jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengya Wang | Kunrong Mei
Source: Studies in health technology and informatics (article, 2023-11-23)
Field: Materials Science — Materials Chemistry

Expression and Purification of SMGL-1 Recombinant protein expression is a crucial technique in biology, with E. coli being the most widely used expression system. However, due to growth pressure, the expression of large molecular weight proteins in E. coli has remained a challenging task. SMGL-1, a newly discovered protein in C. elegans with Rabin 8 function, plays an important role in biology. To better understand the function of SMGL-1, we first predicted and analyzed its protein structure and properties using artificial intelligence. We then conducted studies on its expression and purification. Through optimization of IPTG concentration and expression strains, we successfully expressed SMGL-1 in E. coli, providing guidance for the expression of large proteins in E. coli. Furthermore, we explored the purification of SMGL-1 using GST affinity chromatography, Nickel affinity chromatography, and ammonium sulfate precipitation methods, laying the foundation for future purification work on SMGL-1. Affinity chromatography ; lac operon ; Escherichia coli ; Recombinant DNA ; Protein expression ; Protein purification ; Ammonium sulfate precipitation ; Function (biology) ; Protein precipitation ; Biology ; Biochemistry ; Chemistry ; Chromatography ; Molecular biology ; Computational biology ; Cell biology ; Gene ; High-performance liquid chromatography ; Size-exclusion chromatography ; Enzyme Enzyme Structure and Function ; Fungal and yeast genetics research ; Biofuel production and bioconversion
Authors: Harmony Werth | Kazi Zihan Hossain | M. Rashed Khan | Khan, M. Rashed
Source: arXiv (Cornell University) (preprint, 2023-01-30)
Field: Materials Science — Polymers and Plastics

Data-driven Investigation of Cotton Fabric Behavior Modified by Straight and Zig-Zag Stitches In this article, we demonstrate a data-driven approach to investigate the behavior of cotton fabric modified by straight and zig-zag stitches. Existing literature in understanding the mechanical behavior of soft materials (e.g., textile-based fibers or fabrics) heavily relies on stress-strain analyses. However, the strain-induced deformation behavior can be further analyzed by taking advantage of data-driven constitutive models. Such an approach reveals intermolecular parameters that can be utilized further in design and development analyses. For that, we exhibit the altered mechanics of base cotton fabric induced by two types of singular stitches (straight and zig-zag). We have sewn simple straight and zig-zag cotton stitches to investigate the mechanics of the base cotton fabrics using uniaxial stress-strain experimental data. Then, we leveraged the constitutive models (i.e., three-network model, TNM) obtained from MCalibration software to reveal eleven intermolecular parameters for data-driven investigations. Our experimental analyses, combined with the data, suggest a 99.99% confidence in assessing the mechanical impact of stitches on cotton fabrics. We have also used distributed strain energy to analyze the mechanics and failure of the base and stitched fabrics. Once adopted, our study may contribute to an improved understanding of the production of smart wearables and e-textiles. Textile ; Image stitching ; Rheology ; Materials science ; Base (topology) ; Composite material ; Mechanical engineering ; Structural engineering ; Computer science ; Engineering ; Mathematics ; Artificial intelligence Textile materials and evaluations ; Muscle Physiology and Disorders
Authors: Matthias Neumann | Tom Philipp | Marcel Häringer | Gregor Neusser | Joachim R. Binder | Christine Kranz
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Surfaces, Coatings and Films

Stochastic 3D modeling of nanostructured NVP/C active material particles for sodium-ion batteries A data-driven modeling approach is presented to quantify the influence of morphology on effective properties in nanostructured sodium vanadium phosphate $\mathrm{Na}_3\mathrm{V}_2(\mathrm{PO}_4)_3$/ carbon composites (NVP/C), which are used as cathode material in sodium-ion batteries. This approach is based on the combination of advanced imaging techniques, experimental nanostructure characterization and stochastic modeling of the 3D nanostructure consisting of NVP, carbon and pores. By 3D imaging and subsequent post-processing involving image segmentation, the spatial distribution of NVP is resolved in 3D, and the spatial distribution of carbon and pores is resolved in 2D. Based on this information, a parametric stochastic model, specifically a Pluri-Gaussian model, is calibrated to the 3D morphology of the nanostructured NVP/C particles. Model validation is performed by comparing the nanostructure of simulated NVP/C composites with image data in terms of morphological descriptors which have not been used for model calibration. Finally, the stochastic model is used for predictive simulation to quantify the effect of varying the amount of carbon while keeping the amount of NVP constant. The presented methodology opens new possibilities for a ressource-efficient optimization of the morphology of NVP/C particles by modeling and simulation. Nanostructure ; Materials science ; Parametric statistics ; Calibration ; Stochastic modelling ; Carbon fibers ; Ion ; Parametric model ; Morphology (biology) ; Nanoparticle ; Chemical engineering ; Nanotechnology ; Composite material ; Physics ; Mathematics ; Composite number ; Statistics Electron and X-Ray Spectroscopy Techniques ; Medical Image Segmentation Techniques ; Transition Metal Oxide Nanomaterials
Authors: Kevin McKinnon | Melissa Ness | Constance M. Rockosi | Puragra Guhathakurta
Source: arXiv (Cornell University) (preprint, 2023-07-11)
Field: Chemistry — Analytical Chemistry

Data-driven Discovery of Diffuse Interstellar Bands with APOGEE Spectra Data-driven models of stellar spectra are useful tools to study non-stellar information, such as the Diffuse Interstellar Bands (DIBs) caused by intervening interstellar material. Using $\sim 55000$ spectra of $\sim 17000$ red clump stars from the APOGEE DR16 dataset, we create 2nd order polynomial models of the continuum-normalized flux as a function of stellar parameters ($T_{eff}$, $\log g$, [Fe/H], [$α$/Fe], and Age). The model and data show good agreement within uncertainties across the APOGEE wavelength range, although many regions reveal residuals that are not in the stellar rest-frame. We show that many of these residual features -- having average extrema at the level of $\sim3\%$ in stellar flux on average -- can be attributed to incompletely-removed spectral lines from the Earth's atmosphere and DIBs from the interstellar medium (ISM). After removing most of the remaining contamination from the Earth's sky, we identify 84 absorption features not seen in unreddened sightlights that have $&lt;50\%$ probability of being noise artifacts -- with 25 of these features having $&lt;5\%$ probability of being noise artifacts -- including all 10 previously-known DIBs in the APOGEE wavelength range. Because many of these features occur in the wavelength windows that APOGEE uses to measure chemical abundances, characterization and removal of this non-stellar contamination is an important step in reaching the precision required for chemical tagging experiments. Proper characterization of these features will benefit Galactic ISM science and the currently-ongoing Milky Way Mapper program of SDSS-V, which relies on the APOGEE spectrograph. Physics ; Astrophysics ; Spectral line ; Sky ; Stars ; Milky Way ; Astronomy ; Wavelength ; Galaxy ; Optics Spectroscopy and Chemometric Analyses ; Stellar, planetary, and galactic studies ; Astronomy and Astrophysical Research
Authors: Martin Z. Bazant
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Keynote) Learning Electrochemical Models from Images As illustrated by the work of Ralph White, traditional methods of electrochemical engineering are based on human intelligence: Mathematical models encoding physical hypotheses are proposed, tested against experimental data and refined by fitting adjustable parameters. Artificial intelligence is beginning to challenge this paradigm, since predictions can be made directly from data without the need for models, but such knowledge is often not transferrable to new situations. This talk will present a hybrid approach of solving PDE-constrained inverse problems to derive new multiscale models, in the context of Li-ion batteries. Examples include learning models of reaction kinetics and surface heterogeneities from x-ray adsorption images of phase separation in lithium iron phosphate nanoparticles [1], x-ray diffraction spectra for fictitious phase separation in nickel-rich oxide porous electrodes [2], and optical videos of lithium plating on graphite particles [3]. [1] H. Zhao, H. D. Deng, A. E. Cohen, J. Lim, Y. Li, D. Fraggedakis, B. Jiang, B. D. Storey, W. C. Chueh, R. D. Braatz, and M. Z. Bazant, Learning heterogeneous reaction kinetics from X-ray movies pixel-by-pixel, preprint, https://www.researchsquare.com/article/rs-2320040/v1 [2] J. Park, H. Zhao, S. D. Kang, K. Lim, C.-C. Chen, Y.-S. Yu, R. D. Braatz, D. A. Shapiro, J. Hong, M. F. Toney, M. Z. Bazant, and W. C. Chueh, Fictitious Phase Separation in Li Layered Oxides Driven by Electro-Autocatalysis, Nature Materials 20, 991-999 (2021). [3] T. Gao, Y. Han, D. Fraggedakis, S. Das, T. Zhou, C.-N. Yeh, S. Xu, W. C. Chueh, J. Li, M. Z. Bazant, Interplay of lithium intercalation and plating on a single graphite particle, Joule 5, 1-22 (2021). Context (archaeology) ; Electrochemistry ; Artificial intelligence ; Nanoparticle ; Lithium (medication) ; Materials science ; Computer science ; Chemistry ; Electrode ; Analytical Chemistry (journal) ; Nanotechnology ; Physical chemistry ; Psychology Machine Learning in Materials Science ; Grey System Theory Applications ; Non-Destructive Testing Techniques
Authors: Prachi Gupta | Satinder K. Sharma
Source:  (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Facile and Cost-Effective TFA doped In<sub>2</sub>O<sub>3</sub> photodetectors for Deep Ultra Violet Applications Deep ultraviolet (DUV) photodetectors have critical applications in artificial intelligence, communications, missile detection, wearable technology, lithography aligners etc. However, commercially available DUV optoelectronic devices still possess low selectivity and stability. Here, we report facile, cost-effective metal-semiconductor-metal (MSM) (Al-In<inf>2</inf>O<inf>3</inf>-Al), DUV (<tex>$\lambda \sim 254$</tex> nm) photodetectors, by solution processed In<inf>2</inf>O<inf>3</inf> and photoactive tri-flouro acetic acid (TFA) additive In<inf>2</inf>O<inf>3</inf> thin films. The fabricated photodetector device structures exhibit the higher responsivity (2.79 A/W), external quantum efficiency (1362%), and higher detectivity <tex>$(\sim 1.64\ \mathrm{x}\ 10^{11}\ \text{cm}\ \text{ Hz }^{1/2}\mathrm{W}^{-1})$</tex>. Tfa based In<inf>2</inf>O<inf>3</inf> photodetectors exhibit low cost, scalability, with maximum photocurrent <tex>$(\sim 15.5\ \mathrm{x}\ 10^{-6}\ \mathrm{A})$</tex>. This work demonstrates the effects of doping photoactive additives in In<inf>2</inf>O<inf>3</inf> for the highly efficient, stable, and highly selective DUV detection applications. Photodetector ; Doping ; Ultra violet ; Computer science ; Optoelectronics ; Materials science ZnO doping and properties ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: Guohua Cao | Chuanqi Xu | Ping Cui | Zhenyu Zhang
Source: Computer Physics Communications (article, 2023-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Universal scaling between on-site Coulomb repulsion and numbers of core and valence electrons in transition metal trichalcogenides Coulomb ; Scaling ; Core electron ; Valence electron ; Electron ; Atomic orbital ; Valence (chemistry) ; Physics ; Transition metal ; Condensed matter physics ; Statistical physics ; Atomic physics ; Quantum mechanics ; Chemistry ; Geometry ; Mathematics Iron-based superconductors research ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Shuhui Liang | Huan Guan | Hainan Zhang | Xiangge Han | Sue Hao | Shuliang Dou | Haoxin Zhou | Chenchen Geng | Tao Zhao | Jinxin Gu | Hang Wei | Yao Li
Source: Research Square (preprint, 2023-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

VO2 Nanowires Based Composite Films with Broadband and High Tunable Electromagnetic Interference Shielding Performance Abstract Traditional electromagnetic interference (EMI) shielding materials typically have constant shielding efficiencies, which cannot respond to the real-time changing shielding requirements for future electromagnetic (EM) devices. For the first time, the VO2 NWs/poly(vinylidene fluoride-co-hexafluoropropylene) (VO2/PVDF-HFP) composite films have been developed with off/on switchable EMI shielding performance, which exhibit excellent tunability in X-band and Ku-band with maximum values of 44.8 dB and 59.4 dB, respectively. The unique insulator-to-metal transition (IMT) and perfect conductive network of VO2 NWs result in three orders of magnitude in relative conductivity change, which leads to excellent off/on switchable EMI shielding performance. Furthermore, the off/on switchable EMI shielding performance is also confirmed with good cycling stability and the EMI shielding performance of the composite film can be precisely modulated using temperature. This study provides a unique approach for dynamic switching of EM response with the potential to construct multifunctional smart EM response systems for various applications such as of aerospace, artificial intelligence, advanced electronics, and stealth technology. Electromagnetic shielding ; EMI ; Materials science ; Electromagnetic interference ; Composite number ; Broadband ; Optoelectronics ; Electrical conductor ; Electronics ; Nanowire ; Electrical engineering ; Composite material ; Computer science ; Telecommunications ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Electrical Contact Performance and Analysis
Authors: Chani Kim | Jeongsu Kim | Jungeun Lee | Byeong-Woo Kim
Source:  (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Data-driven Modeling Techniques for SMA Spring A data-driven modeling technique for shape memory alloy (SMA) springs was developed in this study. The conventional mathematical formula-based modeling technique requires SMA property parameter information. Thus, we developed a technique that did not require the information. A dataset was built through experiments to train the model. Based on this dataset, a regression model was also trained to estimate the force and responsiveness properties of SMA springs. Then, the SMA spring's force was modeled using a regression model's estimation results as the first-order system based on the SMA spring's properties. To validate the effectiveness and performance of the data-driven modeling method, root mean square error (RMSE) and R-squared were used. The RMSE was 91.61 g<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf> for the maximum force and 1.5 s for the time constant. The coefficient of determination was higher than 0.8. This confirmed that the regression model accurately estimated the maximum force and time constant. Finally, the validity of the suggested modeling technique was demonstrated by comparing the outcomes of the first-order system design with the real data based on the regression model findings. SMA* ; Mean squared error ; Regression analysis ; Regression ; Computer science ; Spring (device) ; Constant (computer programming) ; Coefficient of determination ; Coil spring ; Linear regression ; Mathematics ; Statistics ; Algorithm ; Engineering ; Machine learning ; Structural engineering Shape Memory Alloy Transformations
Authors: Luisa Fleig | Melvin Liebsch | Stephan Russenschuck | Sebastian Schöps
Source: arXiv (Cornell University) (preprint, 2023-06-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Update of B(H) Curves of Iron Yokes in Normal Conducting Accelerator Magnets Constitutive equations are used in electromagnetic field simulations to model a material response to applied fields or forces. The $B(H)$ characteristic of iron laminations depends on thermal and mechanical stresses that may have occurred during the manufacturing process. Data-driven modelling and updating of the $B(H)$ characteristic are therefore well known necessities. In this work the $B(H)$ curve of an iron yoke of an accelerator magnet is updated based on observed magnetic flux density data by solving a non-linear inverse problem. The inverse problem is regularized by restricting the solution to the function space that is spanned by the truncated Karhunen Loeve expansion of a stochastic $B(H)$-curve model based on material measurements. It is shown that this method is able to retrieve a previously selected ground truth $B(H)$-curve. With the update of the $B(H)$ characteristic, the numerical model gains predictive capacities for excitation currents that were not included in the data. Yoke (aeronautics) ; Magnet ; Inverse ; Inverse problem ; Physics ; Mathematical analysis ; Work (physics) ; Magnetic field ; Flux (metallurgy) ; Curve fitting ; Computational physics ; Mechanics ; Materials science ; Mathematics ; Thermodynamics ; Geometry ; Quantum mechanics ; Statistics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Superconducting Materials and Applications
Authors: А. О. Еркимбаев | В.Ю. Зицерман | Г. А. Кобзев
Source: Научно-техническая информация Серия 2 Информационные процессы и системы (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

MATERIALS DATA TYPOLOGY Технологии хранения и обработки обширных массивов данных открыли новый этап в развитии материаловедения, основанный на применении методов искусственного интеллекта к результатам многолетних исследований. Большие объемы разнородных данных в сочетании с мощной аналитикой позволили существенно расширить масштабы и темпы исследований в сравнении с эмпирическими методами подбора материалов с заданными свойствами. Сделан акцент на специфике этих данных, в основном определяющей уровень и возможности информационных технологий в современном материаловедении. Выявлены их главные особенности, гарантирующие достаточную полноту информации, необходимой в процессах создания и использования различных материалов. Среди этих особенностей: охват, наряду со свойствами, данных по микроструктуре и технологии материала; большой объем качественной информации; полуструктурированный тип данных, т. е. отсутствие регулярной схемы представления. На примере ряда инфраструктурных проектов рассмотрены возможности организации материаловедческих данных, учитывающие их объем, логическую структуру и формат. Technologies for storing and processing vast amounts of data have opened a new stage in the development of materials science, based on the application of artificial intelligence methods to the results of many years of research. Large volumes of heterogeneous data combined with powerful analytic facilities allowed us to significantly expand the range and rate of research in comparison with empirical methods of selecting materials with specified properties. The emphasis is placed on the specifics of these data, which mainly determines the level and capabilities of information technologies in modern materials science. Their main features are revealed, which guarantee sufficient completeness of the information needed for creating and using various materials. Among these features: coverage, along with properties, of data on the microstructure and technology of the material; a large amount of qualitative information; semi-structured data type, i.e. the absence of a regular presentation scheme. Using the example of a number of infrastructure projects, the possibilities of management materials science data, taking into account their volume, logical structure and format, are considered. Typology ; History ; Archaeology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Alexander E. Mayer | Polina N. Mayer
Source: Челябинский физико-математический журнал (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

DETECTION OF VOID NUCLEATION, COALESCENCE AND COLLAPSE IN ATOMISTIC SIMULATIONS Extraction of required information from atomic configurations is crucial for efficient use of molecular dynamics (MD) simulations, and automating this process allows the collection of statistical data to develop and validate physical models and data-driven approaches. Here we update the previously developed pore searching algorithm to automatically detect the events of void nucleation, collapse and coalescence by comparing pairs of atomic configurations. We calculate an intersection matrix, which shows common regions between voids of current and previous time frames, and analysis of this intersection matrix gives a numerical criterion for determination of the considered events. The updated algorithm is verified in the case of small MD system and further used to analyze a large MD system with many hundreds of voids. The predominance of void collapse is confirmed in the case of uniform triaxial tension of solid aluminum, while void coalescence instead of collapse is detected in the case of a more complex loading with axial compression and lateral tension. This dependence of the predominant mechanism on the loading path correlates with previous finite element analysis in the literature. Another interesting finding is a continuing nucleation and collapse of voids far beyond the fracture beginning at a level of negative pressure much lower than the spall strength, which can be attributed to a developed defect structure of material produced by plastic growth of voids. Coalescence (physics) ; Nucleation ; Spall ; Void (composites) ; Mechanics ; Materials science ; Mesoscopic physics ; Statistical physics ; Physics ; Composite material ; Thermodynamics ; Condensed matter physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Ion-surface interactions and analysis