jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dawit Bogale Alemayehu | Masahiro TODOH
Source: Preprints.org (preprint, 2024-03-18)
Field: Materials Science — Biomaterials

Enhanced Energy Absorption in Bioinspired Combined TPMS-Gyroid and Walled TPMS-Gyroid Lattice Structure Manufactured via Fused Filament Fabrication (FFF) Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop&amp;#039;s Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice&amp;#039;s relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance. Gyroid ; Fabrication ; Materials science ; Lattice (music) ; Fused filament fabrication ; Protein filament ; Nanotechnology ; Composite material ; Copolymer ; 3D printing ; Polymer ; Physics ; Acoustics Calcium Carbonate Crystallization and Inhibition
Authors: Fatma Nur Depboylu | Evren Yasa | Özgür Poyraz | Feza Korkusuz | Andrei-Alexandru Popa
Source: Materials Today Communications (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Choosing between commercially pure titanium and Ti-6Al-4V gyroid structures for orthopedic applications: An analysis through Timoshenko beam theory, the Gibson-Ashby model and experimental methods For decades, research has focused on using gyroid lattice structures to prevent stress-induced osteopenia and improve osseointegration by promoting cell adhesion and transition, particularly in cases where implant removal is not desired after complete healing. Despite these advantages, in-depth research is still needed for innovative biomedical implants to reach optimum functionality. Although commercially pure titanium (Cp-Ti) and Ti6Al4V are both popular materials for standalone bone implants, they are not likely paired within the same application due to mechanical property differences. This study proposes a case-specific workflow to support orthopedic implant design considerations, by analytically and experimentally investigating the mechanical response of additively manufactured lattice gyroid structures in both Cp-Ti and Ti6Al4V with different porosity rates. The experimental results of both materials indicate a significant decrease in the elastic modulus against two employed analytical methods, Gibson-Ashby model and Timoshenko beam theory. This is linked to inherent manufacturing-induced stress concentrations on the physical gyroids. The experimental outcome presents itself significantly closer to the predictions of Timoshenko beam theory compared to Gibson Ashby method. Both gyroid titanium structures are loyal to Timoshenko theory, displaying a combination of multi deformation behaviors instead of one-way deformation. The deformation response of Ti6Al4V showed a 45⁰ shear band while Cp-Ti exhibited smoother deformation during compression. Higher porosity rates led to an increase in ductility due to extended horizontal plateau regions in both Cp-Ti and Ti6Al4V lattice structures. The results provide valuable comparative insight and considerations on the material choice for gyroid lattice structures within bone implants. Gyroid ; Materials science ; Timoshenko beam theory ; Osseointegration ; Titanium alloy ; Titanium ; Deformation (meteorology) ; Composite material ; Implant ; Finite element method ; Structural engineering ; Alloy ; Metallurgy Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Cellular and Composite Structures
Authors: Thomas A. de Bruin | Wilfried van Sark
Source: Advanced Photonics Research (article, 2024-11-29)
Field: Chemistry — Physical and Theoretical Chemistry

Shining Lights on Limits: Optimizing Luminescent Solar Concentrators for Solar Windows The luminescent solar concentrator (LSC) can function as an electricity‐generating window whose performance is hindered by an inherent trade‐off between power conversion efficiency and visible light transmission. To optimize this trade‐off, a numerical algorithm is used to find the power conversion efficiency of 92 luminophores, ensuring an average visible transmission &gt;55% and a color rendering index &gt;70. Furthermore, double‐ and triple‐stacked LSC configurations and single LSCs with two‐ or three‐embedded luminophores are optimized. All tested LSC configurations are found to be limited to a power conversion efficiency of around 1% for window‐appropriate transparencies. Window (computing) ; Energy conversion efficiency ; Materials science ; Optics ; Optoelectronics ; Concentrator ; Transmission (telecommunications) ; Rendering (computer graphics) ; Computer science ; Telecommunications ; Computer graphics (images) ; Physics Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research ; Lanthanide and Transition Metal Complexes
Authors: Andrea Ramírez‐Sagredo | Anju Teresa Sunny | Kellye A. Cupp‐Sutton | Trishika Chowdhury | Zhitao Zhao | Si Wu | Ying Ann Chiao
Source: Clinical Proteomics (article, 2024-09-29)
Field: Chemistry — Spectroscopy

Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics By combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart. Proteomics ; Computational biology ; Cell biology ; Biology ; Bioinformatics ; Genetics Advanced Proteomics Techniques and Applications ; Mitochondrial Function and Pathology ; Metabolomics and Mass Spectrometry Studies
Authors: Jade K. Macdonald | Cassandra L. Clift | Janet Saunders | Stephen Zambrzycki | Anand S. Mehta | Richard R. Drake | Peggi M. Angel
Source: Journal of the American Society for Mass Spectrometry (article, 2024-02-08)
Field: Chemistry — Spectroscopy

Differential Protease Specificity by Collagenase as a Novel Approach to Serum Proteomics That Includes Identification of Extracellular Matrix Proteins without Enrichment Circulating extracellular matrix (ECM) proteins are serological biomarkers of interest due to their association with pathologies involving disease processes such as fibrosis and cancers. In this study, we investigate the potential for serum biomarker research using differential protease specificity (DPS), leveraging alternate protease specificity as a targeting mechanism to selectively digest circulating ECM protein serum proteins. A proof-of-concept study is presented using serum from patients with cirrhotic liver or hepatocellular carcinoma. The approach uses collagenase DPS for digestion of deglycosylated serum and liquid-chromatography-trapped ion mobility-tandem mass spectrometry (LC-TIMS-MS/MS) to enhance the detection of ECM proteins in serum. It requires no sample enrichment and minimizes the albumin average precursor intensity readout to less than 1.2%. We further demonstrate the capabilities for using the method as a high-throughput matrix-assisted laser/desorption ionization mass spectrometry (MALDI-MS) assay coupled with reference library searching. A goal is to improve the depth and breadth of biofluid proteomics for noninvasive assays. Chemistry ; Proteomics ; Collagenase ; Extracellular matrix ; Protease ; Biomarker discovery ; Mass spectrometry ; Blood proteins ; Matrix-assisted laser desorption/ionization ; Tandem mass spectrometry ; Biomarker ; Chromatography ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Protease and Inhibitor Mechanisms ; Glycosylation and Glycoproteins Research
Authors: Takudzwa Martin Mashamba | Jiale Wen | Catalina Spataru | Yiwu Weng | Xiaojing Lv
Source: Applied Sciences (article, 2024-04-21)
Field: Materials Science — Materials Chemistry

Characteristic Investigation of a Novel Aircraft SOFC/GT Hybrid System under Varying Operational Parameters In this study, the implementation of a solid oxide fuel cell–gas turbine hybrid engine for primary propulsion and electric power generation in aircraft is investigated. The following three parameters, which are crucial in attaining optimal performance at any point in the flight profile, were identified: the oxygen-to-carbon ratio of the catalytic partial oxidation reformer, the fuel utilization factor of the fuel cell, and the airflow split ratio at the outlet of the high-pressure compressor. The study assesses the impact of varying these parameters within specified ranges on the performance of the hybrid system. At the design point, the system yielded a total power output of 1.96 MW, with 102.5 kW of electric power coming from the fuel cell and 7.9 kN (1.86 MW) of thrust power coming from the gas turbine. The results indicate that varying the oxygen-to-carbon ratio affected the fuel cell’s fuel utilization and resulted in a slight decrease in gas turbine thrust. The fuel utilization factor primarily affected the power output of the fuel cell stack, with a minor impact on thrust. Notably, varying the airflow split ratio showed the most significant influence on the overall system performance. This analysis provides insights into the system’s sensitivities and contributes to the development of more sustainable aircraft energy systems. Computer science ; Environmental science Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: F. Gómez-Silva | R. Zaera | Harm Askes
Source: Thin-Walled Structures (article, 2024-07-04)
Field: Materials Science — Materials Chemistry

Discrete-continuum-discrete approach for the modeling of the dynamic behavior of 2D lattice systems This work presents a new methodology to model elastic lattice systems through a two-step approach that permits to reliably capture its dynamic behavior with a lower computational cost than modeling the lattice explicitly. The first step consists of a non-standard continualization accounting for scale effects. Several methods are explored to derive new continuum models, whose dispersive and vibrational behaviors are compared to that of the lattice, considered as a reference. Non-classical models with micro-inertia reveal high accuracy, not presenting physical inconsistencies. The second step follows a FEM spatial discretization of the developed continua, accounting for micro inertia terms in the mass matrix. Finally, the FEM formulation allows the use of element sizes larger (up to four times) than the physical length scale of the lattice system, thus significantly reducing the computational cost while maintaining accuracy and enabling a versatile application to materials, geometries and boundary conditions. The methodology is tested here for a 2D system with displacements in the plane, but can be extended to other lattice typologies as well. Discretization ; Lattice (music) ; Inertia ; Finite element method ; Statistical physics ; Physical system ; Boundary value problem ; Computer science ; Algorithm ; Mathematics ; Classical mechanics ; Mathematical analysis ; Physics ; Structural engineering ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Vibration and Dynamic Analysis ; Acoustic Wave Phenomena Research
Authors: Jinyuan Sun | Auston Li | Yifan Deng | Jiabo Li
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

ChatMol Copilot: An Agent for Molecular Modeling and Computation Powered by LLMs Computation ; Computer science ; Environmental planning ; Environmental science ; Programming language Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhiling Zheng | Federico Florit | Brooke Jin | Haoyang Wu | Shih‐Cheng Li | Kakasaheb Y. Nandiwale | Chase A. Salazar | Jason Mustakis | William H. Green | Klavs F. Jensen
Source: Angewandte Chemie (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions Abstract Electrochemical C−H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine learning and large language models to streamline the exploration of electrochemical C−H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text‐mined literature data to augment the training set. The resulting ML models for reactivity prediction achieved high accuracy (&gt;90 %) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were prompted to generate code that iteratively improved yields. This human‐AI collaboration proved effective, efficiently identifying high‐yield conditions for 8 drug‐like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 12 different LLMs–including LLaMA series, Claude series, OpenAI o1, and GPT‐4‐on code generation and function calling related to ML based on natural language prompts given by chemists to showcase potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C−H oxidation reactions. Computer science ; Electrochemistry ; Nanotechnology ; Chemistry ; Materials science ; Electrode Machine Learning in Materials Science
Authors: Kishore Kumar Nandipati | Sonika Pal | Ritayan Mitra
Source: Sensors (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4-10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement-disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual. Crossover ; Simple (philosophy) ; Computer science ; Psychology ; Artificial intelligence Advanced Thermoelectric Materials and Devices ; Green IT and Sustainability ; Environmental Education and Sustainability
Authors: Brett Emery | Kelsey L. Snapp | Daniel Revier | Vivek Sarkar | Masa Nakura | Keith A. Brown | Jeffrey I. Lipton
Source: Advanced Science (article, 2024-09-27)
Field: Materials Science — Polymers and Plastics

Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process-property relationships. In this work, a self-driving lab is utilized that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, an underlying self-stabilizing characteristic of VTP layer thickness is discovered as an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable-driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing. Thread (computing) ; Materials science ; Polylactic acid ; Computer science ; Extrusion ; 3D printing ; Mechanical engineering ; Padding ; Soft robotics ; Thermoplastic polyurethane ; Composite material ; Actuator ; Artificial intelligence ; Engineering ; Polymer ; Elastomer Polymer composites and self-healing ; Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics
Authors: Thomas J. Penfold | Luke Watson | Clelia Middleton | Tudur Wyn David | Sneha Verma | Thomas Pope | J. Kaczmarek | Conor D. Rankine
Source: Machine Learning Science and Technology (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy Abstract Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (x-ray) spectroscopies, whose prevalence has increased following the advent of modern x-ray facilities including third-generation synchrotrons and x-ray free-electron lasers. While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable ‘ black-box ’ approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational x-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental x-ray spectroscopic studies. Spectroscopy ; X-ray spectroscopy ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Astronomy Machine Learning in Materials Science ; Advanced X-ray and CT Imaging
Authors: Logan J. Augustine | Yufei Wang | Sara L. Adelman | Enrique R. Batista | Stosh A. Kozimor | Danny Pérez | Joshua Schrier | Ping Yang
Source: ACS Sustainable Chemistry & Engineering (article, 2024-10-29)
Field: Chemistry — Inorganic Chemistry

Advancing Rare-Earth (4<i>f</i>) and Actinide (5<i>f</i>) Separation through Machine Learning and Automated High-Throughput Experiments Identifying improved and sustainable alternatives to "classic" separation techniques is an active research field due to its potential widespread impact in fundamental and applied chemistry. As basic purification methodologies, like liquid-liquid extraction, undergo continuous refinement by chemists and engineers, identifying new conditions that outperform existing techniques can be difficult. A major contributor to this challenging problem is the need to explore a vast experimental space to identify the precise conditions that optimize the separation procedure. The advent of artificial intelligence and the advancement of robotic technologies offer the potential to shift the traditional design paradigm. Toward that end, we applied a combination of Bayesian Optimization and high-throughput robotic experiments on the liquid-liquid extraction of thorium (Th<sup>4+</sup>) and demonstrated that this approach speeds up discovery and significantly accelerates the optimization process. By using Bayesian Optimization as a guide, our automated instrument carried out a total of 339 distribution ratio measurements, corresponding to 113 unique conditions, identifying the optimal experimental conditions with reduced experimental efforts by an estimated 74% compared to a traditional full screening approach. This time and cost saving is particularly significant for radioactive materials, as it not only is more economical and sustainable but also minimizes human exposure to radioactivity. Throughput ; Computer science ; Separation (statistics) ; Actinide ; Field (mathematics) ; Extraction (chemistry) ; Process engineering ; Artificial intelligence ; Biochemical engineering ; Machine learning ; Engineering ; Chemistry ; Chromatography ; Mathematics Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering