jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luigi Calabrese | Massimiliano Galeano | E. Proverbio
Source: Corrosion and Materials Degradation (article, 2023-12-06)
Field: Materials Science — Metals and Alloys

Data Mining Applied to the Electrochemical Noise Technique in the Time/Frequency Domain for Stress Corrosion Cracking Recognition In this paper, time/frequency domain data processing was proposed to analyse the EN signal recorded during stress corrosion cracking on precipitation-hardening martensitic stainless steel in a chloride environment. Continuous Wavelet Transform, albeit with some limitations, showed a suitable support in the discriminatory capacity among transient signals related to the different stress corrosion cracking mechanisms. In particular, the aim is to propose the analysis of electrochemical noise signals under stress corrosion cracking conditions in the time–frequency domain by using the Hilbert–Huang approach. The Hilbert–Huang Transform (performed by the Empirical Mode Decomposition approach) was finally proposed to carry out an identification of the corrosion mechanisms in comparison to conventional data processing methods. By using this approach, a detailed simultaneous decomposition of the original electrochemical noise data in the time and frequency domain was carried out. The method gave useful information about transitions among different corrosion mechanisms, allowing us to (i) identify a specific characteristic response for each corrosion damaging phenomenon induced by stress corrosion cracking, (ii) time each corrosion of the damaging phenomenon, and (iii) provide a topological description of the advancing SCC damaging stages. This characteristic evidences that the Hilbert–Huang Transform is a very powerful technique to potentially recognize and distinguish the different corrosion mechanisms occurring during stress corrosion cracking. Electrochemical noise ; Corrosion ; Stress corrosion cracking ; Materials science ; Hilbert–Huang transform ; Time domain ; Frequency domain ; Cracking ; Stress (linguistics) ; Pitting corrosion ; Noise (video) ; Metallurgy ; Computer science ; Electrochemistry ; Composite material ; Electrode ; Artificial intelligence ; Physics ; White noise Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Paolo Pegolo | Stefano Baroni | Federico Grasselli
Source: The Journal of Chemical Physics (article, 2023-09-07)
Field: Chemistry — Electrochemistry

Self-interaction and transport of solvated electrons in molten salts The dynamics of (few) electrons dissolved in an ionic fluid-as when a small amount of metal is added to a solution while upholding its electronic insulation-manifests interesting properties that can be ascribed to nontrivial topological features of particle transport (e.g., Thouless' pumps). In the adiabatic regime, the charge distribution and the dynamics of these dissolved electrons are uniquely determined by the nuclear configuration. Yet, their localization into effective potential wells and their diffusivity are dictated by how the self-interaction is modeled. In this article, we investigate the role of self-interaction in the description of the localization and transport properties of dissolved electrons in non-stoichiometric molten salts. Although the account for the exact (Fock) exchange strongly localizes the dissolved electrons, decreasing their tunneling probability and diffusivity, we show that the dynamics of the ions and of the dissolved electrons are largely uncorrelated, irrespective of the degree to which the electron self-interaction is treated and in accordance with topological arguments. Electron ; Chemical physics ; Ionic bonding ; Solvated electron ; Ion ; Thermal diffusivity ; Diffusion ; Chemistry ; Adiabatic process ; Quantum tunnelling ; Charged particle ; Materials science ; Physics ; Condensed matter physics ; Thermodynamics ; Physical chemistry ; Aqueous solution ; Quantum mechanics Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Ionic liquids properties and applications
Authors: Mahesh A. Barmade | Prakhar Agrawal | Sweta R. Rajput | Prashant R. Murumkar | Bhavika Rana | Dinkar Sahal | Mange Ram Yadav
Source: RSC Medicinal Chemistry (article, 2023-12-08)
Field: Chemistry — Organic Chemistry

Novel quinolinepiperazinyl-aryltetrazoles targeting the blood stage of <i>Plasmodium falciparum</i> The emergence of drug resistance against the frontline antimalarials is a major challenge in the treatment of malaria. In view of emerging reports on drug-resistant strains of <i>Plasmodium</i> against artemisinin combination therapy, a dire need is felt for the discovery of novel compounds acting against novel targets in the parasite. In this study, we identified a novel series of quinolinepiperazinyl-aryltetrazoles (QPTs) targeting the blood stage of <i>Plasmodium</i>. <i>In vitro</i> anti-plasmodial activity screening revealed that most of the compounds showed IC<sub>50</sub> < 10 μM against chloroquine-resistant <i>Pf</i>INDO strain, with the most promising lead compounds 66 and 75 showing IC<sub>50</sub> values of 2.25 and 1.79 μM, respectively. Further, compounds 64-66, 68, 75-77 and 84 were found to be selective (selectivity index >50) in their action against <i>Pf</i> over a mammalian cell line, with compounds 66 and 75 offering the highest selectivity indexes of 178 and 223, respectively. Explorations into the action of lead compounds 66 and 75 revealed their selective cidal activity towards trophozoites and schizonts. In a ring-stage survival assay, 75 showed cidal activity against the early rings of artemisinin-resistant <i>Pf</i>Cam3.1<sup>R539T</sup>. Further, 66 and 75 in combination with artemisinin and pyrimethamine showed additive to weak synergistic interactions. Of these two <i>in vitro</i> lead molecules, only 66 restricted rise in the percentage of parasitemia to about 10% in <i>P. berghei</i>-infected mice with a median survival time of 28 days as compared to the untreated control, which showed the percentage of parasitemia >30%, and a median survival of 20 days. Promising antimalarial activity, high selectivity, and additive interaction with artemisinin and pyrimethamine indicate the potential of these compounds to be further optimized chemically as future drug candidates against malaria. Plasmodium falciparum ; Malaria ; Plasmodium (life cycle) ; Parasite hosting ; Virology ; Biology ; Immunology ; World Wide Web ; Computer science Synthesis of Tetrazole Derivatives ; Computational Drug Discovery Methods ; Synthesis and biological activity
Authors: Dexin Zhao | Aniket Patel | Aaron Barbosa | Marcus Høy Hansen | Ainiu L. Wang | Jiaqi Dong | Yuwei Zhang | Tejas Umale | İbrahim Karaman | Patrick J. Shamberger | Sarbajit Banerjee | Matt Pharr | Kelvin Y. Xie
Source: Ultramicroscopy (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

A reference-area-free strain mapping method using precession electron diffraction data Diffraction ; Residual ; Materials science ; Sapphire ; Strain (injury) ; Electron diffraction ; Optics ; Standard deviation ; Computer science ; Physics ; Algorithm ; Mathematics ; Statistics Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Microstructure and Mechanical Properties of Steels
Authors: Minjun Kim | Hyun-Ju Ahn | Vanna Chrismas Silalahi | Damun Heo | Samir Adhikari | Y. D. Jang | Jong‐Min Lee | D. Lee
Source: International Journal of Molecular Sciences (article, 2023-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-Dewetting Process for Self-Assembled Nanoparticle Clusters in Wafer Scale Plasmonic molecules, which are geometrically well-defined plasmonic metal nanoparticle clusters, have attracted significant attention due to their enhancement of light-matter interactions owing to a stronger electric field enhancement than that by single particles. High-resolution lithography techniques provide precise positioning of plasmonic nanoparticles, but their fabrication costs are excessively high. In this study, we propose a lithography-free, self-assembly fabrication method, termed the dual-dewetting process, which allows the control of the size and density of gold nanoparticles. This process involves depositing a gold thin film on a substrate and inducing dewetting through thermal annealing, followed by a second deposition and annealing. The method achieves a uniform distribution of particle size and density, along with increased particle density, across a 6-inch wafer. The superiority of the method is confirmed by a 30-fold increase in the signal intensity of surface-enhanced Raman scattering following the additional dewetting with an 8 nm film, compared to single dewetting alone. Our findings indicate that the dual-dewetting method provides a simple and efficient approach to enable a variety of plasmonic applications through efficient plasmonic molecule large-area fabrication. Dewetting ; Materials science ; Nanotechnology ; Lithography ; Plasmonic nanoparticles ; Plasmon ; Wafer ; Fabrication ; Nanoparticle ; Annealing (glass) ; Optoelectronics ; Raman scattering ; Raman spectroscopy ; Thin film ; Optics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Fluid Dynamics and Thin Films ; Plasmonic and Surface Plasmon Research
Authors: Thomas D. Swinburne
Source: Physical Review Letters (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors Atomic simulations of materials require significant resources to generate, store, and analyze. Here, descriptor functions are proposed as a general, metric latent space for atomic structures, ideal for use in large-scale simulations. Descriptors can regress a broad range of properties, including character-dependent dislocation densities, stress states, or radial distribution functions. A vector autoregressive model can generate trajectories over yield points, resample from new initial conditions and forecast trajectory futures. A forecast confidence, essential for practical application, is derived by propagating forecasts through the Mahalanobis outlier distance, providing a powerful tool to assess coarse-grained models. Application to nanoparticles and yielding of nanoscale dislocation networks confirms low uncertainty forecasts are accurate and resampling allows for the propagation of smooth property distributions. Yielding is associated with a collapse in the intrinsic dimension of the descriptor manifold, which is discussed in relation to the yield surface. Granularity ; Statistical physics ; Computer science ; Resampling ; Mahalanobis distance ; Algorithm ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Zihao Wan | Hong Yang | Mingyu Gao | Jipan Xu | Hongbo Mu | Dawei Qi | Shuxia Han
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

NIR-VGGNet19: A Novel Deep Convolutional Neural Network for Pinus NIR Spectra Classification Wood is an indispensable non-renewable resource and plays an important role in our life. So, it is a necessity to apply accurate classification techniques. In this paper, a novel model of deep one-dimensional convolutional neural network NIR-VGGNet19 is proposed to classify seven pius wood by combining near-infrared spectroscopy. NIR-VGGNet19 uses a deep convolutional neural network to automatically extract features, eliminate noise, and solve the problem of spectral overlapping peaks; Initially eliminates random noise by adding 1×7 convolutional kernels; then uses an attention mechanism to extract more accurate features. According to the results, NIR-VGGNet19 achieved a classification accuracy of 98.41% on the test set. When comparing NIR-VGGNet19 with LeNet, AlexNet, VGGNet-19, ResNet-34, backpropagation neural network, and support vector machine for testing respectively, NIR-VGGNet19 has the best classification accuracy, which is higher than 70.95% of LeNet, 92.54% of AlexNet, 93.02% of VGGNet-19, 96.67% of ResNet-34, 76.19% of backpropagation neural network, and 71.26% of support vector machine. Thus, it suggests that the NIR-VGGNet19 can improve the accuracy of classifying wood of the homogeneous genus. Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Backpropagation ; Support vector machine ; Artificial neural network ; Near-infrared spectroscopy ; Test set ; Noise (video) Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Xianmei Chen | Artemiy V. Khamenok | Shaukat Khusainov | Mikhail V. Shestakov | V. V. Moshchalkov
Source: Optics (article, 2023-01-17)
Field: Materials Science — Materials Chemistry

White Photoluminescence in Dy-Doped Oxyfluoride Glasses Dy3+-doped, Ag and Dy3+-co-doped, and Tm3+ and Dy3+-co-doped oxyfluoride glasses have been prepared by a conventional melt-quenching method. White and yellowish light emissions were generated in the glasses under excitation in the range from 350 to 390 nm. Changes in the excitation wavelength in the ultraviolet (UV) range do not significantly alter the emission tint of the Dy and Ag and Dy-co-doped glasses. On the contrary, the tint of the Tm and Dy co-doped glasses considerably varies under excitation in the same range. Doping ; Photoluminescence ; Materials science ; Excitation ; Ultraviolet ; Excitation wavelength ; Analytical Chemistry (journal) ; White light ; Wavelength ; Mineralogy ; Optics ; Optoelectronics ; Chemistry ; Physics Luminescence Properties of Advanced Materials ; Glass properties and applications ; Solid State Laser Technologies
Authors: Kevin Mcdonnell | Enda Howley | Florence Abram
Source: Computational and Structural Biotechnology Journal (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed <i>de novo</i>. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of <i>de novo</i> peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of <i>de novo</i> peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art <i>de novo</i> peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of <i>de novo</i> peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of <i>de novo</i> algorithms as well as alleviating the reliance on database searches for training data. Computer science ; Artificial intelligence ; Identification (biology) ; Context (archaeology) ; Noise (video) ; Machine learning ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Samuel Cajahuaringa | Daniel L. Z. Caetano | Leandro N. Zanotto | Guido Araújo | Munir S. Skaf
Source: Journal of Chemical Information and Modeling (article, 2023-05-15)
Field: Chemistry — Spectroscopy

MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies Ion mobility mass spectrometry (IM-MS) techniques have become highly valued as a tool for structural characterization of biomolecular systems since they yield accurate measurements of the rotationally averaged collision cross-section (CCS) against a buffer gas. Despite its enormous potential, IM-MS data interpretation is often challenging due to the conformational isomerism of metabolites, lipids, proteins, and other biomolecules in the gas phase. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS data. In this work, we present MassCCS, a parallelized open-source code for computing CCS of molecules ranging from small organic compounds to massive protein assemblies at the trajectory method level of description using atomic and molecular buffer gas particles. The performance of the code is comparable to other available software for small molecules and proteins but is significantly faster for larger macromolecular assemblies. We performed extensive tests regarding accuracy, performance, and scalability with system size and number of CPU cores. MassCCS has proven highly accurate and efficient, with execution times under a few minutes, even for large (84.87 MDa) virus capsid assemblies with very modest computational resources. MassCCS is freely available at https://github.com/cces-cepid/massccs. Scalability ; Biomolecule ; Computer science ; Computational science ; Software ; Collision ; Macromolecule ; Gas phase ; Small molecule ; Supercomputer ; Chemistry ; Biological system ; Nanotechnology ; Parallel computing ; Materials science ; Operating system Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Weiwei Chen | Yi‐Ying Chen | Xianhu Zhu | Miaomiao Xu | Zhihao Han | Lianhui Wang | Lixing Weng
Source: Chemosensors (article, 2023-08-05)
Field: Materials Science — Materials Chemistry

NaBH4-Mediated Co-Reduction Synthesis of Glutathione Stabilized Gold/Silver Nanoclusters for Detection of Magnesium Ions The content of magnesium ions (Mg2+) in drinking water is relatively high and the excessive Mg2+ ingestion may lead to pathological lesions in the human body system. At present, the detection of Mg2+ still relies on costly devices or/and complex organic fluorescence probes. To solve this problem, this work proposed a NaBH4-mediated co-reduction strategy for the synthesis of glutathione-stabilized bimetallic AuAg nanoclusters (GSH@AuAg NCs) with performance recognition to Mg2+. The preparation of GSH@AuAg NCs was simple and rapid and could be performed at mild conditions. The reaction parameters and sampling orders were optimized to understand the formation mechanism of GSH@AuAg NCs. The GSH@AuAg NCs exhibited a sensitive “light on” fluorescence response to Mg2+ due to the re-molding of the interfacial physicochemical environment following the Mg2+ coordination, which affected the surface charge transfer process, and thus led to a novel method for fluorescence detection of Mg2+ with admirable selectivity for Mg2+. The proposed method showed a detection limit of 0.2 μM, and its practical utility for the detection of Mg2+ in a real sample of purified drinking water was also demonstrated, confirming its practicability in monitoring the Mg2+ concentration in drinking water. Nanoclusters ; Bimetallic strip ; Glutathione ; Detection limit ; Fluorescence ; Magnesium ; Chemistry ; Selectivity ; Ion ; Metal ions in aqueous solution ; Metal ; Inorganic chemistry ; Materials science ; Catalysis ; Chromatography ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications
Authors: Byeong Sub Kwak | Taehyun Jeon | Ahreum Kim | Eun Young Kim | Jae-Kon Kim | Youngje Min | Hyunjoo Park
Source: Applied Sciences (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Chemical Property Evaluation and Tensile Strength Correlation of XLPE Insulators Based on Accelerated Thermal Aging Cross-linked polyethylene (XLPE) cable is a representative power transmission cable. XLPE has excellent mechanical properties, chemical and heat resistance, and insulation. However, XLPE insulation deteriorates during operation due to electrical, mechanical, and thermal stresses. Among these, thermal stress is a major factor and reduces insulation properties due to a change in molecular structure. Therefore, XLPE characteristic evaluation by heat exposure is essential for power cable condition evaluation. Herein, deteriorated XLPE samples were characterized by tensile strength, X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectroscopy after exposure to various temperatures and durations. Comparing the tensile strength with other analysis results yielded correlations. Each characteristic showed a linear relationship. The correlation between tensile strength and carbonyl index was the strongest, and the coefficient of determination, R2, was 0.9299. Therefore, these results will provide important information on chemical properties when establishing operational management standards for XLPE insulators in the future. Materials science ; Ultimate tensile strength ; Composite material ; Polyethylene ; Differential scanning calorimetry ; Cross-linked polyethylene ; Fourier transform infrared spectroscopy ; Optics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Mert Akın İnsel | Selcan Karakuş | Gizem Temelcan | Hale Gonce Köçken | İnci Albayrak
Source: Physica Scripta (article, 2023-01-24)
Field: Materials Science — Polymers and Plastics

Handling uncertainty in rheological properties of green eggshell nanocomposites by a fuzzy-hybrid modeling approach: a comparative study Abstract Fuzzy regression is an attractive application due to its ability to solve problems in the absence of accurate mathematical models. In this study, rheological properties of a bio-nanocomposite were predicted using fuzzy regression. The aim of study is to introduce the methodology and application of fuzziness in detail for handling uncertainty in the experimental data. In this context, rheological properties of green eggshell bio-nanocomposites are investigated using different operating parameters. Since the determination of surface tension is prone to experimental errors, a novel fuzzy-hybrid model is proposed in addition to a crisp model. Statistical error analysis is conducted to assess the validity of the multivariate models proposed. While both models provided satisfactory results, it is observed that the fuzzy-hybrid model outperforms the crisp model in both accuracy and generality. This study highlights that the proposed models have an impressive potential to be used for modeling experimental studies in nanotechnological manufacturing applications. Fuzzy logic ; Context (archaeology) ; Generality ; Rheology ; Computer science ; Multivariate statistics ; Experimental data ; Biological system ; Biochemical engineering ; Materials science ; Mathematics ; Machine learning ; Artificial intelligence ; Statistics ; Engineering ; Composite material ; Geology Textile materials and evaluations ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Polymer crystallization and properties