jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiayan Liao | Jiajia Zhou | Yiliao Song | Baolei Liu | Jie Lü | Dayong Jin
Source: The Journal of Physical Chemistry Letters (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the potential in creating nanoscale barcodes carrying high-capacity information. With the increasing creation of optical information, it poses more challenges in decoding them in an accurate, high-throughput, and speedy fashion. Here, we reported that the deep-learning approach can recognize the complexity of the optical fingerprints from different UCNPs. Under a wide-field microscope, the lifetime profiles of hundreds of single nanoparticles can be collected at once, which offers a sufficient amount of data to develop deep-learning algorithms. We demonstrated that high accuracies of over 90% can be achieved in classifying 14 kinds of UCNPs. This work suggests new opportunities in handling the diverse properties of nanoscale optical barcodes toward the establishment of vast luminescent information carriers. Computer science ; Nanoscopic scale ; Photon upconversion ; Nanotechnology ; Fingerprint (computing) ; Deep learning ; Nanoparticle ; Throughput ; Decoding methods ; Materials science ; Artificial intelligence ; Luminescence ; Optoelectronics ; Telecommunications Luminescence and Fluorescent Materials ; Machine Learning in Materials Science ; Biosensors and Analytical Detection
Authors: Edoardo Lopriore | Enrique G. Marín | Gianluca Fiori
Source: Nanoscale Horizons (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

An ultrafast photodetector driven by interlayer exciton dissociation in a van der Waals heterostructure Ultrafast photodetectors based on two-dimensional materials suffer from low responsivities and high dark currents. Interlayer exciton dissociation in type-II vertical heterojunctions of transition metal dichalcogenides is a viable mechanism for achieving higher responsivities with picosecond response times. Here, we propose a novel device concept based on these structures, with potential for self-powered photodetector applications characterized by an unprecedented trade-off between speed and responsivity with zero dark current. In order to assess the realistic performance to be expected in the proposed device, we have purposely devised a simulation approach able to provide a detailed investigation of the physics at play, while showing excellent predictive capabilities when compared with experiments on interlayer exciton transport available in the literature. The proposed high-performance photodetectors with tunable responsivities are at reach with available fabrication techniques and could help in paving the way towards monolithically integrated artificial neural networks for ultrafast machine vision in speed sensitive applications. Heterojunction ; van der Waals force ; Exciton ; Ultrashort pulse ; Dissociation (chemistry) ; Photodetector ; Materials science ; Optoelectronics ; Condensed matter physics ; Chemistry ; Physics ; Optics ; Quantum mechanics ; Physical chemistry ; Molecule 2D Materials and Applications ; Semiconductor Quantum Structures and Devices ; Strong Light-Matter Interactions
Authors: Omar Abu-Rub | Qasim Khan | Shady S. Refaat | Hazem Nounou
Source: Canadian Journal of Electrical and Computer Engineering (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Cable Insulation Fault Identification Using Partial Discharge Patterns Analysis This study deals with internal defects existing or occurs in cable insulation due to stress over its operation. The most popular tool for identifying and assessing insulation-based flaws is partial discharge (PD) analysis for every power cable and solid insulant. Characterization of defects is of utmost significance for overall degradation intensity and possible deterioration evaluation. In this article, a machine learning-based diagnostics scheme is proposed to identify and characterize PD signals formed by different internal sources in solid insulation. The internal discharge sources are differently shaped and sized voids created in polymeric insulation. A dissimilar shaped cavity produces distinct PD patterns. The PD signal is recorded and denoised using the wavelet analysis to remove unwanted consistent noise interference efficiently. The feature matrixes are formed by implementing features obtained from statistical operators and phase-resolved PD (PRPD) signal characteristics based on different sizes. The proposed scheme is efficient with forms of support vector machines (SVMs) and ensemble algorithm tools to achieve the high accuracy of defect identification and classification. The accuracy band of the proposed machine learning-based diagnosis to identify and characterize the defect scale is from 96% to 92%. Partial discharge ; Interference (communication) ; Support vector machine ; Identification (biology) ; Noise (video) ; Fault (geology) ; Wavelet ; SIGNAL (programming language) ; Pattern recognition (psychology) ; Computer science ; Electronic engineering ; Feature (linguistics) ; Artificial intelligence ; Engineering ; Voltage ; Channel (broadcasting) ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Wawan Joko Pranoto | Sarmad Ghazi Al‐Shawi | Paitoon Chetthamrongchai | Tzu-Chia Chen | Ekaterina Pavlovna Petukhova | Natalia Nikolaeva | Walid Kamal Abdelbasset | N Yushchenko | A. Surendar
Source: Food Science and Technology (article, 2021-11-22)
Field: Chemistry — Analytical Chemistry

Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification Vegetable oils (VOs) come in a wide range of flavors and trademarks. VOs are very similar in appearance, flavor, and taste, and it's frequently difficult to tell them from just by looking at them. Approaches for classifying these oils are sometimes expensive and time-intensive, and they frequently include analytical chemical techniques as well as mathematical algorithms like as Artificial Neural Networks (ANNs), Properties of Partial Least Squares (PLS), Principal Components Regression (PCR), and Principal Component Analysis (PCA) to enhance their effectiveness. Because of the large range of goods available, more productive techniques for qualifying, characterizing, and classifying these substances are required, as the ultimate cost should indicate the quality of the commodity that reaches the user. This study provides a technique for classifying VOs such as different manufacturers' soybean, corn, sunflower, and canola. This method utilized a Charge-Coupled Device (CCD) array sensor, a light emission diode, and a straightforward mathematical approach to capture the generated fluorescence spectrum (FS) in diluted oil. The spectrum classifications are performed using an ANN with three layers, each having four neurons. The approach can categorize VO and enables rapid network training with a 72% success rate utilizing only a few mathematical changes in the spectra data. Artificial neural network ; Principal component analysis ; Partial least squares regression ; Identification (biology) ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Range (aeronautics) ; Sunflower oil ; Mathematics ; Machine learning ; Engineering ; Chemistry ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Christopher Kucha | Li Liu | Michael Ngadi | C. Gariépy
Source: Meat Science (article, 2021-02-15)
Field: Chemistry — Analytical Chemistry

Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics Hyperspectral imaging ; Partial least squares regression ; Intramuscular fat ; Predictability ; Anisotropy ; Orientation (vector space) ; Chemometrics ; Wavelength ; Backpropagation ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Biological system ; Artificial intelligence ; Physics ; Statistics ; Optics ; Chemistry ; Machine learning ; Food science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Bee Products Chemical Analysis
Authors: Samuel Thompson | F. Teixeira‐Dias | Mariana Paulino | A. Hamilton
Source: Defence Technology (article, 2021-08-11)
Field: Materials Science — Materials Chemistry

Ballistic response of armour plates using Generative Adversarial Networks It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V50 ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V50 BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process. Armour ; Projectile ; Ballistic limit ; Process (computing) ; Ballistic impact ; Generative grammar ; Computer science ; Ballistics ; Simulation ; Artificial intelligence ; Materials science ; Nanotechnology ; Layer (electronics) High-Velocity Impact and Material Behavior ; Adversarial Robustness in Machine Learning ; Integrated Circuits and Semiconductor Failure Analysis
Authors: David Higton | Martin Palmer | Johannes P.C. Vissers | Lauren Mullin | Robert S. Plumb | Ian D. Wilson
Source: Analytical Chemistry (article, 2021-05-13)
Field: Chemistry — Spectroscopy

Use of Cyclic Ion Mobility Spectrometry (cIM)-Mass Spectrometry to Study the Intramolecular Transacylation of Diclofenac Acyl Glucuronide 1--O-Acyl-glucuronides (AGs) are common metabolites of carboxylic acid-containing xenobiotics, including, e.g., many nonsteroidal anti-inflammatory drugs (NSAIDs). They are of concern to regulatory authorities because of the association of these metabolites with the hepatotoxicity that has resulted in drug withdrawal. One factor in assessing the potential risk posed by AGs is the rate of transacylation of the biosynthetic 1--O-acyl form to the 2-, 3-, and 4-O-acyl isomers. While transacylation can be measured using 1 H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS), the process can be time consuming and involve significant method development. The separation of these positional isomers by ion mobility spectrometry (IMS) has the potential to allow their rapid analysis, but conventional instruments lacked the resolving power to do this. Prediction of the collision cross section (CCS) using a machine learning model suggested that greater IMS resolution might be of use in this area. Cyclic IMS was evaluated for separating mixtures of isomeric AGs of diclofenac and was compared with a conventional ultraperformance liquid chromatography (UPLC)-MS method as a means for studying transacylation kinetics. The resolution of isomeric AGs was not seen using a conventional traveling wave IMS device; however, separation was seen after several passes around a cyclic IMS. The cyclic IMS enabled the degradation of the 1--O-acyl-isomer to be analyzed much more rapidly than by LC-MS. The ability of cyclic IMS to monitor the rate of AG transacylation at different pH values, without the need for a prior chromatographic separation, should allow high-throughput, real-time, monitoring of these types of reactions. Chemistry ; Ion-mobility spectrometry ; Mass spectrometry ; Chromatography ; Structural isomer ; Stereochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Forensic Toxicology and Drug Analysis
Authors: Nguyen Thi Han | Vo Khuong Dien | Ming-Fa Lin
Source: Scientific Reports (article, 2021-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Excitonic effects in the optical spectra of Li2SiO3 compound Li<sub>2</sub>SiO<sub>3</sub> compound exhibits unique electronic and optical properties. The state-of-the-art analyses, which based on first-principle calculations, have successfully confirmed the concise physical/chemical picture and the orbital bonding in Li-O and Si-O bonds. Especially, the unusual optical response behavior includes a large red shift of the onset frequency due to the extremely strong excitonic effect, the polarization of optical properties along three-directions, various optical excitations structures and the most prominent plasmon mode in terms of the dielectric functions, energy loss functions, absorption coefficients and reflectance spectra. The close connections of electronic and optical properties can identify a specific orbital hybridization for each distinct excitation channel. The presented theoretical framework will be fully comprehending the diverse phenomena and widen the potential application of other emerging materials. Plasmon ; Excitation ; Optical spectra ; Spectral line ; Orbital hybridisation ; Polarization (electrochemistry) ; Molecular orbital ; Molecular physics ; Dielectric ; Chemical bond ; Materials science ; Absorption spectroscopy ; Electronic structure ; Chemistry ; Optoelectronics ; Computational chemistry ; Optics ; Physics ; Molecule ; Physical chemistry ; Molecular orbital theory Crystal Structures and Properties ; Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials
Authors: Moazzam Ali | Ijaz Hussain | Irfan Mehmud | Muhammad Umair | Sukai Hu | Hafiz Muhammad Adeel Sharif
Source: Nanomaterials (review, 2021-12-06)
Field: Materials Science — Materials Chemistry

Recent Breakthroughs and Advancements in NOx and SOx Reduction Using Nanomaterials-Based Technologies: A State-of-the-Art Review Nitrogen and sulpher oxides (NO<sub>x</sub>, SO<sub>x</sub>) have become a global issue in recent years due to the fastest industrialization and urbanization. Numerous techniques are used to treat the harmful exhaust emissions, including dry, traditional wet and hybrid wet-scrubbing techniques. However, several difficulties, including high-energy requirement, limited scrubbing-liquid regeneration, formation of secondary pollutants and low efficiency, limit their industrial utilization. Regardless, the hybrid wet-scrubbing technology is gaining popularity due to low-costs, less-energy consumption and high-efficiency removal of air pollutants. The removal/reduction of NO<sub>x</sub> and SO<sub>x</sub> from the atmosphere has been the subject of several reviews in recent years. The goal of this review article is to help scientists grasp the fundamental ideas and requirements before using it commercially. This review paper emphasizes the use of green and electron-rich donors, new breakthroughs, reducing GHG emissions, and improved NO<sub>x</sub> and SO<sub>x</sub> removal catalytic systems, including selective/non-catalytic reduction (SCR/SNCR) and other techniques (functionalization by magnetic nanoparticles; NP, etc.,). It also explains that various wet-scrubbing techniques, synthesis of solid iron-oxide such as magnetic (Fe<sub>3</sub>O<sub>4</sub>) NP are receiving more interest from researchers due to the wide range of its application in numerous fields. In addition, EDTA coating on Fe<sub>3</sub>O<sub>4</sub> NP is widely used due to its high stability over a wide pH range and solid catalytic systems. As a result, the Fe<sub>3</sub>O<sub>4</sub>@EDTA-Fe catalyst is projected to be an optimal catalyst in terms of stability, synergistic efficiency, and reusability. Finally, this review paper discusses the current of a heterogeneous catalytic system for environmental remedies and sustainable approaches. NOx ; Wet scrubber ; Data scrubbing ; Selective catalytic reduction ; Process engineering ; Environmental science ; Waste management ; Nanotechnology ; Materials science ; Catalysis ; Scrubber ; Chemistry ; Engineering Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Gas Sensing Nanomaterials and Sensors
Authors: Siavash Zare | Mohammad Javad Abdolhosseini Qomi
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Biomaterials

Reactive force fields for aqueous and interfacial magnesium carbonate formation We develop Mg/C/O/H ReaxFF parameter sets for two environments: an aqueous force field for magnesium ions in solution and an interfacial force field for minerals and mineral-water interfaces. Since magnesium is highly ionic, we choose to fix the magnesium charge and model its interaction with C/O/H through Coulomb, Lennard-Jones, and Buckingham potentials. We parameterize the forcefields against several crystal structures, including brucite, magnesite, magnesia, magnesium hydride, and magnesium carbide, as well as Mg<sup>2+</sup> water binding energies for the aqueous forcefield. Then, we test the forcefield for other magnesium-containing crystals, solvent separated and contact ion-pairs and single-molecule/multilayer water adsorption energies on mineral surfaces. We also apply the forcefield to the forsterite-water and brucite-water interface that contains a bicarbonate ion. We observe that a long-range proton transfer mechanism deprotonates the bicarbonate ion to carbonate at the interface. Free energy calculations show that carbonate can attach to the magnesium surface with an energy barrier of about 0.22 eV, consistent with the free energy required for aqueous Mg-CO<sub>3</sub> ion pairing. Also, the diffusion constant of the hydroxide ions in the water layers formed on the forsterite surface are shown to be anisotropic and heterogeneous. These findings can help explain the experimentally observed fast nucleation and growth of magnesite at low temperature at the mineral-water-CO<sub>2</sub> interface in water-poor conditions. Brucite ; Magnesium ; Chemistry ; Magnesite ; Aqueous solution ; Inorganic chemistry ; Forsterite ; Chemical physics ; Mineralogy ; Physical chemistry Calcium Carbonate Crystallization and Inhibition ; High-pressure geophysics and materials ; CO2 Sequestration and Geologic Interactions
Authors: M. Khaіry | E.M. Kamar | M. Yehia | Emad M. Masoud | T Ramesh | D Kirana | A Ashwini | T Manasa | J Chen | H Hu | J Yang | H Xue | Y Tian | K Fan | Z Zeng | R Wang | Y Liu | G Muedas-Taipe | I Maza Meja | F Santillan | C Velsquez | Y Asencios | K Shen | M Gondal | P Sirajudheen | M Nikitha | P Karthikeyan | S Meenakshi | G Iervolino | V Vaiano | V Palma | L Mais | A Vacca | M Mascia | E Usai | S Tronci | S Palmas | M Basheer | M Zafar | Q Dar | F Nawaz | M Iqbal | M Nazar | S Santos | D Frana | L Castellano | P Trigueiro | E Silva Filho | I Santos | M Fonseca | F Ali | N Ali | I Bibi | A Said | S Nawaz | Z Ali | S Salman | H Iqbal | M Bilal | R Orozco | M Martnez-Juan | J Garca-Snchez | F Urea-Nez | R Aliabadi | N Mahmoodi | A Nicoara | V Ene | B Voicu | M Bucur | I Neacsu | B Vasile | F Iordache | W Zhao | S Liu | S Zhang | K Wang | I Jang | H Leong | H Noh | T Kang | S Kong | S.-G Oh | H Reddam | R Elmail | S Lloria | G Toms | Z Reddam | F Coloma-Pascual | A De | S Boxi | P Kharazi | R Rahimi | M Rabbani | A Hashemi Monfared
Source: Biointerface Research in Applied Chemistry (article, 2021-04-26)
Field: Materials Science — Polymers and Plastics

High Removal Efficiency of Methyl Orange Dye by Pure and (Cu, N) Doped TiO2/Polyaniline Nanocomposites Pure polyaniline and pure and copper, nitrogen-doped TiO2/Polyaniline nanocomposites have been successfully synthesized by in-situ oxidative of aniline in aqueous media containing pure or doped TiO2 nanoparticles. The as-prepared materials were characterized by TGA, XRD, FT-IR, BET, and TEM. The samples obtained have been utilized as an adsorbent to remove methyl orange from an aqueous solution. The effect of particular significant parameters such as pH, initial dye concentration, and contact time on the elimination efficiency was examined in batch experiments. The equilibrium sorption data on Ti/PANI composite were matched into Langmuir, Freundlich, and Temkin isotherms, and their parameters were fixed of the three adsorption isotherms. It is found that the R2 value of the Langmuir isotherm model was the highest, with maximum removal of 94% was obtained at a pH of 7 for an adsorbent dose of 0.1 g/100 mL. The kinetic data corresponded to several models and closely followed the pseudo-second-order model for both components. Results obtained indicate that PANI and its TiO2 or (Cu or N)TiO2/polyaniline nanocomposites could be used as an effective adsorbent significantly more than the PANI powder for dye deletion from water. Polyaniline ; Freundlich equation ; Methyl orange ; Adsorption ; Aqueous solution ; Langmuir adsorption model ; Langmuir ; Nanocomposite ; Materials science ; Aniline ; Sorption ; Nuclear chemistry ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Photocatalysis ; Polymer ; Polymerization Conducting polymers and applications ; Water Quality Monitoring and Analysis ; Adsorption and biosorption for pollutant removal
Authors: Songge Yang | Yi Wang | Zi‐Kui Liu | Yu Zhong
Source: Calphad (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Ab initio simulations on the pure Cr lattice stability at 0K: Verification with the Fe-Cr and Ni-Cr binary systems CALPHAD ; Ab initio ; Thermodynamics ; Ab initio quantum chemistry methods ; Lattice (music) ; Binary number ; Materials science ; Stability (learning theory) ; Phase diagram ; Statistical physics ; Chemistry ; Phase (matter) ; Computer science ; Physics ; Mathematics ; Molecule ; Machine learning ; Organic chemistry Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Intermetallics and Advanced Alloy Properties