jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin Larkin | Abigail Hunter | Miles Buechler
Source: International Journal of Plasticity (article, 2022-08-27)
Field: Materials Science — Materials Chemistry

Simulation of dislocation evolution in microparticle impacts over a wide range of impact velocities Materials science ; Flow stress ; Microparticle ; Dislocation ; Mechanics ; Deformation (meteorology) ; Range (aeronautics) ; Strain hardening exponent ; Strain rate ; Forensic engineering ; Metallurgy ; Composite material ; Physics ; Engineering ; Optics High-Velocity Impact and Material Behavior ; Particle Dynamics in Fluid Flows ; Granular flow and fluidized beds
Authors: Cheng Zhang | Yujie Zhan | Yongxue Qiu | Leilei Xu | Jianguo Guan
Source: PhotoniX (article, 2022-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Planar metasurface-based concentrators for solar energy harvest: from theory to engineering Abstract Solar energy is an inexhaustible renewable energy resource, which is a potential solution to global warming and aids sustainable development. The use of solar-thermal collectors to harness solar energy facilitates low-cost heat storage and can improve the stability of power grids based on renewable energy. In solar-thermal collectors, traditional concentrators, such as parabolic troughs and dishes, are typically used but inevitably require high-precise supports and complex tracking sun systems, which increase the cost of solar-thermal power stations and hinder their further applications. In contrast, planar meta-lenses (so-called metasurface-based concentrators) consisting of two-dimensional nanostructured arrays are allowed to engineer the frequency dispersion and angular dispersion of the incident light through delicately arranging the aperture phase distribution, thereby correcting their inherent aberrations. Accordingly, the novel meta-lenses offer tremendous potentials to effectively capture broadband, wide-angle sunlight without the extra tracking system. This review summarizes the research motivation, design principles, building materials, and large-area fabrication methods of meta-lens for solar energy harvesting in terms of focusing efficiency, operation bandwidth, and angular dependence. In addition, the main challenges and future goals are examined. Renewable energy ; Solar energy ; Solar power ; Photovoltaic system ; Heliostat ; Optics ; Computer science ; Engineering physics ; Thermal ; Thermal energy ; Materials science ; Environmental science ; Power (physics) ; Engineering ; Electrical engineering ; Meteorology ; Physics Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Advanced Antenna and Metasurface Technologies
Authors: Joel J. Pawlak | Ryen Frazier | Ramón E. Vera | Yuhan Wang | Ronalds González
Source: BioResources (article, 2022-03-22)
Field: Materials Science — Polymers and Plastics

Review: The softness of hygiene tissue The hygiene tissue industry has an extensive global market that is quickly growing. Market research has indicated that softness is one of consumers’ most highly desired properties. For certain hygiene tissue products (specifically bath tissue), this property can influence prices. A better understanding of the science of softness would allow companies to engineer soft tissue more economically and efficiently. Softness is a subjective perception related to physical aspects that make it challenging to express and measure. Human handfeel panel testing, which ranks the specimens through physical tests, has been recognized as the most reliable method to measure tissue softness. Much effort has been expanded in correlating the panel test results with some measurable properties. In this regard, equipment has been recently developed by combining several different mechanical, surface, and acoustic properties to characterize softness. In comparison with panel tests, these instruments (e.g., tissue softness analyzer) have been found to give equivalent softness metrics. A combination of materials selection and manufacturing operations are used to create softer tissue sheets. This paper reviews the sensation of softness as perceived by the human touch, techniques for measuring softness, the influence of fiber on softness, manufacturing techniques, and additives used for softness enhancement. Property (philosophy) ; Biomedical engineering ; Computer science ; Measure (data warehouse) ; Hygiene ; Biochemical engineering ; Process engineering ; Materials science ; Mechanical engineering ; Engineering ; Medicine ; Data mining ; Pathology Textile materials and evaluations
Authors: Boris Shurygin | Igor Smirnov | Andrey Chilikin | Dmitriy Khort | Alexey Kutyrev | S. A. Zhukovskaya | Alexei Solovchenko
Source: Horticulturae (article, 2022-11-26)
Field: Chemistry — Analytical Chemistry

Mutual Augmentation of Spectral Sensing and Machine Learning for Non-Invasive Detection of Apple Fruit Damages Non-invasive techniques for the detection of apple fruit damages are central to the correct operation of sorting lines ensuring storability of the collected fruit batches. The choice of optimal method of fruit imaging and efficient image processing method is still a subject of debate. Here, we have dissected the information content of hyperspectral images focusing on either spectral component, spatial component, or both. We have employed random forest (RF) classifiers using different parameters as inputs: reflectance spectra, vegetation indices (VIs), and spatial texture descriptors (local binary patterns, or LBP), comparing their performance in the task of damage detection in apple fruit. The amount of information in raw hypercubes was found to be over an order of magnitude excessive for the end-to-end problem of classification. Converting spectra to vegetation indices has resulted in a 60-fold compression with no significant loss of information relevant for phenotyping and more robust performance with respect to varying illumination conditions. We concluded that the advanced machine learning approaches could be more efficient if complemented by spectral information about the objects in question. We discuss the potential advantages and pitfalls of the different approaches to the machine learning-based processing of hyperspectral data for fruit grading. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Spectral signature ; Random forest ; Remote sensing ; Mathematics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Joseph M. Monti | James A. Stewart | Joyce Custer | David P. Adams | Diederik Depla | Rémi Dingreville
Source: Acta Materialia (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Linking simulated polycrystalline thin film microstructures to physical vapor deposition conditions We present a generalized multi-phase-field model to predict the growth of polycrystalline thin films fabricated by physical vapor deposition. The model accounts for the explicit transport of atomic species to the substrate and the competing diffusion processes on the surface and in the bulk of the film leading to the formation of films with specific microstructures. We used magnetron sputtering conditions (pressure, voltage, working distance, substrate orientation) to calculate the energy and direction of the arriving atoms at the substrate using Monte Carlo simulations with the SiMTRA code. Our simulation results capture the dependence of the microstructure on deposition conditions, and delineate the relationship between process parameters and the formation of columnar microstructures and surface roughness characteristic of thin films. These simulation predictions are in agreement with transmission electron microscopy characterization of sputtered films. Through our systematic investigation of competing growth mechanisms, we provide insights into the complex relationships between deposition conditions and bulk and surface morphologies. Materials science ; Microstructure ; Thin film ; Sputter deposition ; Crystallite ; Deposition (geology) ; Physical vapor deposition ; Substrate (aquarium) ; Surface roughness ; Surface energy ; Sputtering ; Transmission electron microscopy ; Surface diffusion ; Diffusion ; Composite material ; Chemical physics ; Nanotechnology ; Metallurgy ; Thermodynamics ; Physical chemistry Solidification and crystal growth phenomena ; Fluid Dynamics and Thin Films ; Optical Coatings and Gratings
Authors: M. Sohaib Alam | S. Belomestnykh | Nicholas Bornman | Gustavo Cancelo | Yu-Chiu Chao | Mattia Checchin | Vinh San Dinh | Anna Grassellino | Erik Gustafson | Roni Harnik | Corey Rae McRae | Ziwen Huang | Keshav Kapoor | Taeyoon Kim | Jim Kowalkowski | M. J. Kramer | Yulia Krasnikova | Prem Kumar | Dog̃a Murat Kürkçüog̃lu | Henry Lamm | A. L. Lyon | Despina Milathianaki | Akshay A. Murthy | Josh Mutus | Ivan Nekrashevich | Jin‐Su Oh | A. Barış Özgüler | Gabriel Perdue | Matthew J. Reagor | Alexander Romanenko | J. A. Sauls | Leandro Stefanazzi | Tubman, Norm M. | Davide Venturelli | Changqing Wang | Xinyuan You | D. M. T. van Zanten | Lin Zhou | Shaojiang Zhu | Silvia Zorzetti
Source: arXiv (Cornell University) (preprint, 2022-04-19)
Field: Materials Science — Materials Chemistry

Quantum computing hardware for HEP algorithms and sensing Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Quantum computer ; Computer science ; Quantum algorithm ; Quantum ; Fermilab ; Computer engineering ; Quantum information ; Qubit ; Computational science ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Quantum Computing Algorithms and Architecture ; Advancements in Semiconductor Devices and Circuit Design
Authors: Ralf Wanzenböck | Marco Arrigoni | Sebastian Bichelmaier | Florian Buchner | Jesús Carrete | Georg K. H. Madsen
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Neural-network-backed evolutionary search for SrTiO<sub>3</sub>(110) surface reconstructions The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO <sub><i>x</i></sub> overlayer structures on SrTiO<sub>3</sub>(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO<sub>3</sub>(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions. Artificial neural network ; Surface (topology) ; Computer science ; Evolutionary biology ; Artificial intelligence ; Biology ; Mathematics ; Geometry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced X-ray and CT Imaging
Authors: Giuseppe Ferrara | Valerio Marcotuli | Angelo Didonna | Anna Maria Stellacci | M. Palasciano | Andrea Mazzeo
Source: Horticulturae (article, 2022-07-07)
Field: Chemistry — Analytical Chemistry

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape cultivars (Cotton Candy™, Summer Royal, Allison™, and Autumncrisp®) were collected during the season to obtain spectral measurements and quality data for developing predictive models based on NIR spectroscopy to be practically used in the vineyard. A SCiO™ sensor was used in 2019 for predicting the ripening parameters of Cotton Candy™; in particular, total soluble solids (TSS) (R2 = 0.95; RMSE = 0.60, RPD = 13.13), titratable acidity (R2 = 0.97; RMSE = 0.40, RPD = 7.31), and pH (R2 = 0.96; RMSE = 0.07, RPD = 26.06). With these promising results, in the year 2020, the above-mentioned table grape cultivars were all tested for TSS prediction with successful outcomes: Cotton Candy™ (R2 = 0.97; RMSE = 0.68, RPD = 7.48), Summer Royal (R2 = 0.96; RMSE = 0.83, RPD = 7.13), Allison™ (R2 = 0.97; RMSE = 0.72, RPD = 8.70) and Autumncrisp® (R2 = 0.96; RMSE = 0.60, RPD = 9.73). In conclusion, a rapid and economic sensor such as the SCiO™ device can enable a practical application in the vineyard to assess ripening (quality) parameters of table grapes. Thus, this device or similar ones can be also used for a fast sorting and screening of quality throughout the supply chain, from vineyard to cold storage. Ripeness ; Vineyard ; Cultivar ; Table grape ; Ripening ; Titratable acid ; Mathematics ; Mean squared error ; Dry matter ; Horticulture ; Environmental science ; Chemistry ; Agronomy ; Food science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Bee Products Chemical Analysis
Authors: Yan Hu | Youli Wu | Jie Sun | Jinping Geng | Rongsheng Fan | Zhiliang Kang
Source: Foods (article, 2022-08-05)
Field: Chemistry — Analytical Chemistry

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification. Chemometrics ; Principal component analysis ; Hyperspectral imaging ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Feature selection ; Mathematics ; Outlier ; Chemistry ; Linear discriminant analysis ; Food science ; Biological system ; Computer science ; Chromatography ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Achuit K. Singh | Pallavi Mishra | Sarvesh Pratap Kashyap | Suhas Gorakh Karkute | Prabhakar Singh | Nagendra Rai | Anant Bahadur | Tusar Kanti Behera
Source: Frontiers in Plant Science (review, 2022-10-25)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular insights into mechanisms underlying thermo-tolerance in tomato Plant productivity is being seriously compromised by climate-change-induced temperature extremities. Agriculture and food safety are threatened due to global warming, and in many cases the negative impacts have already begun. Heat stress leads to significant losses in yield due to changes in growth pattern, plant phonologies, sensitivity to pests, flowering, grain filling, maturity period shrinkage, and senescence. Tomato is the second most important vegetable crop. It is very sensitive to heat stress and thus, yield losses in tomato due to heat stress could affect food and nutritional security. Tomato plants respond to heat stress with a variety of cellular, physiological, and molecular responses, beginning with the early heat sensing, followed by signal transduction, antioxidant defense, osmolyte synthesis and regulated gene expression. Recent findings suggest that specific plant organs are extremely sensitive to heat compared to the entire plant, redirecting the research more towards generative tissues. This is because, during sexual reproduction, developing pollens are the most sensitive to heat. Often, just a few degrees of temperature elevation during pollen development can have a negative effect on crop production. Furthermore, recent research has discovered certain genetic and epigenetic mechanisms playing key role in thermo-tolerance and have defined new directions for tomato heat stress response (HSR). Present challenges are to increase the understanding of molecular mechanisms underlying HS, and to identify superior genotypes with more tolerance to extreme temperatures. Several metabolites, genes, heat shock factors (HSFs) and microRNAs work together to regulate the plant HSR. The present review provides an insight into molecular mechanisms of heat tolerance and current knowledge of genetic and epigenetic control of heat-tolerance in tomato for sustainable agriculture in the future. The information will significantly contribute to improve breeding programs for development of heat tolerant cultivars. Biology ; Osmolyte ; Food security ; Heat shock protein ; Biotechnology ; Adaptation (eye) ; Gene ; Agriculture ; Genetics ; Botany ; Ecology ; Neuroscience thermodynamics and calorimetric analyses ; Plant Stress Responses and Tolerance ; Plant biochemistry and biosynthesis
Authors: Yu Shi | Shuxin Zheng | Guolin Ke | Yifei Shen | Jiacheng You | Jiyan He | Shengjie Luo | Chang Liu | Di He | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2022-03-09)
Field: Materials Science — Materials Chemistry

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple modifications, Graphormer could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on 2D and 3D molecular graph modeling tasks. In addition, we show that with a global receptive field and an adaptive aggregation strategy, Graphormer is more powerful than classic message-passing-based GNNs. Empirically, Graphormer could achieve much less MAE than the originally reported results on the PCQM4M quantum chemistry dataset used in KDD Cup 2021. In the meanwhile, it greatly outperforms the competitors in the recent Open Catalyst Challenge, which is a competition track on NeurIPS 2021 workshop, and aims to model the catalyst-adsorbate reaction system with advanced AI models. All codes could be found at https://github.com/Microsoft/Graphormer. Benchmarking ; Computer science ; Competitor analysis ; Molecular dynamics ; Scale (ratio) ; Simple (philosophy) ; Graph ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Cloud Computing and Resource Management ; Advanced Graph Neural Networks
Authors: Ivan R. Sasselli | Zois Syrgiannis | Nicholas A. Sather | Liam C. Palmer | Samuel I. Stupp
Source: The Journal of Physical Chemistry B (article, 2022-01-14)
Field: Materials Science — Biomaterials

Modeling Interactions within and between Peptide Amphiphile Supramolecular Filaments Many peptides are able to self-assemble into one-dimensional (1D) nanostructures, such as cylindrical fibers or ribbons of variable widths, but the relationship between the morphology of 1D objects and their molecular structure is not well understood. Here, we use coarse-grained molecular dynamics (CG-MD) simulations to study the nanostructures formed by self-assembly of different peptide amphiphiles (PAs). The results show that ribbons are hierarchical superstructures formed by laterally assembled cylindrical fibers. Simulations starting from bilayer structures demonstrate the formation of filaments, whereas other simulations starting from filaments indicate varying degrees of interaction among them depending on chemical structure. These interactions are verified by observations using atomic force microscopy of the various systems. The interfilament interactions are predicted to be strongest in supramolecular assemblies that display hydrophilic groups on their surfaces, while those with hydrophobic ones are predicted to interact more weakly as confirmed by viscosity measurements. The simulations also suggest that peptide amphiphiles with hydrophobic termini bend to reduce their interfacial energy with water, which may explain why these systems do not collapse into superstructures of bundled filaments. The simulations suggest that future experiments will need to address mechanistic questions about the self-assembly of these systems into hierarchical structures, namely, the preformation of interactive filaments vs equilibration of large assemblies into superstructures. Supramolecular chemistry ; Amphiphile ; Chemical physics ; Bilayer ; Molecular dynamics ; Nanostructure ; Self-assembly ; Nanotechnology ; Materials science ; Peptide ; Crystallography ; Hydrophobic effect ; Atomic force microscopy ; Chemistry ; Membrane ; Computational chemistry ; Polymer ; Copolymer ; Crystal structure ; Composite material Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Lipid Membrane Structure and Behavior