jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dragoljub L. Miladinović | Marija V. Dimitrijević | Jelena Mrmošanin | Mаrijа Mаrković | Aleksandra Pavlović
Source: Research Square (preprint, 2022-05-09)
Field: Chemistry — Analytical Chemistry

Evaluating seasonal variation in trace element content of savory Abstract Background and aims Satureja kitaibelii Wierzb. ex Heuff. (savory) is one of the most popular herbs in Serbia, used as a culinary plant, as well as tea in traditional medicine. The objective of this study was an analysis of seasonal variation in trace element contents in the soil and S. kitaibelii, at the Kravlje village, southeastern Serbia, with emphasis on potential aspects of health promotion.MethodsWe studied the total content of B, Si, Cr, Mn, Ni, Cu and Zn in the soil and savory using inductively coupled plasma-optical emission spectrometry. The obtained results were analyzed by chemometric methods: hierarchical cluster analysis (HCA) and principal component analysis (PCA).ResultsChemical, statistical and chemometric analysis confirmed a variation in trace element content of studied soil and plant samples. The lowest contents of the studied elements in the soil, except for silicon, were recorded in the vegetative stage. In the plant, the boron content is the highest: 10.5-14.9 mg/kg and the chromium content is the lowest: 0.17-1.2 mg/kg. The highest values of soil-to-plant transfer factor were recorded for five elements (except Si and Cr) in the vegetative stage.ConclusionThe present study revealed that savory from Serbia can be considered an accumulator of boron and a potential source of valuable trace elements. A significant percentage of daily intake of B, Cr and Ni, can be provided with three cups of tea per day of plants collected in the vegetative and flowering stages. Trace element ; Inductively coupled plasma atomic emission spectroscopy ; Chemistry ; Boron ; Horticulture ; Environmental chemistry ; Inductively coupled plasma ; Environmental science ; Biology Heavy Metals in Plants ; Plant Micronutrient Interactions and Effects ; Heavy metals in environment
Authors: Johann Ostmeyer
Source: Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022) (article, 2022-11-07)
Field: Materials Science — Materials Chemistry

Stochastic and Tensor Network simulations of the Hubbard Model The Hubbard model is an important tool to understand the electrical properties of various materials. More specifically, on the honeycomb lattice it is used to describe graphene predicting a quantum phase transition from a semimetal to a Mott insulating state. In this work two different numerical techniques are presented that have been employed for simulations of the Hubbard model: The Hybrid Monte Carlo algorithm on the one hand allowed us to simulate unprecedentedly large lattices, whereas Tensor Networks can be used to completely avoid the sign problem. Respective strengths and weaknesses of the methods are discussed. Hubbard model ; Statistical physics ; Lattice (music) ; Bose–Hubbard model ; Tensor (intrinsic definition) ; Quantum Monte Carlo ; Monte Carlo method ; Quantum ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Superconductivity Graphene research and applications ; Theoretical and Computational Physics ; Quantum and electron transport phenomena
Authors: Nihal Yumak
Source: Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Python Tabanlı Destek Vektörü Regresyon Modeli Kullanılarak Farklı Isıtma Hızlarında Yaşlandırılmış β Titanyum Alaşımının Sürtünme Katsayılarının Değerlendirilmesi Bu çalışmada, Ti-15V-3Al-3Sn-3Cr metastabil β titanyum alaşımının mikro yapısal ve aşınma özelliklerine yaşlandırma sıcaklığına ısıtma hızının etkisi incelenmiştir. Alaşım yaşlandırma sıcaklığına 0,4°C/dk, 4°C/dk, 25°C/dk ve 50°C/dk olmak üzere dört farklı hızda ısıtılmıştır. Isıl işlemler sonrasında alaşımın mikro yapısal özellikleri Taramalı Elektron Mikroskobu (SEM) analiziyle incelenmiştir. Alaşımın mekanik özellikleri ise mikro sertlik ve aşınma testleri uygulanarak belirlenmiştir. En küçük boyda α fazları (393±43nm) 0,4°C/dk hızla yaşlandırma sıcaklığına ısıtılmış numunede elde edilmiştir. Isıtma hızının artmasıyla birlikte ise α fazları büyümüş ve böylece en yüksek mikro sertlik 0,4°C/dk hızla ısıtılan numunede, en düşük mikro sertlik ise 50°C/dk hızla ısıtılan numunede elde edilmiştir. Aşınma testleri 3N ve 10N olmak üzere iki farklı yükte ve 150 metre ve 400 metre olmak üzere iki farklı kayma mesafesinde uygulanmıştır. Aşınma testi sonrası kütle kaybı yük ve kayma mesafesinin artmasıyla birlikte tüm numune gruplarında artmıştır. Aşınma testleri sonrasında elde edilen verilere, istatistiki veri analizi uygulanarak sürtünme katsayıları tayin edilmiştir. Ayrıca aşınma testinde elde edilen sürtünme katsayısı verileri, %40 test ve %60 eğitim olmak üzere iki sete bölünmüştür. Model performansı, ortalama hata karesi, ortalama karekök sapması ve regresyon değeri dikkate alınarak değerlendirilmiştir. Model, farklı ısıtma hızlarında yaşlandırma sıcaklığına ısıtılmış numunelerin sürtünme katsayılarını %76’nın üzerinde doğrulukla tahmin edebilmiştir. Physics ; Molecular biology ; Biology Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Metallurgy and Material Forming
Authors: Fucong Lu | Kun Zhang | Yuhang Hou | Zhiwen Wu
Source: Advances in Materials Science and Engineering (article, 2022-05-13)
Field: Materials Science — Polymers and Plastics

Investigation on Temperature-Dependent Multiaxial Ratchetting of Polycarbonate by a Novel Experimental Method A novel method to heat the multiaxial hollow thin-wall specimen was proposed, where its gauge length section can be heated by the liquid (i.e., water) filled inside the specimen instead of the closed furnace. This method realizes the direct measurement of multiaxial strain on the surface of specimen at different temperatures by the noncontact digital image correlation. By utilizing the proposed method, the multiaxial stress-control cyclic tests were performed to investigate the multiaxial ratchetting of polycarbonate (PC) at different temperatures. It is found that the multiaxial ratchetting of PC depends greatly on the test temperatures and the multiaxial ratchetting strain increases with increasing the test temperature. The temperature-dependent multiaxial ratchetting is also influenced by valley stresses. The unrecoverable part of deformation in the multiaxial ratchetting strain increases with increasing temperature. Materials science ; Polycarbonate ; Composite material ; Strain (injury) ; Strain gauge ; Digital image correlation ; Deformation (meteorology) ; Stress (linguistics) Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Mechanical Behavior of Composites
Authors: Arzu Çilli | Murat Beken | Nursaç KURT
Source: DergiPark (Istanbul University) (article, 2022-07-31)
Field: Materials Science — Materials Chemistry

Determination of Theoretical Fracture Criteria of Layered Elastic Composite Material by ANFIS Method from Artificial Intelligence The theoretical breaking boundary of the composite elastic composite material consisting of two is otropichomogeneous layers was investigated [6,7]. Three-dimensional theory of elasticity and a fragment edhomogeneous body model were used to find the theoretical breaking limit [3,6,7]. The defects of this material, which has local defects, are of the same length hand is changing with the angle β. The purpose of the articles [10,11] was to examine the effect of theoretical refraction criterion limit values on the change of the angle β. It has been observed that the theoretical refraction limit values obtained for four different composite materials increase as the angle art increases. In this study, the change values with the β angle of the theoretical refraction limit obtained in [11] article were recovered from the artificial intelligence of the MATLAB platform by ANFIS (Sugeno) method. It was seen that the values obtained with the Anfis method are the same with the theoretical results. Composite number ; Fracture (geology) ; Materials science ; Composite material ; Structural engineering ; Computer science ; Engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability
Authors: Andrea Konečná | Fadıl İyikanat | F. Javier Garcı́a de Abajo
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Entangling free electrons and optical excitations The inelastic interaction between flying particles and optical nanocavities gives rise to entangled states in which some excitations of the latter are paired with changes in the energy or momentum of the former. In particular, entanglement of free electrons and nanocavity modes opens appealing opportunities associated with the strong interaction capabilities of the electrons. However, the degree of entanglement that is currently achievable by electron interaction with optical cavities is severely limited by the lack of external selectivity over the resulting state mixtures. Here, we propose a scheme to generate pure entanglement between designated optical excitations in a cavity and separable free-electron states. Specifically, we shape the electron wave-function profile to dramatically reduce the number of accessible cavity modes and simultaneously associate them with targeted electron scattering directions. We exemplify this concept through a theoretical description of free-electron entanglement with degenerate and nondegenerate plasmon modes in silver nanoparticles as well as atomic vibrations in an inorganic molecule. The generated entanglement can be further propagated through its electron component to extend quantum interactions beyond currently explored protocols. Quantum entanglement ; Electron ; Physics ; Degenerate energy levels ; Plasmon ; Free electron model ; Quantum mechanics ; Atomic physics ; Quantum Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Quantum Information and Cryptography
Authors: Weihao Yang | Jun Qin | Jiawei Long | Yan Wei | Yucong Yang | Chaoyang Li | En Li | Juejun Hu | Longjiang Deng | Qingyang Du | Lei Bi
Source: Research Square (preprint, 2022-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnet-free nonreciprocal metasurface for on-demand bi-directional phase modulation Modulation (music) ; Phase (matter) ; Magnet ; Phase modulation ; On demand ; Optoelectronics ; Optics ; Materials science ; Computer science ; Physics ; Electrical engineering ; Engineering ; Acoustics ; Multimedia Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Vinayak W. Bembrekar | R. N. Yerrawar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-09-29)
Field: Materials Science — General Materials Science

OPTIMIZATION OF PROCESS PARAMETERS ANALYSIS OF FRICTION DRILLING ON MS AND AL 1008 BY USING ROTATING CONICAL TOOL Abstract In the thermal drilling process friction heat generated between conical tool and workpiece is used to make a hole in sheet metal operation, square metallic tube, circular tube, hard materials, and composite materials. This is a bur free operation with a natural formation bush that will be useful in form threading process and sheet metal operation. Conical surface ; Process (computing) ; Drilling ; Materials science ; Mechanical engineering ; Computer science ; Engineering drawing ; Engineering Material Properties and Applications ; Mechanical and Thermal Properties Analysis ; Metallurgy and Material Forming
Authors: Gizem Tutkun | Ahmet Ozan ÖZGEN | Uğur Bilge
Source: DergiPark (Istanbul University) (article, 2022-12-23)
Field: Chemistry — Physical and Theoretical Chemistry

Models of spike, envelope and membrane proteins of SARS-CoV-2 variants for possible therapeutics ABSTRACT<br> Background: SARS-CoV-2 associated with severe acute respiratory syndrome is a common, rapidly growing infectious disease with high morbidity and mortality. Mutations in the sequence of the three structural proteins of this virus may have a significant impact on the progression of the pandemic.<br> In this study, it was aimed to reveal possible targets for future drug and vaccine research by modeling and analyzing mutations in five variants of SARS-CoV-2 of concern.<br> Materials and Methods: RNA sequences of five variants identified by WHO as Variants of Concern (VoC) were obtained from NCBI. The translation of these sequences to amino acid sequence, sequence alignment and sequence comparison between variants was performed in RStudio 1.4.1717 with reference to 2020 study. Structural proteins and PDB files of the Wuhan variant were downloaded from studies conducted with the I-TASSER server. 3D representations of structural proteins were performed in LLC, Schrödinger, PyMOL Molecular Graphics System 1.2r3pre.<br> Results: The amino acid sequences of three structural proteins belonging to the VoC and the Wuhan lineage and the mutations are shown. The mutation numbers in Alpha, Beta, Delta and Gamma were as follows; 10, 8, 10 and 12. The envelope and membrane proteins had one mutation each in Beta and Delta, respectively.<br> Conclusions: The results show that several mutations occur the spike proteins of the SARS-CoV-2 variants. Only a small number of mutations are observed in envelope and membrane proteins. It may be beficial to focus on less mutated structural proteins for future vaccine and drug development studies. <br> Spike (software development) ; Spike Protein ; Envelope (radar) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Computational biology ; Coronavirus disease 2019 (COVID-19) ; 2019-20 coronavirus outbreak ; Biology ; Virology ; Medicine ; Computer science thermodynamics and calorimetric analyses ; SARS-CoV-2 and COVID-19 Research ; Lipid Membrane Structure and Behavior
Authors: K Gopinath | G Rajendran
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-05-01)
Field: Chemistry — Spectroscopy

Protein Remote Homology Detection-Methods and Evaluation Metrics Protein Remote Homology Detection (PRHD) is a concept that aims to discover remote evolutionary links between proteins. PRHD research is currently vital for assessing protein structures and function. A variety of computational approaches have been developed in recent decades to overcome this challenge which requires constant-width characteristics to specify the Protein Sequences (PSs). However, with only a rudimentary knowledge of proteins, identifying their discrimination characteristics is not an easy task. Therefore, a brief comparative review and comparison of different computation methods is essential for PRHD. In this paper, a review of various PRHD methods with the help of different computational methods is presented. In addition, their benefits and drawbacks are discussed in a tabular form. Lastly, the whole survey is summarized and future directions are suggested to improve the efficiency of protein classification based on amino acid sequences, especially with low sequence identity between proteins. Homology (biology) ; Computational biology ; Computer science ; Biology ; Biochemistry ; Amino acid Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Machine Learning in Bioinformatics
Authors: Xiao Liang | Zhou Zhou | Zile Li | Jiaxin Li | Chang Peng | Hao Cui | Kai Wei | Zhixue He | Shaohua Yu | Guoxing Zheng
Source: Research Square (preprint, 2022-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiplexed optical differentiator enabled by single-size nanostructured metasurface Differentiator ; Multiplexing ; Materials science ; Optoelectronics ; Computer science ; Telecommunications ; Bandwidth (computing) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Thomas W. Hansen | William Bang Lomholdt | Matthew Helmi Leth Larsen | Cuauhtémoc Núñez Valencia | Jens Kling | Daniel Kelly | Pei Liu | Jakob Schiøtz
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Approaches to Monitoring Structural Modification Using <i>In Situ</i> Electron Microscopy Understanding the atomic-scale structure of materials and devices, as well as the potential structural changes that may occur during use in real-world operating conditions, is essential to developing next generation of functional materials. Such materials can provide new solutions within a diverse range of fields including chemistry, electronics, and medicine. In the energy sector, advanced nanomaterials are used in energy storage, power-to-X catalysts and many other devices. The core challenge of characterizing such materials is this: how do we obtain the most detailed atomic-resolution pictures of systems and devices under real-world conditions, without affecting them by our imaging process? Stateof-the-art transmission electron microscopy techniques can obtain sub-angstrom spatial resolution and high spectroscopic and temporal resolution datasets of devices operating in gas or liquid environments. However, making these observations relevant to real world applications requires minimization of electron beam effects and reliable analysis of data acquired under low dose rate conditions. In situ ; Electron microscope ; Materials science ; Content (measure theory) ; Nanotechnology ; Computer science ; Optics ; Chemistry ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Anodic Oxide Films and Nanostructures
Authors: Imma Ferrer
Source: LCGC North America (article, 2022-09-01)
Field: Chemistry — Spectroscopy

What’s Your Workflow? Non-Targeted Analysis of Water Samples In recent years, there has been an effort to categorize how emerging contaminants are identified in water samples by liquid chromatography–mass spectrometry (LC–MS). This process is often referred to as a workflow, and it has been the focus of many talks and conference sessions around the world. Many scientists have looked to unify and generalize the workflow, but do we really need a generic workflow to identify a non-target in an environmental sample? Would the scientific method approach (applied to each individual problem to solve) be enough and reliable? Workflow ; Categorization ; Computer science ; Focus (optics) ; Workflow engine ; Workflow technology ; Process (computing) ; Sample (material) ; Data science ; Chromatography ; Chemistry ; Database ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Toxic Organic Pollutants Impact
Authors: Jim Boelrijk | Bernd Ensing | Patrick Forré | Bob W.J. Pirok
Source: ChemRxiv (preprint, 2022-12-13)
Field: Chemistry — Spectroscopy

Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized. Bayesian optimization ; Computer science ; Bayesian probability ; Scalability ; Kernel (algebra) ; Gaussian process ; Multi-objective optimization ; Artificial intelligence ; Covariance ; Mathematical optimization ; Machine learning ; Gaussian ; Mathematics ; Chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Emre Kantar | Jaume M. Cascallo | Torstein Grav Aakre | Nina Marie Thomsen | Espen Eberg
Source: Proceedings of the Nordic Insulation Symposium (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

Use of Data-Driven Approaches for Defect Classification in Stator Winding Insulation Partial discharges (PD) in the high voltage insulation systems are both a symptom and cause of terminal and impending failures. The use of data-driven methods based on PD measurements will enable predictive strategies to replace traditional maintenance strategies. This paper employs machine learningbased classification models to identify and characterizePD signals originating from lab-made artificial defects in epoxy-mica material samples. Three different PD sources were studied: surface discharges in air, corona discharges, and discharges caused by internal cavities/delaminations. To generate high-quality datasets for the training, validation, and testing of classification models, Phase-Resolved PD (PRPD) data for each test object was obtained at room temperature under 50 Hz AC excitation at 10 % above the PD inception voltage (PDIV) of each sample. Relevant statistical and deterministic features were extracted for each observation and were labeled based on the defect type (supervised learning). Finally, the trained and validated ML models were used to identify PD sources in the service-aged stator winding insulation. Support vector machines (SVM), ensemble, and k-nearest neighbor (kNN) algorithms achieved significantly high accuracy (≥ 95 %) of defect identification. Stator ; Support vector machine ; Partial discharge ; Pattern recognition (psychology) ; Computer science ; Voltage ; Identification (biology) ; Sample (material) ; Artificial intelligence ; k-nearest neighbors algorithm ; High voltage ; Data mining ; Machine learning ; Materials science ; Engineering ; Mechanical engineering ; Physics ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques