jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raphael Brechbühler | Miloš Selaković | Philipp Scheidegger | Herbert Looser | André Kupferschmid | Stéphane Blaser | Jérémy Butet | Lukas Emmenegger | Béla Tuzson
Source: Analytical Chemistry (article, 2023-01-26)
Field: Chemistry — Spectroscopy

Rapid Detection of Volatile Organic Compounds by Switch–Scan Tuning of Vernier Quantum-Cascade Lasers Volatile organic compounds (VOCs) exhibit typically broad and mutually overlapping ro-vibrational absorption fingerprints. This complexity has so far limited the applicability of laser-based spectroscopy for VOC measurements in complex gas matrices. Here, we exploit a Vernier-type quantum-cascade laser (QCL) as an electrically tunable multiwavelength source for selective and sensitive VOC analysis. This emerging class of lasers provides access to several spectral windows by discrete Vernier tuning ("switching") and continuous coverage within these windows ("scanning"). We present a versatile driving technique that efficiently combines the two tuning mechanisms. Applied to our Vernier QCL, it enables the rapid acquisition (within 360 ms) of high-resolution spectra from six individual spectral windows, distributed over a wide range from 1063 to 1102 cm<sup>-1</sup>. Gaining access to the broad absorption envelopes of VOCs at multiple frequencies, along with their superimposed fine structure, which are especially pronounced at a reduced sample pressure, offers completely new opportunities in VOC analysis. The potential of this approach is assessed in a direct-laser-absorption setup with acetaldehyde, ethanol, and methanol as benchmark compounds with significant spectral overlaps. A measurement precision of 1-10 ppb is obtained after integration for 10 s at amount fractions below 10 ppm, and excellent linearity is found over at least 3 orders of magnitude. Combined with our dedicated spectral fitting algorithm, we demonstrate highly selective multicompound analyses with less than 3.5% relative expanded uncertainty, even in the presence of a 40× excess of an interfering compound with complete spectral overlap. Vernier scale ; Chemistry ; Laser ; Quantum cascade laser ; Cascade ; Spectroscopy ; Spectral line ; Analytical Chemistry (journal) ; Fabry–Pérot interferometer ; Optoelectronics ; Optics ; Materials science ; Chromatography Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Laser Design and Applications
Authors: Jingyi Wu | Anton Yu. Bykov | Alexey V. Krasavin | Mazhar E. Nasir | Anatoly V. Zayats
Source: Applied Physics Letters (article, 2023-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal control of polarization of light with nonlocal plasmonic anisotropic metamaterials Plasmonic nanostructures have demonstrated significant potential for engineering the intensity and polarization state of light with further opportunities to actively manipulate them by external stimulation using nonlinear effects. Plasmonic metamaterials composed of arrays of vertically oriented metallic nanorods have shown a dynamically tunable optical response based on the change of the electron temperature. The modulation of the optical properties is particularly pronounced in the epsilon-near-zero regime and can be further enhanced by nonlocal effects. Here, we experimentally study the optical properties of gold nanorod metamaterials with a strong nonlocal response under optically-induced heating, exploiting temperature dependence of the metal permittivity. Recovering the Stokes parameters of light transmitted through the metamaterial, we demonstrate the change in the polarization of the transmitted light by more than 20% for temperature changes under hundred degrees. Combined with a numerical analysis, this shows the possibility of controlling transmission and polarization state of light by using metamaterial-assisted thermal modulation. Metamaterial ; Nanorod ; Plasmon ; Materials science ; Polarization (electrochemistry) ; Optoelectronics ; Optics ; Anisotropy ; Permittivity ; Ray ; Dielectric ; Physics ; Nanotechnology ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Philipp Rüßmann | Masoud Bahari | Stefan Blügel | Björn Trauzettel
Source: Physical Review Research (article, 2023-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interorbital Cooper pairing at finite energies in Rashba surface states Multiband effects in hybrid structures provide a rich playground for unconventional superconductivity. We combine two complementary approaches based on density-functional theory (DFT) and effective low-energy model theory in order to investigate the proximity effect in a Rashba surface state in contact with an $s$-wave superconductor. We discuss these synergistic approaches and combine the effective model and DFT analysis at the example of a Au/Al heterostructure. This allows us to predict finite-energy superconducting pairing due to the interplay of the Rashba surface state of Au, and hybridization with the electronic structure of superconducting Al. We investigate the nature of the induced superconducting pairing, and we quantify its mixed singlet-triplet character. Our findings demonstrate general recipes to explore real material systems that exhibit interorbital pairing away from the Fermi energy. Pairing ; Superconductivity ; Condensed matter physics ; Physics ; Cooper pair ; Density functional theory ; Singlet state ; Heterojunction ; Surface (topology) ; Fermi surface ; Quantum mechanics ; Geometry ; Mathematics Iron-based superconductors research ; Surface and Thin Film Phenomena ; Electronic and Structural Properties of Oxides
Authors: Amit Kumar Prasad | Jakub Šebesta | Raquel Esteban-Puyuelo | Pablo Maldonado | Shaozheng Ji | Biplab Sanyal | Oscar Grånäs | Jonas Weissenrieder
Source: ACS Nano (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Nonequilibrium Phonon Dynamics and Its Impact on the Thermal Conductivity of the Benchmark Thermoelectric Material SnSe Thermoelectric materials play a vital role in the pursuit of a sustainable energy system by allowing the conversion of waste heat to electric energy. Low thermal conductivity is essential to achieving high-efficiency conversion. The conductivity depends on an interplay between the phononic and electronic properties of the nonequilibrium state. Therefore, obtaining a comprehensive understanding of nonequilibrium dynamics of the electronic and phononic subsystems as well as their interactions is key for unlocking the microscopic mechanisms that ultimately govern thermal conductivity. A benchmark material that exhibits ultralow thermal conductivity is SnSe. We study the nonequilibrium phonon dynamics induced by an excited electron population using a framework combining ultrafast electron diffuse scattering and nonequilibrium kinetic theory. This in-depth approach provides a fundamental understanding of energy transfer in the spatiotemporal domain. Our analysis explains the dynamics leading to the observed low thermal conductivity, which we attribute to a mode-dependent tendency to nonconservative phonon scattering. The results offer a penetrating perspective on energy transport in condensed matter with far-reaching implications for rational design of advanced materials with tailored thermal properties. Thermal conductivity ; Non-equilibrium thermodynamics ; Phonon ; Thermoelectric materials ; Thermoelectric effect ; Materials science ; Thermal conduction ; Phonon scattering ; Condensed matter physics ; Scattering ; Physics ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Charlotte Soneson | Vytautas Iešmantavičius | Daniel Heß | Michael Stadler | Jan Seebacher
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-07-30)
Field: Chemistry — Spectroscopy

einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data ABSTRACT We describe einprot, an R package providing easy-to-use reproducible workflows for quality control, statistical analysis and visualization of quantitative proteomics data. einprot is applicable to tabular output from MaxQuant, Proteome Discoverer and FragPipe, and a single function call generates an html report that describes the full analysis pipeline applied to the data and contains static and interactive figures and tables for further exploration. This has the potential to facilitate routine analyses as well as to provide a standardized, yet comprehensive way to communicate results to collaborators and the broader community. The source file underlying the report is also returned, giving the user full flexibility to further modify the workflow according to their needs. Workflow ; Computer science ; Pipeline (software) ; Flexibility (engineering) ; Visualization ; Statistical analysis ; Data science ; R package ; Function (biology) ; Proteome ; Data mining ; Software engineering ; Information retrieval ; Database ; Programming language ; Bioinformatics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Metabolomics and Mass Spectrometry Studies
Authors: W. Kamal | Rehab Mahmoud | Abeer Enaiet Allah | Ahmed A. Farghali | Abdalla Abdelwahab | Dalal Hussien M. Alkhalifah | Wael N. Hozzein | Manar Bahaa El Din Mohamed | Sahar Abdel Aleem Abdel Aziz
Source: Microorganisms (article, 2023-10-07)
Field: Chemistry — Inorganic Chemistry

Controlling Multi-Drug-Resistant Traits of Salmonella Obtained from Retail Poultry Shops Using Metal–Organic Framework (MOF) as a Novel Technique <i>Salmonella</i> spp. is considered one of the most important causes of food-borne illness globally. Poultry and its products are usually incriminated in its spread. Treatment with antibiotics is the first choice to deal with such cases; however, multi-drug resistance and biofilm formation have been recorded in animals and humans. This study aimed to detect the antibiotic profile of isolated traits from different sources and to find innovative alternatives, such as MOFs. A total of 350 samples were collected from randomly selected retailed poultry shops in Beni-Suef Province, Egypt. Their antimicrobial susceptibility against eight different antibiotics was tested, and multi-drug resistance was found in most of them. Surprisingly, promising results toward MOF were detected. Cu/Ni/Co-MOF (MOF3) showed superior antibacterial efficiency to Cu/Ni-MOF (MOF2) and Cu-MOF (MOF1) at <i>p</i> value ≤ 0.01. These findings highlight the tendency of <i>Salmonella</i> spp. to develop MDR to most of the antibiotics used in the field and the need to find new alternatives to overcome it, as well as confirming the ability of the environment to act as a source of human and animal affection. Salmonella ; Antibiotics ; Poultry farming ; Antibiotic resistance ; Antimicrobial ; Multi drug resistant ; Drug resistance ; Biotechnology ; Veterinary medicine ; Food science ; Biology ; Microbiology ; Medicine ; Bacteria Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities For centuries, researchers have sought out ways to connect disparate areas of knowledge. While early scholars (Galileo, da Vinci, etc.) were experts across fields, specialization has taken hold later. With the advent of Artificial Intelligence, we can now explore relationships across areas (e.g., mechanics-biology) or disparate domains (e.g., failure mechanics-art). To achieve this, we use a fine-tuned Large Language Model (LLM), here for a subset of knowledge in multiscale materials failure. The approach includes the use of a general-purpose LLM to distill question-answer pairs from raw sources followed by LLM fine-tuning. The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas. While the model has some ability to recall knowledge from training, we find that LLMs are particularly useful to extract structural insights through Ontological Knowledge Graphs. These interpretable graph structures provide explanatory insights, frameworks for new research questions, and visual representations of knowledge that also can be used in retrieval-augmented generation. Three versions of MechGPT are discussed, featuring different sizes from 13 billion to 70 billion parameters, and reaching context lengths of more than 10,000 tokens. This provides ample capacity for sophisticated retrieval augmented strategies, as well as agent-based modeling where multiple LLMs interact collaboratively and/or adversarially, the incorporation of new data from the literature or web searches, as well as multimodality. Computer science ; Context (archaeology) ; Data science ; Modalities ; Artificial intelligence ; Sociology ; Geography Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Hao Qiang | Qun Wang | Hui Niu | Zhaoqi Wang | Jianfeng Zheng
Source: Energies (article, 2023-03-22)
Field: Materials Science — Materials Chemistry

A Partial Discharge Localization Method Based on the Improved Artificial Fish Swarms Algorithm Accurate localization of partial discharge in GIS equipment remains a key focus of daily maintenance for substations, which can be achieved through advanced detection and location techniques, as well as regular maintenance and testing of the equipment. However, there is currently an issue with low accuracy in the localization algorithm. Aiming at the problems of low precision and local optimization of the swarm intelligence algorithm in partial discharge localization system of GIS equipment, this paper proposes a 3D localization algorithm based on a time difference of arrival (TDOA) model of the improved artificial fish swarm algorithm (IAFSA). By introducing the investigation behaviour of the artificial bee colony(ABC) algorithm into the artificial fish swarms algorithm (AFSA), this algorithm is more efficient to jump out of the local extremum, enhance the optimization performance, improve the global search ability and overcome the premature convergence. Furthermore, more precise positioning can be achieved with dynamic parameters. The results of the testing function show that IAFSA is significantly superior to AFSA and particle swarm optimization (PSO) in terms of positioning accuracy and stability. When applied to partial discharge localization experiments, the maximum relative positioning error is less than 2.5%. This validates that the proposed method in this paper can achieve high-precision partial discharge localization, has good engineering application value, and provides strong support for the safe operation of GIS equipment. Partial discharge ; Multilateration ; Particle swarm optimization ; Swarm behaviour ; Algorithm ; Computer science ; Convergence (economics) ; Premature convergence ; Mathematical optimization ; Engineering ; Artificial intelligence ; Mathematics ; Voltage High voltage insulation and dielectric phenomena ; Underwater Vehicles and Communication Systems ; Indoor and Outdoor Localization Technologies
Authors: Ryo Tamura | Kei Terayama | Masato Sumita | Koji Tsuda
Source: Physical Review Materials (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Ranking Pareto optimal solutions based on projection free energy Based on available datasets prepared by numerical simulations and machine learning, maps of properties for materials that have not yet been synthesized can be developed. These maps can be used to select promising materials for synthetic experiments. With a single objective function, the ranking of the optimal solutions can be simply obtained based on the values of the target property. However, applications with multiple target properties require the calculation of Pareto optimal solutions to visualize trade-offs. These solutions are generally ranked manually, selecting the weight of the multiple objectives based on prior knowledge. In this study, to provide an automated ranking of Pareto solutions, we introduced the most-isolated Pareto solution (MIPS) score, which is defined by a projection free energy. Using the MIPS ranking, it is possible to appropriately select the most isolated materials predicted in the property space. To verify the effectiveness of the proposed method, we used a database of semiconductors created by density-functional theory. Our method was able to correctly select and rank the most isolated solutions in both convex and concave two-dimensional Pareto frontiers, outperforming the most relevant outlier detection methods. We also demonstrated that our approach can be easily extended to three-dimensional property spaces. Ranking (information retrieval) ; Property (philosophy) ; Pareto principle ; Projection (relational algebra) ; Outlier ; Pareto optimal ; Rank (graph theory) ; Mathematical optimization ; Computer science ; Multi-objective optimization ; Energy (signal processing) ; Regular polygon ; Algorithm ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Andi Barbour | Yong Q. Cai | Andrei Fluerasu | Guillaume Freychet | Masafumi Fukuto | Oleg Gang | Eliot Gann | Ricarda Laasch | Ruipeng Li | B. M. Ocko | Esther H. R. Tsai | Patryk Wąsik | Lutz Wiegart | Kevin G. Yager | Lin Yang | Honghu Zhang | Yugang Zhang
Source: Synchrotron Radiation News (article, 2023-03-04)
Field: Materials Science — Materials Chemistry

X-Ray Scattering for Soft Matter Research at NSLS-II Click to increase image sizeClick to decrease image size AcknowledgmentsThis article is dedicated to Ron Pindak, a distinguished physicist, one of the pioneers and long-time leaders for synchrotron-based soft matter research, and an esteemed colleague and mentor to many of us in the community. The authors gratefully acknowledge Mikhail Zhernenkov and the efforts of current and former fellow staff and collaborators at Brookhaven National Laboratory, the National Institute of ­Standards and Technology, the Air Force Research Laboratory, and Lawrence Berkeley National Laboratory.Additional informationFundingThis article highlights research that used the CHX, CMS, IXS, LiX, SMI, and SST-1 beamlines of the National Synchrotron Light Source II and the Center for Functional Nanomaterials, which are U.S. Department of Energy (DOE) Office of Science User Facilities operated for the DOE Office of Science by Brookhaven National Laboratory (BNL) under Contract No. DE-SC0012704. The LiX beamline is part of the Center for BioMolecular Structure, primarily supported by the National Institutes of Health through a P30 Grant (P30GM133893), and by the DOE Office of Biological and Environmental Research (KP1605010). The SST-1 beamline was built and is supported by the National Institute of Standards and Technology. Part of this work was supported by the "Recherches Technologiques de Base" program of the French National Research Agency (ANR). Part of the denoising autoencoder work was supported by a BNL Laboratory Directed Research and Development (LDRD) project 20-038 "Machine Learning for Real-Time Data Fidelity, Healing, and Analysis for Coherent X-ray Synchrotron Data."n National laboratory ; Beamline ; Synchrotron ; Research council ; Library science ; Basic research ; Research center ; Research program ; Physics ; Engineering physics ; Engineering ; Political science ; Nuclear physics ; Computer science ; Optics ; Beam (structure) Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Yutao Zhang | Xilin Yang | Chengwei Zhang | Zhi‐Hui Zhang | An Su | Yuanbin She
Source: Processes (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Exploring Bayesian Optimization for Photocatalytic Reduction of CO2 The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO2 reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO2, partial pressure of H2O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH4. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction. Bayesian optimization ; Photocatalysis ; Design of experiments ; Reduction (mathematics) ; Bayesian probability ; Computer science ; Gaussian ; Catalysis ; Biological system ; Materials science ; Chemistry ; Mathematics ; Statistics ; Computational chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts