jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tiago L. P. Galvão | Gerard Novell-Leruth | Alena Kuznetsova | João Tedim | José R. B. Gomes
Source: The Journal of Physical Chemistry C (article, 2020-02-17)
Field: Materials Science — Materials Chemistry

Elucidating Structure–Property Relationships in Aluminum Alloy Corrosion Inhibitors by Machine Learning Organic corrosion inhibitors are playing a crucial role to substitute traditional protective technologies, which have acute toxicity problems associated. However, why some organic compounds inhibit corrosion and others do not, is still not well understood. Therefore, we tested different machine learning (ML) methods to distinguish efficient corrosion inhibitors for aluminum alloys commonly used in aeronautical applications. In this work, we have obtained information that can greatly contribute to automate the search for new and more efficient protective solutions in the future: i) a ML algorithm was selected that is able to classify correctly efficient inhibitors (i.e., with more than 50 % efficiency) and non-inhibitors (i.e. with lower-equal than 50 % efficiency), even when information about different alloys at different pHs is included in the same dataset, which can significantly increase the information available to train the model; ii) new descriptors related to the self-association of the molecules were evaluated, but improvements to the predictive power of the models are limited; iii) average differences concerning the descriptors in this work were identified for inhibitors and non-inhibitors, having the potential to serve as guidelines to select potentially inhibitive molecular systems. This work demonstrates that ML can significantly accelerate research in the field by serving as a tool to perform an initial virtual screen of the molecules. Corrosion ; Virtual screening ; Work (physics) ; Set (abstract data type) ; Computer science ; Quantitative structure–activity relationship ; Machine learning ; Training set ; Property (philosophy) ; Molecular descriptor ; Artificial intelligence ; Data mining ; Materials science ; Chemistry ; Engineering ; Computational chemistry ; Mechanical engineering ; Metallurgy ; Molecular dynamics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Byungjai Kim | Michael Schär | HyunWook Park | Hye‐Young Heo
Source: NeuroImage (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qualitative MT ratio (MTR) metric commonly used in conventional MTC imaging is limited in the assessment of quantitative semisolid macromolecular proton exchange rates and concentrations. In addition, CEST signals measured by MTR asymmetry analysis are unavoidably contaminated by upfield nuclear Overhauser enhancement (NOE) signals of mobile and semisolid macromolecules. To address these issues, we developed an MTC-MR fingerprinting (MTC-MRF) technique to quantify tissue parameters, which further allows an estimation of accurate MTC signals at a certain CEST frequency offset. A pseudorandomized RF saturation scheme was used to generate unique MTC signal evolutions for different tissues and a supervised deep neural network was designed to extract tissue properties from measured MTC-MRF signals. Through detailed Bloch equation-based digital phantom and in vivo studies, we demonstrated that the MTC-MRF can quantify MTC characteristics with high accuracy and computational efficiency, compared to a conventional Bloch equation fitting approach, and provide baseline reference signals for CEST and NOE imaging. For validation, MTC-MRF images were synthesized using the tissue parameters estimated from the deep-learning method and compared with experimentally acquired MTC-MRF images as the reference standard. The proposed MTC-MRF framework can provide quantitative 3D MTC, CEST, and NOE imaging of the human brain within a clinically acceptable scan time. Magnetization transfer ; Bloch equations ; Nuclear magnetic resonance ; Artificial intelligence ; Magnetic resonance imaging ; Transfer of learning ; Imaging phantom ; Flip angle ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Nuclear medicine ; Biomedical engineering ; Physics ; Medicine ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Junjie Hou | Hai Lin | Weilin Xu | Yuze Tian | You Wang | Xintong Shi | Feng Deng | Lijie Chen
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM’s design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces. Metamaterial ; Computer science ; Inverse ; Artificial intelligence ; Materials science ; Optoelectronics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Matthew J. Turner | Nicholas Langellier | Rachel Bainbridge | Dan Walters | Srujan Meesala | Thomas M. Babinec | Pauli Kehayias | Amir Yacoby | Evelyn Hu | Marko Lončar | Ronald L. Walsworth | Edlyn V. Levine
Source: Physical Review Applied (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

Magnetic Field Fingerprinting of Integrated-Circuit Activity with a Quantum Diamond Microscope Current density distributions in active integrated circuits result in patterns of magnetic fields that contain structural and functional information about the integrated circuit. Magnetic fields pass through standard materials used by the semiconductor industry and provide a powerful means to fingerprint integrated-circuit activity for security and failure analysis applications. Here, we demonstrate high spatial resolution, wide field-of-view, vector magnetic field imaging of static magnetic field emanations from an integrated circuit in different active states using a quantum diamond microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (N-$V$) quantum defects near the surface of a transparent diamond substrate placed on the integrated circuit to image magnetic fields. We show that QDM imaging achieves a resolution of approximately $10\phantom{\rule{0.1em}{0ex}}\ensuremath{\mu}\mathrm{m}$ simultaneously for all three vector magnetic field components over the $3.7\ifmmode\times\else\texttimes\fi{}3.7\phantom{\rule{0.2em}{0ex}}{\mathrm{mm}}^{2}$ field of view of the diamond. We study activity arising from spatially dependent current flow in both intact and decapsulated field-programmable gate arrays, and find that QDM images can determine preprogrammed integrated-circuit active states with high fidelity using machine learning classification methods. Magnetic field ; Diamond ; Integrated circuit ; Microscope ; Substrate (aquarium) ; Quantum ; Quantum dot ; Current (fluid) ; Electronic circuit Diamond and Carbon-based Materials Research ; Advanced Surface Polishing Techniques ; Electronic and Structural Properties of Oxides
Authors: Liliana Marinescu | Denisa Ficai | Ovidiu Oprea | Alexandru Marin | Anton Ficai | Ecaterina Andronescu | Alina Maria Holban
Source: Journal of Nanomaterials (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Optimized Synthesis Approaches of Metal Nanoparticles with Antimicrobial Applications According to the literature data, metal nanoparticles can be synthesized by various methods but the chemical reduction methods are mostly applied getting more advantageous comparing with the other methods. This work emphasizes also that the combination of synthetized methods could lead to the spectacular results depending on the application. Among the chemical methods, this work analyzed the polyol method, radiolytic process, microemulsion method, solvo-thermal method, microwave-assisted synthesis, and electrochemical synthesis. It also presents the main application of metal nanoparticles in biomedical fields, empathizing on their antimicrobial potential. Materials science ; Nanoparticle ; Nanotechnology ; Metal ; Microemulsion ; Polyol ; Chemical engineering ; Metallurgy Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Graphene and Nanomaterials Applications
Authors: Fady Nahra | David J. Nelson | Steven P. Nolan
Source: Trends in Chemistry (article, 2020-11-05)
Field: Chemistry — Organic Chemistry

Design Concepts for N-Heterocyclic Carbene Ligands Supramolecular chemistry ; Carbene ; Organometallic chemistry ; Chemistry ; Steric effects ; Combinatorial chemistry ; Supramolecular catalysis ; Design elements and principles ; Reactivity (psychology) ; Catalysis ; Nanotechnology ; Organic chemistry ; Materials science ; Computer science ; Molecule N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Synthetic Organic Chemistry Methods
Authors: Mahesh R. Gadekar | M. Mansoor Ahammed
Source: Applied Water Science (article, 2020-06-11)
Field: Chemistry — Analytical Chemistry

Use of water treatment residuals for colour removal from real textile dye wastewater Abstract The use of readily available water treatment residuals (WTR) as a low-cost material for removal of colour from real textile wastewater was investigated. WTR was used in three forms, namely in raw wet form as a coagulant, in the dried form as an adsorbent and as a filtration media in column tests. The results showed a maximum colour removal of 55 and 36% by coagulation and adsorption, respectively, and the corresponding COD removals were 35 and 37%. Coagulation and batch sorption tests showed the effect of initial pH on the colour removal, and maximum colour removal was obtained at an initial pH of 3.0. Long-duration continuous-flow column test using WTR as a filtration/sorption media showed that a maximum colour removal of 60% can be achieved. In column studies, complete exhaustion of the media occurred at 180 and 120 bed volumes, respectively, for initial pHs of 3.0 and 6.2. The study thus shows the potential of WTR for primary treatment of real textile dye wastewater. Sorption ; Wastewater ; Filtration (mathematics) ; Adsorption ; Chemistry ; Coagulation ; Chromatography ; Pulp and paper industry ; Water treatment ; Textile ; Waste management ; Environmental engineering ; Environmental science ; Materials science ; Mathematics ; Composite material ; Organic chemistry Dye analysis and toxicity ; Dyeing and Modifying Textile Fibers ; Enzyme-mediated dye degradation
Authors: Kyowon Jeong | Jihyung Kim | Manasi Gaikwad | Siti Nurul Hidayah | Laura Heikaus | Hartmut Schlüter | Oliver Kohlbacher
Source: Cell Systems (article, 2020-02-01)
Field: Chemistry — Spectroscopy

FLASHDeconv: Ultrafast, High-Quality Feature Deconvolution for Top-Down Proteomics Top-down mass spectrometry (TD-MS)-based proteomics analyzes intact proteoforms and thus preserves information about individual protein species. The MS signal of these high-mass analytes is complex and challenges the accurate determination of proteoform masses. Fast and accurate feature deconvolution (i.e., the determination of intact proteoform masses) is, therefore, an essential step for TD data analysis. Here, we present FLASHDeconv, an algorithm achieving higher deconvolution quality, with an execution speed two orders of magnitude faster than existing approaches. FLASHDeconv transforms peak positions (m/z) within spectra into log m/z space. This simple transformation turns the deconvolution problem into a search for constant patterns, thereby greatly accelerating the process. In both simple and complex samples, FLASHDeconv reports more genuine feature masses and substantially fewer artifacts than other existing methods. FLASHDeconv is freely available for download here: https://www.openms.org/flashdeconv/. A record of this paper's Transparent Peer Review process is included in the Supplemental Information. Deconvolution ; Feature (linguistics) ; Proteomics ; Quality (philosophy) ; Computer science ; Computational biology ; Biological system ; Biology ; Algorithm ; Physics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Mass Spectrometry Techniques and Applications
Authors: Sukunya Ross | Ren-Siang Wu | Shih‐Chun Wei | Gareth M. Ross | Huan‐Tsung Chang
Source: Journal of Food and Drug Analysis (review, 2020-12-02)
Field: Materials Science — Materials Chemistry

The analytical and biomedical applications of carbon dots and their future theranostic potential: A review In recent years, carbon dots (C-dots) have gained appreciable interest owing to their unique optical properties, including tunable fluorescence, stability against photobleaching and photoblinking, and strong fluorescence. Simple and low-cost hydrothermal and electrochemical approaches have been widely used in the preparation of biocompatible and high-quality C-dots. Various C-dots have been used for the quantitation of small analytes, mostly based on analyte induced fluorescence quenching. Depending on the nature of precursors, synthetic conditions (such as reaction temperature and time), and surface conjugation, multi-function C-dots can be prepared and used in diagnostics and therapeutics. Their strong fluorescence and photostability, enables use in cell imaging. Their biological activity from the surface residues and capability of generating reactive oxygen species, have allowed many C-dots to become candidates as antibacterial and anticancer reagents. After suitable conjugation, biocompatible and fluorescent C-dots can be used for diagnostics and therapeutics, thus, showing their great potential in the area of theranostics. Nanotechnology ; Analyte ; Biocompatible material ; Photobleaching ; Fluorescence ; Quenching (fluorescence) ; Materials science ; Quantum dot ; Biosensor ; Surface modification ; Chemistry ; Chromatography ; Biomedical engineering Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Shikai Xian | Yuhan Lin | Hao Wang | Jing Li
Source: Small (review, 2020-11-03)
Field: Chemistry — Inorganic Chemistry

Calcium‐Based Metal–Organic Frameworks and Their Potential Applications Metal-organic frameworks (MOFs) built on calcium metal (Ca-MOFs) represent a unique subclass of MOFs featuring high stability, low toxicity, and relatively low density. Ca-MOFs show considerable potential for molecular separations, electronic, magnetic, and biomedical applications, although they are not investigated as extensively as transition metal-based MOFs. Compared to MOFs made of other groups of metals, Ca-MOFs may be particularly advantageous for certain applications such as adsorption and storage of light molecules because of their gravimetric benefit, and drug delivery due to their high biocompatibility. This review intends to provide an overview on the recent development of Ca-MOFs, including their synthesis, crystal structures, important properties, and related applications. Various synthetic methods and techniques, types of building blocks, structure and porosity features, selected physical properties, and potential uses will be discussed and summarized. Representative examples will be illustrated for each type of important applications with a focus on their structure-property relations. Metal-organic framework ; Nanotechnology ; Materials science ; Biocompatibility ; Gravimetric analysis ; Drug delivery ; Porosity ; Adsorption ; Chemistry ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization
Authors: He Li | João Conde | Ana Guerreiro | Gonçalo J. L. Bernardes
Source: Angewandte Chemie International Edition (article, 2020-06-18)
Field: Chemistry — Organic Chemistry

Tetrazine Carbon Nanotubes for Pretargeted In Vivo “Click‐to‐Release” Bioorthogonal Tumour Imaging The bioorthogonal inverse-electron-demand Diels-Alder (IEDDA) cleavage reaction between tetrazine and trans-cyclooctene (TCO) is a powerful way to control the release of bioactive agents and imaging probes. In this study, a pretargeted activation strategy using single-walled carbon nanotubes (SWCNTs) that bear tetrazines (TZ@SWCNTs) and a TCO-caged molecule was used to deliver active effector molecules. To optimize a turn-on signal by using in vivo fluorescence imaging, we developed a new fluorogenic near-infrared probe that can be activated by bioorthogonal chemistry and image tumours in mice by caging hemicyanine with TCO (tHCA). With our pretargeting strategy, we have shown selective doxorubicin prodrug activation and instantaneous fluorescence imaging in living cells. By combining a tHCA probe and a pretargeted bioorthogonal approach, real-time, non-invasive tumour visualization with a high target-to-background ratio was achieved in a xenograft mice tumour model. The combined advantages of enhanced stability, kinetics and biocompatibility, and the superior pharmacokinetics of tetrazine-functionalised SWCNTs could allow application of targeted bioorthogonal decaging approaches with minimal off-site activation of fluorophore/drug. Bioorthogonal chemistry ; Tetrazine ; Pretargeting ; Fluorophore ; Chemistry ; Nanoprobe ; Click chemistry ; In vivo ; Fluorescence ; Combinatorial chemistry ; Prodrug ; Molecular imaging ; Nanotechnology ; Biophysics ; Fluorescence-lifetime imaging microscopy ; Materials science ; Biochemistry ; Nanoparticle ; Organic chemistry Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: Mengqi Sun | Xiaoqi Fu | Kexun Chen | Hui Wang
Source: ACS Applied Materials & Interfaces (article, 2020-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-Plasmonic Gold@Copper Sulfide Core–Shell Nanoparticles: Phase-Selective Synthesis and Multimodal Photothermal and Photocatalytic Behaviors Although the intriguing plasmonic properties of noble metal nanoparticles originate from the collective oscillations of free electrons in the conduction band, nanoparticles of doped semiconductors may also exhibit metal-like, plasmonic features that are dictated by the resonantly excited free hole oscillations in the valence band. Here, we combine Au, a representative free electron metal, with copper sulfides, a class of plasmonic <i>p</i>-type semiconductors, in a core-shell nanoparticle geometry to construct dual-plasmonic hetero-nanostructures displaying unique multiplex optical characteristics dominated by plasmonic hole oscillations in the semiconductor shells, plasmonic electron oscillations in the metallic cores, and interband electronic transitions from the valence to conduction bands. Through deliberately designed colloidal synthesis, we are able to selectively grow nanoshells comprising copper sulfides of specifically targeted crystalline phases and Cu/S stoichiometries, such as covellite (CuS), digenite (Cu<sub>1.8</sub>S), and nonstoichiometric Cu<sub>2-<i>x</i></sub>S, on the surfaces of Au nanoparticle cores. Our synthetic approach enables us not only to finely control the core and shell dimensions but also to systematically adjust the free hole concentrations in the semiconductor shells, which forms the keystone for the fine tuning of multiple optical resonance modes supported by these hybrid hetero-nanostructures. The dual-plasmonic Au@copper sulfide core-shell nanoparticles exhibit unique multimodal photothermal and photocatalytic behaviors upon selective photoexcitations of different optical transitions at their characteristic resonant frequencies, allowing us to quantitatively evaluate and rigorously compare the intrinsic photothermal and photocatalytic efficacies of multiple types of hot charge carriers, all photoexcited in the same hybrid nanoparticles but with distinct photophysical origins, excited-state lifetimes, energy distributions, and transfer pathways. Materials science ; Photothermal therapy ; Copper sulfide ; Plasmon ; Copper ; Photocatalysis ; Nanoparticle ; Photothermal effect ; Colloidal gold ; Nanotechnology ; Cadmium sulfide ; Sulfide ; Plasmonic nanoparticles ; Phase (matter) ; Optoelectronics ; Catalysis ; Metallurgy ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Steven M. Maley | Doo‐Hyun Kwon | Nick E. Rollins | Johnathan C. Stanley | Orson L. Sydora | Steven M. Bischof | Daniel H. Ess
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene. Selectivity ; Catalysis ; Olefin fiber ; Transition state ; Phosphine ; Chemistry ; Transition metal ; Computational chemistry ; Materials science ; Computer science ; Biological system ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions