jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ana Rita Oliveira | Cristiano Mota | Guilherme Vilela‐Alves | Rita R. Manuel | Neide Pedrosa | Vincent Fourmond | Kateryna Klymanska | Christophe Léger | Bruno Guigliarelli | Maria João Romão | Inês A. C. Pereira
Source: Nature Chemical Biology (article, 2023-11-20)
Field: Chemistry — Inorganic Chemistry

An allosteric redox switch involved in oxygen protection in a CO2 reductase Allosteric regulation ; Redox ; Chemistry ; Oxygen ; Reactive oxygen species ; Cell biology ; Biochemistry ; Enzyme ; Biology ; Inorganic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Metalloenzymes and iron-sulfur proteins ; CO2 Reduction Techniques and Catalysts
Authors: Yang He | Junyan Zhang | Felipe Polo‐Garzon | Zili Wu
Source: The Journal of Physical Chemistry Letters (review, 2023-01-10)
Field: Materials Science — Materials Chemistry

Adsorbate-Induced Strong Metal–Support Interactions: Implications for Catalyst Design Since the discovery of strong metal-support interactions (SMSIs) over supported metal catalysts in the 1970s, researchers have studied ways to harness this type of catalyst reconstruction to achieve enhanced stability of metal particles against sintering and to create catalytic sites with novel electronic and bonding properties. The motivation to elucidate performance-structure relationships in catalytic transformations has led researchers to take a closer look into catalytic surfaces under reaction conditions rather than a postreaction analysis. These investigations of operating catalysts have made it clear that SMSIs are more common than initially thought. Recent reports show how various adsorbed species, rather than traditional H<sub>2</sub>/O<sub>2</sub> treatment, can promote SMSI in various catalytic systems, a phenomenon named adsorbate-induced SMSI (A-SMSI). Researching the occurrence of A-SMSI has allowed fundamental understanding of catalyst stability, catalytic rates, and product selectivity. The present Perspective discusses the state-of-the-art regarding A-SMSI, the current challenges, and the opportunities ahead in heterogeneous catalysis. Catalysis ; Sintering ; Nanotechnology ; Metal ; Materials science ; Adsorption ; Heterogeneous catalysis ; Chemical engineering ; Chemistry ; Chemical physics ; Physical chemistry ; Organic chemistry ; Engineering ; Composite material ; Metallurgy Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Amir Khajavinia | Anas El‐Aneed
Source: Analytical Chemistry (review, 2023-01-10)
Field: Materials Science — Materials Chemistry

Carbon-Based Nanoparticles and Their Surface-Modified Counterparts as MALDI Matrices ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTCarbon-Based Nanoparticles and Their Surface-Modified Counterparts as MALDI MatricesAmir KhajaviniaAmir KhajaviniaCollege of Pharmacy and Nutrition, Drug Discovery and Development Research Group, University of Saskatchewan, Saskatoon, SK S7N 5E5, CanadaMore by Amir KhajaviniaView Biographyhttps://orcid.org/0000-0002-1170-9213 and Anas El-Aneed*Anas El-AneedCollege of Pharmacy and Nutrition, Drug Discovery and Development Research Group, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada*Tel.: +1 3069662013. Email: [email protected]More by Anas El-AneedView Biographyhttps://orcid.org/0000-0003-1060-3609Cite this: Anal. Chem. 2023, 95, 1, 100–114Publication Date (Web):January 10, 2023Publication History Received14 October 2022Published online10 January 2023Published inissue 10 January 2023https://pubs.acs.org/doi/10.1021/acs.analchem.2c04537https://doi.org/10.1021/acs.analchem.2c04537review-articleACS PublicationsCopyright © 2023 American Chemical SocietyRequest reuse permissionsArticle Views1491Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Carbon nanomaterials,Carbon nanotubes,Mass spectrometry,Nanoparticles,Two dimensional materials Get e-Alerts Altmetrics ; Citation ; Pharmacy ; Social media ; Library science ; Computer science ; World Wide Web ; Medicine ; Family medicine Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Graphene and Nanomaterials Applications
Authors: Petr Grigorev | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | James R. Kermode | Thomas D. Swinburne
Source: Acta Materialia (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods Ab initio ; Materials science ; Dislocation ; Vacancy defect ; Extrapolation ; Ab initio quantum chemistry methods ; Helium ; Context (archaeology) ; Condensed matter physics ; Molecular physics ; Atomic physics ; Physics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Florian M. Dietrich | Xavier R. Advincula | Gianpaolo Gobbo | Michael A. Bellucci | Matteo Salvalaglio
Source: Journal of Chemical Theory and Computation (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Machine Learning Nucleation Collective Variables with Graph Neural Networks The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs. Nucleation ; Computer science ; Metadynamics ; Graph ; Process (computing) ; Biological system ; Statistical physics ; Transferability ; Artificial intelligence ; Machine learning ; Chemistry ; Molecular dynamics ; Theoretical computer science ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Material Dynamics and Properties
Authors: Xianzhi Huang | Ling Xia | Gongke Li
Source: Chemosensors (article, 2023-03-01)
Field: Chemistry — Analytical Chemistry

Recent Progress of Molecularly Imprinted Optical Sensors Molecularly imprinted polymers (MIPs) have been widely utilized in the field of sensing due to their specific and high affinity towards target molecules. Combining the selective preconcentration capability of MIPs and the rapid quantitation merit of optical analytical techniques, MIP optical sensors have been applied to the analysis of various kinds of samples and have received considerable attention in recent years. In this review, we overviewed the progress of MIP sensors in combination with various optical detection methods, including fluorescence, surface plasmon resonance, Raman scattering, and chemiluminescence. The construction, characterization, working principle, and application of four kinds of MIP optical sensors are covered in detail. Finally, the opportunities and challenges currently encountered by MIP optical sensors are summarized. Molecularly imprinted polymer ; Surface plasmon resonance ; Materials science ; Nanotechnology ; Raman scattering ; Optical sensing ; Chemiluminescence ; Characterization (materials science) ; Figure of merit ; Computer science ; Raman spectroscopy ; Optoelectronics ; Chemistry ; Optics ; Chromatography ; Nanoparticle ; Selectivity ; Physics Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Molecular Sensors and Ion Detection
Authors: Janith Weerasinghe | Karthika Prasad | Joice Mathew | Eduardo Trifoni | Oleg Baranov | Igor Levchenko | Kateryna Bazaka
Source: Nanomaterials (review, 2023-05-30)
Field: Materials Science — Materials Chemistry

Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites Recent advancements in space technology and reduced launching cost led companies, defence and government organisations to turn their attention to low Earth orbit (LEO) and very low Earth orbit (VLEO) satellites, for they offer significant advantages over other types of spacecraft and present an attractive solution for observation, communication and other tasks. However, keeping satellites in LEO and VLEO presents a unique set of challenges, in addition to those typically associated with exposure to space environment such as damage from space debris, thermal fluctuations, radiation and thermal management in vacuum. The structural and functional elements of LEO and especially VLEO satellites are significantly affected by residual atmosphere and, in particular, atomic oxygen (<i>AO</i>). At VLEO, the remaining atmosphere is dense enough to create significant drag and quicky de-orbit satellites; thus, thrusters are needed to keep them on a stable orbit. Atomic oxygen-induced material erosion is another key challenge to overcome during the design phase of LEO and VLEO spacecraft. This review covered the corrosion interactions between the satellites and the low orbit environment, and how it can be minimised through the use of carbon-based nanomaterials and their composites. The review also discussed key mechanisms and challenges underpinning material design and fabrication, and it outlined the current research in this area. Spacecraft ; Low earth orbit ; Aerospace ; Aerospace engineering ; Orbit (dynamics) ; Space environment ; Space debris ; Atmosphere (unit) ; Astrobiology ; Materials science ; Satellite ; Computer science ; Systems engineering ; Remote sensing ; Engineering ; Physics ; Meteorology ; Astronomy ; Geology Silicone and Siloxane Chemistry ; Energetic Materials and Combustion ; Diamond and Carbon-based Materials Research
Authors: Yanying Zhang | Yuanzhong Wang
Source: Food Chemistry X (review, 2023-09-03)
Field: Chemistry — Analytical Chemistry

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated. Quality (philosophy) ; Computer science ; Artificial intelligence ; Biotechnology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Metabolomics and Mass Spectrometry Studies
Authors: Yaxin Li | Hongzhi Lu | Shoufang Xu
Source: The Analyst (review, 2023-11-24)
Field: Materials Science — Materials Chemistry

The construction of dual-emissive ratiometric fluorescent probes based on fluorescent nanoparticles for the detection of metal ions and small molecules With the rapid development of fluorescent nanoparticles (FNPs), such as CDs, QDs, and MOFs, the construction of FNP-based probes has played a key role in improving chemical sensors. Ratiometric fluorescent probes exhibit distinct advantages, such as resistance to environmental interference and achieving visualization. Thus, FNP-based dual-emission ratiometric fluorescent probes (DRFPs) have rapidly developed in the field of metal ion and small molecule detection in the past few years. In this review, firstly we introduce the fluorescence sensing mechanisms; then, we focus on the strategies for the fabrication of DRFPs, including hybrid FNPs, single FNPs with intrinsic dual emission and target-induced new emission, and DRFPs based on auxiliary nanoparticles. In the section on hybrid FNPs, methods to assemble two types of FNPs, such as chemical bonding, electrostatic interaction, core satellite or core-shell structures, coordination, and encapsulation, are introduced. In the section on single FNPs with intrinsic dual emission, methods for the design of dual-emission CDs, QDs, and MOFs are discussed. Regarding target-induced new emission, sensitization, coordination, hydrogen bonding, and chemical reaction induced new emissions are discussed. Furthermore, in the section on DRFPs based on auxiliary nanoparticles, auxiliary nanomaterials with the inner filter effect and enzyme mimicking activity are discussed. Finally, the existing challenges and an outlook on the future of DRFP are presented. We sincerely hope that this review will contribute to the quick understanding and exploration of DRFPs by researchers. Fluorescence ; Metal ions in aqueous solution ; Chemistry ; Nanoparticle ; Molecule ; Metal ; Photochemistry ; Dual (grammatical number) ; Ion ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Optics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques
Authors: Sambit Das | Bikash Kanungo | Vishal Subramanian | Gourab Panigrahi | Phani Motamarri | David Rogers | Paul M. Zimmerman | Vikram Gavini
Source:  (article, 2023-11-11)
Field: Materials Science — Materials Chemistry

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer. Ab initio ; Density functional theory ; Quantum ; Ab initio quantum chemistry methods ; Solver ; Computational science ; Statistical physics ; Computer science ; Computational physics ; Materials science ; Quantum mechanics ; Physics ; Mathematics ; Mathematical optimization ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Physics of Superconductivity and Magnetism
Authors: Jiawei Zhan | Marco Govoni | Giulia Galli
Source: Journal of Chemical Theory and Computation (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Nonempirical Range-Separated Hybrid Functional with Spatially Dependent Screened Exchange Electronic structure calculations based on density functional theory (DFT) have successfully predicted numerous ground-state properties of a variety of molecules and materials. However, exchange and correlation functionals currently used in the literature, including semilocal and hybrid functionals, are often inaccurate to describe the electronic properties of heterogeneous solids, especially systems composed of building blocks with large dielectric mismatch. Here, we present a dielectric-dependent range-separated hybrid functional, screened-exchange range-separated hybrid (SE-RSH), for the investigation of heterogeneous materials. We define a spatially dependent fraction of exact exchange inspired by the static Coulomb-hole and screened-exchange (COHSEX) approximation used in many-body perturbation theory, and we show that the proposed functional accurately predicts the electronic structure of several nonmetallic interfaces, three- and two-dimensional, pristine, and defective solids and nanoparticles. Hybrid functional ; Density functional theory ; Coulomb ; Dielectric ; Range (aeronautics) ; Perturbation theory (quantum mechanics) ; Statistical physics ; Electronic structure ; Chemical physics ; Materials science ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Electron Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications ; Physics of Superconductivity and Magnetism
Authors: Juniper Foxley | Kenneth L. Knappenberger
Source: Annual Review of Physical Chemistry (review, 2023-01-25)
Field: Materials Science — Materials Chemistry

Magneto-Optical Properties of Noble Metal Nanostructures The magneto-optical signatures of colloidal noble metal nanostructures, spanning both discrete nanoclusters (<2 nm) and plasmonic nanoparticles (>2 nm), exhibit rich structure-property correlations, impacting applications including photonic integrated circuits, light modulation, applied spectroscopy, and more. For nanoclusters, electron doping and single-atom substitution modify both the intensity of the magneto-optical response and the degree of transient spin polarization. Nanoparticle size and morphology also modulate the magnitude and polarity of plasmon-mediated magneto-optical signals. This intimate interplay between nanostructure and magneto-optical properties becomes especially apparent in magnetic circular dichroism (MCD) and magnetic circular photoluminescence (MCPL) spectroscopic data. Whereas MCD spectroscopy informs on a metal nanostructure's steady-state extinction properties, its MCPL counterpart is sensitive to electronic spin and orbital angular momenta of transiently excited states. This review describes the size- and structure-dependent magneto-optical properties of nanoscale metals, emphasizing the increasingly important role of MCPL in understanding transient spin properties and dynamics. Nanoclusters ; Magnetic circular dichroism ; Materials science ; Photoluminescence ; Plasmon ; Nanostructure ; Spectroscopy ; Magnetism ; Circular polarization ; Photonics ; Excited state ; Condensed matter physics ; Optoelectronics ; Chemical physics ; Nanotechnology ; Optics ; Atomic physics ; Physics ; Spectral line Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Melih Yilmaz | William E. Fondrie | Wout Bittremieux | Carlo Melendez | Rowan Nelson | Varun Ananth | Sewoong Oh | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-01-03)
Field: Chemistry — Spectroscopy

Sequence-to-sequence translation from mass spectra to peptides with a transformer model Abstract A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequence databases are widely used and effective for well-characterized model organisms, such methods cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to the acquired tandem mass spectra without prior information— de novo peptide sequencing—is valuable for gaining biological insights for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this de novo sequencing problem, it remains an outstanding challenge, in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo’s superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome. Computer science ; Computational biology ; Tandem mass spectrometry ; Leverage (statistics) ; Proteogenomics ; Peptide ; Peptide sequence ; Metaproteomics ; Proteome ; Artificial intelligence ; Sequence (biology) ; Transformer ; Proteomics ; Mass spectrometry ; Biology ; Chemistry ; Bioinformatics ; Genomics ; Biochemistry ; Genome ; Engineering ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; RNA and protein synthesis mechanisms