jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kundo Park | Youngsoo Kim | Minki Kim | Chihyeon Song | Jinkyoo Park | Seunghwa Ryu
Source:  (preprint, 2021-06-14)
Field: Materials Science — Materials Chemistry

Designing staggered platelet composite structure with Gaussian process regression based Bayesian optimization The staggered platelet composite structure, one of the most well-known examples of biomimetics, is inspired by the microstructure of nacre, where stiff mineral platelets are stacked with a small fraction of soft polymer in a brick-and-mortar style. Significant efforts have been made to establish a framework for designing a staggered platelet pattern that achieves an excellent balance of toughness and stiffness. However, because no analytical formula for accurately predicting its toughness is available because of the complexity of the failure mechanism of realistic composites, existing studies have investigated either idealized composites with simplified material properties or realistic composites designed by heuristics. In the present study, we propose a Bayesian optimization framework to design a staggered platelet structure that renders high toughness. Gaussian process regression (GPR) was adopted to model statistically the complex relationship between the shape of the staggered platelet array and the resultant toughness. The Markov chain Monte Carlo algorithm was used to determine the optimal kernel hyperparameter set for the GPR. Starting with 14 initial training data collected with uniaxial tensile tests, a GPR-based Bayesian optimization using the expected improvement (EI) acquisition function was carried out. As a result, it was possible to design a staggered platelet pattern with a toughness 11% higher than that of the best sample in the initial training set, and this improvement was achieved after only three iterations of our optimization cycle. As this optimization framework does not require any material theories and models, this process can be easily adapted and applied to various other material optimization problems based on a limited set of experiments or computational simulations. Kriging ; Toughness ; Gaussian process ; Computer science ; Materials science ; Algorithm ; Mathematical optimization ; Gaussian ; Composite material ; Mathematics ; Machine learning Machine Learning in Materials Science
Authors: Alicia Richards | Kuei‐Ho Chen | Damien B. Wilburn | Erica Stevenson | Benjamin J. Polacco | Brian C. Searle | Danielle L. Swaney
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-07-15)
Field: Chemistry — Spectroscopy

Data-independent acquisition protease-multiplexing enables increased proteome sequence coverage across multiple fragmentation modes Abstract The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments, however due to the additional analysis time required, it has not been widely adapted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 27% increase non-redundant amino acid detections. Importantly, this resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as proteoform analysis. Proteases ; Trypsin ; Proteome ; Protease ; Multiplexing ; Proteomics ; Computational biology ; Peptide sequence ; Chemistry ; Computer science ; Biology ; Bioinformatics ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Advanced Biosensing Techniques and Applications
Authors: Katrín Blöndal | Khachik Sargsyan | David H. Bross | Franklin Goldsmith
Source:  (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Adsorbate Partition Functions via Phase Space Integration: Quantifying the Effect of Translational Anharmonicity on Thermodynamic Properties. A new\nmethod for computing anharmonic thermophysical properties\nfor adsorbates on metal surfaces is presented. Classical Monte Carlo\nphase space integration is performed to calculate the partition function\nfor the motion of a hydrogen atom on Cu(111). A minima-preserving\nneural network potential energy surface is used within the integration\nroutine. Two different sampling schema for generating the training\ndata are presented, and two different density functionals are used.\nThe results are benchmarked against direct state counting results\nby using discrete variable representation. The phase space integration\nresults are in excellent quantitative agreement with the benchmark\nresults. Additionally, both the discrete variable representation and\nthe phase space integration results confirm that the motion of H on\nCu(111) is highly anharmonic. The results were applied to calculate\nthe free energy of dissociative adsorption of H&lt;sub&gt;2&lt;/sub&gt; and the\nresulting Langmuir isotherms at 400, 800, and 1200 K in a partial\npressure range of 0–1 bar. It shows that the anharmonic effects\nlead to significantly higher predicted surface site fractions of hydrogen. Anharmonicity ; Partition (number theory) ; Phase space ; Phase (matter) ; Partition function (quantum field theory) ; Thermodynamics ; Chemistry ; Materials science ; Physics ; Mathematics ; Organic chemistry ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science
Authors: A. Basit | Ziad M. Ali
Source: The Nucleus (article, 2021-04-16)
Field: Chemistry — Analytical Chemistry

Detection of Disease Onset in Rice Plant Leaves in Monochrome Light Rice is one of the most important crops in the world because of its vast usage irrespective of age andgender. Machine vision and image processing techniques are widely used to detect diseases in plantleaves. In this paper, a prototype system is developed for rice disease onset detection from images inmonochrome light. Initially, images of leaves of rice plant are acquired in controlled environment. Afterpreprocessing, local binary patterns and local ternary patterns are extracted as features of the image.Support vector machine and k-nearest neighbors are applied as classifier to identify the healthy anddiseased image. Training is completed on 70% of the images while testing is done on the remaining 30%images. Experimental values of the results are 0.94, 0.93, 0.98 and 0.93 for Precision, Sensitivity,Specificity and F1 Score respectively. Overall accuracy of the method is 93.55%. The results showencouraging performance of the proposed method. Monochrome ; Rice plant ; Biology ; Botany ; Agronomy ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Vitaliy Fedotov | S Yu Solovykh
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Modernization of information-measuring systems for grain processing The paper discusses the basic operation principles of information-measuring systems for optimization wheat grain processing. The quality of grain processing products (cereals, flour, etc.) is influenced by both weather and climatic factors and grinding technologies. The modern development of information technologies makes it possible to modernize the existing information-measuring systems for grain processing and create new ones through the development of algorithms for analyzing the physical characteristics of the grain mass. During the study, test grinding of wheat grains of different varieties was carried out in a laboratory mill. To increase the yield of the finished product, digitalization of the selection of optimal grain separation modes was used. The obtained mathematical models allow predicting the quality of grain separation in separators of various types. The digitalization of the grain processing industry includes the use of artificial neural networks to analyze images of the grain mass using computer vision algorithms. It is promising to increase the information content of granulometric analysis using modern intelligent (information-measuring) systems. For the classification of wheat according to the milling properties, it is proposed to use the grain hardness. The studies used computer vision and artificial neural networks to find and organize the particles of grain grinding by geometric properties. The characteristics of the contours of the images of the grinding particles were taken into account. The values obtained by the developed information-measuring system were compared with that obtained using the Russian State Standard GOST methods. The error in assessing the grain hardness by the new method does not exceed 3.5%. The use of modern information tools allows improving the quality of wheat grain processing. Grinding ; Process engineering ; Artificial neural network ; Computer science ; Grain size ; Artificial intelligence ; Mechanical engineering ; Materials science ; Engineering ; Metallurgy Spectroscopy and Chemometric Analyses ; Food composition and properties ; Food Industry and Aquatic Biology
Authors: Lei Ge | Ning Yang | Jian Wang | Weidong Chu | Suqing Duan | Yan Xie | Yingxin Wang | Lianhe Li | E. H. Linfield
Source: Frontiers in Physics (article, 2021-09-22)
Field: Chemistry — Spectroscopy

Self-Mixing Signal Characteristics of Complex-Coupled Distributed-Feedback Terahertz Quantum-Cascade Lasers Self-mixing interference (SMI) in terahertz quantum cascade lasers (THz QCLs) is one of the significant approaches for coherent THz imaging and sensing techniques. Here, the output characteristics of SMI in distributed feedback (DFB) THz QCLs from the index-to the gain-coupling regimes are studied using the coupled wave theory and the multi-mode rate equation method. A mode hopping phenomenon is found to occur when the DFB coupling factor changes from index-coupling to gain-coupling, and the characteristics of the self-mixing signals of DFB-QCLs change greatly with this mode hopping. With the modulus of the coupling factor fixed and its argument varied from 0 to π/2, an extreme point of the self-mixing frequency and power signals of DFB-QCLs is found at π/9 due to the mode hopping. For index-coupling dominated DFB-QCLs, both the varying ranges of the self-mixing frequency signals and amplitudes of power signals increase with increasing DFB coupling factor argument. For gain-coupling dominated DFB-QCLs, with increasing argument value, the amplitude of the self-mixing power signal increases, but the varying range of the self-mixing frequency signal decreases. With the argument of the coupling factor fixed, we also found that the varying ranges of the self-mixing frequency signals decrease with increasing modulus for both index-coupling dominated and gain-coupling dominated DFB-QCLs. For index-coupling dominated DFB-QCLs, the amplitudes of the self-mixing power signals decrease with increasing modulus; however, the amplitudes of the self-mixing power signals of gain-coupling dominated DFB-QCLs increase. With the argument of the coupling factor fixed, for index-coupling dominated DFB-QCLs, we found that the varying ranges of the self-mixing frequency signals and amplitudes of power signals decrease with the increasing modulus. For gain-coupling dominated DFB-QCLs, with the coupling factor modulus increasing, the varying ranges of the self-mixing frequency signals decrease, however, the amplitudes of the self-mixing power signals increase. These results may help with the application of DFB-QCLs to self-mixing interferometers. Mixing (physics) ; Coupling (piping) ; Cascade ; Amplitude ; Terahertz radiation ; Physics ; SIGNAL (programming language) ; Optics ; Materials science ; Quantum mechanics ; Chemistry ; Computer science Spectroscopy and Laser Applications ; Semiconductor Lasers and Optical Devices ; Photonic and Optical Devices
Authors: Şükran Efe | Murat Çolak
Source: Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2021-06-06)
Field: Materials Science — Polymers and Plastics

Emme manifoldu malzeme seçiminde bilgisayar destekli yazılım kullanılması Drt zamanl bir iten yanmal motor 30 000'den fazla paradan olumaktadr. Bu paralar birou, motorlardaki alma prensipleri nedeniyle yksek scaklk, yksek basn, yksek gerilmelere maruz kalabilmektedir. Motorlarda gerekleen evrimlerin yksek motor performans ve yksek enerji verimlilii iin kararl yapda olmas gerekmektedir Material selection ; Selection (genetic algorithm) ; Mechanical engineering ; Combustion ; Computer science ; Software ; Computer-aided ; Process engineering ; Engineering drawing ; Materials science ; Automotive engineering ; Engineering ; Composite material ; Chemistry ; Operating system ; Artificial intelligence Textile materials and evaluations
Authors: Mohammad Abdullah Al Shohel | Vidya A. Chhabria | Sachin S. Sapatnekar
Source: arXiv (Cornell University) (preprint, 2021-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Linear-Time Algorithm for Steady-State Analysis of Electromigration in General Interconnects Electromigration (EM) is a key reliability issue in deeply scaled technology nodes. Traditional EM methods first filter immortal wires using the Blech criterion, and then perform EM analysis based on Black's equation on the remaining wires. The Blech criterion is based on finding the steady-state stress in a two-terminal wire segment, but most on-chip structures are considerably more complex. Current-density-based assessment methodologies, i.e., Black's equation and the Blech criterion, which are predominantly used to detect EM-susceptible wires, do not capture the physics of EM, but alternative physics-based methods involve the solution of differential equations and are slow. This paper uses first principles, based on solving fundamental stress equations that relate electron wind and back-stress forces to the stress evolution in an interconnect, and devises a technique that analyzes any general tree or mesh interconnect structure to test for immortality. The resulting solution is extremely computationally efficient and its computation time is linear in the number of metal segments. Two variants of the method are proposed: a current-density-based method that requires traversals of the interconnect graph, and a voltage-based formulation negates the need for any traversals. The methods are applied to large interconnect networks for determining the steady-state stress at all nodes and test all segments of each network for immortality. The proposed model is applied to a variety of tree and mesh structures and is demonstrated to be fast. By construction, it is an exact solution and it is demonstrated to match much more computationally expensive numerical simulations. Electromigration ; Interconnection ; Algorithm ; Stress (linguistics) ; Computer science ; Computation ; Reliability (semiconductor) ; Tree (set theory) ; Topology (electrical circuits) ; Mathematics ; Physics ; Engineering ; Mathematical analysis ; Electrical engineering ; Telecommunications Copper Interconnects and Reliability ; Electronic Packaging and Soldering Technologies ; Surface and Thin Film Phenomena
Authors: Lavinia Gambelli | Michail N. Isupov | Rebecca Conners | Mathew McLaren | Annett Bellack | Vicki A. M. Gold | Reinhard Rachel | Bertram Daum
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

New insights into the architecture and dynamics of archaella Abstract Archaea swim by means of a unique molecular machine called the archaellum. The archaellum consists of an ATP-powered intracellular motor that drives the rotation of an extracellular filament, allowing the cell to rapidly propel itself through liquid media. The archaellum filament comprises multiple copies of helically organised subunits named archaellins. While in many species several archaellin homologs are encoded in the same operon, structural studies conducted to date have suggested that archaella consist of only one protein species. Thus, the role of the remaining archaellin genes remains elusive. Here we present the structure of the Methanocaldococcus villosus archaellum filament at 3.08 Å resolution. We find that the filament is composed of two alternating archaellins - ArlB1 and ArlB2, suggesting that the architecture and assembly of archaella is more complex than previously thought. Moreover, we identify two major structural elements that enable the archaellum filament to move. Our findings provide new insights into archaeal motility and challenge the current view on the archaellum architecture and assembly. Protein filament ; Archaea ; Biology ; Operon ; Molecular machine ; Molecular motor ; Motility ; Flagellum ; Computational biology ; Evolutionary biology ; Gene ; Biophysics ; Cell biology ; Genetics Enzyme Structure and Function ; Photosynthetic Processes and Mechanisms ; Protein Structure and Dynamics
Authors: Kiran Mujeeb | Muhammad Faryad | Akhlesh Lakhtakia | Julio Urbina
Source: Chinese Optics Letters (article, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Surface-plasmonic sensor using a columnar thin film in the grating-coupled configuration [Invited] The excitation of a surface-plasmon-polariton (SPP) wave guided by a columnar thin film (CTF) deposited on a one-dimensional metallic surface-relief grating was investigated for sensing the refractive index of a fluid infiltrating that CTF. The Bruggemann homogenization formalism was used to determine the relative permittivity scalars of the CTF infiltrated by the fluid. Change in the refractive index of the fluid was sensed by determining the change in the incidence angle for which an SPP wave was excited on illumination by a p-polarized plane wave, when the plane of incidence was taken to coincide with the grating plane but not with the morphologically significant plane of the CTF. Multiple excitations of the same SPP wave were found to be possible, depending on the refractive index of the fluid, which can help increase the reliability of results by sensing the same fluid with more than one excitations of the SPP wave. Optics ; Grating ; Surface plasmon polariton ; Materials science ; Refractive index ; Excitation ; Surface wave ; Surface plasmon ; Plasmon ; Plane of incidence ; Plane wave ; Physics Optical Coatings and Gratings ; Plasmonic and Surface Plasmon Research ; Advanced Fiber Optic Sensors
Authors: Şükran Katmer | Çetin Karataş
Source: Pamukkale University Journal of Engineering Sciences (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Part quality in injection moulding of shape memory polyurethane Akıllı malzemenin bir türü olan şekil hafızalı polimerler (ŞHP) son yıllarda endüstriyel uygulamalarda yaygınlaşmaktadır. Şekil hafızalı polimerlerin bir türü olan termoplastik şekil hafızalı poliüretan (ŞHPU) kolay kalıplanabilme ve tekrar kullanılabilme özellikleri ile ilgi Polyurethane ; Injection moulding ; Materials science ; Composite material ; Quality (philosophy) ; Physics Polymer composites and self-healing
Authors: Xiao Wan | Dengke Ma | Dongkai Pan | Lina Yang | Nuo Yang
Source: arXiv (Cornell University) (preprint, 2021-04-05)
Field: Materials Science — Materials Chemistry

Optimizing Thermal Transport in Graphene Nanoribbon Based on Phonon Resonance Hybridization As a critical way to modulate thermal transport in nanostructures, phonon resonance hybridization has become an issue of great concern in the field of phonon engineering. In this work, we optimized phonon transport across graphene nanoribbon and obtained minimized thermal conductance by means of designing pillared nanostructures based on resonance hybridization. Specifically, the optimization of thermal conductance was performed by the combination of atomic Green` s function and Bayesian optimization. Interestingly, it is found that thermal conductance decreases non-monotonically with the increasing of number for pillared structure, which is severed as resonator and blocks phonon transport. Further mode-analysis and atomic Green` s function calculations revealed that the anomalous tendency originates from decreased phonon transmission in a wide frequency range. Additionally, nonequilibrium molecular dynamics simulations are performed to verify the results with the consideration of high-order phonon scattering. This finding provides novel insights into the control of phonon transport in nanostructures. Phonon ; Graphene ; Condensed matter physics ; Thermal conductivity ; Materials science ; Resonance (particle physics) ; Scattering ; Phonon scattering ; Non-equilibrium thermodynamics ; Nanostructure ; Nanotechnology ; Physics ; Optics ; Thermodynamics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Alessandro Casa | Tom F. O’Callaghan | Thomas Brendan Murphy
Source: arXiv (Cornell University) (preprint, 2021-01-29)
Field: Chemistry — Analytical Chemistry

Parsimonious Bayesian Factor Analysis for modelling latent structures in spectroscopy data In recent years animal diet has been receiving increased attention, in particular examining the impact of pasture-based feeding strategies on the quality of milk and dairy products, in line with the increased prevalence of grass-fed dairy products appearing on market shelves. To date, there are limited testing methods available for the verification of grass-fed dairy therefore these products are susceptible to food fraud and adulteration. Hence statistical tools studying potential differences among milk samples coming from animals on different feeding systems are required, thus providing increased security around the authenticity of the products. Infrared spectroscopy techniques are widely used to collect data on milk samples and to predict milk related traits. While these data are routinely used to predict the composition of the macro components of milk, each spectrum provides a reservoir of unharnessed information about the sample. The interpretation of these data presents a number of challenges due to their high-dimensionality and the relationships amongst the spectral variables. In this work we propose a modification of the standard factor analysis to induce a parsimonious summary of spectroscopic data. The procedure maps the observations into a low-dimensional latent space while simultaneously clustering observed variables. The method indicates possible redundancies in the data and it helps disentangle the complex relationships among the wavelengths. A flexible Bayesian estimation procedure is proposed for model fitting, providing reasonable values for the number of latent factors and clusters. The method is applied on milk mid-infrared spectroscopy data from dairy cows on different pasture and non-pasture based diets, providing accurate modelling of the data correlation, the clustering of variables and information on differences between milk samples from cows on different diets. Latent variable ; Principal component analysis ; Cluster analysis ; Curse of dimensionality ; Bayesian probability ; Sample (material) ; Statistics ; Dairy cattle ; Chemometrics ; Factor analysis ; Computer science ; Mathematics ; Econometrics ; Biology ; Machine learning ; Chemistry ; Animal science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies