jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nor Pana Yupa | Henny Purwaningsih | Tetty Kemala
Source: Jurnal Kimia Sains dan Aplikasi (article, 2023-10-09)
Field: Materials Science — Biomaterials

Synthesis and Characterization of Corn Husk (Zea Mays L.) Cellulose Using Microwave-Assisted Extraction (MAE) This research focuses on synthesizing cellulose from corn husks using microwave-assisted extraction (MAE), which aims to produce cellulose from corn husks through liquefaction, delignification, and bleaching processes. The total time required is only 14 minutes, thus shortening the process time. This study used three power variations: 450 W, 600 W, and 800 W. The results showed that the optimum conditions occurred at 800 W power. Hence, the higher the power caused the high cellulose content obtained, which amounted to 75.23% and decreased the lignin value to 15.78%, along with the increase in microwave power. FTIR analysis also confirmed the cellulose results obtained and indicated that the lignin groups were weakened or lost. Furthermore, morphological analysis of cellulose fibers by SEM showed that the surface of cellulose fibers was rough, oval, and irregular due to cracks caused by erosion. Then, the result of cellulose crystallinity using XRD was 45.8%. Compared to conventional methods, microwave-assisted cellulose synthesis exhibits better and more promising potential. This is because microwave radiation can be used to improve process time efficiency and achieve higher yields. Moreover, the proposed method is easier, faster, and straightforward. Husk ; Cellulose ; Lignin ; Crystallinity ; Extraction (chemistry) ; Microwave power ; Cellulose fiber ; Chemistry ; Microwave ; Fiber ; Pulp and paper industry ; Materials science ; Nuclear chemistry ; Chemical engineering ; Composite material ; Botany ; Organic chemistry ; Biology Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Lignin and Wood Chemistry
Authors: Ivan Stojanović | Mirta Logar | Ivan Fatović | Vesna Alar | Daniela Rakela-Ristevski
Source: Coatings (article, 2023-07-30)
Field: Materials Science — Polymers and Plastics

Experimental Study of Atmospherically and Infrared-Dried Industrial Topcoats In this paper, five different solvent-borne industrial topcoats were dried with infrared (IR) radiation and under atmospheric conditions. A comparison of physical, mechanical, chemical, and electrochemical properties of differently dried topcoats was made. The results of differential scanning calorimetry (DSC), Fourier-Transform Infrared Spectroscopy (FTIR), and adhesion of a topcoat to the metal substrate (determined by the pull-off test) indicate a higher degree of crosslinking of examined topcoats, which improves the coating’s protective properties. Scratch hardness was determined by the pencil hardness test. Impact resistance was examined with a falling-weight test. Changes in the shade of the coating were examined by visual inspection and using a gloss meter. The electrochemical measurements of open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) were conducted. The thermal stability of topcoats was tested by thermogravimetric analysis (TGA). The results show overall better properties of IR-dried topcoats. In addition, topcoats dry significantly faster when IR radiation is applied, which makes this drying method very interesting for industrial application. Gloss (optics) ; Materials science ; Fourier transform infrared spectroscopy ; Differential scanning calorimetry ; Thermogravimetric analysis ; Composite material ; Coating ; Dielectric spectroscopy ; Infrared ; Chemical engineering ; Electrochemistry ; Optics ; Chemistry Polymer composites and self-healing ; Building materials and conservation ; Polymer Nanocomposites and Properties
Authors: Yu-Tang Luo | Zhehan Zhou | Cheng‐Yang Wu | Li-Ching Chiu | Jia‐Yang Juang
Source: Nanomaterials (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Analysis of Hazy Ga- and Zr-Co-Doped Zinc Oxide Films Prepared with Atmospheric Pressure Plasma Jet Systems Co-doped ZnO thin films have attracted much attention in the field of transparent conductive oxides (TCOs) in solar cells, displays, and other transparent electronics. Unlike conventional single-doped ZnO, co-doped ZnO utilizes two different dopant elements, offering enhanced electrical properties and more controllable optical properties, including transmittance and haze; however, most previous studies focused on the electrical properties, with less attention paid to obtaining high haze using co-doping. Here, we prepare high-haze Ga- and Zr-co-doped ZnO (GZO:Zr or ZGZO) using atmospheric pressure plasma jet (APPJ) systems. We conduct a detailed analysis to examine the interplay between Zr concentrations and film properties. UV-Vis spectroscopy shows a remarkable haze factor increase of 7.19% to 34.8% (+384%) for the films prepared with 2 at% Zr and 8 at% Ga precursor concentrations. EDS analysis reveals Zr accumulation on larger and smaller particles, while SIMS links particle abundance to impurity uptake and altered electrical properties. XPS identifies Zr mainly as ZrO<sub>2</sub> because of lattice stress from Zr doping, forming clusters at lattice boundaries and corroborating the SEM findings. Our work presents a new way to fabricate Ga- and Zr-co-doped ZnO for applications that require low electrical resistivity, high visible transparency, and high haze. Materials science ; Doping ; Dopant ; X-ray photoelectron spectroscopy ; Haze ; Transmittance ; Electrical resistivity and conductivity ; Chemical engineering ; Thin film ; Analytical Chemistry (journal) ; Optoelectronics ; Nanotechnology ZnO doping and properties ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Source: Biointerface Research in Applied Chemistry (article, 2023-10-15)
Field: Materials Science — Materials Chemistry

Synthesis and Unveiling the Effect of Nonmagnetic Zn2+ Ions on Enrichment of Structural Properties of Barium-Nickel Ferrites The effects of zinc substitution on barium-based and nickel-based hexagonal ferrites have been investigated. The ceramic technique prepared three series of hexagonal ferrites with nominal compositions, BaNi2-xZnxFe16O27 (x = 0.0, 0.4, 0.8, 1.2, 1.6, 2.0). The powder samples' structural properties have been investigated using the X-ray diffraction (XRD) technique. X-ray diffraction (XRD) patterns of samples confirm the single phase w-type hexagonal structure with p63/mmc space group. The average crystallite size of ferrites was within the range of 35 -36 nm. The lattice constant was increased with the increase in zinc concentration in all samples. Bulk density increased with the increase in zinc concentration in all sample ferrites, whereas X-ray density decreased with the increase in zinc concentration in all sample ferrites. Though the constant lattice increases, the increase in mass follows up with the increase in volume, so X-ray density increases. Porosity decreased with the increase in zinc concentration in all samples. The decrease in the surface area may be due to the increase in crystallite size. The ferrimagnetic materials with crystallite size lower than 50 nm can be potentially employed for the reduction of signal-to-noise ratio in numerous high-density recording and microwave applications. Tetrahedral bond length, octahedral bond length, tetrahedral edge, and shared and unshared octahedral edges increase as the Zn2+ content x increases. This could be related to the larger radius of Zn2+ ions as compared with Ni2+ ions. Crystallite ; Lattice constant ; Materials science ; Barium ; Nickel ; Spinel ; Analytical Chemistry (journal) ; Zinc ; Ferrimagnetism ; Grain size ; Hexagonal phase ; Crystallography ; Diffraction ; Chemistry ; Hexagonal crystal system ; Metallurgy ; Magnetization Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Multiferroics and related materials
Authors: Lívia Mačák | Oksana Velgosová | Erika Múdra | Marek Vojtko | Silvia Dolinská
Source: Polymers (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Transfer of AgNPs’ Anti-Biofilm Activity into the Nontoxic Polymer Matrix A biological method was successfully applied to synthesize spherical silver nanoparticles (AgNPs) while using the extract of lavender (Ex-L) (lat. <i>Lavandula angustifolia</i>) as the reducing and stabilizing agent. The produced nanoparticles were spherical with an average size of 20 nm. The AgNPs' synthesis rate confirmed the extract's excellent ability to reduce silver nanoparticles from the AgNO<sub>3</sub> solution. The presence of good stabilizing agents was confirmed by the excellent stability of the extract. Nanoparticles' shapes and sizes did not change. UV-Vis absorption spectrometry, Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) were used to characterize the silver nanoparticles. The silver nanoparticles were incorporated into the PVA polymer matrix by the "ex situ" method. The polymer matrix composite with AgNPs was prepared in two ways: as a composite film and nanofibers (nonwoven textile). The anti-biofilm activity of AgNPs and the ability of AgNPs to transfer toxic properties into the polymer matrix were proved. Silver nanoparticle ; Fourier transform infrared spectroscopy ; Materials science ; Polymer ; Nanoparticle ; Chemical engineering ; Scanning electron microscope ; Transmission electron microscopy ; Matrix (chemical analysis) ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Composite material Nanoparticles: synthesis and applications ; Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging
Authors: Siyu Xia | Huiling Du | Zhuo Li | Fan Zhao | Qianqian Li | Yuxuan Hu | Le Kang
Source: Coatings (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

Improvement of Piezoelectricity of (Bi0.5Na0.5)0.94Ba0.06TiO3 Ceramics Modified by a Combination of Porosity and Sm3+ Doping Porous lead-free piezoelectric ceramics are characterized by their environment-friendly, light weight, and large specific surface area. The optimization of porous Na0.5Bi0.5TiO3-based lead-free piezoelectric ceramics can improve piezoelectric properties, enhance force–electric coupling characteristics, and effectively promote energy conversion, expanding the application in force-electric coupling devices. This study aimed to prepare [Smx(Bi0.5Na0.5)1−3x/2]0.94Ba0.06TiO3 (x = 0, 0.01, 0.02, 0.03, 0.04) lead-free ceramics with porous structures, resulting in the piezoelectric constant d33 = 131 pC/N and the plane electromechanical coupling coefficient kp = 0.213 at x = 0.01. The presence of pores in lead-free ceramics has a direct impact on the domain structure and can cause the depolarization process to relax. Then, the soft doping of Sm3+ makes the A-site ion in porous (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics occupancy inhomogeneous and generates cation vacancies, which induces lattice distortion and makes the domain wall motion easier, resulting in the improvement of piezoelectric properties and electromechanical coupling parameters. Furthermore, the piezoelectric oscillator exhibits greater resistance to resonant coupling in the radial extension vibration mode. These results infer that a combination of porosity and Sm3+ doping renders (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics base material for piezoelectric resonators, providing a scientific basis for their application in force–electric coupling devices, such as piezoelectric resonant gas sensors. Piezoelectricity ; Materials science ; Porosity ; Electromechanical coupling coefficient ; Doping ; Piezoelectric coefficient ; Ceramic ; Resonator ; Composite material ; Coupling (piping) ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Dielectric materials and actuators
Authors: Chien-Hsun Chen | Chih-Ju Chou
Source: Applied Sciences (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Deep Learning and Long-Duration PRPD Analysis to Uncover Weak Partial Discharge Signals for Defect Identification This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques. Partial discharge ; Pattern recognition (psychology) ; Artificial intelligence ; Convolutional neural network ; Materials science ; Voltage ; Computer science ; Biological system ; Engineering ; Electrical engineering ; Biology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Xingyi Qi | Yaofeng Hu | Ruoyu Wang | Yaqing Yang | Yufei Zhao
Source: Acta Chimica Sinica (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Recent Advance of Machine Learning in Selecting New Materials The new material industry is the foundation of technological change in many related fields, and also the forerunner of the development of new energy, aerospace, electronic information and other high-tech industries.Traditional means cannot meet the development needs of modern society because of disadvantages such as high cost, low efficiency and long commercial cycle.In recent years, with the application of big data combined with artificial intelligence in a deeper degree, data-driven machine learning has made great progress in the design, screening and performance prediction of new materials, which has greatly promoted the development and application of new materials.In this review, the basic process of machine learning, the algorithms commonly used in materials science and the relevant materials database are summarized.This review focuses on the application of machine learning in different functions, as well as the performance prediction in the fields of catalyst materials, lithium-ion batteries, semiconductor materials and alloy materials, presenting the latest progress in materials development.Finally, machine learning in the application of new materials are analyzed and prospected. Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Aparna S. Varde | Jianyu Liang
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties; deep learning via advances such as convolutional neural networks to explore grain size detection; and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90\% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes; creating domain-specific tools using machine learning for decision support in agile manufacturing; and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science. Artificial intelligence ; Computer science ; Machine learning ; Sustainability ; Decision support system ; Artificial neural network ; Random forest ; Agile software development ; Robustness (evolution) ; Software engineering Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Ioan-Bogdan Magdău | Daniel J. Arismendi‐Arrieta | Holly Smith | Clare P. Grey | Kersti Hermansson | Gábor Csányi
Source: ChemRxiv (preprint, 2023-06-22)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field for Molecular Liquids:EC/EMC Binary Solvent Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems. Molecular dynamics ; Force field (fiction) ; Ab initio ; Component (thermodynamics) ; Binary number ; Molecule ; Chemical physics ; Work (physics) ; Materials science ; Field (mathematics) ; Electrolyte ; Phase (matter) ; Chemistry ; Computer science ; Nanotechnology ; Statistical physics ; Computational chemistry ; Physics ; Thermodynamics ; Artificial intelligence ; Physical chemistry ; Organic chemistry ; Electrode ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Various Chemistry Research Topics
Authors: Wei Zhang | Christof Schütte
Source: PAMM (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Understanding recent deep‐learning techniques for identifying collective variables of molecular dynamics Abstract High‐dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning‐based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning‐based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples. Autoencoder ; Deep learning ; Computer science ; Artificial intelligence ; Identification (biology) ; Transfer of learning ; Machine learning Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: David Dalmau | Juan V. Alegre‐Requena
Source: ChemRxiv (preprint, 2023-11-07)
Field: Materials Science — Materials Chemistry

ROBERT: Bridging the Gap between Machine Learning and Chemistry Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing 18–4,149 entries. Furthermore, we demonstrated the program’s ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT’s practicality in real-life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies. Bridging (networking) ; Benchmarking ; Computer science ; Workflow ; Field (mathematics) ; Data science ; Sustainability ; Artificial intelligence ; Software engineering ; Database ; Ecology ; Management Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Anif Jamaluddin | Annisa Dwi Nursanti | Anafi Nur’aini | Rekyan Regasari Mardi Putri | Muhammad Usama Arshad
Source: INDONESIAN JOURNAL OF APPLIED PHYSICS (article, 2023-11-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene as an Active Material for Supercapacitors: A Machine Learning Approach &lt;p class="Abstract"&gt;Graphene is a promising material for supercapacitors due to its unique properties, which influence the device's supercapacitor. This study aims to investigate the key factor of graphene properties in supercapacitors (, with the goal of improving their performance. Also, we observe the machine learning models for predicting capacitance of supercapacitor including four algorithms of machine learning: Linear Regression (LR), lazy IBK, Decision Table (DT), and Random Forest (RF). Machine learning model showed that the RF model demonstrated the highest correlation value of 0.745, surpassing other models. Also, the study revealed that graphene has a high specific surface area and highly porous structure, which enhanced the high capacitance values. Finally, these machine learning models are suitable to apply in materials sciences field for understanding the materials properties in supercapacitor.&lt;/p&gt; Supercapacitor ; Capacitance ; Graphene ; Random forest ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Nanotechnology ; Physics Supercapacitor Materials and Fabrication ; Brain Tumor Detection and Classification ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry