jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammadhossein Nahavandian | Eda Aydogan | Jesper Byggmästar | Matheus A. Tunes | Enrique Martínez | Osman El‐Atwani
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Design Kinetic Parameters for Improved Resilience of Materials under Irradiation High entropy alloys (HEAs) have captured much attention in recent years due to their conceivably improved radiation resistance compared to pure metals and traditional alloys. However, among HEAs, there are millions of design possibilities considering all potential compositions. In this study, we develop criteria to design HEAs with improved radiation resilience taking into consideration defect properties to promote interstitial-vacancy recombination. First, we conduct rate theory calculations on defects followed by molecular dynamics (MD) simulations on pure W and W-based multicomponent concentrated alloys. It is found that when the diffusion coefficients for single vacancies and interstitials become similar and the effective migration energies of defects is minimum (maximum diffusivities), defect recombination becomes optimal, and the concentration of defects is significantly reduced. This is supported by MD simulations indicating improved radiation resistance of V- and Cr-based alloys, which satisfy the above-stated criteria. Furthermore, experimental observations also reinforce the proposed approach. This study sheds light on the design criteria for improved radiation resistance and helps material selection without the need of extensive experimental work. Resilience (materials science) ; Kinetic energy ; Irradiation ; Materials science ; Environmental science ; Computer science ; Composite material ; Physics ; Nuclear physics ; Classical mechanics Nuclear Materials and Properties ; Nuclear materials and radiation effects ; Fusion materials and technologies
Authors: Ömer Genç
Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Nanoakışkanların elektriksel iletkenlik ve stabilitelerinin YSA ile modellenmesi: kütlesel oran ve sıcaklığa bağlı korelasyon türetilmesi Hazırlanma sürelerinin uzun olması ve termofiziksel özelliklerinin belirlenmesinin zahmetli olması nanoakışkan çalışmalarını yapay zeka destekli modelleme çalışmalarına yönlendirmiştir. Bununla birlikte yapılan modelleme çalışmaları ağırlıklı olarak ısıl iletkenlik ve viskozite üzerine yoğunlaşmıştır. Bu çalışmada, nanoakışkan çalışmalarında genellikle ihmal edilen stabilite ve elektriksel iletkenlik göz önüne alınarak, %0.1-%3 kütlesel oran ve 20°C-70°C sıcaklık aralığındaki MgO/EG nanoakışkanların pH, elektriksel iletkenlik ve zeta potansiyelini tahmin etmek için bir yapay sinir ağları modeli geliştirilmiştir. Geliştirilen modelin MSE ve R değeri sırasıyla 0.011118 ve 0.99987 iken, pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri ise sırasıyla %0.11, %0.78 ve %0.74 olarak belirlenmiştir. Ayrıca model verileri kullanılarak literatürde ilk defa bu üç özellik için de geçerli, katsayıları birbirinden farklı ortak bir korelasyon ortaya koyulmuştur. Yeni korelasyonun pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri sırasıyla %0.35, %2.08 ve %1.54’tür. Physics ; Mathematics ; Molecular biology ; Biology Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Electrohydrodynamics and Fluid Dynamics
Authors: Sahar Behroozinia | Qing Gu
Source: arXiv (Cornell University) (preprint, 2024-09-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Leveraging Multiplexed Metasurfaces for Multi-Task Learning with All-Optical Diffractive Processors Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80% for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems. Multiplexing ; Task (project management) ; Computer science ; Computer architecture ; Telecommunications ; Engineering ; Systems engineering Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: C Chokkalingam | Bharath Kumar B. R | G. Sai Abishek | B. Balaji
Source: International Research Journal of Multidisciplinary Technovation (review, 2024-11-30)
Field: Materials Science — Polymers and Plastics

A Review on Natural Fibre Reinforcement on Composite Material The production of eco-friendly and biodegradable fibre-reinforced composite materials has become the subject of extensive research with a view to managing the growing environmental concerns worldwide. In light of this, this review aims to raise awareness regarding the utilization of natural fibres in composite materials, which have been shown to reduce greenhouse gas emissions and carbon footprint significantly. However, despite the numerous benefits of natural fibres, several challenges must be addressed, such as poor compatibility, undesirable interfacial adhesion between natural fibres and matrix, low impact strength, and poor fire resistance. This article briefly assesses the need to meet the demand for eco-friendly materials through prominent modification and processing methods and investigates their properties and applications. Additionally, this article also analyzes the impact of hybridization through a comprehensive comparative analysis. The purpose of this article is to provide a better understanding of the role of natural fibres in reinforcing composites and to emphasize the importance of finding innovative solutions to overcome the challenges associated with their use. In conclusion, this review highlights the potential benefits of using natural fibres in composite materials and emphasizes the need for further research to address the challenges associated with their utilization. This would ultimately lead to the development of sustainable materials that contribute significantly towards reducing the environmental impact of manufacturing processes. Reinforcement ; Composite number ; Composite material ; Materials science ; Natural (archaeology) ; Geology Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Innovations in Concrete and Construction Materials
Authors: Matthias Overberg | Emilia Zalewska | Anwar Abdkader | Chokri Cherif
Source: Fibres and Textiles (article, 2024-09-01)
Field: Materials Science — Polymers and Plastics

IMAGE-BASED CROSS-SECTIONAL ANALYSIS AND MICROMECHANICAL MODELING OF YARN AND COMPOSITE MATERIALS This study aims to establish a comprehensive methodology for determining the microstructural properties of hybrid yarns used in composite materials. By developing accurate models of hybrid yarns and composites based on detailed microstructural information such as fibre orientation, fibre diameter and distribution, this approach lays the foundation for future advances. These models, enriched with accurate microstructural data, will facilitate the creation of new modelling techniques that can be used in future research to explore the correlation between microstructural properties and mechanical performance of composite materials. Yarn ; Composite number ; Composite material ; Materials science ; Computer science Textile materials and evaluations
Authors: Suliman Sharif
Source: Biophysical Journal (article, 2024-02-01)
Field: Chemistry — Physical and Theoretical Chemistry

A computational and organic approach to balance the van der Waals force Force fields (FFs) are the foundation of molecular dynamics (MD) simulations. FFs are an empirical energy function, consisting of bonded and non-bonded terms, in combination with a collection of parameters that dictates the forces acting on the atoms in the system. In practice a force field is built by assigning specific atom types to the functional groups in a molecular system with the parameters associated with the specific atom types. The Lennard-Jones (LJ) parameters in the CHARMM drude and additive force fields are imperative for accurate modeling of the van der Waals attraction term between atoms. LJ Parameters have been known to accurately improve Parameters between different atom types are referred to as off-diagonal LJ parameters. The off-diagonal parameters are in general not explicitly optimized but calculated through combining rules which is an approximation. Thus, our goal of the research is to explicitly optimize the off-diagonal LJ parameters. A chemical list was derived of small model compounds that include those functional groups that encompasses all atom types of the biological and “drug-like” molecules in the force fields. Quantum mechanical (QM) interaction potential energy scans (PES) between all pairs of the model compounds will then be used as target data to optimize the off-diagonal LJ parameters. We wrote a software tool coined the “LJ Drill” to aid in automation of PES to rapidly gather large amounts of data of all combinatorial pairs. MM and QM surface scans are passed into global optimization algorithms to achieve an overall fit of LJ parameters that best agree across all chemical species in the force field. We performed simulations of mixed solvents at different mole fraction compositions and calculated condensed phase properties of binary mixtures using experimental data as the target goal. van der Waals force ; Force field (fiction) ; Diagonal ; Molecular dynamics ; Atom (system on chip) ; Statistical physics ; Density functional theory ; Function (biology) ; Computational chemistry ; Chemistry ; Computer science ; Molecule ; Physics ; Quantum mechanics ; Mathematics thermodynamics and calorimetric analyses ; Spectroscopy and Quantum Chemical Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Amir Hajibabaei | William J. Baldwin | Gábor Csányi | Stephen J. Cox
Source: arXiv (Cornell University) (preprint, 2024-09-23)
Field: Chemistry — Inorganic Chemistry

DFT dataset for "Symmetry Breaking in the Superionic Phase of Silver-Iodide" In the superionic phase of silver iodide, we observe a distorted tetragonal structure characterized by symmetry breaking in the cation distribution. This phase competes with the well known bcc phase, with a symmetric cation distribution at an energetic cost of only a few meV/atom. The small energy difference suggests that these competing structures may both be thermally accessible near the superionic transition temperature. We also find that the distribution of silver ions depends on the low-temperature parent polymorph, with memory persisting in the superionic phase on the nanosecond time scales accessible in our simulations. Furthermore, simulations on the order 100ns reveal that even at temperatures where the bcc phase is stable, significant fluctuations toward the tetragonal lattice structure remain. Our results are consistent with many "anomalous" experimental observations and offer a molecular mechanism for the "memory effect" in silver iodide. Silver iodide ; Symmetry (geometry) ; Iodide ; Phase (matter) ; Symmetry breaking ; Materials science ; Condensed matter physics ; Theoretical physics ; Crystallography ; Chemical physics ; Physics ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Mathematics ; Quantum mechanics ; Geometry Inorganic Fluorides and Related Compounds ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Thomas Harbich | Independent Researcher, Am Brüdenrain, Weissach im Tal, Germany | Richard Gordon | Gulf Specimen Marine Lab and Aquarium, Panacea, FL 32346 USA | S A Cohn | Matt P. Ashworth | Vadim V. Annenkov | Johannes W. Goessling
Source: Limnology and Freshwater Biology (article, 2024-01-01)
Field: Materials Science — Biomaterials

“Diatoms: Life in Glass Houses” revisited: Updates and Comments The film “Diatoms: Life in Glass Houses”, produced in 2003, covers various aspects of this ecologically important class of algae, such as their occurrence, cell biology including cell division and reproduction, morphology, morphogenesis, motility, and the formation of colonies. The aim of this work is to review and comment on some of the aspects presented in the video in the light of current knowledge. Special attention will be given to the constraints imposed by the solid silica wall and how diatoms cope with them. No attempt is made to be comprehensive. Algae ; Diatom ; Morphogenesis ; Division (mathematics) ; Reproduction ; Class (philosophy) ; Ecology ; Biology ; Computer science ; Artificial intelligence ; Mathematics Diatoms and Algae Research ; Algal biology and biofuel production ; Aquatic Ecosystems and Phytoplankton Dynamics
Authors: Timothy P. Jenkins | Kevin Eloff | Konstantinos Kalogeropoulos | Oliver Morell | Amandla Mabona | Jakob Berg Jespersen | Wesley Williams | Sam van Beljouw | Marcin J. Skwark | Andreas H. Laustsen | Anne Ljungars | Erwin M. Schoof | Jeroen Van Goey | Ulrich auf dem Keller | Karim Beguir | Nicolás López Carranza | Stan J. J. Brouns
Source: Research Square (preprint, 2024-05-02)
Field: Chemistry — Spectroscopy

De novo peptide sequencing with InstaNovo: Diffusion-powered, peptide identification for large scale proteomics experiments Proteomics ; Peptide ; Identification (biology) ; Computational biology ; Scale (ratio) ; Biology ; Biochemistry ; Geography ; Gene ; Ecology ; Cartography Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Heliza Rahmania Hatta | Sri Nurdiati | Irman Hermadi | Maman Turjaman
Source: JOIV International Journal on Informatics Visualization (article, 2024-12-31)
Field: Chemistry — Organic Chemistry

Grade Classification of Agarwood Sapwood Using Deep Learning The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to buy cheap. So, deep learning can be used to classify the agarwood sapwood class. One of the uses of deep learning is in image processing. Image processing is used to help humans recognize or classify objects quickly and precisely and can process many data simultaneously. One of the deep learning algorithms used in image processing is the Convolutional Neural Network (CNN). In this study, it is proposed that the deep learning model used is CNN with batch normalization. The dataset used is 72 agarwood sapwood images with a white background, each consisting of 24 Super A, 24 Super B data, and 24 Super Double data. The dataset is divided into 80% training and 20% testing data. The evaluation results of the proposed method at 100 epochs show an accuracy of 87.5%. The research implications will help agarwood tree farmers differentiate and classify agarwood sapwood so that farmers get the right price from buyers. Agarwood ; Artificial intelligence ; Computer science ; Medicine Wood and Agarwood Research ; Forest Ecology and Biodiversity Studies ; Forest ecology and management
Source: Jordan Journal of Mechanical and Industrial Engineering (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Effect of Nb Content on Behavior of (Ti-Nb) Shape Memory Alloys for Biomedical Applications Titanium-niobium (Ti-Nb) binary alloys have lately been given attention and attraction due to their non-toxic properties and low Young's modulus.All alloys manufactured by powder metallurgy and studied through the influence of Nb content (13, 14, 15, 23, 24 and 25) at% on the shape memory effect, the surfaces properties (Vickers microhardness, Contact angle (CA), Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD), DSC and Corrosion resistance (in Hank solution) of Ti-xNb alloys).The results of XRD illustrated that the (Ti-xNb) alloys included α″ and β Ti phases, such that as the amount of Nb in the alloy increased the β phase increased proportionately.Regarding the shape memory effect (SME%) of alloys, the treatment was determined by using microhardness after and before the heat treatment, according to the result of DSC test, by two different temperature (50 and 150) ºC in which the result was far less than 5%.All Ti-xNb alloys displayed noticeably higher hardness, compressive strength and decreased contact angle, low Corrosion was founded on Ti-xNb alloys in hank solutions.In overall, binary Ti-xNb at% alloys are a suitable candidate for dental and orthopedic implants owing to their enhanced mechanical characteristic and Biological performance that is comparable. Materials science ; Shape-memory alloy ; Content (measure theory) ; Psychology ; Metallurgy ; Nanotechnology ; Mathematics Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials
Authors: Hassan Dawood Salman | Anwar Qasim Saeed | Emad Kamil Hussein | Hussein Kadhim Sharaf | Thiago F. Santos | Caroliny M. Santos
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2024-12-25)
Field: Materials Science — General Materials Science

Comparative Study on the Creep Behavior of a Hybrid Composite Material Doped with Nano Material Today, we are facing a technological revolution in the improvement of engineering materials where it is required to produce a specific material with relatively high strength associated with lightweight to be used in many industrial applications, so the core idea behind this investigative study is enhancing creep strain property for a specific composite material composed of carbon fiber plus epoxy resin mixed with hardener. The improvement procedure was done by adding additive material named multi-wall carbon nanotubes (MWCNTs), where the mixing style was a percentage weight base. The total suggested weight was 1 Kg for both cases, where the reference composite was composed of one kilogram of carbon fiber plus epoxy resin with hardener. Meanwhile, the second composite consists of the above-mentioned components (0.9 Kg) with multi-wall carbon nanotubes (0.1Kg). Final experimental results showed the average percentage creep strain for the reference is 0.19566, and the associated value for the hybrid one is 0.173 with a reduction in creep strength resistance of about 11.58% under 120 hours of continuous loading of weight 5 kilograms under 25°C in both two cases. Scanning electron microscopy showed that the internal undesired gaps with the reference composite have been totally recovered due to adding the nanomaterial with the suggested percentage ratio, making the produced composite more challenging and sustaining dimension changes during ordinary loading. Creep ; Materials science ; Doping ; Composite number ; Nano- ; Composite material ; Structural engineering ; Engineering ; Optoelectronics Material Properties and Applications ; Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes
Authors: Marin Senila
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Referee report. For: Assessment of heavy metals in food and drug packaging materials [version 2; peer review: 1 approved, 2 not approved] Heavy metals ; Food packaging ; Packaging and labeling ; Drug ; Plastic packaging ; Food products Heavy Metals in Plants ; Heavy Metal Exposure and Toxicity ; Plant-Derived Bioactive Compounds
Authors: Jefferson Ricardo Pereira | Emanuel J. A. dos Santos | J. Ribeiro | Douglas Soares Galvão
Source: arXiv (Cornell University) (preprint, 2024-09-18)
Field: Materials Science — Materials Chemistry

How does Goldene Stack? The recent synthesis of Goldene, a 2D atomic monolayer of gold, has opened new avenues in exploring novel materials. However, the question of when multilayer Goldene transitions into bulk gold remains unresolved. This study used density functional theory calculations to address this fundamental question. Our findings reveal that multilayer Goldene retains an AA-like stacking configuration of up to six layers, with no observation of Bernal-like stacking as seen in graphene. Goldene spontaneously transitions to a bulk-like gold structure at seven layers, adopting a rhombohedral (ABC-like) stacking characteristic of bulk face-centered cubic (FCC) gold. The atomic arrangement converges entirely to the bulk gold lattice for more than ten layers. Quantum confinement significantly impacts the electronic properties, with monolayer and bulk Goldene exhibiting a single Dirac cone at the X-point of the Brillouin zone. In contrast, multilayer Goldene shows two Dirac cones at the X- and Y-points. Additionally, monolayer Goldene exhibits anisotropic optical absorption, which is absent in bulk gold. This study provides a deeper understanding of multilayer Goldene's structural and electronic properties and stacked 2D materials in general. Stack (abstract data type) ; Computer science ; Programming language Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena