jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoying Zhang | Xin Wang | Mengling Ning | Peng Wang | Wen Wang | Xiaozhou Zhang | Zhiming Liu | Yanjiao Zhang | Shaoxin Li
Source: Nanomaterials (article, 2022-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast Synthesis of Au Nanoparticles on Metal–Phenolic Network for Sweat SERS Analysis The biochemical composition of sweat is closely related to the human physiological state, which provides a favorable window for the monitoring of human health status, especially for the athlete. Herein, an ultra-simple strategy based on the surface-enhanced Raman scattering (SERS) technique for sweat analysis is established. Metal-phenolic network (MPN), an outstanding organic-inorganic hybrid material, is adopted as the reductant and platform for the in situ formation of Au-MPN, which displays excellent SERS activity with the limit of detection to 10<sup>-15</sup> M for 4-mercaptobenzoic acid (4-MBA). As an ultrasensitive SERS sensor, Au-MPN is capable of discriminating the molecular fingerprints of sweat components acquired from a volunteer after exercise, such as urea, uric acid, lactic acid, and amino acid. For pH sensing, Au-MPN/4-MBA efficiently presents the pH values of the volunteer's sweat, which can indicate the electrolyte metabolism during exercise. This MPN-based SERS sensing strategy unlocks a new route for the real-time physiological monitoring of human health. Nanoparticle ; Nanotechnology ; Materials science ; Metal ; SWEAT ; Metallurgy ; Medicine Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Monika Topa-Skwarczyńska | Andrzej Świeży | Dominika Krok | Katarzyna Starzak | Paweł Niezgoda | Bartosz Oksiuta | Weronika Wałczyk | Joanna Ortyl
Source: International Journal of Molecular Sciences (article, 2022-09-09)
Field: Chemistry — Organic Chemistry

Novel Formulations Containing Fluorescent Sensors to Improve the Resolution of 3D Prints Three-dimensional printing in SLA (stereolithography) and DLP (digital light processing) technologies has recently been experiencing a period of extremely rapid development. This is due to the fact that researchers recognise the many advantages of 3D printing, such as the high resolution and speed of the modelling and printing processes. However, there is still a search for new resin formulations dedicated to specific 3D printers allowing for high-resolution prints. Therefore, in the following paper, the effects of dyes such as BODIPY, europium complex, and Coumarin 1 added to light-cured compositions polymerised according to the radical mechanism on the photopolymerisation process speed, polymerisation shrinkage, and the final properties of the printouts were investigated. The kinetics of the photopolymerisation of light-cured materials using real-time FT-IR methods, as well as printouts that tangibly demonstrate the potential application of 3D printing technology in Industry 4.0, were examined. These studies showed that the addition of dyes has an effect on obtaining fluorescent prints with good resolution. Stereolithography ; 3D printing ; Fluorescence ; Shrinkage ; BODIPY ; Process engineering ; Computer science ; Process (computing) ; Nanotechnology ; Materials science ; Resolution (logic) ; Chemistry ; Engineering ; Optics ; Artificial intelligence ; Composite material Photopolymerization techniques and applications ; Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials
Authors: Biaosheng Huang | Jiang Liu | Jun-Ying Jiao | Jing Lü | Danjv Lv | Jiawei Mao | Youjie Zhao | Yan Zhang
Source: Scientific Reports (article, 2022-05-25)
Field: Chemistry — Analytical Chemistry

Applications of machine learning in pine nuts classification Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951-957 nm, 1,147-1,154 nm, 1,907-1,927 nm, 2,227-2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts. Random forest ; Pinus <genus> ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Pinus massoniana ; Convolutional neural network ; Range (aeronautics) ; Mathematics ; Botany ; Biology ; Materials science Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Wood and Agarwood Research
Authors: Junsoo Han | Oumaïma Gharbi
Source: Current Opinion in Electrochemistry (article, 2022-09-08)
Field: Materials Science — Metals and Alloys

Current state of electrochemical techniques and corrosion rate analysis for next-generation materials Corrosion ; Materials science ; Electrochemistry ; Alloy ; Current (fluid) ; Microstructure ; Metallurgy ; Engineering ; Electrode ; Chemistry ; Electrical engineering Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors ; Corrosion Behavior and Inhibition
Authors: A. I. Alateyah | Waleed H. El-Garaihy | Majed O. Alawad | Samar El‐Sanabary | Sally Elkatatny | Hany A. Dahish | Hanan Kouta
Source: Materials (article, 2022-08-02)
Field: Materials Science — Biomaterials

The Effect of ECAP Processing Conditions on Microstructural Evolution and Mechanical Properties of Pure Magnesium—Experimental, Mathematical Empirical and Response Surface Approach In this study, a quantitative evaluation approach was used to investigate how certain ECAP processing parameters affect the microstructural evolution, Vicker's microhardness values and tensile properties of pure Mg. The ECAP processing parameters were number of passes, ECAP die channel angle and processing route type. The response surface methodology (RSM) technique was used to design 16 runs of the experiment using Stat-Ease design expert software. Billets of pure Mg were processed up to four passes of routes Bc, A and C at 225 °C. Two ECAP dies were used with internal channel angles of 90° and 120°. Experimental findings were used to establish empirical models to assess the influence of the ECAP processing parameters on grain size and mechanical properties of ECAPed billets. The established relationships were examined and validated for their adequacy and significance using ANOVA as well as several statistical criteria. Response surface plots and contour graphs were established to offer better understanding of the intended relationships. In addition, the optimum processing parameters for grain size, hardness values and tensile properties were defined. Both experimental results and the theoretical model revealed that route Bc is the most effective route in grain refining. The experimental findings showed that four passes of route Bc through the die channel angle 90° revealed a significant reduction in the grain size by 86% compared to the as-annealed counterparts. Similar to the grain size refining, four-passes processing through the ECAP die with an internal channel angle of 90° leads to improved Vicker's microhardness values. Additionally, four passes of route Bc using the 90° die angle recorded a significant HV increase at the edge and central areas by 112% and 78%, respectively, compared to the as-annealed counterpart. On the other hand, according to the optimization findings, two passes of route Bc using a die angle of 120° resulted in the best ultimate tensile strength for pure Mg, whereas four passes of route Bc revealed the optimum ductility at fracture. Materials science ; Magnesium ; Metallurgy ; Response surface methodology ; Microstructure ; Composite material ; Die (integrated circuit) ; Nanotechnology ; Mathematics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Haiyan Gong | Jie He | Xiaotong Zhang | Lei Duan | Ziqi Tian | Wei Zhao | Fuzhou Gong | Tong Liu | Zongguo Wang | Haifeng Zhao | Weipeng Jia | Lei Zhang | Xue Jiang | Wencong Chen | Shilong Liu | Hao Xiu | Wenjin Yang | Jiawang Wan
Source: Scientific Data (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

A repository for the publication and sharing of heterogeneous materials data National Materials Data Management and Service platform (NMDMS) is a materials data repository for the publication and sharing of heterogeneous materials scientific data and follows the FAIR principles: Findable, Accessible, Interoperable, and Reusable. To ensure data are 'Interoperable, NMDMS uses a user-friendly semi-structured scientific data model, named dynamic container', to define, exchange, and store heterogeneous scientific data. Then, a personalized yet standardized data submission subsystem, a rigorous project data review and publication subsystem, and a multi-granularity data query and retrieval subsystem collaboratively make data 'Reusable', 'Findable', and 'Accessible'. Finally, China's "National Key R&D Program: Material Genetic Engineering Key Special Project" has adopted NMDMS to publish and share its project data. There are 12,251,040 pieces of data published in NMDMS since 2018, under 87 categories and 1,912 user-defined schemas from 45 projects. The platform has been accessed 908875 times, and 2403,208 pieces of data have been downloaded. In short, NMDMS effectively accelerates the publication and sharing of material project data in China. Interoperability ; Computer science ; Data sharing ; Data exchange ; Metadata ; Publication ; World Wide Web ; Data management ; Key (lock) ; Service (business) ; Database ; Data science ; Information retrieval Machine Learning in Materials Science ; Research Data Management Practices ; X-ray Diffraction in Crystallography
Authors: Stefano Falletta | Alfredo Pasquarello
Source: npj Computational Materials (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

Hubbard U through polaronic defect states Abstract Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+ U functional has been used for predicting properties of correlated materials by applying on-site effective Coulomb interactions to specific orbitals. However, the determination of the Hubbard U parameter has remained under intense discussion despite the multitude of approaches proposed. Here, we define a selection criterion based on the use of polaronic defect states for the enforcement of the piecewise linearity of the total energy upon electron occupation. A good agreement with results from piecewise linear hybrid functionals is found for the electronic and structural properties of polarons, including the formation energies. The values of U determined in this way are found to give a robust description of the polaron energetics upon variation of the considered state. In particular, we also address a polaron hopping pathway, finding that the determined value of U leads to accurate energetics without requiring a configurational-dependent U . It is emphasized that the selection of U should be based on physical properties directly associated with the orbitals to which U is applied, rather than on more global properties such as band gaps and band widths. For comparison, we also determine U through a well-established linear-response scheme finding noticeably different values of U and consequently different formation energies. Possible origins of these discrepancies are discussed. As case studies, we consider the self-trapped electron in BiVO 4 , the self-trapped hole in MgO, the Li-trapped hole in MgO, and the Al-trapped hole in α -SiO 2 . Polaron ; Atomic orbital ; Condensed matter physics ; Band gap ; Physics ; Electron ; Hubbard model ; Quantum mechanics Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics ; Ga2O3 and related materials
Authors: Sooraj Baijnath | Ibrahim Kaya | Anna Nilsson | Mohammadreza Shariatgorji | Per E. Andrén
Source: Trends in Pharmacological Sciences (review, 2022-07-05)
Field: Chemistry — Spectroscopy

Advances in spatial mass spectrometry enable in-depth neuropharmacodynamics Mass spectrometry imaging (MSI) is a powerful technique that combines the ability of microscopy to provide spatial information about multiple molecular species with the specificity of mass spectrometry (MS) for unlabeled mapping of analytes in diverse biological tissues. Initial pharmacological applications focused on drug distributions in different organs, including the compartmentalized brain. However, recent technological advances in instrumentation, software, and chemical tools have allowed its use in quantitative spatial omics. It now enables visualization of distributions of diverse molecules at high lateral resolution in studies of the pharmacokinetic and neuropharmacodynamic effects of drugs on functional biomolecules. Therefore, it has become a versatile technique with a multitude of applications that have transformed neuropharmacological research and enabled research into brain physiology at unprecedented resolution, as described in this review. Mass spectrometry imaging ; Biomolecule ; Computational biology ; Mass spectrometry ; Drug discovery ; Chemistry ; Molecular imaging ; Biology ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Prabu Ravindran | Frank C. Owens | Adam C. Wade | Rubin Shmulsky | Alex C. Wiedenhoeft
Source: Frontiers in Plant Science (article, 2022-01-21)
Field: Chemistry — Organic Chemistry

Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods Availability of and access to wood identification expertise or technology is a critical component for the design and implementation of practical, enforceable strategies for effective promotion, monitoring and incentivisation of sustainable practices and conservation efforts in the forest products value chain. To address this need in the context of the multi-billion-dollar North American wood products industry 22-class, image-based, deep learning models for the macroscopic identification of North American diffuse porous hardwoods were trained for deployment on the open-source, field-deployable XyloTron platform using transverse surface images of specimens from three different xylaria and evaluated on specimens from a fourth xylarium that did not contribute training data. Analysis of the model performance, in the context of the anatomy of the woods considered, demonstrates immediate readiness of the technology developed herein for field testing in a human-in-the-loop monitoring scenario. Also proposed are strategies for training, evaluating, and advancing the state-of-the-art for developing an expansive, continental scale model for all the North American hardwoods. Context (archaeology) ; Software deployment ; Identification (biology) ; Liberian dollar ; Computer science ; Deep learning ; Product (mathematics) ; Expansive ; Artificial intelligence ; Engineering ; Business ; Ecology ; Archaeology ; Materials science ; Software engineering ; Geography ; Biology ; Mathematics Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Katsuhiro Endo | Yoshiki Matsuda | Shu Tanaka | Mayu Muramatsu
Source: Scientific Reports (article, 2022-06-24)
Field: Materials Science — Materials Chemistry

A phase-field model by an Ising machine and its application to the phase-separation structure of a diblock polymer A novel model to be applied to next-generation accelerators, Ising machines, is formulated on the basis of the phase-field model of the phase-separation structure of a diblock polymer. Recently, Ising machines including quantum annealing machines, attract overwhelming attention as a technology that opens up future possibilities. On the other hand, the phase-field model has demonstrated its high performance in material development, though it takes a long time to achieve equilibrium. Although the convergence time problem might be solved by the next-generation accelerators, no solution has been proposed. In this study, we show the calculation of the phase-separation structure of a diblock polymer as the equilibrium state using phase-field model by an actual Ising machine. The proposed new model brings remarkable acceleration in obtaining the phase-separation structure. Our model can be solved on a large-scale quantum annealing machine. The significant acceleration of the phase-field simulation by the quantum technique pushes the material development to the next stage. Ising model ; Phase (matter) ; Field (mathematics) ; Computer science ; Polymer ; Materials science ; Statistical physics ; Physics ; Mathematics ; Composite material ; Quantum mechanics Block Copolymer Self-Assembly ; Solidification and crystal growth phenomena ; Theoretical and Computational Physics
Authors: Xueting Ma | Huaping Luo | Fei ZHANG | Feng GAO
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology This paper explores the influence of the selection method of the region of interest (ROI) on the results in the total sugar of apple detection based on hyperspectral imaging technology. Taking Fuji apple as the detection object, the hyperspectral images of the samples were collected based on the 900~1750 nm hyperspectral imaging system, and the total sugar content of the samples was obtained based on the anthrone colorimetric method. The square ROI and circular ROI of different sizes were extracted. The average spectrum of the region was used to establish a quantitative analysis model of apple's total sugar content by partial least squares (PLS). The results show that apple's total sugar detection model established by extracting a circular ROI with a diameter of 25 pixels has the highest accuracy and strongest prediction ability(Rc = 0.8977, RMSEC = 0.6459, RP = 0.8836, RMSEP = 0.6627). The research shows that selecting ROI with a suitable shape and size for the research object is of great significance for improving the accuracy of the prediction model of apple's total sugar content and giving play to the advantages of hyperspectral images. Hyperspectral imaging ; Sugar ; Pixel ; Region of interest ; Artificial intelligence ; Content (measure theory) ; Mathematics ; Computer science ; Computer vision ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing and Land Use
Authors: Aditya Garg | Elieser Mejia | Wonil Nam | Peter J. Vikesland | Wei Zhou
Source: Small (article, 2022-09-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Biomimetic Transparent Nanoplasmonic Meshes by Reverse‐Nanoimprinting for Bio‐Interfaced Spatiotemporal Multimodal SERS Bioanalysis Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo. Nanotechnology ; Bioanalysis ; Materials science ; Biomolecule ; Rhodamine 6G ; Chemistry ; Molecule Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Maimunah Mohd Ali | Norhashila Hashim | Samsuzana Abd Aziz | Ola Lasekan
Source: Agriculture (article, 2022-07-13)
Field: Chemistry — Analytical Chemistry

Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, i.e., 5, 10, and 25 °C and a relative humidity of 85% to 90%. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among the different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100%. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars, which could enhance the grading and sorting processes of the fruit. Artificial intelligence ; Support vector machine ; Linear discriminant analysis ; Machine learning ; Mathematics ; Principal component analysis ; Cultivar ; Algorithm ; Pattern recognition (psychology) ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Advanced Chemical Sensor Technologies