jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianyang Zhang | Xiao Wang | Zhenyu Wu | Tianyu Yang | Han Zhao | Jia‐Wei Wang | Hui Huang | Yang Liu | Zhenhui Kang
Source: Nanoscale Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Highly stable and bright blue light-emitting diodes based on carbon dots with a chemically inert surface The manufacture of blue light-emitting diodes (LEDs) has always been a tough problem to solve in the display and illumination fields. Inorganic/organic semiconductors and carbon dots (CDs) with a wide band gap still face obstacles such as a low external quantum efficiency (EQE) and poor stability. Herein, we synthesized highly stable and blue emission CDs with a chemically inert surface, and the photoluminescence (PL) peak (in ultra-pure water) of which is located at 446 nm with an absolute PL quantum yield (PLQY) of 26.4%. The LEDs based blue emission CDs exhibit an electroluminescence (EL) peak located at 456 nm and a high brightness of 223 cd m<sup>-2</sup> with an EQE of 0.856%. The Commission Internationale del'Eclairage (CIE) coordinates are located at (0.21, 0.23) and the device lifetime with 65% brightness (<i>T</i> <sub>65</sub>) reaches over 217 h because of the chemically inert surface of the CDs. The results mean the devices are the most stable CDs-LEDs reported to date. This work represents a novel route for the preparation of low cost, highly stable and very bright CDs-LEDs with a short wavelength emission. Inert ; Materials science ; Light-emitting diode ; Diode ; Blue light ; Surface modification ; Optoelectronics ; Carbon fibers ; Blueshift ; Photochemistry ; Nanotechnology ; Chemical engineering ; Chemistry ; Photoluminescence ; Composite material ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Thomas Pardoen | Nathan Klavzer | Sarah F. Gayot | Frederik Van Loock | Jérémy Chevalier | Xavier Morelle | Vincent Destoop | Frédéric Lani | P.P. Camanho | Laurence Brassart | Bernard Nysten | Christian Bailly
Source: Comptes Rendus Physique (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Nanomechanics serving polymer-based composite research Tremendous progress in nanomechanical testing and modelling has been made during the last two decades. This progress emerged from different areas of materials science dealing with the mechanical behaviour of thin films and coatings, polymer blends, nanomaterials or microstructure constituents as well as from the rapidly growing field of MEMS. Nanomechanical test methods include, among others, nanoindentation, in-situ testing in a scanning or transmission electron microscope coupled with digital image correlation, atomic force microscopy with new advanced dynamic modes, micropillar compression or splitting, on-chip testing, or notched microbeam bending. These methods, when combined, reveal the elastic, plastic, creep, and fracture properties at the micro- and even the nanoscale. Modelling techniques including atomistic simulations and several coarse graining methods have been enriched to a level that allows treating complex size, interface or surface effects in a realistic way. Interestingly, the transfer of this paradigm to advanced long fibre-reinforced polymer composites has not been as intense compared to other fields. Here, we show that these methods put together can offer new perspectives for an improved characterisation of the response at the elementary fibre-matrix level, involving the interfaces and interphases. Yet, there are still many open issues left to resolve. In addition, this is the length scale, typically below 10 micrometres, at which the current multiscale modelling paradigm still requires enhancements to increase its predictive potential, in particular with respect to non-linear plasticity and fracture phenomena. Nanoindentation ; Nanomechanics ; Materials science ; Fracture (geology) ; Microbeam ; Creep ; Digital image correlation ; Nanotechnology ; Characterization (materials science) ; Nanoscopic scale ; Composite material ; Microstructure ; Bending ; Micromechanics ; Nanomaterials ; Compression (physics) ; Composite number ; Atomic force microscopy ; Optics Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Composite Material Mechanics
Authors: Alexey Kartsev | S.I. Malkovsky | Andrey N. Chibisov
Source: Nanomaterials (article, 2021-11-05)
Field: Materials Science — Materials Chemistry

Analysis of Ionicity-Magnetism Competition in 2D-MX3 Halides towards a Low-Dimensional Materials Study Based on GPU-Enabled Computational Systems The acceleration of parallel high-throughput first-principle calculations in the context of 3D (three dimensional) periodic boundary conditions for low-dimensional systems, and particularly 2D materials, is an important issue for new material design. Where the scalability rapidly deflated due to the use of large void unit cells along with a significant number of atoms, which should mimic layered structures in the vacuum space. In this report, we explored the scalability and performance of the Quantum ESPRESSO package in the hybrid central processing unit - graphics processing unit (CPU-GPU) environment. The study carried out in the comparison to CPU-based systems for simulations of 2D magnets where significant improvement of computational speed was achieved based on the IBM ESSL SMP CUDA library. As an example of physics-related results, we have computed and discussed the ionicity-covalency and related ferro- (FM) and antiferro-magnetic (AFM) exchange competitions computed for some CrX3 compounds. Further, it has been demonstrated how this exchange interplay leads to high-order effects for the magnetism of the 1L-RuCl3 compound. Scalability ; Magnetism ; Computational science ; Computer science ; Graphics processing unit ; Context (archaeology) ; Graphics ; IBM ; Supercomputer ; CUDA ; Materials science ; Parallel computing ; Nanotechnology ; Physics ; Condensed matter physics ; Computer graphics (images) ; Operating system ; Geology 2D Materials and Applications ; Inorganic Chemistry and Materials ; Graphene research and applications
Authors: Michela Palumbo | Bernardo Pace | Maria Cefola | Francesco Fabiano Montesano | Francesco Serio | Giancarlo Colelli | G. Attolico
Source: Agronomy (article, 2021-07-01)
Field: Chemistry — Analytical Chemistry

Self-Configuring CVS to Discriminate Rocket Leaves According to Cultivation Practices and to Correctly Attribute Visual Quality Level Computer Vision Systems (CVS) represent a contactless and non-destructive tool to evaluate and monitor the quality of fruits and vegetables. This research paper proposes an innovative CVS, using a Random Forest model to automatically select the relevant features for classification, thereby avoiding their choice through a cumbersome and error-prone work of human designers. Moreover, three color correction techniques were evaluated and compared, in terms of classification performance to identify the best solution to provide consistent color measurements. The proposed CVS was applied to fresh-cut rocket, produced under greenhouse soilless cultivation conditions differing for the irrigation management strategy and the fertilization level. The first aim of this study was to objectively estimate the quality levels (QL) occurring during storage. The second aim was to non-destructively, and in a contactless manner, identify the cultivation approach using the digital images of the obtained product. The proposed CVS achieved an accuracy of about 95% in QL assessment and about 65–70% in the discrimination of the cultivation approach. Computer science ; Quality (philosophy) ; Rocket (weapon) ; Random forest ; Artificial intelligence ; Agricultural engineering ; Greenhouse ; Product (mathematics) ; Digital image ; Environmental science ; Remote sensing ; Computer vision ; Mathematics ; Image processing ; Engineering ; Agronomy ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Hsin-Pai Cheng | Tunhou Zhang | Yixing Zhang | Shiyu Li | Feng Liang | Yan Feng | Meng Li | Vikas Chandra | Hai Li | Yiran Chen
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

NASGEM: Neural Architecture Search via Graph Embedding Method Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a latent space without considering graph similarity. Ignoring graph similarity in node-based search space may induce a large inconsistency between similar graphs and their distance in the continuous encoding space, leading to inaccurate encoding representation and/or reduced representation capacity that can yield sub-optimal search results. To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information. By precisely estimating the graph distance and using an auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize additional structural information to get more accurate graph representation to improve the search efficiency. GEMNet, a set of networks discovered by NASGEM, consistently outperforms networks crafted by existing search methods in classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%- 21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object detection. In both one-stage and twostage detectors, our GEMNet surpasses its manually-crafted and automatically-searched counterparts. Computer science ; Graph embedding ; Embedding ; Graph ; Scalability ; Theoretical computer science ; ENCODE ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Advanced Neural Network Applications ; Machine Learning and Data Classification
Authors: Lucia Meng Qi Liao | Rachel A. V. Gray | Dale D. O. Martin
Source: Journal of Visualized Experiments (article, 2021-04-09)
Field: Chemistry — Spectroscopy

Optimized Incorporation of Alkynyl Fatty Acid Analogs for the Detection of Fatty Acylated Proteins using Click Chemistry Fatty acylation, the covalent addition of saturated fatty acids to protein substrates, is important in regulating a myriad of cellular functions in addition to its implications in cancer and neurodegenerative diseases. Recent developments in fatty acylation detection methods have enabled efficient and non-hazardous detection of fatty acylated proteins, particularly through the use of click chemistry with bio-orthogonal labeling. However, click chemistry detection can be limited by the poor solubility and potential toxic effects of adding long chain fatty acids to cell culture. Described here is a labeling approach with optimized delivery using saponified fatty acids in combination with fatty-acid free BSA, as well as delipidated media, which can improve detection of hard to detect fatty acylated proteins. This effect was most pronounced with the alkynyl-stearate analog, 17-ODYA, which has been the most commonly used fatty acid analog in click chemistry detection of acylated proteins. This modification will improve cellular incorporation and increase sensitivity to acylated protein detection. In addition, this approach can be applied in a variety of cell types and combined with other assays such as pulse-chase analysis, stable isotope labeling with amino acids in cell culture, and mass spectrometry for quantitative profiling of fatty acylated proteins. Click chemistry ; Fatty acid ; Acylation ; Chemistry ; Biochemistry ; Saponification ; Free fatty acid receptor ; Fatty acid-binding protein ; Polyunsaturated fatty acid ; Organic chemistry Advanced Proteomics Techniques and Applications ; Click Chemistry and Applications ; Peptidase Inhibition and Analysis
Authors: Claudio I. Fernández | Brigitte Leblon | Jinfei Wang | Ata Haddadi | Keri Wang
Source: Canadian Journal of Plant Science (article, 2021-11-19)
Field: Chemistry — Analytical Chemistry

Cucumber powdery mildew detection using hyperspectral data This study aimed to understand the spectral changes induced by Podosphaera xanthii, the causal agent of powdery mildew, in cucumber leaves from the moment of inoculation until visible symptoms are apparent. A principal component analysis (PCA) was applied to the spectra to assess the spectral separability between healthy and infected leaves. A spectral ratio between infected and healthy leaf spectra was used to determine the best wavelengths for detecting the disease. Additionally, the spectra were used to compute two spectral variables [i.e., the red-well point (RWP) and the red-edge inflexion point (REP)]. A linear support vector machine (SVM) classifier was applied to certain spectral features to assess how well these features can separate the infected leaves from the healthy ones. The PCA showed that a good separability could be achieved from 4 days post-inoculation (DPI). The best model to fit the RWP and REP wavelengths corresponded to a linear model. The linear model had a higher adjusted R 2 for the infected leaves than for the healthy leaves. The SVM trained with five first principal components scores achieved an overall accuracy of 95% at 4 DPI (i.e., two days before the visible symptoms). With the RWP and REP parameters, the SVM accuracy increased as a function of the day of inoculation, reaching 89% and 86%, respectively, when symptoms were visible at 6 DPI. Further research must consider a higher number of samples and more temporal repetitions of the experiment. Powdery mildew ; Principal component analysis ; Hyperspectral imaging ; Mathematics ; Support vector machine ; Pattern recognition (psychology) ; Spectral line ; Artificial intelligence ; Horticulture ; Biology ; Statistics ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Rémi Delaporte-Mathurin | Mykola Ialovega | E.A. Hodille | Jonathan Mougenot | Y. Charles | E. Bernard | C. Martin | C. Grisolia
Source: Scientific Reports (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Influence of exposure conditions on helium transport and bubble growth in tungsten Helium diffusion, clustering and bubble nucleation and growth is modelled using the finite element method. The existing model from Faney et al. (Model Simul Mater Sci Eng 22:065010, 2018; Nucl Fusion 55:013014, 2015) is implemented with FEniCS and simplified in order to greatly reduce the number of equations. A parametric study is performed to investigate the influence of exposure conditions on helium inventory, bubbles density and size. Temperature is varied from 120 K to 1200 K and the implanted flux of 100 eV He is varied from [Formula: see text] to [Formula: see text]. Bubble mean size increases as a power law of time whereas the bubble density reaches a maximum. The maximum He content in bubbles was approximately [Formula: see text] He at [Formula: see text]. After 1 h of exposure, the helium inventory varies from [Formula: see text] at low flux and high temperature to [Formula: see text] at high flux and low temperature. The bubbles inventory varies from [Formula: see text] bubbles m[Formula: see text] to [Formula: see text] bubbles m[Formula: see text]. Comparison with experimental measurements is performed. The bubble density simulated by the model is in quantitative agreement with experiments. Bubble ; Tungsten ; Helium ; Materials science ; Nuclear engineering ; Computer science ; Chemistry ; Engineering ; Metallurgy ; Operating system Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Adrián Gómez‐Sánchez | Mónica Marro | María Marsal | Sara Zacchetti | Rodrigo Rocha de Oliveira | Pablo Loza‐Álvarez | Anna de Juan
Source: Scientific Reports (article, 2021-09-20)
Field: Chemistry — Analytical Chemistry

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Multiset ; Context (archaeology) ; Pattern recognition (psychology) ; Image fusion ; Sample (material) ; Orientation (vector space) ; Image resolution ; Protocol (science) ; Computer vision ; Sensor fusion ; Representation (politics) ; Image (mathematics) ; Remote sensing ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Saeed Kamarian | Ruiwen Yu | Jung‐il Song
Source: Nanotechnology Reviews (article, 2021-12-27)
Field: Materials Science — Polymers and Plastics

Synergistic effects of halloysite nanotubes with metal and phosphorus additives on the optimal design of eco-friendly sandwich panels with maximum flame resistance and minimum weight Abstract The present work addresses the optimal design of sandwich panels made of flax fabric (FF)/vinyl ester (VE) composite face sheets and honeycomb VE core. The sandwich structures are first optimized in terms of flammability by obtaining the best combination of ammonium polyphosphate (APP), halloysite nanotube (HNT), and magnesium hydroxide (MH) as three flame retardants (FRs). Using the Taguchi method and horizontal burning test, it is shown that [6, 3, and 3%] and [1, 0.5, and 0%] are the optimal combinations of APP, HNT, and MH for the face sheets and core, respectively. Cone calorimeter test results indicate that the optimal FR combinations significantly decrease the mass lost rate (MLR), heat rate release (HRR), total smoke release (TSR), and maximum average release heat emission (MARHE). The FR sandwich structures are then geometrically optimized under compressive loads based on their weight. Different failure modes are considered as the design constraints of the optimization problem. Imperialist competitive algorithm (ICA), as a powerful meta-heuristic algorithm, is implemented to considerably reduce the computational cost of the optimization process. The results of this study show that proper combinations of FR additives can increase the flame retardancy while decreasing the weight of sandwich panels. Cone calorimeter ; Halloysite ; Materials science ; Fire retardant ; Ammonium polyphosphate ; Flammability ; Composite material ; Composite number ; Environmentally friendly ; Chemical engineering ; Char ; Engineering Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; biodegradable polymer synthesis and properties
Authors: Behzad Ghanbarian | Allen G. Hunt | Marco Bittelli | Markus Tuller | Emmanuel Arthur
Source: Soil Science Society of America Journal (article, 2021-02-15)
Field: Chemistry — Physical and Theoretical Chemistry

Estimating specific surface area: Incorporating the effect of surface roughness and probing molecule size Abstract The pore–solid interface and its characteristics play a key role in chemical interactions between minerals in the solid soil matrix and the liquid in pore space and, consequently, solute transport in soils. Specific surface area (SSA), typically measured to characterize the pore–solid interface, depends not only on the particle size distribution (PSD) but also on particle shapes and surface roughness. In this note, we investigate the effects of surface roughness and probing molecule size on SSA estimation, use concepts from fractals, and theoretically estimate SSA from PSD and the water retention curve (WRC). The former is used to characterize the particle sizes and the latter to approximately quantify the pore–solid interface roughness by determining the surface fractal dimension. To evaluate our approach, we use five Washington and 21 Arizona soils for which both PSDs and WRCs were accurately measured over a wide range of particle sizes and matric potentials. Comparison with the experiments show that the proposed method estimates the SSA reasonably well, with RMSE = 16.8 and 30.1 m 2 g –1 and average relative error = –56 and –35% for the Washington and Arizona datasets, respectively. Surface roughness ; Surface (topology) ; Surface finish ; Materials science ; Molecule ; Environmental science ; Soil science ; Geology ; Mathematics ; Geometry ; Chemistry ; Composite material Electrostatics and Colloid Interactions ; Iron oxide chemistry and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Mohammed Kamruzzaman
Source: Meat and Muscle Biology (article, 2021-08-19)
Field: Chemistry — Analytical Chemistry

Fraud Detection in Meat Using Hyperspectral Imaging Fraud detection in meat is a challenging task for researchers, consumers, industries, and regulatory agencies. Traditional approaches for fraud detection are time-consuming, complicated, laborious, and expensive; they require technical skills. Therefore, much effort has been devoted in academia and industry to developing rapid and nondestructive optical techniques for fraud detection in meat. Among them, hyperspectral imaging has gained enormous attention and curiosity throughout the world. Hyperspectral imaging is an emerging analytical technique that combines spectroscopy and imaging in one system to acquire spectra and spatial information from an object simultaneously. Hyperspectral imaging is the only analytical technology that answers commonly asked analytical questions such as what chemical species are in the samples, how much, and most importantly, where they are located. Therefore, the technology will undoubtedly play indispensable roles in research and industry for fraud detection in the coming days. Hyperspectral imaging ; Curiosity ; Computer science ; Task (project management) ; Data science ; Artificial intelligence ; Systems engineering ; Engineering ; Psychology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality