jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Irina M. Benson | Thomas E. Helser | Giovanni Marchetti | Beverly K. Barnett
Source: Canadian Journal of Fisheries and Aquatic Sciences (article, 2023-07-04)
Field: Chemistry — Analytical Chemistry

The future of fish age estimation: deep machine learning coupled with Fourier transform near-infrared spectroscopy of otoliths Our novel approach for fish age prediction uses quantitative analysis of Fourier transform near-infrared (FT-NIR) spectra of otoliths by means of multimodal convolutional neural networks (MMCNN). We integrate two key data modalities that are related to fish ages: the entire range of wavenumbers of FT-NIR spectra and corresponding biological and geospatial data for nearly 9000 walleye pollock ( Gadus chalcogrammus) specimens. The proposed model extracts informative spectral features automatically and elucidates hidden structural relationships associated with fish growth to improve age predictions. Absorbance associated with 7000 to 4000 cm −1 wavenumbers had the highest influence on model predictions followed by fish length, latitude, depth, and temperature. The optimal model resulted in good overall performance with an R 2 of 0.93 and RMSE of 0.83 for training data set and R 2 of 0.92 and RMSE of 0.91 for test data set. MMCNN's age predictions were comparable to microscope-based ages yielding as good or slightly better precision. Moreover, the model outperformed classical partial least squares analysis of otolith spectra and remedied prediction bias at older ages of fish. Otolith ; Mean squared error ; Fourier transform ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Data set ; Mathematics ; Convolutional neural network ; Absorbance ; Partial least squares regression ; Statistics ; Fish <Actinopterygii> ; Computer science ; Fishery ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Marine and fisheries research ; Identification and Quantification in Food
Authors: Vianney Andrew Yiga | Moses Katamba | Michael Lubwama | Karin H. Adolfsson | Minna Hakkarainen | Edwin Kamalha
Source: Journal of Thermal Analysis and Calorimetry (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Combustion, kinetics and thermodynamic characteristics of rice husks and rice husk-biocomposites using thermogravimetric analysis Abstract Pyrolysis of rice husk (RH), alkali-treated cellulose-rich rice husk (RHC), chemically modified RHC (RHCM) and RH-biocomposites by thermogravimetric analysis was carried out to determine combustion and kinetic parameters at three different heating rates of 20, 40 and 50 °C min −1 . Combustion performance was analyzed from results of ignition temperature, burnout temperature, combustion rates, flammability index and combustion characteristic index. Increase in heating rate from 20 to 40 and further to 50 °C min −1 increased the onset of degradation, burnout and peak temperatures as observed by curve shifts to the right. Maximum combustion rates were around 0.57–0.59% min −1 , 1.03% min −1 and 0.63–0.69% min −1 for RH, RHC and RHCM, respectively. For the RH-biocomposites, the maximum combustion rates were in a 0.76–0.97% min −1 range. Their average pre-exponential factors using KAS method were in the 2.24E-03–8.07E-03 range, respectively, while those for OFW method were in the 7.75E + 04–4.55E + 06 range, respectively. Average activation energies of RH-biocomposites were in the 41.0–58.2 kJ mol −1 and 48.3–67.7 kJ mol −1 ranges for KAS and OFW methods, respectively. The data were well fitting with coefficient of determination ( R 2 ) values close to 1. Average Δ G value ranges for RH-biocomposites ranged between 148.2 and 161.7 kJ mol −1 . The low-energy barrier (≤ 5.4 kJ mol −1 ) between activation energy and enthalpy changes indicated that reaction initiation occurs easily. Thermogravimetric analysis ; Husk ; Combustion ; Materials science ; Activation energy ; Enthalpy ; Autoignition temperature ; Analytical Chemistry (journal) ; Atmospheric temperature range ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Thermodynamics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Flame retardant materials and properties
Authors: Amr Fouda | Khalid S. Alshallash | Mohammed Ibrahim Alghonaim | Ahmed M. Eid | Ahmed M. Alemam | Mohamed A. Awad | Mohammed F. Hamza
Source: Chemistry (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

The Antimicrobial and Mosquitocidal Activity of Green Magnesium Oxide Nanoparticles Synthesized by an Aqueous Peel Extract of Punica granatum An aqueous extract of Punica granatum peel was used as a biocatalyst for magnesium oxide nanoparticle (MgO-NP) synthesis, which was characterized via UV-Vis spectroscopy, TEM, EDX, FT-IR, XRD, DLS, and zeta potential. Data showed the efficacy of the plant aqueous extract in forming spherical, crystalline-nature, well-arranged MgO-NPs with sizes in the range of 10–45 nm with average sizes of 24.82 ± 8.85 nm. Moreover, EDX analysis revealed that the highest weight and atomic percentages were recorded for Mg and O ions. The green synthesized MgO-NPs showed antimicrobial activity against Bacillus subtilis, Staphylococcus aureus, E. coli, Pseudomonas aeruginosa, and Candida albicans in a concentration-dependent manner with clear zones in the range of 8.7 ± 0.6 to 19.7 ± 0.5 mm with various concentrations. Also, the MIC value was varied to be 25 µg mL−1 for Gram-negative bacteria, B. subtilis, and C. albicans and 50 µg mL−1 for S. aureus. Moreover, MgO-NPs showed high activity against the 3rd-instar larvae of Culex quinquefasciatus. The mortality percentages were concentration- and time-dependent. Data analysis showed that the highest mortality was 88.3 ± 3.2%, attained at a concentration of 100 µg mL−1 after 72 h. Also, all originated pupae were malformed and did not hatch to adults, with mortality percentages of 100% at all concentrations. Overall, the P. granatum-mediated MgO-NPs showed promising activity in inhibiting the growth of pathogenic microbes and the hatching of C. quinquefasciatus larvae to adults. Punica ; Bacillus subtilis ; Nuclear chemistry ; Aqueous solution ; Antibacterial activity ; Zeta potential ; Chemistry ; Phytochemical ; Magnesium ; Staphylococcus aureus ; Antimicrobial ; Food science ; Biology ; Nanoparticle ; Bacteria ; Materials science ; Biochemistry ; Organic chemistry ; Horticulture ; Nanotechnology Nanoparticles: synthesis and applications
Authors: Thomas Savage | Nausheen Basha | Jonathan McDonough | Omar K. Matar | Ehecatl Antonio del Rio‐Chanona
Source: Computers & Chemical Engineering (article, 2023-09-16)
Field: Materials Science — Materials Chemistry

Multi-fidelity data-driven design and analysis of reactor and tube simulations Mixing (physics) ; Fidelity ; High fidelity ; Parameterized complexity ; Computer science ; Nonlinear system ; Chemical reactor ; Optimal design ; Mathematical optimization ; Gaussian ; Algorithm ; Simulation ; Engineering ; Mathematics ; Chemistry ; Physics ; Machine learning Machine Learning in Materials Science ; Advanced Control Systems Optimization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Huiqiang Hu | Tingting Wang | Yunpeng Wei | Zhenyu Xu | Shiyu Cao | Ling Fu | Huaxing Xu | Xiaobo Mao | Luqi Huang
Source: Frontiers in Plant Science (article, 2023-10-25)
Field: Chemistry — Analytical Chemistry

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R<sup>2</sup>) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry. Daidzin ; Puerarin ; Hyperspectral imaging ; Artificial intelligence ; Isoflavones ; Computer science ; Partial least squares regression ; Multispectral image ; Process analytical technology ; Preprocessor ; Convolutional neural network ; Machine learning ; Pattern recognition (psychology) ; Daidzein ; Biological system ; Chemistry ; Engineering ; Biology ; Work in process Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Axel Zuber | Véronique Gauthier‐Brunet | Jérôme Roger | Jesús González‐Julián | T. Ouisse | S. Dubois
Source: Journal of the European Ceramic Society (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Cr2AlC high temperature oxidation under dry and wet air: Understanding of the oxidation mechanism Materials science ; Mechanism (biology) ; Chemical engineering ; Mineralogy ; Chemistry MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Steven A. Spronk | Zachary L. Glick | Derek P. Metcalf | C. David Sherrill | Daniel L. Cheney
Source: Scientific Data (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions Fast and accurate calculation of intermolecular interaction energies is desirable for understanding many chemical and biological processes, including the binding of small molecules to proteins. The Splinter ["Symmetry-adapted perturbation theory (SAPT0) protein-ligand interaction"] dataset has been created to facilitate the development and improvement of methods for performing such calculations. Molecular fragments representing commonly found substructures in proteins and small-molecule ligands were paired into >9000 unique dimers, assembled into numerous configurations using an approach designed to adequately cover the breadth of the dimers' potential energy surfaces while enhancing sampling in favorable regions. ~1.5 million configurations of these dimers were randomly generated, and a structurally diverse subset of these were minimized to obtain an additional ~80 thousand local and global minima. For all >1.6 million configurations, SAPT0 calculations were performed with two basis sets to complete the dataset. It is expected that Splinter will be a useful benchmark dataset for training and testing various methods for the calculation of intermolecular interaction energies. Maxima and minima ; Intermolecular force ; Interaction energy ; Quantum chemical ; Molecule ; Small molecule ; Computer science ; Benchmark (surveying) ; Intermolecular interaction ; Computational chemistry ; Physics ; Statistical physics ; Biological system ; Chemical physics ; Chemistry ; Biology ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Filippo Pinelli | Chiara Piras | Liebert Parreiras Nogueira | Filippo Rossi
Source: Gels (article, 2023-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the Sorbent Ability and Reusability of Graphene-Oxide–Chitosan Aerogels for the Removal of Dyes from Wastewater One of the most persistent issues affecting people worldwide is water contamination due to the indiscriminate disposal of pollutants, causing severe environmental problems. Dyes are among the most harmful contaminants because of their high chemical stability and consequently difficult degradation. To remove contaminants from water, adsorption is the most widely used and effective method. In this work, we recall the results already published about the synthesis, the characterization and the use of porous graphene-oxide-chitosan aerogels as a sorbent material. Those systems, prepared by mixing GO sheets and CS chains, using APS as a cross-linking agent, and by further lyophilization, were further characterized using nano-computed tomography, supplying more understanding about their micro and nano-structure. Their sorbent ability has been investigated also by the study of their isotherm of adsorption of two different anionic dyes: Indigo Carmine and Cibacron Brilliant Yellow. Those analyses confirmed the potentialities of the aerogels and their affinity for those anionic dyes. Moreover, the possibility of regenerating and reusing the material was evaluated as a key aspect for applications of this kind. The treatment with NaOH, to promote the desorption of adsorbed dyes, and subsequent washing with HCl, to re-protonate the system, ensured the regeneration of the gels and their use in multiple cycles of adsorption with the selected water contaminants. Sorbent ; Adsorption ; Chitosan ; Indigo carmine ; Materials science ; Chemical engineering ; Wastewater ; Oxide ; Graphene ; Reusability ; Desorption ; Portable water purification ; Nanotechnology ; Chemistry ; Organic chemistry ; Waste management ; Computer science Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal
Authors: Simbarashe Fashu | Vera Trabadelo
Source: Metals (review, 2023-10-31)
Field: Materials Science — Materials Chemistry

Development and Performance of High Chromium White Cast Irons (HCWCIs) for Wear–Corrosive Environments: A Critical Review There is a huge demand for high-performance materials in extreme environments involving wear and corrosion. High chromium white cast irons (HCWCIs) display better performance than many materials since they are of sufficient hardness for wear protection and can be tailored in chemical compositions to improve corrosion resistance; however, their performance is often still inadequate. This article reviews the chemical composition and microstructure design aspects employed to tailor and develop HCWCIs with combined corrosion and wear resistance. The performance of these alloys under wear and corrosion is reviewed to highlight the influence of these parameters in the industry. Existing challenges and future opportunities, mainly focusing on metallurgical alloy development aspects like chemical composition, casting, and heat treatment design, are highlighted. This is followed by suggestions for potential developments in HCWCIs to improve the performance of materials in these aggressive environments. Many variables are involved in the design to obtain suitable microstructures and matrix composition for wear–corrosion resistance. Computational modeling is a promising approach for optimizing multi-design variables; however, reliable field performance data of HCWCIs in wear–corrosion environments are still inadequate. Quantitative evaluation of the wear–corrosion performance of HCWCIs requires the development of laboratory and field tests using standard conditions like abrasive type and sizes, severity of loading, slurry velocity, pH, and temperature to develop wear–corrosion maps to guide alloy development. Corrosion ; Metallurgy ; Materials science ; Abrasive ; Microstructure ; Alloy ; Slurry ; Casting ; Composite material Metal Alloys Wear and Properties ; Advanced materials and composites ; High Entropy Alloys Studies
Authors: Michal Krbaťa | Róbert Cíger | Marcel Kohutiar | Maroš Eckert | Igor Barényi | Bohdan Trembach | Andrej Dubec | Jana Escherová | Matúš Gavalec | Naďa Beronská
Source: Materials (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Microstructural Changes and Determination of a Continuous Cooling Transformation (CCT) Diagram Using Dilatometric Analysis of M398 High-Alloy Tool Steel Produced by Microclean Powder Metallurgy The paper deals with the dilatometric study of high-alloy martensitic tool steel with the designation M398 (BÖHLER), which is produced by the powder metallurgy process. These materials are used to produce screws for injection molding machines in the plastic industry. Increasing the life cycle of these screws leads to significant economic savings. This contribution focuses on creating the CCT diagram of the investigated powder steel in the range of cooling rates from 100 to 0.01 °C/s. JMatPro<sup>®</sup> API v7.0 simulation software was used to compare the experimentally measured CCT diagram. The measured dilatation curves were confronted with a microstructural analysis, which was evaluated using a scanning electron microscope (SEM). The M398 material contains a large number of carbide particles that occur in the form of M<sub>7</sub>C<sub>3</sub> and MC and are based on Cr and V. EDS analysis was used to evaluate the distribution of selected chemical elements. A comparison of the surface hardness of all samples in relation to the given cooling rates was also carried out. Subsequently, the nanoindentation properties of the formed individual phases as well as the carbides, where the nanohardness and reduced modulus of elasticity (carbides and matrix) were evaluated. Materials science ; Continuous cooling transformation ; Metallurgy ; Powder metallurgy ; Carbide ; Tool steel ; Alloy ; Nanoindentation ; Martensite ; Alloy steel ; Scanning electron microscope ; Microstructure ; Composite material ; Bainite Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials ; Advanced materials and composites
Authors: Aaron R. Finney | Matteo Salvalaglio
Source: The Journal of Chemical Physics (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

A variational approach to assess reaction coordinates for two-step crystallization Molecule- and particle-based simulations provide the tools to test, in microscopic detail, the validity of classical nucleation theory. In this endeavor, determining nucleation mechanisms and rates for phase separation requires an appropriately defined reaction coordinate to describe the transformation of an out-of-equilibrium parent phase for which myriad options are available to the simulator. In this article, we describe the application of the variational approach to Markov processes to quantify the suitability of reaction coordinates to study crystallization from supersaturated colloid suspensions. Our analysis indicates that collective variables (CVs) that correlate with the number of particles in the condensed phase, the system potential energy, and approximate configurational entropy often feature as the most appropriate order parameters to quantitatively describe the crystallization process. We apply time-lagged independent component analysis to reduce high-dimensional reaction coordinates constructed from these CVs to build Markov State Models (MSMs), which indicate that two barriers separate a supersaturated fluid phase from crystals in the simulated environment. The MSMs provide consistent estimates for crystal nucleation rates, regardless of the dimensionality of the order parameter space adopted; however, the two-step mechanism is only consistently evident from spectral clustering of the MSMs in higher dimensions. As the method is general and easily transferable, the variational approach we adopt could provide a useful framework to study controls for crystal nucleation. Nucleation ; Statistical physics ; Crystallization ; Reaction coordinate ; Curse of dimensionality ; Computer science ; Biological system ; Thermodynamics ; Chemistry ; Physics ; Physical chemistry ; Artificial intelligence Material Dynamics and Properties ; Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions
Authors: Tom Pan | Shikai Jin | Mitchell D. Miller | Anastasios Kyrillidis | G.N. Phillips
Source: IUCrJ (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

A deep learning solution for crystallographic structure determination The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept. Computer science ; Convolutional neural network ; Simple (philosophy) ; Protein Data Bank (RCSB PDB) ; Deep learning ; Protein Data Bank ; Artificial neural network ; Artificial intelligence ; Phase (matter) ; Protein structure ; Algorithm ; Theoretical computer science ; Crystallography ; Chemistry ; Stereochemistry ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Mohammad Kianezhad | Mehrdad Youzi | Mehran Vaezi | Hossein Nejat Pishkenari
Source: Scientific Reports (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Unidirectional motion of C60-based nanovehicles using hybrid substrates with temperature gradient Abstract With the synthesis of nanocar structures the idea of transporting energy and payloads on the surface became closer to reality. To eliminate the concern of diffusive surface motion of nanocars, in this study, we evaluate the motion of C 60 and C 60 -based nanovehicles on graphene and hexagonal boron-nitride (BN) surfaces using molecular dynamics simulations and potential energy analysis. Utilizing the graphene-hBN hybrid substrate, it has been indicated that C 60 is more stable on boron-nitride impurity regions in the hybrid substrate and an energy barrier restricts the motion to the boron-nitride impurity. Increasing the temperature causes the molecule to overcome the energy barrier frequently. A nanoroad of boron-nitride with graphene sideways is designed to confine the surface motion of C 60 and nanovehicles at 300 K. As expected, the motion of all surface molecules is limited to the boron-nitride nanoroads. Although the motion is restricted to the boron-nitride nanoroad, the diffusive motion is still noticeable in lateral directions. To obtain the unidirectional motion for C 60 and nanocars on the surface, a temperature gradient is applied to the surface. The unidirectional transport to the nanoroad regions with a lower temperature occurs in a short period of time due to the lower energies of molecules on the colder parts. Graphene ; Boron nitride ; Materials science ; Chemical physics ; Substrate (aquarium) ; Nanotechnology ; Boron ; Molecule ; Temperature gradient ; Hexagonal boron nitride ; Condensed matter physics ; Chemistry ; Physics ; Organic chemistry Graphene research and applications ; Molecular Junctions and Nanostructures ; Fullerene Chemistry and Applications