jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nora S. Alrefaei | P.J. Hine | Michael E. Ries
Source: Cellulose (article, 2023-10-06)
Field: Materials Science — Biomaterials

Dissolution of hemp yarns by 1-ethyl-3-methylimidazolium acetate studied with time-temperature superposition Abstract This study investigated the dissolution of hemp yarns in the ionic liquid 1-ethyl-3-methylimidazolium acetate. The yarns were submerged in the ionic liquid at various temperatures and times, then coagulated in water. This resulted in the formation of a composite yarn where two optical microscopy methods were employed to track the growth of the coagulated matrix. In the first, the submerged yarns within water were measured from a side view. In the second, the yarns were dried then analysed by encapsulating in epoxy resin. In both methods, the growth of the swollen ring thickness and the coagulated fraction was tracked as a function of time and temperature. It was found to obey time-temperature superposition, giving a dissolution activation energy of $$78\pm 2 \text{k}\;\text{J}/\text{m}\text{o}\text{l}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>78</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn> <mml:mtext>k</mml:mtext> <mml:mspace/> <mml:mtext>J</mml:mtext> <mml:mo>/</mml:mo> <mml:mtext>mol</mml:mtext> </mml:mrow> </mml:math> . In water the yarn is swollen; the swelling ratios of the different regions were calculated to be $$4.4\pm 0.2 \; \text{a}\text{n}\text{d} \; 1.4\pm 0.1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4.4</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.2</mml:mn> <mml:mspace/> <mml:mtext>and</mml:mtext> <mml:mspace/> <mml:mn>1.4</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.1</mml:mn> </mml:mrow> </mml:math> for the outer dissolved ring and the undissolved core, respectively. A novel finding in this study was that the growth of the coagulated region follows a diffusion process, increasing with the square root of time, and so could be modelled to give a diffusion coefficient for the ionic liquid of $$6.74\times {10}^{-13}\;{\text{ m}}^{2}/{\text{s}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.74</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>13</mml:mn> </mml:mrow> </mml:msup> <mml:mspace/> <mml:msup> <mml:mrow> <mml:mspace/> <mml:mtext>m</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>/</mml:mo> <mml:mtext>s</mml:mtext> </mml:mrow> </mml:math> . This compared well with previously published NMR data for a saturated cellulose solution (23.5%). This strongly suggests that the diffusion of the ions through a saturated layer of cellulose solution controls the dissolution of the yarn. This finding has significant implications for cellulose-based composite production and thus the recycling of cellulose textiles. Dissolution ; Materials science ; Ionic bonding ; Analytical Chemistry (journal) ; Ion ; Chemistry ; Physical chemistry ; Chromatography ; Organic chemistry Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Lignin and Wood Chemistry
Authors: Razan O. M. Aboljadayel | C. J. Kinane | C. A. F. Vaz | David M. Love | Robert S. Weatherup | Philipp Braeuninger‐Weimer | Marie‐Blandine Martin | Adrian Ionescu | Andrew J. Caruana | Timothy Charlton | Justin Llandro | Pedro M. S. Monteiro | C. H. W. Barnes | Stephan Hofmann | S. Langridge
Source: ACS Applied Materials & Interfaces (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Determining the Proximity Effect-Induced Magnetic Moment in Graphene by Polarized Neutron Reflectivity and X-ray Magnetic Circular Dichroism We report the magnitude of the induced magnetic moment in CVD-grown epitaxial and rotated-domain graphene in proximity with a ferromagnetic Ni film, using polarized neutron reflectivity (PNR) and X-ray magnetic circular dichroism (XMCD). The XMCD spectra at the C <i>K</i>-edge confirm the presence of a magnetic signal in the graphene layer, and the sum rules give a magnetic moment of up to ∼0.47 μ<sub>B</sub>/C atom induced in the graphene layer. For a more precise estimation, we conducted PNR measurements. The PNR results indicate an induced magnetic moment of ∼0.41 μ<sub>B</sub>/C atom at 10 K for epitaxial and rotated-domain graphene. Additional PNR measurements on graphene grown on a nonmagnetic Ni<sub>9</sub>Mo<sub>1</sub> substrate, where no magnetic moment in graphene is measured, suggest that the origin of the induced magnetic moment is due to the opening of the graphene's Dirac cone as a result of the strong C p<sub><i>z</i></sub>-Ni 3d hybridization. X-ray magnetic circular dichroism ; Materials science ; Magnetic circular dichroism ; Magnetic moment ; Graphene ; Neutron ; Circular dichroism ; Circular polarization ; Condensed matter physics ; Reflectivity ; Moment (physics) ; Optics ; Nuclear magnetic resonance ; Crystallography ; Nanotechnology ; Physics ; Nuclear physics ; Spectral line Graphene research and applications ; Quantum and electron transport phenomena ; Magnetic properties of thin films
Authors: Alea Miako Tokita | Jörg Behler
Source: PubMed (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

Tutorial: How to Train a Neural Network Potential The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing efficient access to energies and forces, they allow us to perform large-scale simulations of extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach the accuracy of electronic structure calculations, provided that they have been properly trained and validated using a suitable set of reference data. Due to their highly flexible functional form, the construction of MLPs has to be done with great care. In this Tutorial, we describe the necessary key steps for training reliable MLPs, from data generation via training to final validation. The procedure, which is illustrated for the example of a high-dimensional neural network potential, is general and applicable to many types of MLPs. Computer science ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Ilia Igashov | Arne Schneuing | Marwin Segler | Michael M. Bronstein | Bruno E. Correia
Source: arXiv (Cornell University) (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

RetroBridge: Modeling Retrosynthesis with Markov Bridges Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confidence estimates to guide heuristic search algorithms. We model single-step retrosynthesis planning as a distribution learning problem in a discrete state space. First, we introduce the Markov Bridge Model, a generative framework aimed to approximate the dependency between two intractable discrete distributions accessible via a finite sample of coupled data points. Our framework is based on the concept of a Markov bridge, a Markov process pinned at its endpoints. Unlike diffusion-based methods, our Markov Bridge Model does not need a tractable noise distribution as a sampling proxy and directly operates on the input product molecules as samples from the intractable prior distribution. We then address the retrosynthesis planning problem with our novel framework and introduce RetroBridge, a template-free retrosynthesis modeling approach that achieves state-of-the-art results on standard evaluation benchmarks. Retrosynthetic analysis ; Markov chain ; Heuristic ; Bridge (graph theory) ; Computer science ; Markov model ; Markov process ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jian Wen Lin | Mohd Shahrimie Mohd Asaari | Haidi Ibrahim | Mohamad Khairi Ishak | Abdul Sattar Din
Source: Pertanika journal of science & technology (review, 2023-10-02)
Field: Chemistry — Analytical Chemistry

A Review on Analysis Method of Proximal Hyperspectral Imaging for Studying Plant Traits Understanding the response of plant traits towards different growing conditions is crucial to maximizing crop yield and mitigating the effect of the food crisis. At present, many imaging techniques are being explored and utilized within plant science to solve problems in agriculture. One of the most advanced imaging methods is hyperspectral imaging (HSI), as it carries the spectral and spatial information of a subject. However, in most plant studies that utilized HSI, the focus was given to performing an analysis of spectral information. Even though a satisfactory performance was achieved, there is potential for better performance if spatial information is given more consideration. This review paper (1) discusses the potential of the proximal HSI analysis methods for plant traits studies, (2) presents an overview of the acceptance of hyperspectral imaging technology for plant research, (3) presents the basic workflow of hyperspectral imaging in proximal settings concerning the image acquisition settings, image pre-processing, spectral normalization, and spectral analysis, (4) discusses the analysis methods that utilize spatial information, and (5) addresses some technical challenges related to implementing hyperspectral imaging in proximal settings for plant traits analysis. Hyperspectral imaging ; Workflow ; Precision agriculture ; Computer science ; Normalization (sociology) ; Remote sensing ; Spectral imaging ; Artificial intelligence ; Agriculture ; Geography ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Merve Okutan | Abdurrahman Asan | Hacer Ebru SİNGER
Source: Hittite Journal of Science & Engineering (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

The Effect of the Oğuzlar Walnut Extract as a Green Corrosion Inhibitor on AISI 1010 Mild Steel Mild steel is primarily used in reinforced concrete structures, because it has a low corrosion rate due to the formation of a passive oxide film in the alkaline environment. However, when exposed to acidic and atmospheric environments or aqueous environments containing dissolved salts such as sea water, the protective film deteriorates and corrosion occurs due to contamination caused by chloride and carbonation. It is possible to obtain corrosion inhibitors by extraction of bioactive compounds from plants. Thus, inexpensive and environmentally friendly new effective inhibitors are obtained as an alternative to environmentally harmful inhibitors. In this context, the subject of the study was determined as the investigation of the inhibition efficiency of the vanillin phenolic compound in the Çorum Oğuzlar walnut shell extract, which is a local product, on the corrosion of AISI 1010 mild steel in acidic, alkaline and salty environments. The walnut shells, which were cleaned, ground and pre-sieved with a certain grain size, were extracted in seven different solvents. In order to find the appropriate solvent, the total phenolic content (TPC) in the extracts was determined by both the Folin Ciocalteu method and the liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) system. Cyclic voltammetry (CV) and Tafel polarization methods were applied to determine the effect of the extract on the electrochemical behavior of AISI 1010 and its corrosion rate. LC-MS/MS analysis showed the presence of compounds containing p electrons, N and O heteroatoms responsible for the corrosion inhibition. The best inhibition effectiveness was obtained with 86.1% acetone-water mixture in acidic environment (0.2 M HCl). Corrosion ; Tafel equation ; Chemistry ; Aqueous solution ; Chloride ; Vanillin ; Extraction (chemistry) ; Context (archaeology) ; Nuclear chemistry ; Electrochemistry ; Metallurgy ; Materials science ; Chromatography ; Organic chemistry Corrosion Behavior and Inhibition ; Cultural Heritage Materials Analysis ; Concrete Corrosion and Durability
Authors: Hala M. Abdelmigid | Mohammed Baz | Mohammed A. AlZain | Jehad F. Al‐Amri | Hatim Ghazi Zaini﻿ | Maissa M. Morsi | Matokah M. Abualnaja | Nawal Abdallah Alhuthal
Source: Applied Sciences (article, 2023-02-27)
Field: Chemistry — Analytical Chemistry

A Novel Generative Adversarial Network Model Based on GC-MS Analysis for the Classification of Taif Rose Rose oil production is believed to be dependent on only a few genotypes of the famous rose Rosa damascena. The aim of this study was to develop a novel GC-MS fingerprint based on the need to expand the genetic resources of oil-bearing rose for industrial cultivation in the Taif region (Saudi Arabia). Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for determining the volatile composition of distilled rose oil from flower data. Because biosample availability, prohibitive costs, and ethical concerns limit observations in agricultural research, we aimed to enhance the quality of analysis by combining real observations with samples generated in silico. This study proposes a novel artificial intelligence model based on generative adversarial neural networks (GANs) to classify Taif rose cultivars using raw GC-MS data. We employed a variant of the GAN known as conditional stacked GANs (cSGANs) to predict Taif rose’s oil content and other latent characteristics without the need to conduct laboratory tests. A hierarchical stack of conditional GANs is used in this algorithm to generate images. A cluster model was developed based on the dataset provided, to quantify the diversity that should be implemented in the proposed model. The networks were trained using the cross-entropy and minimax loss functions. The accuracy of the proposed model was assessed by measuring losses as a function of the number of epochs. The results prove the ability of the proposed model to perfectly generate new real samples of different classes based on the GC-MS fingerprint. Rose (mathematics) ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Plant biochemistry and biosynthesis ; Fermentation and Sensory Analysis
Authors: Kenan KILIÇ | Kürşat Kiliç | Brian Bino Sinaice | Uğur Özcan
Source: Drvna industrija (article, 2023-12-15)
Field: Chemistry — Organic Chemistry

Wood Species Image Classification Using Two-Dimensional Convolutional Neural Network The woodworking industry&amp;rsquo;s recognition and classification of timber is essential for trade, production and timber science. Traditional methods of identifying wood types are complex, time-consuming, costly and require expertise in wood science. Traditional techniques have been replaced by convolutional neural networks (CNNs), a deep learning tool to better identify wood species. In contrast to earlier studies that used pretrained models, a novel architecture designed explicitly for the WOOD-AUTH dataset was proposed in this study to develop a new 2D CNN model. The data collection encompasses high-level visual representations of 12 distinct types of timber. It is aimed to create a simpler and faster model as an alternative to time-consuming and heavy wood classification models. Compared to previous studies, this research worked with a newly structured 2D CNN network based on 12 wood species. High accuracy and fast computation time were achieved using fewer numbers (three layers) of the convolutional neural network. The proposed model achieved 94 % accuracy, 87 % precision, 81 % recall, 80 % F1 score and 112 minutes 27 seconds computation time. The 2D CNN model performed better than the transfer learning models regarding training epochs. The primary benefit of the model is its ability to achieve high accuracy with lower computation time, even at high epochs compared to other models. The introduced 2D CNN model produced satisfactory outcomes for wood species classification. Convolutional neural network ; Computation ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Woodworking ; Transfer of learning ; Machine learning ; Deep learning ; Engineering ; Algorithm Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Jinfan Shao | Yan Qian | Erjun Kan | Haiping Wu
Source: Journal of Physics D Applied Physics (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

A new superhard material C<sub>5</sub>N<sub>2</sub> assembled from diamane: a first-principles study Abstract Due to the shortage of diamond and cubic boron nitride (BN) and the lack of other potential rivals, searching for suitable superhard materials is still attracting tremendous research interests, especially those with special properties except for semiconducting and insulating characteristics. Beyond previous synthetic methods, here we give a new approach of designing superhard materials (i.e. stacking diamane layers through chemical adsorption of molecules at the interface) via first-principles calculations. As a result, a superhard carbon-rich C 5 N 2 compound with monoclinic C 2 symmetry is designed by inserting CN 4 molecule between diamane layers, and it exhibits dynamical and mechanical stability at ambient environment. The hardness is estimated as 74.9 GPa, showing superhard nature. Different from indirect band gap insulators of diamond and cubic BN, C 5 N 2 behaves as a direct band gap semiconductor with an energy gap of ∼2.10 eV. Besides, the properties of C 5 N 2 can be effectively regulated by controlling the adsorbed molecules, for instance, it would turn into ferromagnetic phase with introduction of sp 3 hybridized three-folded C atoms and sp 2 hybridized two-fold coordinated N atoms. The estimated Curie temperatures of some ferromagnetic superhard phases are preferably above room temperature. Additionally, these ferromagnetic phases show diverse electronic properties, such as spin gapless semiconductor, bipolar magnetic semiconductor, metal, etc. This work opens a potential way to design superhard materials and tailor their physical properties, and can arouse their applications in spintronic field. Materials science ; Spintronics ; Ferromagnetism ; Diamond ; Semiconductor ; Condensed matter physics ; Band gap ; Curie temperature ; Monoclinic crystal system ; Magnetic semiconductor ; Stacking ; Nanotechnology ; Crystallography ; Optoelectronics ; Crystal structure ; Chemistry ; Nuclear magnetic resonance ; Physics ; Composite material Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Nguyen Minh Trieu | Nguyen Truong Thinh
Source: Computer Systems Science and Engineering (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model Nowadays, wood identification is made by experts using hand lenses, wood atlases, and field manuals which take a lot of cost and time for the training process. The quantity and species must be strictly set up, and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging. With the development of science, wood identification should be supported with technology to enhance the perception of fairness of trade. An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established, namely, wood anatomical images collected and used to train for the proposed model. In the convolutional neural network (CNN), the last layers are usually soft-max functions with dense layers. These layers contain the most parameters that affect the speed model. To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy, the structure of the model should be optimized and developed. Therefore, a hybrid of convolutional neural network and random forest model (CNN-RF model) is introduced to wood identification. The accuracy’s hybrid model is more than 98%, and the processing speed is 3 times higher than the CNN model. The highest accuracy is 1.00 in some species, and the lowest is 0.92. These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images. It also facilitates further investigations of wood cells and has implications for wood science. Convolutional neural network ; Identification (biology) ; Computer science ; Process (computing) ; Adaptability ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Wood processing ; Solid wood ; Field (mathematics) ; Machine learning ; Mathematics ; Engineering ; Layer (electronics) ; Ecology ; Materials science ; Mechanical engineering Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Allison Green | Woo Je Chang | Zachary M. Sherman | Zarko Sakotic | Kihoon Kim | Daniel Wasserman | Delia J. Milliron | Thomas M. Truskett
Source: ChemRxiv (preprint, 2023-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Order and Plasmonic Response of Nanoparticle Monolayers Collective plasmon resonances in superlattice assemblies of metallic nanoparticles are influenced by nanoparticle attributes and assembly structure. Although grain boundaries and other structural defects are inherent to superlattices assembled in the lab, their impact on near- and far-field optical properties remains poorly understood. Here, we study variations in structural and optical properties of model 2D superlattices of spherical metallic nanoparticles, focusing on large-scale monolayers of approximately constant area fraction formed by different assembly rates. Our modeling addresses how disorder generates distributions of hot spot intensities and nanoparticle-level resonant frequencies. The highest intensities occur in narrow gaps between particle pairs aligned parallel to the field polarization. Despite the structural diversity of the monolayers investigated, we find the highest hot-spot intensity adjacent to a nanoparticle strongly correlates with the particle’s resonant frequency, though the introduction of realistic variability in nanoparticle dielectric parameters weakens the link. These trends hold for different nanoparticle compositions, and predictions are compared to experimentally prepared monolayers of tin-doped indium oxide nanocrystals. Though defectivity only modestly influences ensemble extinction peak frequency and surface-enhanced infrared absorption, increasing structural disorder broadens spectral lineshapes and boosts surface-enhanced Raman scattering enhancement factors. The magnitude of spectroscopic enhancement and the sensitivity of optical properties to local structural order depends primarily on dipole polarizability contrast. These results can help inform tolerances and tradeoffs relevant to designing materials and assembly protocols to achieve desired optical properties for applications, including sensitive molecular detection. Materials science ; Nanoparticle ; Plasmon ; Polarizability ; Monolayer ; Dielectric ; Superlattice ; Chemical physics ; Dipole ; Optoelectronics ; Nanotechnology ; Raman scattering ; Molecular physics ; Raman spectroscopy ; Optics ; Chemistry ; Molecule ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Quantum Dots Synthesis And Properties