jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anubhab Haldar | Cristian L. Cortes | Pierre Darancet | Sahar Sharifzadeh
Source: Nano Letters (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Microscopic Theory of Plasmons in Substrate-Supported Borophene We compute the dielectric properties of freestanding and metal-supported borophene from first-principles time-dependent density functional theory. We find that both the low- and high-energy plasmons of borophene are fully quenched by the presence of a metallic substrate at borophene-metal distances smaller than ≃9 Å. Based on these findings, we derive an electrodynamic model of the interacting, momentum-dependent polarizability for a two-dimensional metal on a model metallic substrate, which quantitatively captures the evolution of the dielectric properties of borophene as a function of metal-borophene distance. Applying this model to a series of metallic substrates, we show that maximizing the plasmon energy detuning between borophene and substrate is the key material descriptor for plasmonic performance. Borophene ; Plasmon ; Polarizability ; Materials science ; Metal ; Substrate (aquarium) ; Density functional theory ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Physics ; Optoelectronics ; Quantum mechanics ; Graphene ; Molecule ; Metallurgy Graphene research and applications ; Thermal properties of materials ; Molecular Junctions and Nanostructures
Authors: Quanxi Feng | Huazhou Chen | Hai Xie | Ken Cai | Bin Lin | Lili Xu
Source: Computational Intelligence and Neuroscience (article, 2020-07-10)
Field: Chemistry — Analytical Chemistry

A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers. Calibration ; Computer science ; Crossover ; Genetic algorithm ; Algorithm ; Parametric statistics ; Population ; Biological system ; Artificial intelligence ; Mathematics ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Water Quality Monitoring and Analysis
Authors: Andrea Cossettini | Cecilia Laborde | Denis Brandalise | F. Widdershoven | Serge G. Lemay | L. Selmi
Source: IEEE Sensors Journal (article, 2020-10-21)
Field: Chemistry — Electrochemistry

Space and Frequency Dependence of Nanocapacitor Array Sensors Response to Microparticles in Electrolyte We present new experimental evidence and extensive numerical simulations of a few distinct fingerprints generated by dielectric and conductive microparticles in electrolyte environment on the capacitance spectra of nanoelectrode array sensors. Finite element simulations in good agreement with measurements allow us to identify unambiguously the physical origin of these features, and to illustrate their dependence on the system's geometrical and physical properties. In particular, we show that conductive particles induce a response with complex space and frequency dependencies, caused by the formation of an AC electrical double layer at the particle surface, and its interaction with the working and counter electrodes in the array. Furthermore, we highlight features that could lead to false-negative detection events in sensing applications. The theoretical predictions are confirmed by experiments on a state of the art CMOS pixelated nanocapacitor biosensor platform. Capacitance ; Electrolyte ; Electrical conductor ; Materials science ; Dielectric ; Electrode ; Optoelectronics ; Frequency response ; Biosensor ; Depletion region ; Layer (electronics) ; Nanotechnology ; Physics ; Electrical engineering ; Engineering Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Microfluidic and Bio-sensing Technologies
Authors: Weronika Golec | Aleksandra Jakimów-Kostrzewa | Bartosz Mruk | Sergiusz Jóźwiak
Source: Neurologia i Neurochirurgia Polska (review, 2020-09-21)
Field: Materials Science — Materials Chemistry

Role of gadolinium-based contrast agents in neurological disorders Gadolinium-based contrast agents (GBCAs) are widely used in magnetic resonance imaging (MRI) to help with the diagnostic and monitoring processes of many diseases, including neurological disorders. Initially, it was assumed that GBCAs carry minimal risk, are safe and well tolerated. But recent reports of GBCA-associated deposition in many body tissues have raised concerns about the broader health impacts of gadolinium exposure. The aim of this review was to summarise knowledge regarding gadolinium deposition, primarily in the brain structures, and of potential GBCA-associated toxicity. Moreover, we discuss the current recommendations on the use of GBCAs, as well as alternative contrast agents and imaging techniques. Medicine ; Gadolinium ; Magnetic resonance imaging ; Nephrogenic systemic fibrosis ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Nanocluster Synthesis and Applications
Authors: Khalid Al‐Saad | Ahmed A. Issa | Sourour Idoudi | Basem Shomar | Mohammad A. Al‐Ghouti | Nessreen Al‐Hashimi | Marwa El‐Azazy
Source: Molecules (article, 2020-10-20)
Field: Chemistry — Analytical Chemistry

Smart Synthesis of Trimethyl Ethoxysilane (TMS) Functionalized Core–Shell Magnetic Nanosorbents Fe3O4@SiO2: Process Optimization and Application for Extraction of Pesticides In the current study, a smart approach for synthesizing trimethyl ethoxysilane-decorated magnetic-core silica-nanoparticles (TMS-mcSNPs) and its effectiveness as nanosorbents have been exploited. While the magnetite core was synthesized using the modified Mössbauer method, Stöber method was employed to coat the magnetic particles. The objective of this work is to maximize the magnetic properties and to minimize both particle size (PS) and particle size distribution (PSD). Using a full factorial design (2<sup>k</sup>-FFD), the influences of four factors on the coating process was assessed by optimizing the three responses (magnetic properties, PS, and PSD). These four factors were: (1) concentration of tetraethyl-orthosilicate (TEOS); (2) concentration of ammonia; (3) dose of magnetite (Fe<sub>3</sub>O<sub>4</sub>); and (4) addition mode. Magnetic properties were calculated as the attraction weight. Scanning electron microscopy (SEM) was used to determine PS, and standard deviation (±SD) was calculated to determine the PSD. Composite desirability function (D) was used to consolidate the multiple responses into a single performance characteristic. Pareto chart of standardized effects together with analysis of variance (ANOVA) at 95.0 confidence interval (CI) were used to determine statistically significant variable(s). Trimethyl ethoxysilane-functionalized mcSNPs were further applied as nanosorbents for magnetic solid phase extraction (TMS-MSPE) of organophosphorus and carbamate pesticides. Tetraethyl orthosilicate ; Magnetite ; Particle size ; Materials science ; Response surface methodology ; Factorial experiment ; Extraction (chemistry) ; Magnetic nanoparticles ; Chemical engineering ; Analytical Chemistry (journal) ; Chromatography ; Nanotechnology ; Chemistry ; Computer science ; Nanoparticle ; Metallurgy ; Machine learning Analytical chemistry methods development ; Iron oxide chemistry and applications ; Catalytic Processes in Materials Science
Authors: Ana C. N. Pardauil | Thiago P. Nascimento | Marcelo Ricardo Souza Siqueira | Ubiratan Holanda Bezerra | Werbeston Douglas de Oliveira
Source: Energies (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R2 was used for assessing the data trend. Cluster analysis ; Partial discharge ; Random forest ; Data mining ; Computer science ; Identification (biology) ; Generator (circuit theory) ; Process (computing) ; Pattern recognition (psychology) ; Artificial intelligence ; Decision tree ; Machine learning ; Engineering ; Power (physics) ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Krzesimir Ciura | Joanna Fedorowicz | Petar Žuvela | Mario Lovrić | Hanna Kapica | Paweł Baranowski | Wiesław Sawicki | Ming Wah Wong | Jarosław Sączewski
Source: Molecules (article, 2020-10-20)
Field: Chemistry — Spectroscopy

Affinity of Antifungal Isoxazolo[3,4-b]pyridine-3(1H)-Ones to Phospholipids in Immobilized Artificial Membrane (IAM) Chromatography Currently, rapid evaluation of the physicochemical parameters of drug candidates, such as lipophilicity, is in high demand owing to it enabling the approximation of the processes of absorption, distribution, metabolism, and elimination. Although the lipophilicity of drug candidates is determined using the shake flash method (<i>n</i>-octanol/water system) or reversed phase liquid chromatography (RP-LC), more biosimilar alternatives to classical lipophilicity measurement are currently available. One of the alternatives is immobilized artificial membrane (IAM) chromatography. The present study is a continuation of our research focused on physiochemical characterization of biologically active derivatives of isoxazolo[3,4-<i>b</i>]pyridine-3(1<i>H</i>)-ones. The main goal of this study was to assess the affinity of isoxazolones to phospholipids using IAM chromatography and compare it with the lipophilicity parameters established by reversed phase chromatography. Quantitative structure-retention relationship (QSRR) modeling of IAM retention using differential evolution coupled with partial least squares (DE-PLS) regression was performed. The results indicate that in the studied group of structurally related isoxazolone derivatives, discrepancies occur between the retention under IAM and RP-LC conditions. Although some correlation between these two chromatographic methods can be found, lipophilicity does not fully explain the affinities of the investigated molecules to phospholipids. QSRR analysis also shows common factors that contribute to retention under IAM and RP-LC conditions. In this context, the significant influences of WHIM and GETAWAY descriptors in all the obtained models should be highlighted. Antifungal ; Chemistry ; Pyridine ; Affinity chromatography ; Membrane ; Chromatography ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Biology ; Microbiology ; Enzyme Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis ; Organophosphorus compounds synthesis
Authors: Gökhan Kayhan | Erhan Ergün
Source: Balkan Journal of Electrical and Computer Engineering (article, 2020-01-31)
Field: Chemistry — Analytical Chemistry

Medicinal and Aromatic Plants Identification Using Machine Learning Methods In this study, different machine learning (ML) methods were used to classify medicinal and aromatic plants (MAP) namely St. John’s wort (Hypericum perforatum L.), Melissa (Melissa officinalis L.), Echinacea (Echinacea purpurea L.), Thyme (Thymus sp.) and Mint (Mentha angustifolia L.) based on leaf shape, gray and fractal features. Naive Bayes Classifier (NBC), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Probabilistic Neural Network (PNN) classification were used as methods. The results indicated that plant species were successfully recognized the average of correct classification rate. The best classification rate on the NBC was taken: training data for classification rate 98.39% and test data classification rate for 98.00% are obtained. ML could be accurate tools for MAP classification tasks. Artificial intelligence ; Hypericum perforatum ; Naive Bayes classifier ; Pattern recognition (psychology) ; Machine learning ; Support vector machine ; Computer science ; Traditional medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Aswathi Soni | Mahmoud Al-Sarayreh | Marlon M. Reis | Jeremy R. Smith | Kris Tong | Gale Brightwell
Source: Foods (article, 2020-06-26)
Field: Chemistry — Analytical Chemistry

Identification of Cold Spots Using Non-Destructive Hyperspectral Imaging Technology in Model Food Processed by Coaxially Induced Microwave Pasteurization and Sterilization The model food in this study known as mashed potato consisted of ribose (1.0%) and lysine (0.5%) to induce browning via Maillard reaction products. Mashed potato was processed by Coaxially Induced Microwave Pasteurization and Sterilization (CiMPAS) regime to generate an F0 of 6-8 min and analysis of the post-processed food was done in two ways, which included by measuring the color changes and using hyperspectral data acquisition. For visualizing the spectra of each tray in comparison with the control sample (raw mashed-potato), the mean spectrum (i.e., mean of region of interest) of each tray, as well as the control sample, was extracted and then fed to the fitted principal component analysis model and the results coincided with those post hoc analysis of the average reflectance values. Despite the presence of a visual difference in browning, the Lightness (L) values were not significantly (<i>p</i> < 0.05) different to detect a cold spot among a range of 12 processed samples. At the same time, hyperspectral imaging could identify the colder trays among the 12 samples from one batch of microwave sterilization. Browning ; Pasteurization ; Sterilization (economics) ; Food science ; Maillard reaction ; Hyperspectral imaging ; Tray ; Chemistry ; Botany ; Biology ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Hermann B. Frieboes | Shreya Raghavan | Biana Godin
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-08-19)
Field: Materials Science — Biomaterials

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced <i>in vitro</i> 3D tissue models as well as with <i>in silico</i> mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized. Tumor microenvironment ; Nanocarriers ; Drug delivery ; Metastasis ; In silico ; Cancer ; Cancer research ; Cancer cell ; Function (biology) ; Computational biology ; Tumor cells ; Medicine ; Biology ; Nanotechnology ; Cell biology ; Materials science Nanoparticle-Based Drug Delivery ; Mathematical Biology Tumor Growth ; Cancer Cells and Metastasis
Authors: Yangyang Cao | Tetsuya Yomo | Bei‐Wen Ying
Source: Microorganisms (article, 2020-02-26)
Field: Chemistry — Analytical Chemistry

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping Bacterial growth curves, representing population dynamics, are still poorly understood. The growth curves are commonly analyzed by model-based theoretical fitting, which is limited to typical S-shape fittings and does not elucidate the dynamics in their entirety. Thus, whether a certain growth condition results in any particular pattern of growth curve remains unclear. To address this question, up-to-date data mining techniques were applied to bacterial growth analysis for the first time. Dynamic time warping (DTW) and derivative DTW (DDTW) were used to compare the similarity among 1015 growth curves of 28 <i>Escherichia coli</i> strains growing in three different media. In the similarity evaluation, agglomerative hierarchical clustering, assessed with four statistic benchmarks, successfully categorized the growth curves into three clusters, roughly corresponding to the three media. Furthermore, a simple benchmark was newly proposed, providing a highly improved accuracy (~99%) in clustering the growth curves corresponding to the growth media. The biologically reasonable categorization of growth curves suggested that DTW and DDTW are applicable for bacterial growth analysis. The bottom-up clustering results indicate that the growth media determine some specific patterns of population dynamics, regardless of genomic variation, and thus have a higher priority of shaping the growth curves than the genomes do. Dynamic time warping ; Cluster analysis ; Hierarchical clustering ; Bacterial growth ; Benchmark (surveying) ; Similarity (geometry) ; Growth curve (statistics) ; Population ; Dynamics (music) ; Statistic ; Growth chart ; Computer science ; Biological system ; Pattern recognition (psychology) ; Biology ; Statistics ; Mathematics ; Artificial intelligence ; Geography ; Genetics ; Bacteria ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Advanced Chemical Sensor Technologies
Authors: Edwin R. Arboleda | Arnel C. Fajardo | Ruji P. Medina
Source: TELKOMNIKA (Telecommunication Computing Electronics and Control) (article, 2020-06-12)
Field: Chemistry — Analytical Chemistry

Green coffee beans feature extractor using image processing This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the “pixel pair formation” that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively. Prewitt operator ; Sobel operator ; Normalization (sociology) ; Artificial intelligence ; Peak signal-to-noise ratio ; Extractor ; Computer science ; Mean squared error ; Pixel ; Pattern recognition (psychology) ; Linear discriminant analysis ; Edge detection ; Mathematics ; Image processing ; Algorithm ; Statistics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Coffee research and impacts