also been developed for fabricating advanced silk materials with tailored chemical features and nanostructures. Herein, this review summarizes various methods to synthesize functional silk-based materials from different perspectives. This review also highlights the recent advances in the applications of natural and recombinant silks in tissue regeneration, soft robotics, and biosensors, using <i>B. mori</i> SF and silk-elastin-like proteins (SELPs) as examples. Fibroin ; SILK ; Nanotechnology ; Biocompatibility ; Elastin ; Materials science ; Mechanical strength ; Chemistry ; Polymer science ; Biochemical engineering ; Biology ; Organic chemistry ; Engineering Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Advanced Materials and Mechanics
Authors: Maximilian Rixner | Phaedon‐Stelios Koutsourelakis
Source: npj Computational Materials (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Self-supervised optimization of random material microstructures in the small-data regime Abstract While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives. The first contribution of this paper is a flexible, fully probabilistic formulation of materials’ optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal, high-dimensional, process parameters. We employ a probabilistic, data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives. We couple this with a problem-tailored active learning strategy, i.e., a self-supervised selection of training data, which significantly improves accuracy while reducing the number of expensive model simulations. We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase, random media but envision that its applicability encompasses a wide variety of microstructure-sensitive design problems. Probabilistic logic ; Computer science ; Property (philosophy) ; Process (computing) ; Computation ; Identification (biology) ; Variety (cybernetics) ; Mathematical optimization ; Bayesian optimization ; Machine learning ; Industrial engineering ; Artificial intelligence ; Algorithm ; Engineering ; Mathematics Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Advanced Multi-Objective Optimization Algorithms
Authors: H.O. Tekın | Ghada ALMisned | Y. S. Rammah | G. Süsoy | Fatema T. Ali | Duygu Şen Baykal | Wiam Elshami | Hesham M.H. Zakaly | Shams A.M. Issa
Source: Applied Physics A (article, 2022-05-07)
Field: Materials Science — Materials Chemistry

The significant role of WO3 on high-dense BaO–P2O3 glasses: transmission factors and a comparative investigation using commercial and other types of shields Half-value layer ; Materials science ; Radiation shielding ; Tungsten ; Composite material ; Analytical Chemistry (journal) ; Electromagnetic shielding ; Chemistry ; Metallurgy Radiation Shielding Materials Analysis ; Nuclear materials and radiation effects ; Glass properties and applications