jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cuong Ly | Cody A. Nizinski | Clement Vachet | Luther W. McDonald | Tolga Taşdizen
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Chemistry — Spectroscopy

Learning to Estimate the Composition of a Mixture with Synthetic Data An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. Content (measure theory) ; Composition (language) ; Action (physics) ; Computer science ; Mathematics ; Art ; Physics Analytical Chemistry and Chromatography ; Chromatography in Natural Products
Authors: Mengyu Yang | Wensi Wang | Qiang Gao | Zhao Chen | Caole Li | Xiangfei Yang | Jiaxi Li | Xiaoguang Li | Jianglong Cui | Liting Zhang | Yanping Ji | Shuqin Geng
Source: Research Square (preprint, 2021-11-08)
Field: Chemistry — Analytical Chemistry

Automatic Identification of Harmful Algae Based On Multiple Convolutional Neural Networks and Transfer Learning Abstract The monitoring of harmful algae is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive and limited in practice. The automatic classification of algae cell images and the identification of harmful algae images were realized by the combination of multiple Convolutional Neural Networks (CNNs) and deep learning techniques based on transfer learning in this work. 11 common harmful and 31 harmless algae genera were collected as input samples, the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify algae images, and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful algae which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful algae which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful algae could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful algae and greatly reduces the workload of professional personnel. Transfer of learning ; Convolutional neural network ; Identification (biology) ; Algae ; Computer science ; Artificial intelligence ; Machine learning ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Logan S. Richards | Maria D. Flores | Claudia Millán | Calina Glynn | Chih-Te Zee | M.R. Sawaya | Marcus Gallagher-Jones | Rafael J. Borges | Isabel Usón | José A. Rodríguez
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-09-14)
Field: Materials Science — Materials Chemistry

Fragment-based <i>ab initio</i> phasing of peptidic nanocrystals by MicroED Abstract Microcrystal electron diffraction (MicroED) is transforming the visualization of molecules from nanocrystals, rendering their three-dimensional atomic structures from previously unamenable samples. Peptidic structures determined by MicroED include naturally occurring peptides, synthetic protein fragments and peptide-based natural products. However, as a diffraction method, MicroED is beholden to the phase problem, and its de novo determination of structures remains a challenge. ARCIMBOLDO, an automated, fragment-based approach to structure determination. It eliminates the need for atomic resolution, instead enforcing stereochemical constraints through libraries of small model fragments, and discerning congruent motifs in solution space to ensure validation. This approach expands the reach of MicroED to presently inaccessible peptidic structures including segments of human amyloids, and yeast and mammalian prions, and portends a more general phasing solution while limiting model bias for a wider set of chemical structures. Phaser ; Angstrom ; Limiting ; Chemistry ; Nanotechnology ; Crystallography ; Physics ; Materials science ; Optics Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Mass Spectrometry Techniques and Applications
Authors: Yi‐Hao Ma | Liang-Hao Ruan | Jing Wang | Wen‐Sheng Zhao | Yue Hu | Yuhua Cheng | Da‐Wei Wang | Gaofeng Wang
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Spatial Selected Spin Filtering Effect in Z-Shaped MoS<sub>2</sub> Nanoribbon Due to the gapless between any two successive subbands with the same spin, the spin polarization in uniform MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> zigzag nanoribbon (MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -ZR) is weak. Here, through configuring Z-shaped structure, we realize 100% spin polarization in MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -ZR with only exchange field. The filtered spin electrons transmit along a specified edge of the device. This phenomenon is the result of the combined effect of the localized edge states and structure induced inversion of the Mo-edge and S-edge. In our calculation the fully spin polarization appears in several energy regions, so this Z-shaped MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -ZRs can be used in making spintronic devices. Physics ; Spintronics ; Polarization (electrochemistry) ; Spin (aerodynamics) ; Spin polarization ; Condensed matter physics ; Electron ; Crystallography ; Ferromagnetism ; Quantum mechanics ; Chemistry ; Physical chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Wan Nazri Wan Busu | Shan Chen | Muhammad Jefri Mohd Yusof | Sahrim Ahmad
Source: Journal of Metals Materials and Minerals (article, 2021-03-28)
Field: Materials Science — Polymers and Plastics

Graphene enhanced linear low-density polyethylene nanocomposites by premixing and melt compounding In this study, linear low-density polyethylene/graphene nanoplatelets (LLDPE/GNPs) nanocomposites were prepared from conventional melt-mixing method and our new approach; two-step premix mixing technique. Indirect mixing technique (IDT) was employed to fabricate a premix of LLDPE/ GNPs in the ratio of 80:20 wt%. The effects of GNPs loadings and the processing method of nanocomposites on the mechanical strength, decomposition temperature, crystallinity, electrical impedance and morphology were investigated. Irrespective of processing methods, the prepared nanocomposites exhibited crystalline structure due to the presence of GNPs whilst the degradation temperature was recorded to be increased with GNPs loadings that signified improved thermal stability. The inclusion of GNPs provided electrical impedance ability on LLDPE matrix as a result from the formation of conductive networks of GNPs. LLDPE/GNPs nanocomposites prepared from two-step premix mixing technique showed better mechanical properties than those of melt-mixing method. Apparently, two-step premix mixing of LLDPE/GNPs nanocomposites promoted better dispersion of GNPs in matrix based on SEM images. Our findings have proved that our new, profound technique of preparing premix before mixing could result in enhanced quality of nanocomposites that are potentially useful in packaging or electronic applications. Linear low-density polyethylene ; Nanocomposite ; Materials science ; Compounding ; Crystallinity ; Composite material ; Mixing (physics) ; Thermal stability ; Dispersion (optics) ; Polyethylene ; Chemical engineering Polymer Nanocomposite Synthesis and Irradiation ; Advanced Sensor and Energy Harvesting Materials ; Graphene research and applications
Authors: David Dang
Source: Deep Blue (University of Michigan) (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Thermal and Structural Response Modeling of a Woven Thermal Protection System NASA’s future missions include destinations such as Venus, Saturn, and Jupiter, where the heat-intensive environments experienced during atmospheric entry by vehicles or probes often exceed the capability of conventional Thermal Protection System (TPS) materials (e.g., Phenolic Impregnated Carbon Ablator (PICA), Heritage Carbon Phenolic (HCP)). The Woven Thermal Protection System (WTPS), a relatively new concept of ablative TPS design, has been of key interest in the past decade to replace heritage materials. In the design of TPS, characterizing the material’s thermal and structural performance is critical. The focus of this dissertation is on improving the modeling of WTPS, in particular, the Heatshield for Extreme Entry Environment Technology (HEEET), a dual layer WTPS composed of an outer recession-resistant layer and inner insulative layer. The results focus on three aspects. The first aspect is focused on pure thermal response modeling of HEEET in a radiant heating facility. The simulation results are compared to experimental measurements, and improved material and boundary condition models are derived based on the analyses. xi The second aspect is focused on pure structural response modeling of the 24 in. HEEET weave under four point bend mechanical loading. The model is assessed using experimental measurements. Initial simulations are performed using stiffness properties derived from a smaller HEEET weave at room temperature. Comparisons between the experimental measurements and simulation results for deformation indicate that the 24 in. recession layer is 130% stiffer than the smaller weave. The last aspect is focused on coupled thermo-structural response modeling of HEEET subject to combined mechanical and thermal loading. Discrepancies between simulation predictions and experimental measurements of deformation suggest that significant stiffness degradation is occurring. The subsequent stiffness degradation studies indicate that the stiffness of HEEET is monotonically decreasing in the temperature range being modeled. When using the inferred stiffness reduction factors for coupled thermo-structural modeling, the predictions for strain are in much closer agreement with experimental measurements. Limitations of the studies are discussed, which motivate the need for higher-fidelity experimental tests that can be used to deduce improved models. Thermal ; Thermal protection ; Materials science ; Composite material ; Physics ; Thermodynamics Textile materials and evaluations
Authors: Yanxiao Li | Xuetao Hu | Jiyong Shi | Baijing Qiu | Jianbo Xiao
Source: eFood (article, 2021-10-01)
Field: Chemistry — Analytical Chemistry

Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of Enterobacter sp, Acetobacter pasteurianus , and Lactobacillus paracasei colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least‐squares support vector machine (LS‐SVM) by using 23 variables selected by genetic algorithm. The optimal LS‐SVM model with an identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agreed with that of the manual counting method with an average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure. Hyperspectral imaging ; Artificial intelligence ; Chemometrics ; Linear discriminant analysis ; Support vector machine ; Partial least squares regression ; Pattern recognition (psychology) ; Microbial population biology ; Mathematics ; Biology ; Computer science ; Statistics ; Machine learning ; Bacteria Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Graciela Artavia | Fabio Granados-Chinchilla
Source: Akademik Gıda (article, 2021-10-17)
Field: Chemistry — Organic Chemistry

Discrimination, Quantitation, and Identification of Edible Vegetable Oil Blends Based on Their Fatty Acid Profiles Based on the most common vegetable oil blends, binary and ternary analytical mixtures were constructed in mass fractions from 0.50 to 0.97, and their fatty acid profile was determined and represented graphically. The fatty acids with discriminatory power were selected to construct equations to predict commercial oil blend proportions. Three different linear equations resulted from the analysis for i. palm oil-based blends: y = (0.3713 ± 0.0217)x + (11.401 ± 0.68) for C18:2 and (0.4357 ± 0.0254)x + (51.281 ± 2.90) for C16:0 ii. soybean oil-based blends y = (-0.0789 ± 0.0046)x + (30.686 ± 1.71) for C18:1 and (0.0686 ± 0.0040)x - (0.1395 ± 0.0081) for C18:3 and iii. sunflower oil-based blends y = (-0.0552 ± 0.0032)x + (12.167 ± 0.6105) for C16:0. Finally, the fatty acid profiles of n = 10 commercial samples (i.e., vegetable oil blends) were determined, and the model was applied to them with satisfactory results. Sunflower oil ; Ternary operation ; Palm oil ; Vegetable oil ; Sunflower ; Fatty acid ; Materials science ; Chemistry ; Mathematics ; Chromatography ; Food science ; Organic chemistry ; Computer science Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Fatty Acid Research and Health
Authors: Marcus Portelli | Michele Pasquali | Federico Carra | A. Bertarelli | Pierluigi Mollicone | Nicholas Sammut | Óscar Sacristán De Frutos | Jorge Guardia Valenzuela | Erich Neubauer | Michael Kitzmantel | David Grech
Source: Shock and Vibration (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Thermomechanical Characterisation of Copper Diamond and Benchmarking with the MultiMat Experiment The High‐Luminosity Large Hadron Collider upgrade at CERN will result in an increase in the energy stored in the circulating particle beams, making it necessary to assess the thermomechanical performance of currently used and newly developed materials for use in beam intercepting devices such as collimators and absorbers. This study describes the thermomechanical characterisation of a novel copper diamond grade selected for use in tertiary collimators of the HL‐LHC. The data obtained are used to build an elastoplastic material model and implemented in numerical simulations performed to benchmark experimental data obtained from the recently completed MultiMat experiment conducted at CERN’s HiRadMat facility, where various materials shaped as slender rods were tested under particle beam impact. The analyses focus on the dynamic longitudinal and flexural response of the material, with results showing that the material model is capable of replicating the material behaviour to a satisfactory level in both thermal and structural domains, accurately matching experimental measurements in terms of temperature, frequency content, and amplitude. Large Hadron Collider ; Materials science ; Beam (structure) ; Rod ; Diamond ; Benchmark (surveying) ; Bending ; Upgrade ; Mechanical engineering ; Structural engineering ; Nuclear engineering ; Composite material ; Computer science ; Engineering ; Physics ; Nuclear physics ; Geology High-Velocity Impact and Material Behavior ; Superconducting Materials and Applications ; Laser-Plasma Interactions and Diagnostics
Authors: Wendong Yao | Hengwu Xu | Yanhong Chen | Yingying Xu | Feng Zhou | Zeng Wang | Xinjun Cai
Source: Research Square (preprint, 2021-10-18)
Field: Materials Science — Biomaterials

Mannose Modified Co-Loaded Zoledronic Liposomes Deplete M2-tumor-associated Macrophages to Enhance Anti-Tumor Effect of Doxorubicin on TNBC Abstract As the most numerous inflammatory cell group in tumor microenvironment, tumor-associated macrophages (TAMs) have an essential effect in tumor therapy and are potential therapeutic targets. M2 type tumor-associated macrophages (M2-TAMs) were involved in the entire process of tumor development, invasion, and metastasis, obstructing the anti-tumor effect of chemotherapy drugs and nano-medicine. This study aimed to construct a mannose modified co-loaded zoledronic acid and doxorubicin liposomes (Man-LP@ZOL/DOX) for improving the anti-tumor effect by suppressing M2-TAMs on triple-negative breast cancer (TNBC). The size, PDI, and Zeta-potential of Man-LP@ZOL/DOX liposomes were determined to be 212.80±7.74 nm, 0.1413±0.0232, -33.63±0.49 mV, respectively. The Man-LP@ZOL/DOX's EE% and DL% of ZOL as measured were 17.21±2.26% and 1.56±0.21%, while for DOX, they were 84.42±2.05% and 5.12±0.14%. The uptake of Man-LP into cells was increased when it was modified with a TAMs target ligand. The liposomes inhibited the invasion of MDA-MB-231 cells induced by M2-TAMs, and expression of biomarkers on M2-TAMs (Arg1 and CD206 in vitro, CD68, and CD206 in vivo) was apparent. Moreover, co-loaded drugs liposome system remarkably enhanced anticancer effects both in vitro and vivo combined with ZOL. In conclusion, all these results established that Man-LP@ZOL/DOX could enhance anti-tumor effect of DOX via depleting M2-TAMs on TNBC. Doxorubicin ; Liposome ; Zoledronic acid ; Cancer research ; Chemistry ; Medicine ; Oncology ; Internal medicine ; Chemotherapy ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Immune cells in cancer
Authors: Tacettin GEÇKİL | Ceren Beyza İNCE | Eda TÜZÜN ÖZPINAR
Source: Konya Journal of Engineering Sciences (article, 2021-12-02)
Field: Materials Science — Materials Chemistry

NANOSİLİKANIN BİTÜMÜN KIVAMI, VİSKOZİTESİ VE MİKROYAPISINA ETKİLERİ Bu çalışmada, nanosilika katkısının bitümlerin önemli özelliklerinden olan kıvam, viskozite ve mikroyapı üzerindeki etkileri araştırılmıştır. Bu amaçla, penetrasyon sınıfı B 160/220 olan saf bitüme ağırlıkça %1, %3, %5 ve %7 oranlarında nanosilika katkısı eklenerek modifiye bitümler elde edilmiştir. Elde edilen saf ve modifiye bitümlerin fiziksel özellikleri penetrasyon, yumuşama noktası, düktilite ve dönel ince film etüvü (RTFO) testleri ile belirlenmiştir. Ayrıca, bitümlerin sıcaklığa karşı hassasiyetlerinin bir ölçüsü olarak kabul edilen penetrasyon indeksi (PI) değerleri de hesaplanmıştır. Daha sonra, bağlayıcıların yüksek sıcaklıklardaki akıcılıkları dönel viskozimetre (RV) deneyi ile, mikroyapılarında meydana gelen değişim ise taramalı elektron mikroskopu (SEM) analizi ile belirlenmiştir. Fiziksel test sonuçlarına göre, nanosilika katkısıyla bağlayıcıların sertleşerek penetrasyon sınıfının B 100/150 olarak değiştiği, %5 nanosilika oranının kritik bir oran olduğu ve bu oranda sıcaklık hassasiyetinin azaldığı görülmüştür. RV sonuçlarına göre, katkı ilavesi ile bağlayıcıların viskozite değerlerinin artış gösterdiği ve dolayısıyla karıştırma-sıkıştırma sıcaklıklarının da arttığı görülmüştür. SEM sonuçlarına göre, nanosilika ilavesiyle homojen bir bitüm-nanosilika karışımının elde edildiği görülmüştür. Physics High voltage insulation and dielectric phenomena ; Asphalt Pavement Performance Evaluation ; Non-Destructive Testing Techniques
Authors: Alessandra da Rocha Duailibe Monteiro | Thiago Feital | José Carlos Pinto
Source: Processes (article, 2021-09-21)
Field: Chemistry — Analytical Chemistry

A Numerical Procedure for Multivariate Calibration Using Heteroscedastic Principal Components Regression Many methods have been developed to allow for consideration of measurement errors during multivariate data analyses. The incorporation of the error structure into the analytical framework, usually described in terms of the covariance matrix of measurement errors, can provide better model estimation and prediction. However, little effort has been made to evaluate the effects of heteroscedastic measurement uncertainties on multivariate analyses when the covariance matrix of measurement errors changes with the measurement conditions. For this reason, the present work describes a new numerical procedure for analyses of heteroscedastic systems (heteroscedastic principal component regression or H-PCR) that takes into consideration the variations of the covariance matrix of measurement fluctuations. In order to illustrate the proposed approach, near infrared (NIR) spectra of xylene and toluene mixtures were measured at different temperatures and stirring velocities and the obtained data were used to build calibration models with different multivariate techniques, including H-PCR. Modeling of available xylene–toluene NIR data revealed that H-PCR can be used successfully for calibration purposes and that the principal directions obtained with the proposed approach can be quite different from the ones calculated through standard PCR, when heteroscedasticity is disregarded explicitly. Heteroscedasticity ; Principal component regression ; Covariance matrix ; Calibration ; Principal component analysis ; Multivariate statistics ; Covariance ; Statistics ; Observational error ; Mathematics ; Computer science ; Econometrics Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Fault Detection and Control Systems
Authors: Minhee Kim | Se‐Hee Lee
Source: Polymers (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Modified Molecular Chain Displacement Analysis Employing Electro-Mechanical Threshold Energy Condition for Direct Current Breakdown of Low-Density Polyethylene In an HVDC environment, space charge accumulated in polymeric insulators causes severe electric field distortion and degradation of breakdown strength. To analyze the breakdown characteristics, here, the space charge distribution was numerically evaluated using the bipolar charge transport (BCT) model, considering the temperature gradient inside the polymeric insulator. In particular, we proposed an electro-mechanical threshold energy condition, resulting in the modified molecular chain displacement model. The temperature gradient accelerates to reduce the breakdown strength with the polarity-reversal voltage, except during the harshest condition, when the temperature of the entire polymeric insulator was 70 °C. The energy imbalance inside the insulator caused by polarity-reversal voltage reduced the breakdown strength by 82%. Finally, this numerical analysis model can be used universally to predict the breakdown strength of polymeric insulators in various environments, and help in evaluating the electrical performance of polymeric insulators. Materials science ; Insulator (electricity) ; Polarity reversal ; Space charge ; Polyethylene ; Composite material ; Breakdown voltage ; Electric field ; Voltage ; Distortion (music) ; Polarity (international relations) ; Optoelectronics ; Electrical engineering ; Chemistry ; Physics ; Electron High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics