jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adnan Alnehia | Muhammad Hadi | Hisham Alnahari | Annas Al-Sharabi
Source: Scientific Reports (article, 2024-06-22)
Field: Chemistry — Inorganic Chemistry

Optical, structural and antibacterial properties of phase heterostructured Fe2O3–CuO–CuFe2O4 nanocomposite The synthesis of the Fe<sub>2</sub>O<sub>3</sub>-CuO-CuFe<sub>2</sub>O<sub>4</sub> nanocomposite was effectively achieved through the sol-gel technique, utilizing ethanol as a reactive fuel. Investigation of the nanocomposite's structure via X-ray Diffraction confirmed the coexistence of Fe<sub>2</sub>O<sub>3</sub>, CuO, and CuFe<sub>2</sub>O<sub>4</sub> phases within the material. The Scherrer equation was applied to determine an average crystallite size ranging from 60 to 95 nm. UV-visible spectroscopy studies suggested the material possesses an approximate energy bandgap of 4 eV. Scanning Electron Microscopy provided insights into the nanocomposite's surface morphology, which exhibited a porous and heterogeneous aggregation of particles in various sizes and shapes. When tested for antibacterial efficacy, the nanocomposite exhibited activity against gram-positive S. aureus with a maximum zone of inhibition (ZOI) measuring 9 mm at the highest concentration, whereas no inhibitory effect was detected against gram-negative E.coli. Nanocomposite ; Phase (matter) ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology Pigment Synthesis and Properties ; Copper-based nanomaterials and applications ; Magnetic Properties and Synthesis of Ferrites
Authors: Ibtisam Alali | Amina U. Shehu | Robert Mokaya
Source: Energy & Environmental Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Porous carbon composites as clean energy materials with extraordinary methane storage capacity Activated carbon composites containing alumina have high porosity (up to ∼2800 m 2 g −1 and 1.5 cm 3 g −1 ) and packing density (∼1 g cm −3 ), which translates to extraordinary methane volumetric uptake of up to 474 cm 3 (STP) cm −3 at 25 °C and 100 bar. Methane ; Porosity ; Materials science ; Composite material ; Carbon fibers ; Energy storage ; Activated carbon ; Bar (unit) ; Composite number ; Chemistry ; Adsorption Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Quynh N. Nguyen | Eun Mi Kim | Yong Ding | Annemieke Janssen | Chenxiao Wang | Kei Kwan Li | Junseok Kim | Kristen A. Fichthorn | Younan Xia
Source: Journal of the American Chemical Society (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

Elucidating the Role of Reduction Kinetics in the Phase-Controlled Growth on Preformed Nanocrystal Seeds: A Case Study of Ru This study demonstrates the crucial role of reduction kinetics in phase-controlled synthesis of noble-metal nanocrystals using Ru nanocrystals as a case study. We found that the reduction kinetics played a more important role than the templating effect from the preformed seed in dictating the crystal structure of the deposited overlayers despite their intertwined effects on successful epitaxial growth. By employing two different polyols, a series of Ru nanocrystals with tunable sizes of 3-7 nm and distinct patterns of crystal phase were synthesized by incorporating different types of Ru seeds. Notably, the use of ethylene glycol and triethylene glycol consistently resulted in the formation of Ru shell in natural hexagonal close-packed (<i>hcp</i>) and metastable face-centered cubic (<i>fcc</i>) phases, respectively, regardless of the size and phase of the seed. Quantitative measurements and theoretical calculations suggested that this trend was a manifestation of the different reduction kinetics associated with the precursor and the chosen polyol, which, in turn, affected the reduction pathway (solution versus surface) and packing sequence of the deposited Ru atoms. This work not only underscores the essential role of reduction kinetics in controlling the packing of atoms and thus the phase taken by Ru nanocrystals but also suggests a potential extension to other noble-metal systems. Nanocrystal ; Chemistry ; Kinetics ; Ethylene glycol ; Triethylene glycol ; Phase (matter) ; Chemical engineering ; Epitaxy ; Crystal growth ; Hexagonal phase ; Metal ; Nanotechnology ; Crystallography ; Polymer chemistry ; Organic chemistry ; Materials science Copper-based nanomaterials and applications ; nanoparticles nucleation surface interactions ; Quantum Dots Synthesis And Properties
Authors: Aditya Dey | Ahmad Azizimanesh | Stephen M. Wu | Hesam Askari
Source: ACS Applied Materials & Interfaces (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Uniaxial Strain-Induced Stacking Order Change in Trilayer Graphene The layer stacking order in two-dimensional heterostructures, like graphene, affects their physical properties and potential applications. Trilayer graphene, specifically ABC-trilayer graphene, has captured significant interest due to its potential for correlated electronic states. However, achieving a stable ABC arrangement is challenging due to its lower thermodynamic stability compared to the more stable ABA stacking. Despite recent advancements in obtaining ABC graphene through external perturbations, such as strain, the stacking transition mechanism remains insufficiently explored. In this study, we unveil a universal mechanism to achieve ABC stacking, applicable for understanding ABA to ABC stacking changes induced by any mechanical perturbations. Our approach is based on a novel strain engineering technique that induces interlayer slippage and results in the formation of stable ABC domains. We investigate the underlying interfacial mechanisms of this stacking change through computational simulations and experiments. Our findings demonstrate a highly anisotropic and significant transformation of ABA stacking to large and stable ABC domains facilitated by interlayer slippage. Through atomistic simulations and local energy analysis, we systematically demonstrate the mechanism for this stacking transition, that is dependent on specific loading orientation. Understanding such a mechanism allows this material system to be engineered by design compatible with industrial techniques on a device-by-device level. We conduct Raman studies to validate and characterize the formed ABC stacking, highlighting its distinct features compared to the ABA region. Our results contribute to a clearer understanding of the stacking change mechanism and provide a robust and controllable method for achieving stable ABC domains, facilitating their use in developing advanced optoelectronic devices. Stacking ; Materials science ; Graphene ; Molecular dynamics ; Slippage ; Nanotechnology ; Mechanism (biology) ; Raman spectroscopy ; Chemical physics ; Heterojunction ; Computational chemistry ; Optoelectronics ; Composite material ; Physics ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Robert Henning | Irina Kosheleva | V. Šrajer | In‐Sik Kim | Eric Zoellner | Rama Ranganathan
Source: Structural Dynamics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules A major goal in biomedical science is to move beyond static images of proteins and other biological macromolecules to the internal dynamics underlying their function. This level of study is necessary to understand how these molecules work and to engineer new functions and modulators of function. Stemming from a visionary commitment to this problem by Keith Moffat decades ago, a community of structural biologists has now enabled a set of x-ray scattering technologies for observing intramolecular dynamics in biological macromolecules at atomic resolution and over the broad range of timescales over which motions are functionally relevant. Many of these techniques are provided by BioCARS, a cutting-edge synchrotron radiation facility built under Moffat leadership and located at the Advanced Photon Source at Argonne National Laboratory. BioCARS enables experimental studies of molecular dynamics with time resolutions spanning from 100 ps to seconds and provides both time-resolved x-ray crystallography and small- and wide-angle x-ray scattering. Structural changes can be initiated by several methods-UV/Vis pumping with tunable picosecond and nanosecond laser pulses, substrate diffusion, and global perturbations, such as electric field and temperature jumps. Studies of dynamics typically involve subtle perturbations to molecular structures, requiring specialized computational techniques for data processing and interpretation. In this review, we present the challenges in experimental macromolecular dynamics and describe the current state of experimental capabilities at this facility. As Moffat imagined years ago, BioCARS is now positioned to catalyze the scientific community to make fundamental advances in understanding proteins and other complex biological macromolecules. Synchrotron ; Picosecond ; Macromolecule ; Nanosecond ; Nanotechnology ; Molecular dynamics ; Field (mathematics) ; Physics ; Computer science ; Chemistry ; Materials science ; Optics ; Computational chemistry ; Laser Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Photosynthetic Processes and Mechanisms
Authors: Jesse A. Jones | Michael P. Andreas | Tobias W. Giessen
Source: Nature Communications (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Structural basis for peroxidase encapsulation inside the encapsulin from the Gram-negative pathogen Klebsiella pneumoniae Encapsulins are self-assembling protein nanocompartments capable of selectively encapsulating dedicated cargo proteins, including enzymes involved in iron storage, sulfur metabolism, and stress resistance. They represent a unique compartmentalization strategy used by many pathogens to facilitate specialized metabolic capabilities. Encapsulation is mediated by specific cargo protein motifs known as targeting peptides (TPs), though the structural basis for encapsulation of the largest encapsulin cargo class, dye-decolorizing peroxidases (DyPs), is currently unknown. Here, we characterize a DyP-containing encapsulin from the enterobacterial pathogen Klebsiella pneumoniae. By combining cryo-electron microscopy with TP and TP-binding site mutagenesis, we elucidate the molecular basis for cargo encapsulation. TP binding is mediated by cooperative hydrophobic and ionic interactions as well as shape complementarity. Our results expand the molecular understanding of enzyme encapsulation inside protein nanocompartments and lay the foundation for rationally modulating encapsulin cargo loading for biomedical and biotechnological applications. Klebsiella pneumoniae ; Chemistry ; Pathogen ; Biochemistry ; Biology ; Computational biology ; Microbiology ; Escherichia coli Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; Bacteriophages and microbial interactions
Authors: Yongtao Liu | Martí Checa | Rama K. Vasudevan
Source: Machine Learning Science and Technology (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design <sup>*</sup> Abstract With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community. Workflow ; Executable ; Computer science ; Software engineering ; Programming language ; Scientific instrument ; Interface (matter) ; Code (set theory) ; Human–computer interaction ; Artificial intelligence ; Database ; Operating system Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Kaidong Song | A. N. M. Tanvir | Md. Omarsany Bappy | Yanliang Zhang
Source: Small Science (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

New Directions for Thermoelectrics: A Roadmap from High‐Throughput Materials Discovery to Advanced Device Manufacturing Thermoelectric materials, which can convert waste heat into electricity or act as solid-state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high-throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high-throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed-loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low-cost, and energy-efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high-throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid-state cooling. In the end, this article also discusses the future research prospects and directions. Thermoelectric materials ; Thermoelectric effect ; Throughput ; Scalability ; Process engineering ; Process (computing) ; Computer science ; Electricity ; Thermoelectric generator ; Efficient energy use ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Electrical engineering ; Telecommunications ; Wireless Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Ioana Lavinia Dejeu | Laura Grațiela Vicaș | Eleonora Marian | Mariana Ganea | Olimpia Daniela Frenț | Paula Bianca Maghiar | Flaviu Bodea | George Emanuiel Dejeu
Source: Pharmaceutics (review, 2024-11-27)
Field: Materials Science — Biomaterials

Innovative Approaches to Enhancing the Biomedical Properties of Liposomes Liposomes represent a promising class of drug delivery systems that enhance the therapeutic efficacy and safety of various pharmaceutical agents. Also, they offer numerous advantages compared to traditional drug delivery methods, including targeted delivery to specific sites, controlled release, and fewer side effects. This review meticulously examines the methodologies employed in the preparation and characterization of liposomal formulations. With the rising incidence of adverse drug reactions, there is a pressing need for innovative delivery strategies that prioritize selectivity, specificity, and safety. Nanomedicine promises to revolutionize diagnostics and treatments, addressing current limitations and improving disease management, including cancer, which remains a major global health challenge. This paper aims to conduct a comprehensive study on the interest of biomedical research regarding nanotechnology and its implications for further applications. Nanomedicine ; Drug delivery ; Liposome ; Nanotechnology ; Risk analysis (engineering) ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Zohra Benzarti | Nаdiа Аrrousse | R. Serra | Sandra Cruz | A.C. Bastos | João Tedim | Rita Salgueiro | A. Cavaleiro | S. Carvalho
Source: Corrosion Reviews (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Copper corrosion mechanisms, influencing factors, and mitigation strategies for water circuits of heat exchangers: critical review and current advances Abstract This review examines copper corrosion mechanisms and their key influencing factors, including microstructure effects, surface treatments, manufacturing conditions, temperature, water chemistry parameters, fluid velocity, and microbial effects in water-based systems, with a particular focus on heat exchangers. This addresses a critical gap in the existing literature, which often examines copper corrosion in a broader context. By critically analyzing the literature, the review provides an in-depth understanding of the factors that govern copper corrosion in heat exchanger applications. Copper corrosion in heat exchangers can have significant technical and social detrimental consequences, leading to substantial economic losses. By focusing on heat exchangers, the review offers valuable insights and best practices for engineers, researchers, and practitioners working with copper in this domain. Furthermore, the review evaluates the latest mitigation strategies, including advancements in material selection, surface treatments, water treatment techniques, and robust monitoring/maintenance methods. Finally, the review explores promising new concepts for corrosion prevention for long-term performance, paving the way for future research in developing innovative technologies and refining highly effective strategies under diverse operating conditions relevant to combat deleterious copper corrosion effects in heat exchanger applications. Materials science ; Corrosion ; Heat exchanger ; Copper ; Current (fluid) ; Electronic circuit ; Materials processing ; Erosion corrosion of copper water tubes ; Metallurgy ; Process engineering ; Mechanical engineering ; Electrical engineering ; Engineering Corrosion Behavior and Inhibition ; Water Treatment and Disinfection ; Magnesium Oxide Properties and Applications
Authors: Yuheng Ding | Bo Qiang | Qixuan Chen | Yiqiao Liu | Liangren Zhang | Zhenming Liu
Source: Journal of Chemical Information and Modeling (review, 2024-03-15)
Field: Materials Science — Materials Chemistry

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue. Chemical space ; Computer science ; Perspective (graphical) ; Representation (politics) ; Feature (linguistics) ; Artificial intelligence ; Bridge (graph theory) ; Space (punctuation) ; Data science ; Machine learning ; Biochemical engineering ; Drug discovery ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Hannes Kneiding | David Balcells
Source: Chemical Science (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Augmenting genetic algorithms with machine learning for inverse molecular design Evolutionary and machine learning methods have been successfully applied to the generation of molecules and materials exhibiting desired properties. The combination of these two paradigms in inverse design tasks can yield powerful methods that explore massive chemical spaces more efficiently, improving the quality of the generated compounds. However, such synergistic approaches are still an incipient area of research and appear underexplored in the literature. This perspective covers different ways of incorporating machine learning approaches into evolutionary learning frameworks, with the overall goal of increasing the optimization efficiency of genetic algorithms. In particular, machine learning surrogate models for faster fitness function evaluation, discriminator models to control population diversity on-the-fly, machine learning based crossover operations, and evolution in latent space are discussed. The further potential of these synergistic approaches in generative tasks is also assessed, outlining promising directions for future developments. Machine learning ; Computer science ; Artificial intelligence ; Generative grammar ; Crossover ; Perspective (graphical) ; Evolutionary algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Guillermo Vazquez | Daniel Sauceda | Raymundo Arróyave
Source: Acta Materialia (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

Deciphering chemical ordering in High Entropy Materials: A machine learning-accelerated high-throughput cluster expansion approach Materials science ; Cluster expansion ; Cluster (spacecraft) ; Throughput ; High entropy alloys ; Statistical physics ; Thermodynamics ; Computer science ; Microstructure ; Metallurgy ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Chalcogenide Semiconductor Thin Films