jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kridsadakorn Prakinee | Narin Lawan | Aisaraphon Phintha | Surawit Visitsatthawong | P. Chitnumsub | Watcharapa Jitkaroon | Pimchai Chaiyen
Source: Angewandte Chemie International Edition (article, 2024-04-12)
Field: Chemistry — Organic Chemistry

On the Mechanisms of Hypohalous Acid Formation and Electrophilic Halogenation by Non‐Native Halogenases Enzymatic electrophilic halogenation is a mild tool for functionalization of diverse organic compounds. Only a few groups of native halogenases are capable of catalyzing such a reaction. In this study, we used a mechanism-guided strategy to discover the electrophilic halogenation activity catalyzed by non-native halogenases. As the ability to form a hypohalous acid (HOX) is key for halogenation, flavin-dependent monooxygenases/oxidases capable of forming C4a-hydroperoxyflavin (Fl<sub>C4a-OOH</sub>), such as dehalogenase, hydroxylases, luciferase and pyranose-2-oxidase (P2O), and flavin reductase capable of forming H<sub>2</sub>O<sub>2</sub> were explored for their abilities to generate HOX in situ. Transient kinetic analyses using stopped-flow spectrophotometry/fluorometry and product analysis indicate that Fl<sub>C4a-OOH</sub> in dehalogenases, selected hydroxylases and luciferases, but not in P2O can form HOX; however, the HOX generated from Fl<sub>C4a-OOH</sub> cannot halogenate their substrates. Remarkably, in situ H<sub>2</sub>O<sub>2</sub> generated by P2O can form HOI and also iodinate various compounds. Because not all enzymes capable of forming Fl<sub>C4a-OOH</sub> can react with halides to form HOX, QM/MM calculations, site-directed mutagenesis and structural analysis were carried out to elucidate the mechanism underlying HOX formation and characterize the active site environment. Our findings shed light on identifying new halogenase scaffolds besides the currently known enzymes and have invoked a new mode of chemoenzymatic halogenation. Halogenation ; Electrophile ; Chemistry ; Electrophilic addition ; Organic chemistry ; Catalysis Free Radicals and Antioxidants ; Vanadium and Halogenation Chemistry
Authors: Ye Liu | Ran Zhang | Jun Wang | Yajun Wang | Gang Lv | He Yang | Haibo Chen | Tiezhu Li | Bin Hao | Junhua Guo
Source: Fuel (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Soot particles formed by n-heptane and n-heptane/oxymethylene ether-3 in an inverse diffusion flame: A comparative analysis of chemical features Oxymethylene ether-3 (OME3) is a promising synthetic e-fuel and alternative fuel for diesel engines. In contrast to diesel fuel, the use of OME3 leads to a distinct combustion process in which soot particles are generated, which leads to changes in the chemical properties of these particles. These chemical properties are intricately connected to both the concentration and availability of potential radical sites, which, in turn, exert a substantial influence on soot oxidation during diesel particulate filter (DPF) regeneration. Hence, it is crucial to investigate the chemical features of soot samples. Nevertheless, a thorough investigation into the chemical features of n-heptane/OME3 soot samples has not been fully understood. Here, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), elemental analyser, and thermogravimetric analysis (TGA) were used to examine the impact of blending OME3 on chemical features of soot particles at various flame heights of an inverse n-heptane-OME3 flame. XPS results from C1s and O1s peaks showed that OME3 incorporation enhanced the concentrations of C = O, C–O, and O = C–O groups, especially C = O groups, with the percentage increase ranging from 11.93 % to 49.74 %. Similarly, the content of aliphatic C − H groups, along with O and H, increased with higher levels of OME3 blending. Moreover, it was discovered that blending OME3 altered the spatial distribution of hybridisation orbitals of carbon atoms, leading to a decrease in the sp2/sp3 ratio by as much as 40 % with the increase in the OME3 blending ratio. The alteration in these chemical properties due to the addition of OME3 enhanced the oxidative reactivity of soot particles, as manifested by up to a 15 % reduction in the measured activation energy. These findings are useful to optimise control strategies of DPF regeneration with the more widespread use of e-fuel. Soot ; Heptane ; Combustion ; X-ray photoelectron spectroscopy ; Analytical Chemistry (journal) ; Diesel fuel ; Diffusion flame ; Chemistry ; Fourier transform infrared spectroscopy ; Chemical engineering ; Thermogravimetric analysis ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Atmospheric chemistry and aerosols
Authors: Qinxia Wu | Xinqi Xu | Xiaokun Li | Hao Jiang | Xian Qin | Zhongzhu Hong | Xiaofeng Chen | Zhijian Yang | Xiangyu Ou | Lili Xie | Yu He | Sanyang Han | Qiushui Chen | Huanghao Yang
Source: Angewandte Chemie (article, 2024-04-18)
Field: Materials Science — Materials Chemistry

Probing Energy‐Funneling Kinetics in Nanocrystal Sublattices for Superior X‐Ray Imaging Abstract Long‐lasting radioluminescence scintillators have recently attracted substantial attention from both research and industrial communities, primarily due to their distinctive capabilities of converting and storing X‐ray energy. However, determination of energy‐conversion kinetics in these nanocrystals remains unexplored. Here we present a strategy to probe and unveil energy‐funneling kinetics in NaLuF 4 :Mn 2+ /Gd 3+ nanocrystal sublattices through Gd 3+ ‐driven microenvironment engineering and Mn 2+ ‐mediated radioluminescence profiling. Our photophysical studies reveal effective control of energy‐funneling kinetics and demonstrate the tunability of electron trap depth ranging from 0.66 to 0.96 eV, with the corresponding trap density varying between 2.38×10 5 and 1.34×10 7 cm −3 . This enables controlled release of captured electrons over durations spanning from seconds to 30 days. It allows tailorable emission wavelength within the range of 520–580 nm and fine‐tuning of thermally‐stimulated temperature between 313–403 K. We further utilize these scintillators to fabricate high‐density, large‐area scintillation screens that exhibit a 6‐fold improvement in X‐ray sensitivity, 22 lp/mm high‐resolution X‐ray imaging, and a 30‐day‐long optical memory. This enables high‐contrast imaging of injured mice through fast thermally‐stimulated radioluminescence readout. These findings offer new insights into the correlation of radioluminescence dynamics with energy‐funneling kinetics, thereby contributing to the advancement of high‐energy nanophotonic applications. Nanocrystal ; Kinetics ; Materials science ; X-ray ; Nanotechnology ; Crystallography ; Chemical physics ; Chemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Angelica Federici | Orquidea Garcia | Daniel J. Kelly | David A. Hoey
Source: Advanced Functional Materials (article, 2024-08-06)
Field: Materials Science — Biomaterials

Muticomponent Melt‐Electrowritten Vascular Graft to Mimic and Guide Regeneration of Small Diameter Blood Vessels Abstract Cardiovascular disease is a leading cause of morbidity. Current treatments include vessel substitution using autologous/synthetic vascular grafts, but these commonly fail in small diameter applications, largely due to compliance mismatch and clot formation. In this study, a multicomponent vascular graft, that takes inspiration from native vessel architecture, is developed to overcome these limitations. Melt electrowriting (MEW) is used to produce tubular scaffolds with vascular‐mimetic fiber architecture and mechanics, which is combined with a lyophilized fibrinogen matrix with tailored degradation kinetics to generate a hybrid graft. The MEW framework not only contributes to graft mechanics, but also provides contact guidance to direct cell/neotissue orientation and mimic the native tunica media. This construct is further functionalized with heparin, which in combination with the smooth extracellular matrix (ECM) surface, reduced platelet adhesion and clot formation providing a substrate for endothelization, thereby mimicking the function of the intima. Lastly, an outer electrospun layer representing the adventitia is added to improve elasticity and reduce permeability. This graft satisfies ISO implantability requirements, matches the compliance of native vessels, and reestablishes physiological flow with minimal clot formation in a preclinical model. Therefore, this graft represents an innovative off‐the‐shelf alternative to address the unmet clinical need for small‐diameter vascular grafts. Materials science ; Regeneration (biology) ; Vascular graft ; Biomedical engineering ; Cell biology ; Medicine ; Biology Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Advanced Nanomaterials in Catalysis
Authors: Jae-Mo Lihm | Samuel Poncé | Cheol-Hwan Park
Source: Physical review. B./Physical review. B (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Self-consistent electron lifetimes for electron-phonon scattering Acoustic phonons in piezoelectric materials strongly couple to electrons through a macroscopic electric field. We show that this coupling leads to a momentum-dependent divergence of the Fan-Migdal electron linewidth. We then develop a self-consistent theory for calculating electron linewidths, which not only removes this piezoelectric divergence but also considerably modifies the linewidth in nonpiezoelectric, polar materials. Our predictions await immediate experimental confirmation, and this self-consistent method should be broadly used in interpreting various experiments on the electronic properties of real materials. Electron ; Scattering ; Phonon ; Physics ; Electron scattering ; Condensed matter physics ; Atomic physics ; Nuclear physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies ; Ga2O3 and related materials
Authors: Yiming Pan | Fabio Caruso
Source: npj 2D Materials and Applications (article, 2024-07-04)
Field: Materials Science — Materials Chemistry

Strain-induced activation of chiral-phonon emission in monolayer WS2 Abstract We report a theoretical investigation of the ultrafast dynamics of electrons and phonons in strained monolayer WS 2 following photoexcitation. We show that strain substantially modifies the phase space for electron-phonon scattering, unlocking relaxation pathways that are unavailable in the pristine monolayer. In particular, strain triggers a transition between distinct dynamical regimes of the non-equilibrium lattice dynamics characterized by the emission of chiral phonons under high strain and linearly-polarized phonons under low strain. For valley-polarized electronic excitations, this mechanism can be exploited to selectively activate the emission of chiral phonons – phonons carrying a net angular momentum. Our simulations are based on state-of-the-art ab-initio methods and focus exclusively on realistic excitation and strain conditions that have already been achieved in recent experimental studies. Overall, strain emerges as a powerful tool for controlling chiral phonons emission and relaxation pathways in multivalley quantum materials. Monolayer ; Phonon ; Strain (injury) ; Materials science ; Condensed matter physics ; Chemistry ; Physics ; Chemical physics ; Crystallography ; Nanotechnology ; Biology 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications
Authors: V. Tsakaloudi | C. Beatrice | F. Fiorillo | V.T. Zaspalis
Source: Journal of Magnetism and Magnetic Materials (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Magnetic loss versus temperature and role of doping in Mn-Zn ferrites We investigate the effect of different doping schemes on the broadband magnetic losses and their temperature dependence in Mn-Zn ferrites. CaO, Nb2O5, ZrO2, and SiO2 are added with increasing proportions to TiO2-doped prefired powders and, after sintering at either 1275 °C or 1300 °C, the obtained ring samples are tested versus frequency f (DC-1 GHz) and peak polarization Jp (2 mT – 200 mT) up to T = 160 °C. Appropriately enhanced impurity contents are shown to induce further decrease of the energy loss in materials already prepared for best performance at high temperatures (140 – 160 °C). This behavior can be hardly ascribed to the impurity-related increase of the electrical resistivity brough about by extra-doping, being it rather connected to a corresponding monotonical decrease of the effective magnetic anisotropy < Keff > with T. The decreasing anisotropy makes the balance between the contributions of domain wall (dw) displacements and reversible rotations to the magnetization process evolving in favor of the latter. The energy loss correspondingly develops with frequency and peak polarization in a complex fashion, according to the specific dissipative mechanisms sustained by the spins precessing either inside the moving walls or in the bulk. A dividing line in the (Jp − f) plane is identified, which separates dominant dw- and rotation-generated losses. It moves downward (i.e. lower f) with increasing temperature, the higher T the lower the frequency at which the rotations, theoretically assessed via the Landau-Lifshitz equation, supersede the domain wall contribution. Once accomplished, however, the transition to rotations can lead, according to the theoretical model, to higher losses when moving to higher temperatures. Following the experimental trend of the complex resistivity versus frequency at different T values, the calculations and the experiments show that eddy currents start to contribute to the energy loss, in the 5 mm thick ring samples, around a few MHz, accounting for about 50 % of measured loss beyond some 50 MHz. The chief dissipative process at applicative frequencies and induction values is therefore identified, with spin damping, to which the generalized loss decomposition method can be applied. Materials science ; Doping ; Condensed matter physics ; Manganese ; Nuclear magnetic resonance ; Optoelectronics ; Metallurgy ; Physics Magnetic Properties and Synthesis of Ferrites ; Magnetic Properties and Applications ; Magneto-Optical Properties and Applications
Authors: Jiewen Zuo | Yankun Peng | Yongyu Li | Yahui Chen | Tianzhen Yin | Kuanglin Chao
Source: Food Chemistry (article, 2024-11-21)
Field: Chemistry — Analytical Chemistry

Integrating transfer learning and spectroscopy for enhanced pork spoilage assessment using correlation analysis Food spoilage ; Spectroscopy ; Chemistry ; Food science ; Environmental science ; Analytical Chemistry (journal) ; Environmental chemistry ; Biology ; Physics ; Bacteria Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Mayk Caldas Ramos | Christopher J. Collison | Andrew Dickson White
Source: arXiv (Cornell University) (review, 2024-06-26)
Field: Materials Science — Materials Chemistry

A Review of Large Language Models and Autonomous Agents in Chemistry Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science. Astrobiology ; Chemistry ; Computer science ; Physics Machine Learning in Materials Science
Authors: Takamitsu Ishiyama | Koki Nozawa | Takeshi Nishida | Takashi Suemasu | Kaoru Toko
Source: NPG Asia Materials (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films Abstract Studying the properties of thermoelectric materials needs substantial effort owing to the interplay of the trade-off relationships among the influential parameters. In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. Here, we used Bayesian optimization to improve the thermoelectric properties of multicomponent III–V materials; this domain warrants comprehensive investigation due to the need to simultaneously control multiple parameters. We designated the figure of merit ZT as the objective function to improve and search for a five-dimensional space comprising the composition of InGaAsSb thin films, dopant concentration, and film-deposition temperatures. After six Bayesian optimization cycles, ZT exhibited an approximately threefold improvement compared to its values obtained in the random initial experimental trials. Additional analysis employing Gaussian process regression elucidated that a high In composition and low substrate temperature were particularly effective at increasing ZT . The optimal substrate temperature (205 °C) demonstrated the potential for depositing InGaAsSb thermoelectric thin films onto plastic substrates. These findings not only promote the development of thermoelectric devices based on III–V semiconductors but also highlight the effectiveness of using Bayesian optimization for multicomponent materials. Materials science ; Thin film ; Semiconductor ; Crystallite ; Thermoelectric effect ; Optoelectronics ; Engineering physics ; Nanotechnology ; Metallurgy Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal Radiation and Cooling Technologies
Authors: Yongchao Lu | Hong Wang | Lanting Zhang | Ning Yu | Siqi Shi | Hang Su
Source: Scientific Data (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Unleashing the power of AI in science-key considerations for materials data preparation The release of ChatGPT has triggered global attention on artificial intelligence (AI), and AI for science is thus becoming a hot topic in the scientific community. When we think about unleashing the power of AI to accelerate scientific research, the question coming to our mind first is whether there is a continuous supply of highly available data at a sufficiently large scale. Key (lock) ; Data science ; Computer science ; Computer security Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: J. de Curtò | I. de Zarzà | Gemma Roig | Carlos T. Calafate
Source: Signals (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge. X-ray photoelectron spectroscopy ; Materials science ; Nuclear magnetic resonance ; Physics Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Hasan M. Sayeed | Trupti Mohanty | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Annotating Materials Science Text: A Semi-automated Approach for Crafting Outputs with Gemini Pro Recent advancements in large language models (LLMs) have paved the way for automated information extraction in the materials science domain. However, fine-tuning these models, crucial for effective machine learning pipelines in materials science, is hindered by a lack of pre-annotated data. Manual annotation, a laborious process, exacerbates the challenge. To address this, we introduce a tailored semi-automated annotation process, using Google’s Gemini Pro language model. Our approach focuses on two key tasks: extracting information in structured JSON format and generating abstractive summaries from materials science texts. The collaborative process, a symbiotic effort between human annotators and the LLM, driven by structured prompts and user-guided examples, enhances the annotation quality and augments the LLM’s capacity to comprehend materials science intricacies. Importantly, it streamlines human annotation efforts by leveraging the LLM’s proficient starting point. Computer science ; Annotation ; Process (computing) ; JSON ; Key (lock) ; Domain (mathematical analysis) ; Point (geometry) ; Data science ; Information retrieval ; Artificial intelligence ; World Wide Web ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques