jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chuanyang Li | Changhong Zhang | Jinzhuang Lv | Fangwei Liang | Zuodong Liang | Xianhao Fan | Uwe Riechert | Zhen Li | Peng Liu | Jianyi Xue | Cheng Pan | Geng Chen | Lei Zhang | Zhe‐Ming Wang | Wu Lu | Hucheng Liang | Zijun Pan | Weijian Zhuang | Giovanni Mazzanti | Davide Fabiani | Bo Liu | Shaohua Cao | Jianying Zhong | Yuan Deng | Zhenle Nan | Jingen Tang | Jinliang He
Source: iEnergy (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

China's 10-year progress in DC gas-insulated equipment: From basic research to industry perspective The construction of the future energy structure of China under the 2050 carbon-neutral vision requires compact direct current (DC) gas-insulation equipment as important nodes and solutions to support electric power transmission and distribution of long-distance and large-capacity. This paper reviews China's 10-year progress in DC gas-insulated equipment. Important progresses in basic research and industry perspective are presented, with related scientific issues and technical bottlenecks being discussed. The progress in DC gas-insulated equipment worldwide (Europe, Japan, America) is also reported briefly. Perspective (graphical) ; China ; Gas industry ; Engineering ; Political science ; Computer science ; Waste management ; Natural gas High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; High-Voltage Power Transmission Systems
Authors: Yimian Chen | Shuize Wang | Jie Xiong | Guilin Wu | Junheng Gao | Yuan Wu | Guoqiang Ma | Hong‐Hui Wu | Xinping Mao
Source: Journal of Material Science and Technology (article, 2022-07-06)
Field: Materials Science — Metals and Alloys

Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning Charpy impact test ; Alloy ; Toughness ; Materials science ; Work (physics) ; Symbolic regression ; Composition (language) ; Genetic programming ; Metallurgy ; Machine learning ; Mechanical engineering ; Computer science ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Kamal Choudhary | Taner Yildirim | Daniel W. Siderius | A. Gilad Kusne | Austin McDannald | Diana L. Ortiz‐Montalvo
Source: Computational Materials Science (article, 2022-04-14)
Field: Chemistry — Inorganic Chemistry

Graph neural network predictions of metal organic framework CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e488" altimg="si38.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> adsorption properties Adsorption ; Artificial neural network ; Metal-organic framework ; Computer science ; Limiting ; Graph ; Materials science ; Algorithm ; Thermodynamics ; Artificial intelligence ; Physical chemistry ; Chemistry ; Physics ; Theoretical computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Tariq Bashir | Sara Adeeba Ismail | Yuheng Song | Rana Muhammad Irfan | Shiqi Yang | Shaowen Zhou | Zhao Jian-qing | Lijun Gao
Source: Energy Materials (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

A review of the energy storage aspects of chemical elements for lithium-ion based batteries Energy storage devices such as batteries hold great importance for society, owing to their high energy density, environmental benignity and low cost. However, critical issues related to their performance and safety still need to be resolved. The periodic table of elements is pivotal to chemistry, physics, biology and engineering and represents a remarkable scientific breakthrough that sheds light on the fundamental laws of nature. Here, we provide an overview of the role of the most prominent elements, including s-block, p-block, transition and inner-transition metals, as electrode materials for lithium-ion battery systems regarding their perspective applications and fundamental properties. We also outline hybrid materials, such as MXenes, transition metal oxides, alloys and graphene oxide. Finally, the challenges and prospects of each element and their derivatives and hybrids for future battery systems are discussed, which may provide guidance towards green, low-cost, versatile and sustainable energy storage devices. MXenes ; Battery (electricity) ; Nanotechnology ; Energy storage ; Lithium (medication) ; Engineering physics ; Graphene ; Transition metal ; Materials science ; Computer science ; Chemistry ; Engineering ; Physics MXene and MAX Phase Materials ; Advancements in Battery Materials ; Supercapacitor Materials and Fabrication
Authors: Xiaonan Chen | Weikang Shu | Liang Zhao | Jingjing Wan
Source: View (article, 2022-12-28)
Field: Chemistry — Spectroscopy

Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis Abstract In vitro diagnosis (IVD) is one vital component of medical tests that detects biological samples of tissues or bio‐fluids. Recently, mass spectrometry and spectroscopy have been increasingly employed in the field of IVD, due to their high accuracy, facile sample preparation, and rapid detection. Notably, the large datasets generated by these two technology methods provide a wealth of information but subsequently involve complex and time‐consuming processing works. Machine learning (ML), an important branch of artificial intelligence (AI), has emerged as a promising solution for the decoding of big data. ML imitates the human brain to process data, significantly improving accuracy and efficiency compared with traditional processing methods. In this review, we first introduce the commonly used ML algorithms and advanced mass spectrometry and spectroscopy techniques in the field of IVD, respectively. The ML algorithms are summarized as four aspects according to different learning tasks. Then, the combinations of ML with mass spectrometry, spectroscopy, and multi‐modal analysis for IVD are presented, and the roles of ML in these combinations are elucidated by some representative examples. This review aims to provide a systematic and comprehensive summary of the literature on ML‐assisted mass spectrometry or spectroscopy. We believe that it will facilitate researchers to select suitable ML algorithms for supplementing existing detection techniques or to develop the potential of coupling more detection techniques with ML, thus promoting the development of mass spectrometry and spectroscopy in IVD. Mass spectrometry ; Computer science ; Field (mathematics) ; Artificial intelligence ; Spectroscopy ; Machine learning ; Chemistry ; Chromatography ; Mathematics ; Physics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Anqi Ji | Jung‐Hwan Song | Qitong Li | Fenghao Xu | Ching‐Ting Tsai | R. C. Tiberio | Bianxiao Cui | Philippe Lalanne | Pieter G. Kik | David A. B. Miller | Mark L. Brongersma
Source: Nature Communications (article, 2022-12-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantitative phase contrast imaging with a nonlocal angle-selective metasurface Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4 f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing. Zernike polynomials ; Microscope ; Phase imaging ; Phase contrast microscopy ; Optics ; Contrast (vision) ; Optical microscope ; Phase (matter) ; Microscopy ; Optical imaging ; Computer science ; Materials science ; Nanotechnology ; Physics ; Wavefront Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Malik Wagih | Christopher A. Schuh
Source: Physical Review Letters (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Learning Grain-Boundary Segregation: From First Principles to Polycrystals The segregation of solute atoms at grain boundaries (GBs) can strongly impact the structural and functional properties of polycrystals. Yet, due to the limited availability of simulation tools to study polycrystals at the atomistic scale (i.e., interatomic potentials), there is a minimal understanding of the variation of solute segregation tendencies across the very complex space of GB microenvironments and the large range of alloys in which it can occur. Here, we develop an algorithmic framework that can directly learn the full spectrum of segregation energies for a metal solute atom in a metal polycrystal from ab initio methods, bypassing the need for alloy interatomic potentials. This framework offers a pathway to a comprehensive catalog of GB solute segregation with quantum accuracy, for the entire alloy space. As an initial demonstration in this pursuit, we build an extensive GB segregation database for aluminum-based alloys across the periodic table, including dozens of alloys for which there are substantially no prior data. Grain boundary ; Materials science ; Interatomic potential ; Alloy ; Molecular dynamics ; Ab initio ; Atom (system on chip) ; Space (punctuation) ; Embedded atom model ; Range (aeronautics) ; Ab initio quantum chemistry methods ; Chemical physics ; Microstructure ; Physics ; Metallurgy ; Computer science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: Wenyang Jia | Saskia M. van Ruth | N.D. Scollan | Anastasios Koidis
Source: Current Research in Food Science (review, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues. Supply chain ; Hyperspectral imaging ; Quality (philosophy) ; Factory (object-oriented programming) ; Meat packing industry ; Scope (computer science) ; Product (mathematics) ; Food safety ; Computer science ; Production (economics) ; Scale (ratio) ; Risk analysis (engineering) ; Biochemical engineering ; Environmental science ; Business ; Engineering ; Artificial intelligence ; Food science ; Marketing ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Agata Kotulska | Aleksandra Pilch-Wróbel | Satu Lahtinen | Tero Soukka | Artur Bednarkiewicz
Source: Light Science & Applications (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Upconversion FRET quantitation: the role of donor photoexcitation mode and compositional architecture on the decay and intensity based responses Lanthanide-doped colloidal nanoparticles capable of photon upconversion (UC) offer long luminescence lifetimes, narrowband absorption and emission spectra, and efficient anti-Stokes emission. These features are highly advantageous for Förster Resonance Energy Transfer (FRET) based detection. Upconverting nanoparticles (UCNPs) as donors may solve the existing problems of molecular FRET systems, such as photobleaching and limitations in quantitative analysis, but these new labels also bring new challenges. Here we have studied the impact of the core-shell compositional architecture of upconverting nanoparticle donors and the mode of photoexcitation on the performance of UC-FRET from UCNPs to Rose Bengal (RB) molecular acceptor. We have quantitatively compared luminescence rise and decay kinetics of Er<sup>3+</sup> emission using core-only NaYF<sub>4</sub>: 20% Yb, 2% Er and core-shell NaYF<sub>4</sub>: 20% Yb @ NaYF<sub>4</sub>: 20% Yb, 5% Er donor UCNPs under three photoexcitation schemes: (1) direct short-pulse photoexcitation of Er<sup>3+</sup> at 520 nm; indirect photoexcitation of Er<sup>3+</sup> through Yb<sup>3+</sup> sensitizer with (2) 980 nm short (5-7 ns) or (3) 980 nm long (4 ms) laser pulses. The donor luminescence kinetics and steady-state emission spectra differed between the UCNP architectures and excitation schemes. Aiming for highly sensitive kinetic upconversion FRET-based biomolecular assays, the experimental results underline the complexity of the excitation and energy-migration mechanisms affecting the Er<sup>3+</sup> donor responses and suggest ways to optimize the photoexcitation scheme and the architecture of the UCNPs used as luminescent donors. Photoexcitation ; Photon upconversion ; Förster resonance energy transfer ; Luminescence ; Photobleaching ; Materials science ; Excitation ; Resonant inductive coupling ; Optoelectronics ; Photochemistry ; Chemistry ; Fluorescence ; Chemical physics ; Energy transfer ; Optics ; Physics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: Peter Mastracco | Anna Gonzàlez‐Rosell | Joshua Evans | Petko Bogdanov | Stacy M. Copp
Source: ACS Nano (article, 2022-09-20)
Field: Materials Science — Materials Chemistry

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence DNA can stabilize silver nanoclusters (Ag<sub><i>N</i></sub>-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA's vast sequence space challenges the design and discovery of Ag<i><sub>N</sub></i>-DNAs with tailored properties. In particular, Ag<sub><i>N</i></sub>-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive Ag<sub><i>N</i></sub>-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from Ag<sub><i>N</i></sub>-DNA crystal structures, we distill the salient DNA sequence features that determine Ag<sub><i>N</i></sub>-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of Ag<sub><i>N</i></sub>-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive Ag<i><sub>N</sub></i>-DNAs by 12.3 times as compared to training data, nearly doubling the number of known Ag<sub><i>N</i></sub>-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure-property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data. Nanoclusters ; Luminescence ; Fluorescence ; Nucleobase ; DNA ; Infrared ; Nanotechnology ; Materials science ; Optoelectronics ; Chemistry ; Physics ; Optics Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jun Zhu | Zhihao Xu | Sihua Ha | Dongke Li | Kexiong Zhang | Hai Zhang | Jijun Feng
Source: Materials (review, 2022-10-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gallium Oxide for Gas Sensor Applications: A Comprehensive Review Ga<sub>2</sub>O<sub>3</sub> has emerged as a promising ultrawide bandgap semiconductor for numerous device applications owing to its excellent material properties. In this paper, we present a comprehensive review on major advances achieved over the past thirty years in the field of Ga<sub>2</sub>O<sub>3</sub>-based gas sensors. We begin with a brief introduction of the polymorphs and basic electric properties of Ga<sub>2</sub>O<sub>3</sub>. Next, we provide an overview of the typical preparation methods for the fabrication of Ga<sub>2</sub>O<sub>3</sub>-sensing material developed so far. Then, we will concentrate our discussion on the state-of-the-art Ga<sub>2</sub>O<sub>3</sub>-based gas sensor devices and put an emphasis on seven sophisticated strategies to improve their gas-sensing performance in terms of material engineering and device optimization. Finally, we give some concluding remarks and put forward some suggestions, including (i) construction of hybrid structures with two-dimensional materials and organic polymers, (ii) combination with density functional theoretical calculations and machine learning, and (iii) development of optical sensors using the characteristic optical spectra for the future development of novel Ga<sub>2</sub>O<sub>3</sub>-based gas sensors. Fabrication ; Nanotechnology ; Gallium ; Materials science ; Semiconductor ; Emphasis (telecommunications) ; Field (mathematics) ; Computer science ; Engineering physics ; Optoelectronics ; Engineering ; Telecommunications Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Taiwo W. Quadri | Lukman O. Olasunkanmi | Ekemini D. Akpan | Omolola E. Fayemi | Han‐Seung Lee | Hassane Lgaz | Chandrabhan Verma | Lei Guo | Savaş Kaya | Eno E. Ebenso
Source: Materials Today Communications (article, 2022-01-19)
Field: Chemistry — Organic Chemistry

Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors Pyridazine ; Quantitative structure–activity relationship ; Molecular descriptor ; Artificial neural network ; Linear regression ; Density functional theory ; Materials science ; Computational chemistry ; Biological system ; Machine learning ; Computer science ; Chemistry ; Organic chemistry ; Biology Multicomponent Synthesis of Heterocycles ; Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods
Authors: Fujia Dong | Jie Hao | Ruiming Luo | Zhifeng Zhang | Songlei Wang | Kangning Wu | Mengqi Liu
Source: Computers and Electronics in Agriculture (article, 2022-05-10)
Field: Chemistry — Analytical Chemistry

Identification of the proximate geographical origin of wolfberries by two-dimensional correlation spectroscopy combined with deep learning Linear discriminant analysis ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Hyperspectral imaging ; Wavelength ; Mathematics ; Chemistry ; Computer science ; Biological system ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shucheng Xing | Jian Zhou | Xuanguang Zhang | Stephen R. Elliott | Zhimei Sun
Source: Progress in Materials Science (article, 2022-10-22)
Field: Materials Science — Materials Chemistry

Theory, properties and engineering of 2D magnetic materials Spintronics ; Magnetism ; Ferromagnetism ; Curie temperature ; Materials science ; Realization (probability) ; Quantum ; Nanotechnology ; Quantum Hall effect ; Modulation (music) ; Condensed matter physics ; Engineering physics ; Magnetic field ; Physics ; Quantum mechanics 2D Materials and Applications ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Xingyue Liu | Zhao Chen | Tao Wang | Xinrong Jiang | Xuetong Qu | Wei Duan | Fengna Xi | Zhengfu He | Jianmin Wu
Source: ACS Nano (article, 2022-04-13)
Field: Chemistry — Spectroscopy

Tissue Imprinting on 2D Nanoflakes-Capped Silicon Nanowires for Lipidomic Mass Spectrometry Imaging and Cancer Diagnosis Spatially resolved tissue lipidomics is essential for accurate intraoperative and postoperative cancer diagnosis by revealing molecular information in the tumor microenvironment. Matrix-free laser desorption ionization mass spectrometry imaging (LDI-MSI) is an emerging attractive technology for label-free visualization of metabolites distributions in biological specimens. However, the development of LDI-MSI technology that could conveniently and authentically reveal molecular distribution on tissue samples is still a challenge. Herein, we present a tissue imprinting technology by retaining tissue lipids on 2D nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of molybdenum disulfide (MoS<sub>2</sub>) with nitrogen-doped graphene quantum dots (NGQDs), which serve as both intercalation agent and dispersant. The obtained NGQD@MoS<sub>2</sub> nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid NGQD@MoS<sub>2</sub>/SiNWs nanostructure, which display excellent lipid extraction ability, enhanced LDI efficiency and molecule imaging capability. The peak number and total ion intensity of different lipids species on animal lung tissues obtained by tissue imprinting LDI-MSI on NGQD@MoS<sub>2</sub>/SiNWs were ∼4-5 times greater than those on SiNWs substrate. As a proof-of-concept demonstration, the NGQD@MoS<sub>2</sub>/SiNWs nanostructure was further applied to visualize phospholipids on sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal tissue. On the basis of selected feature lipids and machine learning algorithm, a prediction model was constructed to discriminate NSCLC tissues from the adjacent normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for the independent validation cohort. Nanotechnology ; Materials science ; Mass spectrometry ; Mass spectrometry imaging ; Silicon nanowires ; Silicon ; Nanowire ; Cancer ; Chemistry ; Optoelectronics ; Medicine ; Chromatography Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Analytical chemistry methods development