jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Belén Cortés‐Llanos | Sandra M. Ocampo | Leonor de la Cueva | Gabriel F. Calvo | Juan Belmonte-Beitia | Lucas Pérez | Gorka Salas | Ángel Ayuso‐Sacido
Source: Nanomaterials (article, 2021-10-28)
Field: Materials Science — Biomaterials

Influence of Coating and Size of Magnetic Nanoparticles on Cellular Uptake for In Vitro MRI Iron oxide nanoparticles (IONPs) are suitable materials for contrast enhancement in magnetic resonance imaging (MRI). Their potential clinical applications range from diagnosis to therapy and follow-up treatments. However, a deeper understanding of the interaction between IONPs, culture media and cells is necessary for expanding the application of this technology to different types of cancer therapies. To achieve new insights of these interactions, a set of IONPs were prepared with the same inorganic core and five distinct coatings, to study their aggregation and interactions in different physiological media, as well as their cell labelling efficiency. Then, a second set of IONPs, with six different core sizes and the same coating, were used to study how the core size affects cell labelling and MRI in vitro. Here, IONPs suspended in biological media experience a partial removal of the coating and adhesion of molecules. The FBS concentration alters the labelling of all types of IONPs and hydrodynamic sizes ≥ 300 nm provide the greatest labelling using the centrifugation-mediated internalization (CMI). The best contrast for MRI results requires a core size range between 12-14 nm coated with dimercaptosuccinic acid (DMSA) producing <i>R</i><sub>2</sub><sup>*</sup> values of 393.7 s<sup>-1</sup> and 428.3 s<sup>-1</sup>, respectively. These findings will help to bring IONPs as negative contrast agents into clinical settings. Labelling ; Coating ; Internalization ; In vitro ; Nanoparticle ; Magnetic resonance imaging ; Nanotechnology ; Materials science ; Biophysics ; Iron oxide nanoparticles ; Chemistry ; Biomedical engineering ; Nuclear magnetic resonance ; Cell ; Biochemistry ; Medicine ; Biology ; Radiology ; Physics Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; 3D Printing in Biomedical Research
Authors: Shailesh Kumar | Cuo Wu | Danylo Komisar | Yinhui Kan | Liudmilla F. Kulikova | Valery A. Davydov | V. Agafonov | Sergey I. Bozhevolnyi
Source: The Journal of Chemical Physics (article, 2021-01-27)
Field: Materials Science — Materials Chemistry

Fluorescence enhancement of a single germanium vacancy center in a nanodiamond by a plasmonic Bragg cavity Germanium vacancy (GeV) centers in diamonds constitute a promising platform for single-photon sources to be used in quantum information technologies. Emission from these color centers can be enhanced by utilizing a cavity that is resonant at the peak emission wavelength. We investigate circular plasmonic Bragg cavities for enhancing the emission from single GeV centers in nanodiamonds (NDs) at the zero phonon line. Following simulations of the enhancement for different configuration parameters, the appropriately designed Bragg cavities together with out-coupling gratings composed of hydrogen silsesquioxane ridges are fabricated around the NDs containing nitrogen vacancy centers deposited on a silica-coated silver surface. We characterize the fabricated configurations and finely tune the cavity parameters to match the GeV emission. Finally, we fabricate the cavity containing a single GeV-ND and compare the total decay-rate before and after cavity fabrication, finding a decay-rate enhancement of ∼5.5 and thereby experimentally confirming the feasibility of emission enhancement with circular plasmonic cavities. Nanodiamond ; Materials science ; Germanium ; Vacancy defect ; Optoelectronics ; Photon ; Plasmon ; Purcell effect ; Diamond ; Single-photon source ; Optics ; Quantum dot ; Spontaneous emission ; Physics ; Laser ; Silicon ; Condensed matter physics Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Photonic and Optical Devices
Authors: Danting Liu | C. H. Chang | L.-C. Chiang | M.-H. Lee | C. F. Chang | Chia-Hsien Lin | Chengyi Liang | T.-H. Lee | Shang‐Wei Lin | Chenglin Liu | Ching-Shiang Hwang | Jui-Che Huang | C. K. Kuan | H.-S. Wang | Yanjie Liu | F H Tseng | Jian‐Ying Chuang | Wun-Rong Liao | H.-C. Li | Chiu‐Hun Su | Kuei-Fen Liao | Yi‐Qi Yeh | Orion Shih | Wei‐Ru Wu | C.-A. Wang | U‐Ser Jeng
Source: Journal of Synchrotron Radiation (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

Optical design and performance of the biological small-angle X-ray scattering beamline at the Taiwan Photon Source The optical design and performance of the recently opened 13A biological small-angle X-ray scattering (SAXS) beamline at the 3.0 GeV Taiwan Photon Source of the National Synchrotron Radiation Research Center are reported. The beamline is designed for studies of biological structures and kinetics in a wide range of length and time scales, from angstrom to micrometre and from microsecond to minutes. A 4 m IU24 undulator of the beamline provides high-flux X-rays in the energy range 4.0-23.0 keV. MoB<sub>4</sub>C double-multilayer and Si(111) double-crystal monochromators (DMM/DCM) are combined on the same rotating platform for a smooth rotation transition from a high-flux beam of ∼4 × 10<sup>14</sup> photons s<sup>-1</sup> to a high-energy-resolution beam of ΔE/E ≃ 1.5 × 10<sup>-4</sup>; both modes share a constant beam exit. With a set of Kirkpatrick-Baez (KB) mirrors, the X-ray beam is focused to the farthest SAXS detector position, 52 m from the source. A downstream four-bounce crystal collimator, comprising two sets of Si(311) double crystals arranged in a dispersive configuration, optionally collimate the DCM (vertically diffracted) beam in the horizontal direction for ultra-SAXS with a minimum scattering vector q down to 0.0004 Å<sup>-1</sup>, which allows resolving ordered d-spacing up to 1 µm. A microbeam, of 10-50 µm beam size, is tailored by a combined set of high-heat-load slits followed by micrometre-precision slits situated at the front-end 15.5 m position. The second set of KB mirrors then focus the beam to the 40 m sample position, with a demagnification ratio of ∼1.5. A detecting system comprising two in-vacuum X-ray pixel detectors is installed to perform synchronized small- and wide-angle X-ray scattering data collections. The observed beamline performance proves the feasibility of having compound features of high flux, microbeam and ultra-SAXS in one beamline. Beamline ; Undulator ; Synchrotron radiation ; Microsecond ; Optics ; Scattering ; Synchrotron ; Advanced Photon Source ; Photon flux ; Small-angle X-ray scattering ; Photon energy ; Physics ; Synchrotron Radiation Source ; Photon ; Small-angle scattering ; Radiation ; Beam (structure) Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Bruno Pinho | Laura Torrente‐Murciano
Source: Advanced Energy Materials (article, 2021-07-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dial‐A‐Particle: Precise Manufacturing of Plasmonic Nanoparticles Based on Early Growth Information—Redefining Automation for Slow Material Synthesis Abstract Nanomaterials are at the core of many scientific discoveries in catalysis, energy and healthcare to name a few. However, their deployment is limited by the lack of reproducible and precise manufacturing technologies on‐demand. In this work, a precision automated technology is demonstrated for nanoparticles synthesis with wide‐range tunable sizes ( ≈ 4–100 nm). Dial‐a‐particle capabilities are achieved by a combination of a fast integrated multipoint particle sizing combined with a “plug‐n‐play” modular platform with reactors in series, distributed feed and in situ multipoint analysis. Real‐time early growth information accurately predicts the resulting particle properties. Such real‐time simple feedback control can overcome repeatability and stability issues associated with controllable (e.g., conditions) and uncontrollable (e.g., fouling, ageing, and impurities) variations leading to self‐regulated, highly stable multistage systems with no human intervention even with long residence times (from a few minutes to hours). This is a paradigm shift from machine learning (ML) methodologies, which are restricted to trained networks with rich data sets, impractical in non‐reproducible processes and limited to short residence times (e.g., within few minutes). The approach is demonstrated for plasmonic silver and gold nanoparticles showing agile control within minutes, opening the door for automation of more complex multistage procedures such as composites, multielement materials, and particle functionalization. Materials science ; Nanotechnology ; Automation ; Modular design ; Nanoparticle ; Particle (ecology) ; Process engineering ; Plasmon ; Computer science ; Mechanical engineering ; Engineering ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Molecular Communication and Nanonetworks
Authors: Young Jin Yoon | Myung Jun Kim | Jae Jeong Kim
Source: Electrochimica Acta (article, 2021-10-15)
Field: Chemistry — Electrochemistry

Machine learning to electrochemistry: Analysis of polymers and halide ions in a copper electrolyte Electrochemistry ; Electrolyte ; Redox ; Copper ; Halide ; Analyte ; Electrode ; Chemistry ; Polyethylene glycol ; Adsorption ; Inorganic chemistry ; Materials science ; Chromatography ; Organic chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Conducting polymers and applications ; Machine Learning in Materials Science
Authors: Sung Jun Hong | Hoje Chun | Je-Hyun Lee | Byung‐Hyun Kim | Min Ho Seo | Joonhee Kang | Byungchan Han
Source: The Journal of Physical Chemistry Letters (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions Machine-learning (ML) techniques have drawn an ever-increasing focus as they enable high-throughput screening and multiscale prediction of material properties. Especially, ML force fields (FFs) of quantum mechanical accuracy are expected to play a central role for the purpose. The construction of ML-FFs for polymers is, however, still in its infancy due to the formidable configurational space of its composing atoms. Here, we demonstrate the effective development of ML-FFs using kernel functions and a Gaussian process for an organic polymer, polytetrafluoroethylene (PTFE), with a data set acquired by first-principles calculations and <i>ab initio</i> molecular dynamics (AIMD) simulations. Even though the training data set is sampled only with short PTFE chains, structures of longer chains optimized by our ML-FF show an excellent consistency with density functional theory calculations. Furthermore, when integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle, such as a density, melting temperature, coefficient of thermal expansion, and Young's modulus. Molecular dynamics ; van der Waals force ; Polymer ; Materials science ; Density functional theory ; Consistency (knowledge bases) ; Statistical physics ; Chemical physics ; Computer science ; Computational chemistry ; Molecule ; Composite material ; Chemistry ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Xinyu Li | Raymond Chiong | Zhongyi Hu | Alister J. Page
Source: The Journal of Physical Chemistry Letters (article, 2021-07-28)
Field: Materials Science — Materials Chemistry

Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning Pt is a key high-performing catalyst for important chemical conversions, such as biomass conversion and water splitting. Limited Pt reserves, however, demand that we identify more sustainable alternative catalyst materials for these processes. Here, we combine state-of-the-art graph neural networks and crystal graph machine learning representations with active learning to discover new, low-cost Pt alloy catalysts for biomass reforming and hydrogen evolution reactions. We identify 12 Pt-based alloys which have comparable catalytic activity to that of the exemplar Pt(111) surface. Notably, Cu<sub>3</sub>Pt and FeCuPt<sub>2</sub> exhibit near identical catalytic performance as that of Pt(111). These results demonstrate the potential of machine learning for predicting new catalytic materials without recourse to expensive DFT geometry optimizations, the current bottleneck impeding high-throughput materials discovery. We also examine the performance of <i>d</i>-band theory in elucidating trends in binary and ternary Pt alloys. Density functional theory ; Catalysis ; Materials science ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Bin Dong | Ning Xue | Guohao Mu | Mengjun Wang | Zonghua Xiao | Lin Dai | Zhixiang Wang | Dechun Huang | Hongliang Qian | Wei Chen
Source: Ultrasonics Sonochemistry (article, 2021-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of monodisperse spherical AgNPs by ultrasound-intensified Lee-Meisel method, and quick evaluation via machine learning Due to the high reactivity of Ag<sup>+</sup> and uncontrolled growth process, the AgNPs produced by conventional Lee-Meisel method always exhibited larger particle size (30-200 nm) and polydisperse morphology (including spherical, triangular, and rod-like shape). An ultrasound-intensified Lee-Meisel (UILM) method is developed in this study to environmental-friendly and controllable synthesize monodisperse spherical AgNPs (~3.7 nm). Effects of Ag:citrate ratio (1:3 or 5:4), ultrasound power (300 to 1200 W) and reaction time (4 to 24 min) on the physical-chemical properties of AgNPs are investigated systematically. The transmission electron microscope (TEM) images, UV-Vis spectra, average particle size, zeta potential and pH value all demonstrate that crystallization and digestive ripening processes are facilitated in the presence of ultrasound irradiation. Therefore, both chemical reaction rate and mass transfer rate are enhanced to accelerate primary nucleation and inhibit uncontrolled particle growth, leading to the formation of monodisperse spherical AgNPs. Moreover, a machine learning approach - Decision Tree Regressor in conjunction with Shapley value analysis reveal the concentration of reactants is a more important feature affecting the particle. Dispersity ; Nucleation ; Particle (ecology) ; Particle size ; Chemical engineering ; Crystallization ; Zeta potential ; Transmission electron microscopy ; Chemistry ; Materials science ; Nanotechnology ; Nanoparticle ; Organic chemistry ; Physical chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Dajun Yu | Nick Lord | Justin Polk | Kshitiz Dhakal | Song Li | Yun Yin | Susan E. Duncan | Hengjian Wang | Bo Zhang | Haibo Huang
Source: Food Chemistry (article, 2021-08-08)
Field: Chemistry — Analytical Chemistry

Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time Point of delivery ; Sugar ; Starch ; Reflectivity ; Glycine ; Mathematics ; Chemistry ; Horticulture ; Food science ; Biology ; Physics ; Optics ; Amino acid ; Biochemistry Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: Angelo Maiorino | Manuel Gesù Del Duca | Urban Tomc | Jaka Tušek | Andrej Kitanovski | Ciro Aprea
Source: Applied Thermal Engineering (article, 2021-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A numerical modelling of a multi-layer LaFeCoSi Active magnetic regenerator by using Artificial Neural Networks Magnetic refrigeration ; Regenerative heat exchanger ; Artificial neural network ; Adiabatic process ; Materials science ; Layer (electronics) ; Refrigerator car ; Magnetic field ; Computer science ; Mechanical engineering ; Artificial intelligence ; Thermodynamics ; Engineering ; Nanotechnology ; Physics ; Magnetization Magnetic and transport properties of perovskites and related materials
Authors: Arun Mannodi‐Kanakkithodi | Maria K. Y. Chan
Source: Trends in Chemistry (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Computational Data-Driven Materials Discovery Computer science ; Computation ; Throughput ; Drug discovery ; Discovery learning ; Knowledge extraction ; Machine learning ; Data science ; Computational science ; Data mining ; Bioinformatics ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Nesrine Amor | Muhammad Tayyab Noman | Michal Petrů | Aamir Mahmood | Adla Ismail
Source: Scientific Reports (article, 2021-07-01)
Field: Materials Science — Polymers and Plastics

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO<sub>2</sub> NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO<sub>2</sub> NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input-output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature. Artificial neural network ; Multilayer perceptron ; Computer science ; Metaheuristic ; Perceptron ; Sensitivity (control systems) ; Algorithm ; Machine learning ; Artificial intelligence ; Engineering Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Liming Ba | Xingyu Xiong | Zhibin Yang | Ze Lei | Ben Ge | Suping Peng
Source: Journal of Power Sources (article, 2021-05-08)
Field: Materials Science — Materials Chemistry

A novel multi-physics and multi-dimensional model for solid oxide fuel cell stacks based on alternative mapping of BP neural networks Stack (abstract data type) ; Solid oxide fuel cell ; Artificial neural network ; Fuel cells ; Computer science ; Biological system ; Mechanical engineering ; Engineering ; Physics ; Artificial intelligence ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Li Wang | Jieqing Li | Li Tao | Honggao Liu | Yuanzhong Wang
Source: Microchemical Journal (article, 2021-07-23)
Field: Chemistry — Analytical Chemistry

Two-dimensional correlation spectroscopy combined with deep learning method and HPLC method to identify the storage duration of porcini Chemistry ; High-performance liquid chromatography ; Uridine ; Chromatography ; Biological system ; Mathematics Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Phytochemicals and Antioxidant Activities
Authors: Van Thuan Le | Elena-Niculina Drăgoi | Fares Almomani | Yasser Vasseghian
Source: Energies (review, 2021-05-17)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2) production. Catalytic dry reforming is used to facilitate the reforming process. The most popular catalysts for dry reforming are Ni-based catalysts. Due to their inactivation at high temperatures, these catalysts need to use metal supports, which have received special attention from researchers in recent years. Due to the existence of a wide range of metal supports and the need for accurate detection of higher H2 production, in this study, a systematic review and meta-analysis using ANNs were conducted to assess the hydrogen production by various catalysts in the dry reforming process. The Scopus, Embase, and Web of Science databases were investigated to retrieve the related articles from 1 January 2000 until 20 January 2021. Forty-seven articles containing 100 studies were included. To determine optimal models for three target factors (hydrocarbon conversion, hydrogen yield, and stability test time), artificial neural networks (ANNs) combined with differential evolution (DE) were applied. The best models obtained had an average relative error for the testing data of 0.52% for conversion, 3.36% for stability, and 0.03% for yield. These small differences between experimental results and predictions indicate a good generalization capability. Hydrogen production ; Carbon dioxide reforming ; Catalysis ; Yield (engineering) ; Hydrogen ; Biochemical engineering ; Production (economics) ; Process engineering ; Process (computing) ; Artificial neural network ; Environmental science ; Computer science ; Chemistry ; Materials science ; Machine learning ; Syngas ; Engineering ; Organic chemistry ; Metallurgy ; Economics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Hydrodesulfurization Studies