jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolas Keppeler | Matheus Costa Lourenço | Omar A. El Seoud
Source: Journal of Laboratory Chemical Education (article, 2021-10-22)
Field: Materials Science — Biomaterials

Distance Teaching of the Undergraduate Laboratory During Pandemic Time: Dissolution of Cellulose in Mixtures of Ionic Liquids and Dimethyl Sulfoxide and Biopolymer Regeneration as Films We report on distance teaching of an experimental project, taught during an advanced chemistry course given to our chemistry-major students (47, divided into eight groups). The project required 12 h to complete, during three weeks. We chose a subject of socio-economic relevance, namely, the physical dissolution of cellulose (Cel), an important step in processing wood-based Cel to fabricate, e.g., fibers and films. The solvents employed were mixtures of DMSO with (green) ionic liquids (ILs) 1-allyl-3-methylimidazolium acetate- (AlMeImAcO) and chloride (AlMeImCl). The former IL dissolves more Cel than the latter, this difference in efficiency was probed using molecular dynamics (MD) simulations. The dissolved biopolymer was dyed with a reactive dye and regenerated as films. The online activities during the classes included showing videos of the experiments done by the instructor; explaining how MD simulations of Cel dissolution were done, and seminars given by the students on themes related to the project. The seminars were on the commercial production of Viscose and Lyocell Cel fibers, dyeing of cellulosic fibers, and properties (light- and washing fastness) of dyed textiles. Additionally, the students were asked to suggest experiments to <i>corroborate,</i> <i>or</i> <i>refute</i> the results of MD simulations. The students appreciated our active learning approach (85.1%); reported that they learned new material and found the seminar themes interesting (80.9%). They indicated that online classes do not replace face-to-face practical classes (59.6%), and that the activities given do not replace the experience acquired by doing the experiment (66.0%). Regarding their local environment, 74.5% considered the conditions at home as good/very good (internet connection and hardware). After returning to face-to-face teaching, we will continue showing videos of the staff while carrying out the experiments, before going to the laboratory. This is a very useful approach to highlight important experimental precautions/protocols e.g., regarding the proper handling of chemicals and equipment. We recommend this project (distance-, or face-to-face teaching) for students of science courses because of its relevance, safety, and low-cost. Ionic liquid ; Dissolution ; Cellulose ; Lyocell ; Biopolymer ; Dimethyl sulfoxide ; Chemical engineering ; Chemistry ; Materials science ; Organic chemistry ; Engineering ; Polymer ; Fiber Advanced Cellulose Research Studies
Authors: Yaqiong Zhong | Xiaojuan Hu | Debalaya Sarker | Qingrui Xia | Liangliang Xu | Chao Yang | Zhongkang Han | Sergey V. Levchenko | Jiaolin Cui
Source: arXiv (Cornell University) (preprint, 2021-04-16)
Field: Materials Science — Materials Chemistry

Data analytics accelerates the experimental discovery of new thermoelectric materials with extremely high figure of merit Thermoelectric (TE) materials are among very few sustainable yet feasible energy solutions of present time. This huge promise of energy harvesting is contingent on identifying/designing materials having higher efficiency than presently available ones. However, due to the vastness of the chemical space of materials, only its small fraction was scanned experimentally and/or computationally so far. Employing a compressed-sensing based symbolic regression in an active-learning framework, we have not only identified a trend in materials' compositions for superior TE performance, but have also predicted and experimentally synthesized several extremely high performing novel TE materials. Among these, we found Ag$_{0.55}$Cu$_{0.45}$GaTe$_2$ to possess an experimental figure of merit as high as ~2.8 at 827 K, which is a breakthrough in the field. The presented methodology demonstrates the importance and tremendous potential of physically informed descriptors in material science, in particular for relatively small data sets typically available from experiments at well-controlled conditions. Figure of merit ; Computer science ; Thermoelectric effect ; Field (mathematics) ; Energy (signal processing) ; Materials science ; Analytics ; Thermoelectric materials ; Data analysis ; Sustainable energy ; Engineering physics ; Process engineering ; Nanotechnology ; Data science ; Data mining ; Physics ; Optoelectronics ; Engineering ; Mathematics ; Electrical engineering ; Renewable energy Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Ulrike Krewer | Daniel Witt | Oke Schmidt | Fridolin Röder
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

(Invited) Towards Model-Based Predictive Battery Electrode Design Lithium ion batteries contain porous electrodes, whose design determines battery performance, cost and life-time. Yet, we are far from knowing what is the best structure, and it is prohibitively time- and resource-consuming to do experimental trial and error production, as there are too many parameters in the production process and in the electrode itself. This motivates to use physico-chemical models that yield insight into performance and limitations of given electrode structures as well as allow optimal design. The need for models that allow structure optimisation, i.e. rapid evaluation of many design variants, basically excludes large 3-dimensional models with complex inhomogeneous structures. On the other hand, homogeneous pseudo-two-dimensional models face limitations regarding the prediction of new electrode designs as the crucial influence of conducting additives and binder are not considered sufficiently. This talk will elucidate a pathway that allows model-based predictive battery design. It shows the application of microstructure models to identify the effective electric and ionic conductivity and electroactive area of electrode structures containing additives, and the transfer of this knowledge via surrogate models into electrochemical P2D models to evaluate the respective electrochemical performance. The effect of additive and active material content and distribution is demonstrated at the example of Li ion battery cathode and all solid state Li battery electrodes. The models are applicable to predict the performance of electrodes in cells. We further elucidate how uncertainties in the production process of electrodes lead to distributed electrode design parameters and performance. Finally, we show and compare two pathways of identifying optimal electrode designs at the example of dual layer electrodes: a coarse-grained scanning of thousands of design variants using 8 parameters reveals strongly different designs for optimal charge and discharge, whereas a high resolution optimisation of two design parameters shows flat performance plateaus. Model-based design optimization without extensive experimental studies will reduce costs and experimental efforts in the development of next-generation batteries. Battery (electricity) ; Electrode ; Computer science ; Electrochemistry ; Materials science ; Cathode ; Lithium-ion battery ; Process (computing) ; Process engineering ; Nanotechnology ; Engineering ; Electrical engineering ; Chemistry ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Technologies Research
Authors: Jiaxin You | Zhang Kun | Huimin Liang | Xiangdong Feng | Yonggang Ruan
Source: Actuators (article, 2021-01-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program In this paper, a method for the improvement of the calculation accuracy of the distributed parameter model (DPM) of electromagnetic devices is proposed based on the kriging basis function predictive identification program (PIP). Kriging is mainly an optimal interpolation method which uses spatial self-covariance, and takes a polynomial as the basis function. The accuracy of the kriging-based surrogate model can be improved by adjusting the related functions and hyperparameters. Based on the DPM of a solenoid valve, there exist certain errors in the estimation. They can be summarized as follows: Firstly, the estimation error of magnetic flux leakage (MFL) permeance is caused directly by the deviation of the magnetic flux tube due to the segmented magnetic field line. Secondly, the estimation error of soft magnetic resistance because of the nonlinearity of the permeability of soft magnetic material leads to the change of soft magnetic resistance alongside the magnetic flux. In this paper, an improved kriging error correction method is applied to modify the leak permeance and soft magnetic resistance calculation. The kriging basis function is adjusted to adapt to the data curve of the MFL permeance error data. The calculated MFL permeance data are compared with the error variation data to select the appropriate basis function. To improve the computational efficiency, the PIP is proposed to select the appropriate basis function. The modified MFL permeance data and soft magnetic resistance are substituted into the DPM for improving the computational accuracy and efficiency of the solenoid valve. Permeance ; Kriging ; Basis function ; Algorithm ; Spline (mechanical) ; Basis (linear algebra) ; Surrogate model ; Mathematical optimization ; Control theory (sociology) ; Mathematics ; Computer science ; Engineering ; Statistics ; Artificial intelligence ; Mathematical analysis ; Structural engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Metallurgy and Material Forming
Authors: Bessem Debich
Source:  (preprint, 2021-07-02)
Field: Materials Science — General Materials Science

Modeling, simulation and reliability of phase change materials for automotive and aeronautics Increasing the power density of electronic components requires more efficient thermal management. Thermal problems are even becoming the major concern of designers for the conception these components. Phase Change Material (PCM) based heat sinks are widely used in several industrial applications particularly in mechatronics field. These passive cooling techniques allow better thermal management of electronic components. Recently, researchers aim to find an optimaldesign of these systems in order to improve their efficiency. However, uncertainties are not considered for the majority of these studies. This study aims to propose a new methodology that leads to proposing an optimal model of a PCM-based cooling systems considering uncertainties. The Deterministic Design Optimization approach (DDO) provides an optimal design, but its relibility level is not respected. To this end, Reliability-Based Design Optimization (RBDO) techniques are then required. These approaches aim to find a reliable design with an optimal cost by considering uncertainties of the studied model. Classical RBDO methods have been applied only in linear problems and in specific non-linear ones. This study presents an extension of these methods by considering the nonlinearity of the problem. The last part of this thesis consists in studying a problem of multiobjective reliabilitybased design optimization of PCM-based heat sinks. To remedy the complexity of these systems considering uncertainties, surrogate models are then applied. These methods lead to construct an approximate model, which ensures both precision and efficiency of the model. In this study, we propose a new methodology, called MORBDO-RHM, which consists in combining the Robust Hybrid Method (RHM) with the Constrained Non-dominated Sorting Genetic Algorithm (C-NSGA-II). The efficiency of the proposed approach is then verified for a PCM-based thermal storage unit. Reliability (semiconductor) ; Automotive industry ; Aeronautics ; Phase (matter) ; Engineering ; Aerospace engineering ; Automotive engineering ; Computer science ; Physics Material Properties and Applications ; Brake Systems and Friction Analysis ; Engineering Applied Research
Authors: Sumudu Herath | Xiao Xiao | Fehmi Cirak
Source: arXiv (Cornell University) (preprint, 2021-07-12)
Field: Materials Science — Polymers and Plastics

Computational modelling and data-driven homogenisation of knitted membranes Knitting is an effective technique for producing complex three-dimensional surfaces owing to the inherent flexibility of interlooped yarns and recent advances in manufacturing providing better control of local stitch patterns. Fully yarn-level modelling of large-scale knitted membranes is not feasible. Therefore, we use a two-scale homogenisation approach and model the membrane as a Kirchhoff-Love shell on the macroscale and as Euler-Bernoulli rods on the microscale. The governing equations for both the shell and the rod are discretised with cubic B-spline basis functions. For homogenisation we consider only the in-plane response of the membrane. The solution of the nonlinear microscale problem requires a significant amount of time due to the large deformations and the enforcement of contact constraints, rendering conventional online computational homogenisation approaches infeasible. To sidestep this problem, we use a pre-trained statistical Gaussian Process Regression (GPR) model to map the macroscale deformations to macroscale stresses. During the offline learning phase, the GPR model is trained by solving the microscale problem for a sufficiently rich set of deformation states obtained by either uniform or Sobol sampling. The trained GPR model encodes the nonlinearities and anisotropies present in the microscale and serves as a material model for the membrane response of the macroscale shell. The bending response can be chosen in dependence of the mesh size to penalise the fine out-of-plane wrinkling of the membrane. After verifying and validating the different components of the proposed approach, we introduce several examples involving membranes subjected to tension and shear to demonstrate its versatility and good performance. Microscale chemistry ; Homogenization (climate) ; Nonlinear system ; Computer science ; Micromechanics ; Materials science ; Algorithm ; Mathematics ; Composite number ; Physics Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Advanced Numerical Analysis Techniques
Authors: Rebecca Lindsey | Nir Goldman | Laurence E. Fried | Sorin Bastea
Source: ChemRxiv (preprint, 2021-09-08)
Field: Materials Science — Materials Chemistry

Chemistry-Mediated Ostwald Ripening in Carbon-RichC/O Systems at Extreme Conditions There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g. shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e. growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials. Ostwald ripening ; Carbon fibers ; Expansive ; Nanotechnology ; Nanomaterials ; Condensation ; Materials science ; Chemical physics ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Catalysis and Oxidation Reactions
Authors: S. Aria Hosseini | Devin Coleman | Sabah K. Bux | P. Alex Greaney | Lorenzo Mangolini
Source: arXiv (Cornell University) (preprint, 2021-10-26)
Field: Materials Science — Materials Chemistry

Enhanced Thermoelectric ZT in the Tails of the Fermi Distribution via Electron Filtering by Nanoinclusions -- Model Electron Transport in Nanocomposites Silicon carbide nanoparticles with diameters around 8 nm and with narrow size distribution have been finely mixed with doped silicon nanopowders and sintered into bulk samples to investigate the influence of nanoinclusions on electrical and thermal transport properties. We have compared the thermoelectric properties of samples ranging from 0-5% volume fraction of silicon carbide. The silicon carbide nanoinclusions lead to a significant improvement in the thermoelectric figure of merit, ZT, largely due to an enhancement of the Seebeck coefficient. A semiclassical Boltzmann transport equation is used to model the electrical transport properties of the Seebeck coefficient and electrical conductivity. The theoretical analysis confirms that the enhancements in the thermoelectric properties are consistent with the energy selective scattering of electrons induced by the offset between the silicon Fermi level and the carbide conduction band edge. This study proves that careful engineering of the energy-dependent electron scattering rate can provide a route towards relaxing long-standing constraints in the design of thermoelectric materials. Materials science ; Thermoelectric effect ; Seebeck coefficient ; Silicon carbide ; Condensed matter physics ; Boltzmann equation ; Thermoelectric materials ; Scattering ; Fermi level ; Drude model ; Electron ; Thermal conductivity ; Composite material ; Physics ; Thermodynamics ; Optics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design
Authors: Najmeh Zarei | Seyed Ahmad Anvar | Sevan Goenezen
Source: Acta Polytechnica (article, 2021-10-31)
Field: Materials Science — Biomaterials

Subjective approach to optimal cross-sectional design of biodegradable magnesium alloy stent undergoing heterogeneous corrosion Existing biodegradable Magnesium Alloy Stents (MAS) have several drawbacks, such as high restenosis, hasty degradation, and bulky cross-section, that limit their widespread application in a current clinical practice. To find the optimum stent with the smallest possible cross-section and adequate scaffolding ability, a 3D finite element model of 25 MAS stents of different cross-sectional dimensions were analysed while localized corrosion was underway. For the stent geometric design, a generic sine-wave ring of biodegradable magnesium alloy (AZ31) was selected. Previous studies have shown that the long-term performance of MAS was characterized by two key features: Stent Recoil Percent (SRP) and Stent Radial Stiffness (SRS). In this research, the variation with time of these two features during the corrosion phase was monitored for the 25 stents. To find the optimum profile design of the stent subjectively (without using optimization codes and with much less computational costs), radial recoil was limited to 27 % (corresponding to about 10 % probability of in-stent diameter stenosis after an almost complete degradation) and the stent with the highest radial stiffness was selected.The comparison of the recoil performance of 25 stents during the heterogeneous corrosion phase showed that four stents would satisfy the recoil criterion and among these four, the one having a width of 0.161 mm and a thickness of 0.110 mm, showed a 24 % – 49 % higher radial stiffness at the end of the corrosion phase. Accordingly, this stent, which also showed a 23.28 % mass loss, was selected as the optimum choice and it has a thinner cross-sectional profile than commercially available MAS, which leads to a greater deliverability and lower rates of restenosis. Stent ; Materials science ; Recoil ; Elastic recoil ; Corrosion ; Magnesium alloy ; Stiffness ; Cross section (physics) ; Alloy ; Elastic recoil detection ; Phase (matter) ; Composite material ; Structural engineering ; Biomedical engineering ; Surgery ; Physics ; Nanotechnology ; Medicine ; Engineering ; Internal medicine Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Can Xu | Ping Zhu | Zhao Liu
Source: Journal of Mechanical Engineering (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Hierarchical Sensitivity Analysis of Multiscale Stochastic Mechanical Properties for Plain Woven Carbon Fiber Composites 摘要： 平纹机织碳纤维复合材料各尺度的材料属性和空间结构不确定性对材料随机力学性能影响程度不一。为了在多个尺度上确定影响材料随机力学性能的关键参数，基于方差分析逐层级地研究平纹机织碳纤维复合材料各尺度中材料、结构参数关于随机力学性能的敏感性。通过指标集成策略将各尺度敏感性指标组合得到系统级敏感性指标，并对各参数的影响程度进行排序、评估，从根源上识别出关键参数。该方法能够确定考虑不确定性的材料-结构一体化设计过程中的关键设计变量，有助于降低变量维度从而提高设计质量和效率。 Composite material ; Plain weave ; Materials science ; Woven fabric ; Sensitivity (control systems) ; Fiber ; Yarn ; Engineering Textile materials and evaluations ; Manufacturing Process and Optimization ; Mechanical Behavior of Composites
Authors: Yan Hu | Shuo Jin | Zhifen Luo | Hanghang Zeng | Jiahui Wang | Xiaoli Fan
Source: Research Square (preprint, 2021-01-13)
Field: Materials Science — Materials Chemistry

Conversation from anti-ferromagnetic MnBr2 to ferromagnetic Mn3Br8 monolayer with large MAE Ferromagnetism ; Conversation ; Monolayer ; Condensed matter physics ; Materials science ; Physics ; Nanotechnology ; Psychology ; Communication 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Julien Rault | Cheng‐Tai Kuo | Henrique P. Martins | G. Conti | Slavomír Nemšák
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (preprint, 2021-10-22)
Field: Materials Science — Materials Chemistry

High resolution depth profiling using near-total-reflection hard x-ray photoelectron spectroscopy By adjusting the incidence angle of incoming x rays near the critical angle of x-ray total reflection, photoelectron intensity is strongly modulated due to the variation of x-ray penetration depth. Photoelectron spectroscopy combined with near-total reflection exhibits tunable surface sensitivity, providing depth-resolved information. In this Review, we first describe the experimental setup and specific data analysis process. We then review three different examples that show the broad application of this method. The emphasis is on its applications correlated to oxide heterostructures, especially quantitative depth analyses of compositions and electronic states. In the last part, we discuss the limitations of this technique, mostly in terms of the range of samples that can be studied. X-ray photoelectron spectroscopy ; X-ray ; Heterojunction ; Total internal reflection ; Reflection (computer programming) ; Penetration depth ; Materials science ; Optics ; X-ray spectroscopy ; Spectroscopy ; Analytical Chemistry (journal) ; Physics ; Chemistry ; Optoelectronics ; Computer science ; Nuclear magnetic resonance Electronic and Structural Properties of Oxides ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials