jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philipp O. Tsvetkov
Source: MDPI (MDPI AG) (article, 2021-03-15)
Field: Chemistry — Physical and Theoretical Chemistry

An AI-Powered Blood Test to Detect Cancer Using NanoDSF Glioblastoma is the most frequent and aggressive primary brain tumor. Its diagnosis is based on resection or biopsy that could be especially difficult and dangerous in the case of deep location or patient comorbidities. Monitoring disease evolution and progression also requires repeated biopsies that are often not feasible. Therefore, there is an urgent need to develop biomarkers to diagnose and follow glioblastoma evolution in a minimally invasive way. In the present study, we described a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of differential scanning fluorimetry. Using blood samples from 84 glioma patients and 63 healthy controls, we showed that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test. Glioma ; Cancer ; Glioblastoma ; Blood test ; Medicine ; Workflow ; Biopsy ; Computer science ; Pathology ; Radiology ; Internal medicine ; Cancer research thermodynamics and calorimetric analyses ; Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies
Authors: Quan Zhou | Wenqian Huang | Dong Liang | Xi Tian
Source: Sensors (article, 2021-06-22)
Field: Chemistry — Analytical Chemistry

Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100-2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky-Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels. Hyperspectral imaging ; Pattern recognition (psychology) ; Principal component analysis ; Linear discriminant analysis ; Artificial intelligence ; Mathematics ; Smoothing ; Kernel (algebra) ; Feature selection ; Dimensionality reduction ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Danrui Ma | Lijun Jin | Jianming He | Kai Gao
Source: High Voltage (article, 2021-03-03)
Field: Materials Science — Materials Chemistry

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses Abstract Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents. In this study, a new method for identification of discharge severities is proposed. Specifically, a low‐cost ultraviolet (UV) sensor detection system was combined with time–frequency method, texture analysis and support vector machine (SVM) classifier to classify partial discharge severities for ceramic insulators. The visible images and the root‐mean‐square value of leakage currents detected simultaneously are used to classify the UV signals into different discharge faults. The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge. The images of UV signal spectrograms differ significantly at different discharge stages. The density and brightness of image textures are minimal in corona discharge and larger in arc discharge. Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6% for ceramic insulators, and outperform a single time feature or other texture features. SVM outperforms k‐Nearest Neighbour, Naive Bayes and Decision Tree. Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection. It can be further applied to the deterioration diagnosis of power facilities. Partial discharge ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Naive Bayes classifier ; Corona discharge ; Materials science ; Feature extraction ; Acoustics ; Classifier (UML) ; Computer vision ; Electrode ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Image Enhancement Techniques
Authors: Qiang Liu | Shaoxia Chen | Dandan Zhou | Chao Ding | Jiahong Wang | Hongsheng Zhou | Kang Tu | Leiqing Pan | Pengxia Li
Source: Foods (article, 2021-09-29)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (<i>R<sub>p</sub></i><sup>2</sup> = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for <i>L</i>* value (<i>R<sub>p</sub></i><sup>2</sup> = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (<i>R<sub>p</sub></i><sup>2</sup> = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage. Partial least squares regression ; Mathematics ; Near-infrared spectroscopy ; Fourier transform ; Second derivative ; Chemistry ; Analytical Chemistry (journal) ; Statistics ; Chromatography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Water Quality Monitoring and Analysis
Authors: Jing Liu | Wenzhuang Ma | Wei Chen | Yushan Chen | Xuchu Deng | Yu Gu
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Metamaterial Absorber Based on Particle Swarm Optimization Suitable for Earth’s Atmospheric Transparency Window The absorption and reflection of electromagnetic waves by various particles in the earth’s atmosphere allow the passage of only certain electromagnetic wavelengths to reach the ground, called Earth’s atmosphere transparent window. In this study, perfect absorption was theoretically obtained in the range of near- and mid-infrared earth’s atmospheric transparency window using a simple absorber with metal-dielectric-metal structure. The numerical simulations showed the average absorption to reach 96.2% at wavelengths from 2000 to 6000 nm. Also, the broadband absorption was noticed and attributed to combined physical mechanisms, such as anti-reflection effect, localized surface plasmon polariton, propagating surface plasmon polarization, Fabry–Pérot cavity and slow light mode. Meanwhile, the proposed absorber displayed simple-structure, low-cost, wide-angle, and polarization-independent. In sum, the proposed absorber might be useful for future applications related to atmospheric transparency window, such as remote sensing, energy harvesting, infrared detection, and stray light elimination. Transparency (behavior) ; Metamaterial ; Particle swarm optimization ; Window (computing) ; Earth (classical element) ; Particle (ecology) ; Computer science ; Optics ; Physics ; Geology ; Astronomy ; Algorithm Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Timo Küster | Geoffrey D. Bothun
Source: Nanoscale Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

<i>In situ</i> SERS detection of dissolved nitrate on hydrated gold substrates The accurate and fast measurement of nitrate in seawater is important for monitoring and controlling water quality to prevent ecologic and economic disasters. In this work we show that the <i>in situ</i> detection of nitrate in aqueous solution is feasible at nanomolar concentrations through surface enhanced Raman spectroscopy (SERS) using native nanostructured gold substrates without surface functionalization. Spectra were analyzed as collected or after standard normal variate (SNV) normalization, which was shown through Principal Component Analysis (PCA) to reduce spectral variations between sample sets and improve Langmuir adsorption model fits. An additional normalization approach based on the substrate silicon template showed that silicon provided an internal standard that accounted for the spectral variance without the need for SNV normalization. Nitrate adsorption was well-described by the Langmuir adsorption model, consistent with an adsorbed monolayer, and a limit of detection of 64 nM nitrate was obtained in ultrapure water, representing environmentally relevant concentrations. Free energy calculations based on the Langmuir adsorption constants, approximating equilibrium adsorption constants, and calculated self-energy arising from image charge, accounting for electrostatic interactions with a polarizable nanostructured substrate, suggest that nitrate adsorption was partially driven by an entropy gain presumably due to dehydration of the gold substrate and/or nitrate ion. This work is being extended to determine if similar statistical and normalization methods can be applied to nitrate detection in complex natural waters where non-target ions and molecules are expected to interfere. In situ ; Nitrate ; Environmental chemistry ; Chemistry ; Inorganic chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical Analysis and Applications ; Machine Learning and ELM
Authors: Omid Farzadian | Farrokh Yousefi | Christos Spitas | Κωνσταντίνος Κώστας
Source: International Journal of Heat and Mass Transfer (article, 2021-09-10)
Field: Materials Science — Materials Chemistry

Phonon heat transport in two-dimensional phagraphene-graphene superlattice Superlattice ; Thermal conductivity ; Graphene ; Condensed matter physics ; Phonon ; Materials science ; Coherence length ; Physics ; Nanotechnology ; Composite material ; Superconductivity Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Graphene research and applications
Authors: William Heymann | Juliane Glaser | Fabrice Schlegel | Will Johnson | Pablo Rolandi | Eric von Lieres
Source: Journal of Chromatography A (article, 2021-11-20)
Field: Chemistry — Spectroscopy

Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling Least squares estimation of unknown parameters from measurement data is a well-established standard method in chromatography modeling but can suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. We have therefore developed a new score system that separately accounts for the shape, position and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. The proposed method is demonstrated with synthetic and experimental case studies of increasing complexity. All software is freely available as open source code (https://github.com/modsim/CADET-Match). Convergence (economics) ; Software ; Position (finance) ; Matching (statistics) ; Code (set theory) ; Estimation theory ; Genetic algorithm ; Computer science ; Algorithm ; Statistics ; Data mining ; Machine learning ; Mathematics ; Set (abstract data type) Analytical Chemistry and Chromatography ; Protein purification and stability ; Computational Drug Discovery Methods
Authors: István Kertész | Viktória Zsom-Muha | Rebeka András | Ferenc Horváth | Csaba Németh | József Felföldi
Source: Molecules (article, 2021-08-03)
Field: Chemistry — Analytical Chemistry

Development of a Novel Acoustic Spectroscopy Method for Detection of Eggshell Cracks Non-destructive testing (NDT) for eggshell faults is highly important for the egg industry, as cracked eggs account for around 3% of total production. The most commonly used method at present, candling, is labor intensive, while computer vision systems are expensive and complicated. In this paper, we present a simple, yet efficient, novel method for eggshell crack detection by acoustic spectroscopy. Altogether, 693 sound recordings were evaluated by different classification methods. The results show a cross-validated 2.1% total classification error, with only 0.87% false positive rate, which is the crucial metric for fresh eggs. Adapting the developed method to an industrial setting may lead to a reliable, fast and cost-effective detection method. Eggshell ; Nondestructive testing ; Metric (unit) ; Spectroscopy ; Computer science ; Materials science ; Acoustics ; Biological system ; Biology ; Engineering ; Ecology ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Yuan Gao | Mason A. Dearborn | Sagar Vyas | Aditya Kumar | Julie Hemmer | Zhao Wang | Qiong Wu | Omar Alshangiti | Jeffrey S. Moore | Aaron P. Esser‐Kahn | Philippe H. Geubelle
Source: The Journal of Physical Chemistry B (article, 2021-07-06)
Field: Chemistry — Organic Chemistry

Manipulating Frontal Polymerization and Instabilities with Phase-Changing Microparticles Recently presented as a rapid and eco-friendly manufacturing method for thermoset polymers and composites, frontal polymerization (FP) experiences thermo-chemical instabilities under certain conditions, leading to visible patterns and spatially dependent material properties. Through numerical analyses and experiments, we demonstrate how the front velocity, temperature, and instability in the frontal polymerization of cyclooctadiene are affected by the presence of poly(caprolactone) microparticles homogeneously mixed with the resin. The phase transformation associated with the melting of the microparticles absorbs some of the exothermic reaction energy generated by the FP, reduces the amplitude and order of the thermal instabilities, and suppresses the front velocity and temperatures. Experimental measurements validate predictions of the dependence of the front velocity and temperature on the microparticle volume fraction provided by the proposed homogenized reaction-diffusion model. Polymerization ; Materials science ; Exothermic reaction ; Thermosetting polymer ; Front velocity ; Microparticle ; Polymer ; Diffusion ; Composite material ; Amplitude ; Phase (matter) ; Volume fraction ; Thermal ; Instability ; Chemical engineering ; Polymer chemistry ; Thermodynamics ; Front (military) ; Mechanics ; Chemistry ; Optics ; Organic chemistry ; Physics Photopolymerization techniques and applications ; Photochromic and Fluorescence Chemistry ; Dyeing and Modifying Textile Fibers
Authors: Jun Li | Chunpei Cai | Zhihui Li
Source: Physics of Fluids (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Knudsen diffusion differs from Fickian diffusion We investigate the gas transport enhancement through nanotubes, relative to the prediction by the prevailing century-old Knudsen diffusion model. This enhancement is usually attributed to the partly specular molecular reflections at the smooth nanotube surface, which break the model assumption of completely diffusive reflections. However, an oversighted cause of the discrepancy between the measurement and theory that we found is that even for the gas transport with completely diffusive reflections, the Knudsen diffusion model based on Fick's first law is accurate only for long nanotubes. Additionally, for smooth nanotubes with partly specular reflections, the Knudsen diffusion model is also invalid even if the diffusion coefficient is corrected to account for the atomic-scale surface smoothness. On the other hand, the Knudsen diffusion model might be used for interpretations instead of predictions, and then the diffusion coefficient inferred from the measured mass flow rate could be completely different from the actual value. All those discrepancies and confusions stem from the implementation of Fick's first law can be avoided by using the molecular transmission probability obtained by the kinetic theory to quantify the flow rate of the Knudsen diffusion process. This work provides the correction to the Knudsen diffusion model for accurate predictions of gas diffusion through nanotubes and better interpretations of experimental measurements. Knudsen diffusion ; Knudsen number ; Diffusion ; Molecular diffusion ; Specular reflection ; Physics ; Knudsen flow ; Free molecular flow ; Fick's laws of diffusion ; Surface diffusion ; Diffusion process ; Thermodynamics ; Kinetic theory ; Effective diffusion coefficient ; Statistical physics ; Mechanics ; Chemistry ; Physical chemistry ; Optics ; Innovation diffusion Graphene research and applications ; Membrane Separation and Gas Transport ; Thermal properties of materials
Authors: Hizkeal Tsade | Sisay Tadesse Anshebo | Fedlu Kedir Sabir
Source: Journal of Chemistry (article, 2021-04-23)
Field: Materials Science — Biomaterials

Preparation and Characterization of Functionalized Cellulose Nanomaterials (CNMs) for Pb(II) Ions Removal from Wastewater Due to their remarkable properties, cellulose nanomaterials are emerging materials for wastewater (WW) treatment. In this study, both pristine cellulose nanomaterial (CNM) and sodium periodate modified cellulose nanomaterial (NaIO4-CNM) were prepared from the stem of the Erythrina brucei plant for the removal of Pb(II) ions from WW. As-prepared CNMs were characterized by X-ray diffraction (XRD), Fourier transform infrared (FT-IR), scanning electron microscope (SEM), and thermogravimetric analysis with differential thermogravimetry (TGA-DTG) analysis. The as-prepared and characterized CNMs were tested for the removal of Pb(II) ions from secondary run-off wastewater (SERWW). Langmuir and Freundlich adsorption isotherms were certainly fixed to a maximum Pb(II) ions uptake capability (Qmax) of 91.74 and 384.62 mg g−1 by CNM and NaIO4-CNM adsorbents, respectively. The pseudo-second-order (PSO) kinetics model was well fitted to the uptake process. Results revealed that the percentage removal (%R) of Pb(II) ions was decreased by the presence of nitrogen and organic matter, but not affected by the presence of phosphorous in SERWW. Due to its high efficiency, NaIO4-CNM was selected for the regeneration study. The regeneration study was conducted after desorption of Pb(II) ions from the adsorbent by the addition of HCl, and the regenerated sorbent was reused as an adsorbent for at least 13 successive cycles. The results indicated excellent recycling capabilities, and the adsorbent was used as adsorbing material for the removal of Pb(II) ions from SERWW after 13 successive cycles without significant efficient loss. Chemistry ; Adsorption ; Thermogravimetric analysis ; Cellulose ; Nuclear chemistry ; Wastewater ; Freundlich equation ; Langmuir adsorption model ; Langmuir ; Nanomaterials ; Inorganic chemistry ; Organic chemistry ; Chemical engineering ; Waste management Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal
Authors: Shella Permatasari Santoso | Artik Elisa Angkawijaya | Vania Bundjaja | Alfin Kurniawan | Maria Yuliana | Chang‐Wei Hsieh | Alchris Woo Go | Kuan‐Chen Cheng | Felycia Edi Soetaredjo | Suryadi Ismadji
Source: Journal of environmental chemical engineering (article, 2021-12-08)
Field: Materials Science — Biomaterials

Investigation of the influence of crosslinking activation methods on the physicochemical and Cu(II) adsorption characteristics of cellulose hydrogels Epichlorohydrin ; Self-healing hydrogels ; Cellulose ; Adsorption ; Chemical engineering ; Crystallinity ; Thermal stability ; Materials science ; Absorption of water ; Langmuir adsorption model ; Chemistry ; Nuclear chemistry ; Polymer chemistry ; Composite material ; Organic chemistry Advanced Cellulose Research Studies ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal