jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Martínez-Fernández | Miguel Herranz | Katerina Foteinopoulou | Nikos Ch. Karayiannis | Manuel Laso
Source: Polymers (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Local and Global Order in Dense Packings of Semi-Flexible Polymers of Hard Spheres The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. The chain stiffness is controlled by a tunable harmonic potential for the bending angle, whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range between acute and obtuse ones, reaching the limit of rod-like polymers. We analyze how the packing density and chain stiffness affect the chains' ability to self-organize at the local and global levels. The former corresponds to crystallinity, as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures are obtained, ranging between random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, and nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, a delay is observed between the establishment of the long-range nematic order and crystallization as a function of the packing density, while for right-angle chains, both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres. Materials science ; Liquid crystal ; Persistence length ; Polymer ; Bending stiffness ; Crystallography ; Condensed matter physics ; Bending ; Physics ; Composite material ; Chemistry Material Dynamics and Properties ; Polymer crystallization and properties ; Liquid Crystal Research Advancements
Authors: Shahla Alizadeh | Souvik Ta | S. Lakshminarayanan | Ajay K. Ray
Source: The Canadian Journal of Chemical Engineering (article, 2023-05-23)
Field: Chemistry — Analytical Chemistry

Application of artificial neural network for prediction of 10 crude oil properties Abstract This study aims to develop an industrially reliable and accurate method to estimate crude oil properties from their Fourier transform infrared spectroscopy (FTIR) spectra. We used the complete FTIR spectral data of selected crude oil samples from seven different Canadian oil fields to predict 10 important crude oil properties using artificial neural networks (ANNs). The predicted properties include specific gravity, kinematic viscosity, total acid number, micro carbon content, and production of light and heavy naphtha, Kero, and distillate in oil refineries. The 107 different (65 light oil and 42 heavy/medium oil samples) crude oil samples used in this study came from seven oil fields and reservoirs across Canada. In line with standard practice, we used 80% of the dataset for training the ANN models and used the remaining 20% of the crude oil samples to test the models. In the ANN analysis, the mean squared error (MSE) was used as the loss function in models, and the mean absolute prediction error (MAPE) was used as a reference to compare the performance of different neural networks constructed with different numbers of layers. This work demonstrates that FTIR spectroscopy is a promising technique that provides rapid and accurate estimates for the oil properties of interest to the industry. A comparison of the values predicted by the validated ANN models and their corresponding measured (actual) values showed excellent prediction with the acceptable range of error (below 15%) aimed for by our industry partner for all properties except viscosity, for which building models based on the natural logarithmic values of measured viscosities significantly improved the results. API gravity ; Artificial neural network ; Mean squared error ; Light crude oil ; Mean absolute percentage error ; Distillation ; Oil refinery ; Environmental science ; Fourier transform infrared spectroscopy ; Petroleum industry ; Petroleum ; Mathematics ; Crude oil ; Biological system ; Petroleum engineering ; Statistics ; Computer science ; Chemistry ; Engineering ; Artificial intelligence ; Waste management ; Chromatography ; Environmental engineering ; Chemical engineering ; Organic chemistry Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Water Quality Monitoring and Analysis
Authors: Panisa Treepong | Nawanol Theera-Ampornpunt
Source: Current Research in Food Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Early bread mold detection through microscopic images using convolutional neural network Mold on bread in the early stages of growth is difficult to discern with the naked eye. Visual inspection and expiration dates are imprecise approaches that consumers rely on to detect bread spoilage. Existing methods for detecting microbial contamination, such as inspection through a microscope and hyperspectral imaging, are unsuitable for consumer use. This paper proposes a novel early bread mold detection method through microscopic images taken using clip-on lenses. These low-cost lenses are used together with a smartphone to capture images of bread at 50× magnification. The microscopic images are automatically classified using state-of-the-art convolutional neural networks (CNNs) with transfer learning. We extensively compared image preprocessing methods, CNN models, and data augmentation methods to determine the best configuration in terms of classification accuracy. The top models achieved near-perfect <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math> scores of 0.9948 for white sandwich bread and 0.9972 for whole wheat bread. Convolutional neural network ; Artificial intelligence ; Preprocessor ; Computer science ; Pattern recognition (psychology) ; Hyperspectral imaging ; Mold ; Computer vision ; Materials science Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Biosensors and Analytical Detection
Authors: Ke-Jun Fan | Boyuan Liu | Wen‐Hao Su
Source: Sensors (article, 2023-02-28)
Field: Chemistry — Analytical Chemistry

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels. Hyperspectral imaging ; Convolutional neural network ; Tandem ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Nikhil Branson | Pedro R. Cutillas | Conrad Bessant
Source: Bioinformatics Advances (article, 2023-12-22)
Field: Chemistry — Spectroscopy

Comparison of multiple modalities for drug response prediction with learning curves using neural networks and XGBoost See https://github.com/Nik-BB/Learning-curves-for-DRP for the code used. Phosphoproteomics ; Proteomics ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Drug response ; Benchmark (surveying) ; Computational biology ; Bioinformatics ; Data mining ; Biology ; Drug ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Gene expression and cancer classification
Authors: Dimitrios Kolosov | Lemonia-Christina Fengou | Jens Michael Carstensen | Nette Schultz | George‐John E. Nychas | Iosif Mporas
Source: Sensors (article, 2023-04-24)
Field: Chemistry — Analytical Chemistry

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively. Deep learning ; Convolutional neural network ; Computer science ; Multispectral image ; Cloud computing ; Latency (audio) ; Throughput ; Artificial intelligence ; Embedded system ; Artificial neural network ; Computer hardware ; Operating system Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ahmad Ghiaskar | Mohammad Damghani Nouri
Source: Polymer Composites (article, 2023-11-16)
Field: Materials Science — Polymers and Plastics

High‐velocity impact behavior and quasi‐static indentation of new elastomeric nanocomposites reinforced with cellulose nanofibers and lignin powder Abstract The present study investigates the indentation resistance of elastomeric nanocomposites based on natural rubber with different amounts of cellulose nanofibers (CNFs) under quasi‐static indentation (QSI) and high‐velocity impact (HVI) tests. At first, the baking characteristics and hardness of the samples were checked. The results showed that with the increase in CNFs content, the hardness of the samples increases, and the optimal baking time decreases. A high‐quality final product is produced through good dispersion and acceptable physical bonding between the CNF filler and the rubber chain. Further, the results of quasi‐static indentation showed that the amount of energy absorption for optimal loading of 6 phr increased by 43% compared to a control sample (0 phr). Similarly, from the high‐speed impact results for optimal loading (6 phr), the ballistic limit and energy absorption values increased by 23.6 and 52.7%, respectively, compared to the control sample (0 phr). It was also observed for higher values (9,12 phr) that the glass transition temperature, Tg, effect significantly affected the fracture behavior of the samples due to the high‐speed impact. Finally, CNFs provide better bonding with polymer chains to improve properties through good interaction with natural rubber, making them suitable for energy absorption applications. Highlights New elastomeric nanocomposites reinforced with cellulose nanofibers were made. Increasing the amount of CNFs decreases the optimum curing temperature. The hardness of elastomeric nanocomposites is increased by cellulose nanofibers. The indentation properties of nanocomposites have increased significantly. The optimum value of 6‐phr increased the impact resistance of nanocomposites. Materials science ; Composite material ; Nanocomposite ; Elastomer ; Indentation ; Nanofiber ; Cellulose ; Natural rubber ; Curing (chemistry) ; Chemical engineering Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications
Authors: Vimukthi Dananjaya | Yashoda Somarathna | S. Siriwardena | Narayana M. S. Sirimuthu | L. Karunanayake | Chamil Abeykoon
Source: International Journal of Lightweight Materials and Manufacture (article, 2023-12-22)
Field: Materials Science — Polymers and Plastics

Effects of latex type and processed-mica waste loading on the structural and thermo-physical properties of natural rubber latex foam composites Mica waste, a type of commercial waste produced in bulk quantities by the mica mining industry, was processed and added to Natural Rubber Latex Foams (NRLF) made of centrifuged and creamed latex. Following the Dunlop method, NRLF composites with various Processed Mica Waste (PMW) loadings (0, 2, 4, 6, 8, and 10 phr) were prepared, and the thermo-physical characteristics were compared. Thermal conductivity, electrical resistivity of NRLF against the latex type and mica loading were compared for the first time. NRLFs prepared using creamed latex exhibit 33 and 50 μm (width and height) cell diameter, 3 ibf hardness, 281 % swelling index improvements and 0.05 Wm-1K-1 thermal conductivity, and 1 ˚C glass transition temperature (Tg) reductions than centrifuged NRLF and indistinguishable electrical resistivity. With the addition of mica (0-10 Phr), both NRLF types showed a similar ascending trend in hardness (42 ibf), water absorption (16%), Tg (7 ˚C), thermal conductivity (0.54 Wm-1K-1), electrical resistivity (69 × 103ohm m) with decreasing gel time (3 min) and swelling index (550 %). The key objective of this research was to prepare PMW-filled NRLF and compare structural, electrical and thermo-physical properties, for the first time, against mica content and latex type. Mica ; Composite material ; Materials science ; Swelling ; Electrical resistivity and conductivity ; Absorption of water ; Natural rubber Polymer Nanocomposites and Properties ; Natural Fiber Reinforced Composites ; Polymer composites and self-healing
Authors: Yasuhide Mochizuki | Kaede Nagamatsu | Hiroki Koiso | Toshihiro Isobe | Akira Nakajima
Source: The Journal of Physical Chemistry Letters (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Mechanism of Negative Thermal Expansion in Monoclinic Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub> from First Principles Negative thermal expansion (NTE) materials generally have high-symmetry space groups, large average atomic volumes, and corner-sharing octahedral and tetrahedral coordination structures. By contrast, monoclinic α-Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub>, which has a small average atomic volume and edge-sharing structure, has been reported to exhibit NTE, the detailed mechanism of which is unclear. In this study, we investigate the <i>A</i><sub>2</sub><i>B</i><sub>2</sub>O<sub>7</sub> polymorphs and analyze the NTE behavior of α-Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub> using first-principles lattice-dynamics calculations. From the polymorphism investigation in 20 <i>A</i><sub>2</sub><i>B</i><sub>2</sub>O<sub>7</sub> compounds using 6 representative crystal structures, small <i>A</i> and <i>B</i> cationic radii are found to stabilize the α-Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub>-type structure. We then analyze the NTE behavior of α-Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub> using quasi-harmonic approximation. Our calculated thermal expansion coefficients and anisotropic atomic displacement parameters were in good agreement with those of the experimental reports at low temperatures. From the mode-Grüneisen parameter distribution plotted over the entire first-Brillouin zone, we found that the phonon contributing most significantly to NTE emerges not into the special points but between them. In this phonon mode, the O connecting two PO<sub>4</sub> tetrahedra rotates, and the Cu and O vibrate perpendicular to the bottom of the CuO<sub>5</sub> pyramidal unit, which folds the <i>ac</i> lattice plane. This vibration behavior can explain the experimentally reported anisotropic NTE behavior of α-Cu<sub>2</sub>P<sub>2</sub>O<sub>7</sub>. Our results demonstrate that the most negative mode-Grüneisen parameter contributing to NTE behavior is not always located on high-symmetry special points, indicating the importance of lattice vibration analyses for the entire first-Brillouin zone. Monoclinic crystal system ; Negative thermal expansion ; Mechanism (biology) ; Thermal expansion ; Materials science ; Copper ; Thermal ; Crystallography ; Physics ; Chemistry ; Thermodynamics ; Metallurgy ; Crystal structure ; Quantum mechanics Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Ferroelectric and Piezoelectric Materials
Authors: Subhadra Thapa | Finley Clark | Severin T. Schneebeli | Jianing Li
Source: Journal of Chemical Theory and Computation (article, 2023-11-28)
Field: Materials Science — Biomaterials

Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design. Benchmarking ; Computer science ; Peptide ; Oligopeptide ; Characterization (materials science) ; Nanotechnology ; Chemistry ; Materials science ; Biochemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Aaron Scott | Christofer Karlsson | Tirthankar Mohanty | Erik Hartman | Suvi T. Vaara | Adam Linder | Johan Malmström | Lars Malmström
Source: Communications Biology (article, 2023-06-10)
Field: Chemistry — Spectroscopy

Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics. False discovery rate ; False positive paradox ; Identification (biology) ; False positives and false negatives ; Proteome ; Proteomics ; Computer science ; Mass spectrometry ; False positive rate ; Computational biology ; Global Positioning System ; True positive rate ; Data mining ; Bioinformatics ; Artificial intelligence ; Chromatography ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Zhiwei 芷薇 Fan 范 | Jingyuan 靖媛 Qu 渠 | Tao 涛 Wang 王 | Yan 滟 Wen 温 | Ziwen 子文 An 安 | Qitao 琦涛 Jiang 姜 | Wuhong 武红 Xue 薛 | Peng 鹏 Zhou 周 | Xiaohong 小红 Xu 许
Source: Chinese Physics B (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Recent progress on two-dimensional ferroelectrics: Material systems and device applications Ferroelectrics are a type of material with a polar structure and their polarization direction can be inverted reversibly by applying an electric field. They have attracted tremendous attention for their extensive applications in non-volatile memory, sensors and neuromorphic computing. However, conventional ferroelectric materials face insulating and interfacial issues in the commercialization process. In contrast, two-dimensional (2D) ferroelectric materials usually have excellent semiconductor performance, clean van der Waals interfaces and robust ferroelectric order in atom-thick layers, and hold greater promise for constructing multifunctional ferroelectric optoelectronic devices and nondestructive ultra-high-density memory. Recently, 2D ferroelectrics have obtained impressive breakthroughs, showing overwhelming superiority. Herein, firstly, the progress of experimental research on 2D ferroelectric materials is reviewed. Then, the preparation of 2D ferroelectric devices and their applications are discussed. Finally, the future development trend of 2D ferroelectrics is looked at. Ferroelectricity ; Materials science ; Non-volatile memory ; Neuromorphic engineering ; Nanotechnology ; Optoelectronics ; Semiconductor ; Engineering physics ; Polarization (electrochemistry) ; van der Waals force ; Dielectric ; Computer science ; Physics Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications