jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryan Armbruster | Dushyant Kumar | Blake Benyard | Paul Jacobs | Aditi Khandavilli | Fang Liu | Ravi Prakash Reddy Nanga | Shana E. McCormack | Anne R. Cappola | Neil Wilson | Ravinder Reddy
Source: Nature Communications (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Personalized and muscle-specific OXPHOS measurement with integrated CrCEST MRI and proton MR spectroscopy Creatine chemical exchange saturation transfer (CrCEST) MRI is an emerging high resolution and noninvasive method for measuring muscle specific oxidative phosphorylation (OXPHOS). However, CrCEST measurements are sensitive to changes in muscle pH, which might confound the measurement and interpretation of creatine recovery time (τ<sub>Cr</sub>). Even with the same prescribed exercise stimulus, the extent of acidification and hence its impact on τ<sub>Cr</sub> is expected to vary between individuals. To address this issue, a method to measure pH pre- and post-exercise and its impact on CrCEST MRI with high temporal resolution is needed. In this work, we integrate carnosine <sup>1</sup>H- magnetic resonance spectroscopy (MRS) and 3D CrCEST to establish "mild" and "moderate/intense" exercise stimuli. We then test the dependence of CrCEST recovery time on pH using different exercise stimuli. This comprehensive metabolic imaging protocol will enable personalized, muscle specific OXPHOS measurements in both healthy aging and myriad other disease states impacting muscle mitochondria. Carnosine ; Creatine ; Oxidative phosphorylation ; Magnetic resonance imaging ; Nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Creatine kinase ; Medicine ; Chemistry ; Internal medicine ; Biochemistry ; Physics ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Joanna Jedoń | Eugeniusz Zych | Justyna Zeler | V. Gorbenko | Yu. Zorenko
Source: Advanced Optical Materials (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

YAG:Tb@YAG:Pr Single Crystalline Dual‐Layer Phosphor. An Alternative Approach to Multimodal Wide Range Luminescence Thermometry Abstract The SC‐YAG:Tb@YAG:Pr‐SCF composite epitaxial structure consisting of a 1 mm thick single crystal substrate of YAG:Tb garnet and a thin (9 µm) single crystalline film of YAG:Pr garnet is fabricated, and its luminescent properties are examined toward luminescence thermometry. The possibility to efficiently excite, individually or simultaneously, the Tb 3+ and Pr 3+ dopants by their intense allowed 4f→5d transitions is executed to reach relative thermal sensitivity higher than 1% K −1 over the range of temperatures as wide as 100–625 K and above 0.5% K −1 over 13–650 K. The obtained results demonstrate that using intra‐ and inter‐configurational emission transitions of Pr 3+ and other lanthanides makes it possible to design wide‐range luminescence thermometers with high sensitivity spanning the entire operating limits. Materials science ; Phosphor ; Luminescence ; Dual layer ; Range (aeronautics) ; Persistent luminescence ; Atmospheric temperature range ; Optoelectronics ; Layer (electronics) ; Optics ; Nanotechnology ; Composite material Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Terahertz technology and applications
Authors: Songbin Liu | Shan Yang | Junrong Wang | Zhengce An | Junjie Wang | Yu Liao | Ze Zhang | Junjun Tan | Xinyu Ye | Bo Zhou
Source: Laser & Photonics Review (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Tunable Tri‐Channel Orthogonal Full‐Color Luminescence in Nanostructure toward Anticounterfeiting and Information Security Abstract Tunable orthogonal full‐color luminescence has emerged as a new class of smart luminescence phenomenon with wide applications ranging from photonics to biomedicine. However, the current research is focused on complex multilayer core‐shell nanostructures (e.g., 5–8 shell layers) with a single upconversion mode, greatly limiting their synthesis and practical application. Herein, this work proposes a simple core‐shell structure to integrate upconversion and downshifting dual‐mode luminescence based on Gd 3+ ‐mediated interfacial energy transfer and Ce 3+ ‐assisted cross relaxation. This design is able to suppress cross‐talk of multiple emissions and simplify the sample structure by removing the conventionally required intermediate isolation layer. Importantly, it further enables the arbitrarily controllable multicolor output at a single nanoparticle level by adopting the tri‐channel selective excitation wavelengths (980/808/254 nm), greatly expanding the conventional red‐green‐blue (RGB) color gamut. Moreover, the use of these nanoparticles promotes the information security level and the complexity of anti‐counterfeiting modes by adopting a pre‐set logic Morse information encryption and decryption strategy. These results provide effective guidance for the rational nanostructure design of novel orthogonal trichromatic emissive materials for a variety of frontier applications such as advanced anticounterfeiting and information security. Luminescence ; Channel (broadcasting) ; Nanostructure ; Materials science ; Optoelectronics ; Business ; Computer security ; Internet privacy ; Telecommunications ; Computer science ; Nanotechnology Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Carbon and Quantum Dots Applications
Authors: Filip Musiałek | D. Szabra | J. Wojtas
Source: Sensors (article, 2024-03-13)
Field: Chemistry — Spectroscopy

Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm This paper presents the description of the wavelength modulation spectroscopy (WMS) experiment, the parameters of which were established by use of the Artificial Intelligence (AI) algorithm. As a result, a significant improvement in the signal power to noise power ratio (SNR) was achieved, ranging from 1.6 to 6.5 times, depending on the harmonic. Typically, optimizing the operation conditions of WMS-based gas sensors is based on long-term simulations, complex mathematical model analysis, and iterative experimental trials. An innovative approach based on a biological-inspired genetic algorithm (GA) and custom-made electronics for laser control is proposed. The experimental setup was equipped with a 31.23 m Heriott multipass cell, software lock-in, and algorithms to control the modulation process of the quantum cascade laser (QCL) operating in the long-wavelength-infrared (LWIR) spectral range. The research results show that the applied evolutionary approach can efficiently and precisely explore a wide range of WMS parameter combinations, enabling researchers to dramatically reduce the time needed to identify optimal settings. It took only 300 s to test approximately 1.39 × 10<sup>32</sup> combinations of parameters for key system components. Moreover, because the system is able to check all possible component settings, it is possible to unquestionably determine the operating conditions of WMS-based gas sensors for which the limit of detection (LOD) is the most favorable. Genetic algorithm ; Computer science ; Algorithm ; Optimization algorithm ; Machine learning ; Mathematical optimization ; Mathematics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Atmospheric Ozone and Climate
Authors: In Hyeok Choi | Seung Gyo Jeong | Dae-Hun Jeong | S. S. A. Seo | Woo Seok Choi | Jong Seok Lee
Source: Advanced Science (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Engineering the Coherent Phonon Transport in Polar Ferromagnetic Oxide Superlattices Artificial superlattices composed of perovskite oxides serves as an essential platform for engineering coherent phonon transport by redefining the lattice periodicity, which strongly influences the lattice-coupled phase transitions in charge and spin degrees of freedom. However, previous methods of manipulating phonons have been limited to controlling the periodicity of superlattice, rather than utilizing complex mutual interactions that are prominent in transition metal oxides. In this study on oxide superlattices composed of ferromagnetic metallic SrRuO<sub>3</sub> and quantum paraelectric SrTiO<sub>3</sub>, phonon modulation by controlling the geometry of superlattice in atomic-scale precision is realized, demonstrating the coherent phonon engineering using structural and magnetic phase transitions. By modulating the interface density, coherent-incoherent crossover of the phonon transport at room temperature is observed, which is coupled with a change in interfacial structural continuity. Upon cooling, the close relation between phonon transport and multiple phase transitions is identified. In particular, the enhancement of the polar state in SrTiO<sub>3</sub> layer at ≈200 K leads to the weakening of phonon coherence and a further reduction of thermal conductivity in superlattices compared to the bulk limit. These findings provide a guide to developing future thermoelectric nanodevices by engineering the coherence of phonons via the design of complex oxide heterostructures. Superlattice ; Phonon ; Condensed matter physics ; Polar ; Ferromagnetism ; Materials science ; Oxide ; Physics ; Quantum mechanics ; Metallurgy Ferroelectric and Piezoelectric Materials ; Thermal properties of materials ; Acoustic Wave Resonator Technologies
Authors: Peter Minárik | Mária Zemková | Stanislav Šašek | Jan Dittrich | Michal Knapek | František Lukáč | Daniel Koutný | Ján Jaroš | Róbert Král
Source: Journal of Magnesium and Alloys (article, 2024-04-01)
Field: Materials Science — Biomaterials

Comparative analysis of microstructure, mechanical, and corrosion properties of biodegradable Mg-3Y alloy prepared by selective laser melting and spark plasma sintering • W3 alloy was successfully consolidated by SLM and SPS almost without porosity. • Microstructure is significantly affected by the processing method. • Mechanical strength is comparable to the extruded material. • Deformability is primarily affected by porosity, grain structure and residual strain. • SPS material showed convenient combination of mechanical and degradation properties. This work explored possibilities of biodegradable magnesium alloy Mg-3Y preparation by two modern powder metallurgy techniques – spark plasma sintering (SPS) and selective laser melting (SLM). The powder material was consolidated by both methods utilising optimised parameters, which led to very low porosity (∼0.3%) in the SLM material and unmeasurably low porosity in the SPS material. The main aim of the study was the thorough microstructure characterisation and interrelation between the microstructure and the functional properties, such as mechanical strength, deformability, and corrosion resistance. Both materials showed comparable strength of ∼110 MPa in tension and compression and relatively good deformability of ∼9% and ∼21% for the SLM and SPS materials, respectively. The corrosion resistance of the SPS material in 0.1 M NaCl solution was superior to the SLM one and comparable to the conventional extruded material. The digital image correlation during loading and the cross-section analysis of the corrosion layers revealed that the residual porosity and large strained grains have the dominant negative effect on the functional properties of the SLM material. On the other hand, one of the primary outcomes of this study is that the SPS consolidation method is very effective in the preparation of the W3 biodegradable alloy, resulting in material with convenient mechanical and degradation properties that might find practical applications. Spark plasma sintering ; Materials science ; Microstructure ; Selective laser melting ; Alloy ; Metallurgy ; Corrosion ; Sintering ; Selective laser sintering Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Hydrogen Storage and Materials
Authors: Redwan Mohammed Mamu | Necla Toğun
Source: Acta Mechanica (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Thermal effects on nonlinear vibration of nonlocal nanobeam embedded in nonlinear elastic medium Abstract Nanotechnology has an impact on our lives in a many ways, from better medical treatments and more efficient energy sources to stronger and lighter materials and advanced electronics and this article presents the implementation of a perturbation method for the vibration analysis of simply supported and clamped–clamped Euler–Bernoulli nanobeams resting on nonlinear elastic foundations in thermal environment using nonlocal elasticity theory. Hamilton's principle is used to construct the differential equation of motion of a nanobeam in conjunction with appropriate boundary conditions. The equations of motion of the Euler–Bernoulli nanobeam are determined using nonlocal elasticity theory. It is shown how thermal loadings affect the vibration of the Euler–Bernoulli nanobeam. The multiple scale method, which is one of the perturbation method, is used to get an approximated solution for the presented system. The effects of temperature, Winkler, Pasternak and nonlinear foundation parameters on the vibration analysis of simply supported and clamped–clamped nanobeams are determined and results are given in tables and graphs. Solid mechanics ; Nonlinear system ; Vibration ; Thermal ; Mechanics ; Classical mechanics ; Materials science ; Physics ; Acoustics ; Composite material ; Thermodynamics ; Quantum mechanics Nonlocal and gradient elasticity in micro/nano structures ; Thermoelastic and Magnetoelastic Phenomena ; Composite Structure Analysis and Optimization
Authors: Qianyu Xia | Chenhao Ji | Zhixin Zhan | Xiaojia Wang | Zhi Bian | Weiping Hu | Qingchun Meng
Source: International Journal of Fatigue (article, 2024-10-02)
Field: Materials Science — Metals and Alloys

Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material Bimetallic strip ; Welding ; Materials science ; Metallurgy ; Diffusion ; Bronze ; Structural engineering ; Engineering ; Physics ; Thermodynamics Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Xiaoshuang Zhou | Xianhong Zheng | Xu Dong | Xi Wang | Yaoyao Jiang | Guiyang Li | Lvzhou Li | Ningyi Yuan | Jianning Ding
Source: Energy Materials (article, 2024-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in wearable fiber-shaped supercapacitors: materials, design, and applications The rapid development of wearable electronic products necessitates flexible and efficient energy storage solutions. Fiber-shaped supercapacitors (FSSCs) have gained significant attention as promising substitutes owing to their flexibility, low weight, and ability to integrate seamlessly into textile and wearable applications. This review offers an in-depth examination of the latest developments in FSSCs, distinguishing itself by not only summarizing diverse electrode materials such as carbon-based, polymer-based, and novel MXenes but also exploring their unique properties and applications in depth. We critically examine various fabrication techniques that enable the customization of fiber structures, including wet-spinning, electrostatic spinning, melt spinning, co-spinning, and advanced three-dimensional (3D) printing methods. Furthermore, we investigate innovative design strategies aimed at enhancing performance, such as film/sheet twisting for increased surface area and multilayer integrated designs. By discussing the integration of FSSCs into applications such as energy storage devices and sensors, we shed light on their potential to revolutionize the field. The review wraps up by identifying future challenges and directions, underlining the critical need for advancements in material synthesis, device design, and integration strategies. These advancements are crucial for the widespread adoption of FSSCs in flexible and wearable electronics. Continued research holds the potential to deliver next-generation energy storage solutions tailored to the growing demands of the wearable technology industry. Supercapacitor ; Wearable computer ; Fiber ; Materials science ; Wearable technology ; Nanotechnology ; Computer science ; Capacitance ; Composite material ; Embedded system ; Physics ; Electrode Supercapacitor Materials and Fabrication
Authors: Yixiang Ruan | Chenyin Lu | Ning Xu | Jian Zhang | Jun Xuan | Jian‐Zhang Pan | Qun Fang | Hanyu Gao | Xiaodong Shen | Ning Ye | Qiang Zhang | Yiming Mo
Source: ChemRxiv (preprint, 2024-05-07)
Field: Materials Science — Materials Chemistry

Accelerated end-to-end chemical synthesis development with large language models The rapid emergence of large language model (LLM) technology presents significant opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build a multi-agent system to handle fundamental tasks involved throughout the chemical synthesis development process. The multi-agent system comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application was built with the multi-agent system as the backend to allow chemist users to interact with experimental platforms and analyze results via natural language, thus, requiring zero-coding skills to allow easy access for all chemists. We demonstrated this multi-agent system on the development of a recently developed copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, and this LLM multi-agent copiloted end-to-end reaction development process includes: literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. This work showcases the trilogy among chemist users, LLM-based agents, and automated experimental platforms to reform the traditional expert-centric and labor-intensive reaction development workflow. End-to-end principle ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Davi Fébba | Kingsley Egbo | William A. Callahan | Andriy Zakutayev
Source: Digital Discovery (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

From text to test: AI-generated control software for materials science instruments AI-crafted control software for automating scientific instruments can democratize and further accelerate materials research. Control software ; Software ; Instrument control ; Test (biology) ; Control (management) ; Computer science ; Software engineering ; Scientific instrument ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques
Authors: Thomas G. Ciardi | Arafath Nihar | Rounak Chawla | Olatunde Akanbi | Pawan K. Tripathi | Yinghui Wu | Vipin Chaudhary | Roger H. French
Source: MRS Communications (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Materials data science using CRADLE: A distributed, data-centric approach Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract Geospatial analysis ; Scalability ; Sensor fusion ; Analytics ; Materials science ; Data quality ; Data analysis ; Data management ; Segmentation ; Data extraction ; Computer science ; Data aggregator ; Scale (ratio) ; Data science ; Data mining ; Database ; Artificial intelligence ; Remote sensing ; Cartography ; Engineering Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Scientific Computing and Data Management
Authors: Yuwei Zhang | Fang-Yi Chen | Zeyi Liu | Yunzhuo Ju | Dongliang Cui | Jinyi Zhu | Xue Jiang | Xi Guo | Jie He | Lei Zhang | Xiaotong Zhang | Yanjing Su
Source: Scientific Data (article, 2024-06-07)
Field: Materials Science — Materials Chemistry

A materials terminology knowledge graph automatically constructed from text corpus A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation. Terminology ; Computer science ; Knowledge sharing ; Knowledge graph ; Information retrieval ; Graph ; Knowledge representation and reasoning ; Scalability ; Natural language processing ; Term (time) ; Artificial intelligence ; Data science ; Knowledge management ; Theoretical computer science ; Database ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography