jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Biao Liu | Hongxu Zhang | Jieqiang Zhu | Yuan Chen | Yixia Pan | Xingchu Gong | Jizhong Yan | Hui Zhang
Source: Sensors (article, 2024-05-27)
Field: Chemistry — Analytical Chemistry

Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng. The "Red Ginseng-Mycotoxin" (R-M) dataset is established, and optimal parameters for 1D-CNN, residual bidirectional long short-term memory (ResBiLSTM), and 1DCNN-ResBiLSTM-Attention models are determined. The models achieved testing accuracies of 98.75%, 99.03%, and 99.17%, respectively. To simulate real detection scenarios with potential interfering impurities during the sampling process, a "Red Ginseng-Mycotoxin-Interfering Impurities" (R-M-I) dataset was created. The testing accuracy of the 1DCNN-ResBiLSTM-Attention model reached 96.39%, and it successfully predicted pixel-wise classification for other unknown samples. This study introduces a novel method for real-time mycotoxin monitoring in traditional Chinese medicine, with important implications for the on-site quality control of herbal materials. Hyperspectral imaging ; Residual ; TRACE (psycholinguistics) ; Artificial intelligence ; Pixel ; Pattern recognition (psychology) ; Mycotoxin ; Ginseng ; Computer science ; Computer vision ; Remote sensing ; Biology ; Botany ; Geography ; Medicine ; Algorithm Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Food Quality and Safety Studies
Authors: Huaizhong Zhang | Kristen A. Fichthorn
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structural classification of Ag and Cu nanocrystals with machine learning We use machine learning (ML) to classify the structures of mono-metallic Cu and Ag nanoparticles. Our datasets comprise a broad range of structures - both crystalline and amorphous - derived from parallel-tempering molecular dynamics simulations of nanoparticles in the 100-200 atom size range. We construct nanoparticle features using common neighbor analysis (CNA) signatures, and we utilize principal component analysis to reduce the dimensionality of the CNA feature set. To sort the nanoparticles into structural classes, we employed both K-means clustering and the Gaussian mixture model (GMM). We evaluated the performance of the clustering algorithms through the gap statistic and silhouette score, as well as by analysis of the CNA signatures. For Ag, we found five structural classes, with 14 detailed sub-classes, while for Cu, we found two broad classes (crystalline and amorphous), with the same five classes as for Ag, and 15 detailed sub-classes. Our results demonstrate that these ML methods are effective in identifying and categorizing nanoparticle structures to different levels of complexity, enabling us to classify nanoparticles into distinct and physically relevant structural classes with high accuracy. This capability is important for understanding nanoparticle properties and potential applications. Nanocrystal ; Materials science ; Nanotechnology ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Egor S. Rodionov | Victor V. Pogorelko | Victor G. Lupanov | A. G. Fazlitdinova | Polina N. Mayer | Alexander E. Mayer
Source: International Journal of Plasticity (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Dynamic deformation and fracture of brass: Experiments and dislocation-based model Brass ; Materials science ; Dislocation ; Deformation (meteorology) ; Fracture (geology) ; Composite material ; Structural engineering ; Forensic engineering ; Metallurgy ; Engineering ; Copper Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metallurgy and Material Science
Authors: Nada Alfryyan | Norah A. M. Alsaif | Hanan Al–Ghamdi | Shams A.M. Issa | Hesham M.H. Zakaly | A. A. El-Hamalawy | M.S. Sadeq | A. M. Abdelghany | Y. S. Rammah
Source: Optical Materials (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

RETRACTED: Investigation of borate glasses reinforced with iron (III) oxide [Fe2O3]: Preparation, physical, UV–Vis properties and γ-ray attenuation efficacy This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal ( https://www.elsevier.com/about/policies-and-standards/article-withdrawal ). At the request of the Editor-in-Chief, this article, “Investigation of borate glasses reinforced with iron (III) oxide [Fe 2 O 3 ]: Preparation, physical, UV–Vis properties and γ-ray attenuation efficacy,” Optical Materials, Volume 152, June 2024, 115424, has been retracted. The XRD spectra representing samples Fe1.0, Fe0.5, and Fe0.0 are identical to those represented as Pt1.0, Pt0.5, and Pt0.0, respectively, in “Fabrication, physical and machine learning density prediction techniques of newly B2O3–ZnO–BaO–PtO2 glasses,” Optical Materials, Volume 142, August 2023, 113994; https://doi.org/10.1016/j.optmat.2023.113994 . Therefore, the Editor-in-Chief no longer has confidence in the authenticity of the data. In accordance with the journal's publishing policy, these articles are retracted. Boron ; Attenuation ; Materials science ; Iron oxide ; Nuclear chemistry ; Boron oxide ; Oxide ; Inorganic chemistry ; Mineralogy ; Chemistry ; Metallurgy ; Optics ; Physics ; Organic chemistry Radiation Shielding Materials Analysis ; Glass properties and applications ; Pigment Synthesis and Properties
Authors: Melisa Kiran | Zeynep Pinar Haslak | Halit Ates | Vi̇ktorya Avi̇yente | F. Ahu Akin
Source: Journal of Molecular Structure (article, 2024-01-18)
Field: Chemistry — Organic Chemistry

Evaluation of the pK's of Quinazoline derivatives : Usage of quantum mechanical based descriptors Chemistry ; Quinazoline ; Quantum ; Computational chemistry ; Quantum chemical ; Combinatorial chemistry ; Organic chemistry ; Molecule ; Quantum mechanics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Byungcheon Yoo | Chaeyeong Ryu | Seung Hwan Lee | Sanggyun Jeong | Younghoon You | Dahye Baek | Dowon Kim | Inkyu Jeon | Ki‐Seok An | Jongwon Oh | Jiseok Lee
Source: Advanced Materials (article, 2024-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐Throughput Multiplexed Plasmonic Color Encryption of Microgel Architectures via Programmable Dithering‐Mask Flow Microlithography Silver nanoparticles (AgNPs) are known for their unique plasmonic colors and interaction with light, making them ideal for color printing and data encoding. Traditional methods like electron beam lithography (EBL) and focused ion beam (FIB) milling, however, suffer from low throughput and high costs. In this paper, a scalable and cost-efficient method is introduced for producing multiplexed plasmonic colors by in situ photoreducing AgNPs within microgel architectures with controlled porosity. Utilizing a digital micro-mirror device (DMD)-based flow microlithography system combined with a programmable dithering-mask technique, the high-throughput synthesis of shape or barcoded microparticles is facilitated, along with large-scale, high-resolution images embedded with hidden multiplexed plasmonic colors. This approach allows for a hidden multiplexed plasmonic color code library, significant enhancing the encoding capacity of barcode microparticles from 3<sup>3</sup> to 30<sup>3</sup> (a 1000-fold increase). Additionally, quantitative agreement is achieved between chemically encrypted and optically decrypted plasmonic colors using a deep learning classifier. Moreover, the method also supports the production of large scale (>5.6 × 5.6 cm<sup>2</sup>), high-resolution (>300 dpi) microgel arrays encrypted with multiple plasmonic colors in under 30 min. The multiplexed plasmonic coloration strategy in microgel architectures paves a new way for hidden data storage, secure optical labeling, and anti-counterfeiting technologies. Plasmon ; Materials science ; Multiplexing ; Throughput ; Nanotechnology ; Lithography ; Optoelectronics ; Computer science ; Computer hardware ; Telecommunications Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Plasmonic and Surface Plasmon Research
Authors: Riccardo Farris | Konstantin M. Neyman | Albert Bruix
Source: The Journal of Chemical Physics (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Determining the chemical ordering in nanoalloys by considering atomic coordination types The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys. Bimetallic strip ; Convex hull ; Computer science ; Nanoparticle ; Surface (topology) ; Biological system ; Materials science ; Chemistry ; Statistical physics ; Regular polygon ; Nanotechnology ; Mathematics ; Metal ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhouli Hui | Ning Gu | Haohuan Li | Zeguang Shi | Jiale Ren | Yang Cao | Youyi Sun
Source: Journal of Solid State Electrochemistry (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of NiCo2O4 nanoarray morphology for optimizing electrochemical stability of supercapacitor Supercapacitor ; Materials science ; Electrochemistry ; Nanosheet ; Electrode ; Capacitance ; Nanofiber ; Morphology (biology) ; Nanotechnology ; Chemical engineering ; Hydrothermal circulation ; Chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Hongchul Shin | Taeyoung Yoon | Juneseok You | Sungsoo Na
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2024-06-26)
Field: Materials Science — Biomaterials

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies Biomaterial ; Generalization ; Artificial neural network ; Artificial intelligence ; Computer science ; Ultimate tensile strength ; Machine learning ; Support vector machine ; SILK ; Materials science ; Nanotechnology ; Mathematics Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Biochemical and Structural Characterization
Authors: Sargol Mazraedoost | Petar Žuvela | Szymon Ulenberg | Tomasz Bączek | Jay Liu
Source: Analytical and Bioanalytical Chemistry (article, 2024-03-20)
Field: Chemistry — Spectroscopy

Cross-column density functional theory–based quantitative structure-retention relationship model development powered by machine learning Molecular descriptor ; Random forest ; Solvation ; Partial least squares regression ; Chemistry ; Linear regression ; Density functional theory ; Artificial intelligence ; Ensemble forecasting ; Machine learning ; Quantitative structure–activity relationship ; Chromatography ; Computer science ; Computational chemistry ; Molecule Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Norah Alwadai | Maryam Al Huwayz | Z.A. Alrowaili | M.U. Baskin | M.S. Al-Buriahi
Source: Journal of Radiation Research and Applied Sciences (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Predicting the mass attenuation coefficient of glass systems using machine learning approach for radiation applications In this study, we present a machine learning approach to predict the mass attenuation coefficients (MAC) of some glassy materials, aTeO2-bMoO3-cBaO-dSm2O3-eBi2O3, across various weight fractions (a, b, c, d, e) and energy levels between 0.015 and 15 MeV energy range. The compound exhibits a diverse set of weight fractions, ensuring a comprehensive representation of possible compositions. The NIST XCOM database serves as the reference for scattering and coherent values. This study introduces a novel and potentially alternative method for determining MAC in glass systems, with potential extensions to evaluate diverse radiation shielding characteristics. In contrast to traditional software, this approach presents unique advantages. The utilization of this technique holds promise for designing innovative glass compositions tailored for specific radiation shielding applications. NIST ; Attenuation ; Electromagnetic shielding ; Radiation shielding ; Mass attenuation coefficient ; Computer science ; Radiation ; Range (aeronautics) ; Attenuation coefficient ; Materials science ; Computational physics ; Physics ; Nuclear physics ; Optics ; Composite material Radiation Shielding Materials Analysis ; Glass properties and applications ; Luminescence Properties of Advanced Materials
Authors: Ali A. Alizadehmojarad | Sergei M. Bachilo | Anatoly B. Kolomeisky | R. Bruce Weisman
Source: The Journal of Physical Chemistry C (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Morphology Transitions of SDS Coatings on Single-Walled Carbon Nanotubes The morphologies of sodium dodecyl sulfate (SDS) coatings adsorbed on single-walled carbon nanotubes (SWCNTs) were investigated through molecular dynamics (MD) computations. Simulations covering a range of nanotube diameters and SDS concentrations showed regimes giving three different SDS morphologies, characterized as ordered, random, and micellar. In the ordered structures, which were formed at the lowest SDS concentrations, nanotubes were coated with a partial monolayer of SDS molecules whose alkyl tails were aligned parallel to the nanotube axis. Higher SDS concentrations gave the random coating morphology with a partial monolayer of more closely packed and poorly aligned adsorbed molecules. At the highest concentrations, spheroidal micellar clusters of SDS formed on the nanotube surface. The morphologies were quantitatively characterized by computed distribution functions of the SDS alkyl chain orientation and radius of gyration. A machine learning method was also applied to determine the aggregation numbers of SDS micelles in these colloidal systems. Carbon nanotube ; Morphology (biology) ; Materials science ; Chemical engineering ; Composite material ; Nanotechnology Carbon Nanotubes in Composites ; Graphene research and applications ; Fiber-reinforced polymer composites
Authors: Muhammad Achirul Nanda | Kharistya Amaru | S. Rosalinda | Inna Novianty | Walidatush Sholihah | Gema Parasti Mindara | Anifatul Faricha | Tusan Park
Source: Journal of Agriculture and Food Research (article, 2024-09-24)
Field: Chemistry — Analytical Chemistry

Higuchi fractal dimension and deep learning on near-infrared spectroscopy for determination of free fatty acid (FFA) content in oil palm fruit Free fatty acid (FFA) content is an essential parameter with a significant influence on the quality of oil palm, with lower levels showing higher quality. The measurement of this parameter is often carried out with conventional methods in chemical laboratories using solvents and reagents, but these techniques have several limitations. Therefore, this study aimed to develop a rapid and non-destructive technique for determining FFA content based on near-infrared (NIR) spectroscopy. This technique comprised the use of Higuchi fractal dimension (HFD) and deep learning to process NIR spectra as a new chemometric analysis. The sample population consisted of 350 oil palm fruits at various maturity ages collected from the Cikabayan Oil Palm Plantation. Each sample had its NIR spectrum acquired with a wavelength of 1000–1500 nm. Good performance was shown by a low root mean squared error (RMSE) value and a high coefficient of determination (R 2 ). Based on numerical analysis, this study proposed HFD with a maximum discrete time interval ( k max ) value of 10 and deep learning with long short-term memory (LSTM) as a robust architectural model. The results showed that the proposed technique could predict oil palm fruit FFA levels with RMSE and R 2 values of 0.167 and 0.959, respectively. Furthermore, it could identify FFA content at each process step, before and after harvesting. Based on these results, its implementation in oil palm control management was expected to enhance effectiveness and efficiency, leading to the production of high-quality products. • Determination of free fatty acid of oil palm fruit via near infrared spectroscopy. • Higuchi Fractal Dimension was applied to estimate the complexity of NIR spectra. • The Deep Learning was employed to produce a robust model architecture. • The proposed model can predict FFA content with R 2 of 0.959 and RMSE of 0.167. • This technique can work in a nondestructive, accurate, and fast manner. Palm oil ; Fractal dimension ; Dimension (graph theory) ; Fatty acid ; Content (measure theory) ; Food science ; Infrared spectroscopy ; Chemistry ; Infrared ; Spectroscopy ; Near-infrared spectroscopy ; Fractal ; Mathematics ; Horticulture ; Organic chemistry ; Biology ; Physics ; Optics ; Combinatorics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Christine E. Mundy | Boitumelo K. Nokeri
Source: Journal of Chemical Education (article, 2024-07-19)
Field: Chemistry — Physical and Theoretical Chemistry

Investigating the Effects of a Context-Based Laboratory Exercise for Meaningful Learning We used the context of boot-legging of alcohol to support the concept of matter and separation in the laboratory components of a first-year general chemistry course. The boot-legging laboratory exercise was deployed over two years, Year 1 being online and Year 2 being in a traditional laboratory. Findings over both years indicated that students did significantly better in the context-based lab exercise compared to most traditional laboratory exercises. The use of the Meaningful Learning in the Laboratory Inventory (MLLI) revealed that students came in with high expectations and that cognitive and affective laboratory expectations were met or fell just short in both years. Blended questionnaire items, including both the cognitive and affective in one, showed that the students’ expectations were met in Year 1. In Year 2, most students had good cognitive/affective experiences compared to a wide range of incoming expectations. The majority of students enjoyed the laboratory exercise; reasons for this changed from cognitive in Year 1 to affective in Year 2, coinciding with the change to face to face laboratories, where students could enjoy the excitement of performing the experiments for themselves. The applicability of the context-based experiment resonated with many of the students and prompted a small portion of students to think metacognitively about their laboratory experience and the consequences of chemistry in health, society, and industry. Overall, the introduction of a context-based laboratory exercise provided a more meaningful laboratory experience for students, that is, a deeper learning experience relevant to their everyday lives. Context (archaeology) ; Psychology ; Cognition ; Medical education ; Mathematics education ; Applied psychology ; Medicine Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Feiyu Li | Bocheng Xu | Zijie Lu | Jiafei Chen | Yike Fu | Jie Huang | Yizhen Wang | Xiang Li
Source: Small (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine‐Learning for Tumor Therapy Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials. Oncolytic virus ; Cytotoxicity ; Peptide ; Pipeline (software) ; Tumor cells ; Combinatorial chemistry ; Computer science ; Chemistry ; Cancer research ; Computational biology ; Biochemistry ; Biology ; In vitro Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics