jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Özgür Seydibeyoğlu | Alperen Doğru | Jinwu Wang | Mitchell L. Rencheck | Yousoo Han | Lu Wang | Elif Alyamaç | Xianhui Zhao | Kimberly J. Ong | Jo Anne Shatkin | S. Shams Es‐haghi | Sunil Bhandari | Soydan Ozcan | Douglas J. Gardner
Source: Polymers (review, 2023-02-16)
Field: Materials Science — Biomaterials

Review on Hybrid Reinforced Polymer Matrix Composites with Nanocellulose, Nanomaterials, and Other Fibers The use of composite materials has seen many new innovations for a large variety of applications. The area of reinforcement in composites is also rapidly evolving with many new discoveries, including the use of hybrid fibers, sustainable materials, and nanocellulose. In this review, studies on hybrid fiber reinforcement, the use of nanocellulose, the use of nanocellulose in hybrid forms, the use of nanocellulose with other nanomaterials, the applications of these materials, and finally, the challenges and opportunities (including safety issues) of their use are thoroughly discussed. This review will point out new prospects for the composite materials world, enabling the use of nano- and micron-sized materials together and creating value-added products at the industrial scale. Furthermore, the use of hybrid structures consisting of two different nano-materials creates many novel solutions for applications in electronics and sensors. Nanocellulose ; Materials science ; Nanomaterials ; Nanotechnology ; Hybrid material ; Composite number ; Composite material ; Cellulose ; Engineering Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications
Authors: Bokai Liu | Weizhuo Lu | Thomas Olofsson | Xiaoying Zhuang | Timon Rabczuk
Source: Composite Structures (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites Artificial neural network ; Thermal conductivity ; Multiscale modeling ; Gradient boosting ; Finite element method ; Computer science ; Artificial intelligence ; Boosting (machine learning) ; Machine learning ; Context (archaeology) ; Representative elementary volume ; Materials science ; Random forest ; Composite material ; Engineering ; Structural engineering ; Geology Thermal properties of materials ; Composite Material Mechanics ; Probabilistic and Robust Engineering Design
Authors: Pranav Shetty | Arunkumar Chitteth Rajan | Chris Kuenneth | Sonakshi Gupta | Lakshmi Prerana Panchumarti | Lauren Holm | Chao Zhang | Rampi Ramprasad
Source: npj Computational Materials (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature. We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is made available at polymerscholar.org which can be used to locate material property data recorded in abstracts. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with extracted material property information. Pipeline (software) ; Property (philosophy) ; Computer science ; Data extraction ; Extraction (chemistry) ; Component (thermodynamics) ; Natural language processing ; Information retrieval ; Artificial intelligence ; Chemistry ; Programming language ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Tao Li | Chen Chen | Xingjian Xiao | Ji Chen | Shanshan Hu | Shining Zhu
Source: Photonics Insights (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Revolutionary meta-imaging: from superlens to metalens The refractive-lens technique has been well developed over a long period of evolution, offering powerful imaging functionalities, such as microscopes, telescopes, and spectroscopes. Nevertheless, the ever-growing requirements continue to urge further enhanced imaging capabilities and upgraded devices that are more compact for convenience. Metamaterial as a fascinating concept has inspired unprecedented new explorations in physics, material science, and optics, not only in fundamental researches but also novel applications. Along with the imaging topic, this paper reviews the progress of the flat lens as an important branch of metamaterials, covering the early superlens with super-diffraction capability and current hot topics of metalenses including a paralleled strategy of multilevel diffractive lenses. Numerous efforts and approaches have been dedicated to areas ranging from the new fascinating physics to feasible applications. This review provides a clear picture of the flat-lens evolution from the perspective of metamaterial design, elucidating the relation and comparison between a superlens and metalens, and addressing derivative designs. Finally, application scenarios that favor the ultrathin lens technique are emphasized with respect to possible revolutionary imaging devices, followed by conclusive remarks and prospects. Superlens ; Metamaterial ; Lens (geology) ; Optics ; Through-the-lens metering ; Physics ; Computer science ; Nanotechnology ; Materials science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Devon Kohler | Mateusz Staniak | Tsung‐Heng Tsai | Ting Huang | Nicholas Shulman | Oliver M. Bernhardt | Brendan MacLean | Alexey I. Nesvizhskii | Lukas Reiter | Eduard Sabidó | Meena Choi | Olga Vitek
Source: Journal of Proteome Research (article, 2023-04-05)
Field: Chemistry — Spectroscopy

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale The <i>MSstats</i> R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name <i>MSstats</i>, has undergone a series of substantial updates. Its new version <i>MSstats</i> v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with <i>MSstats</i>, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, <i>MSstats</i>' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of <i>MSstats</i> v4.0 to its previous implementations, as well as to the packages <i>MSqRob</i> and <i>DEqMS</i>, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of <i>MSstats</i> v4.0 as compared to existing methods. Computer science ; Usability ; Workflow ; Bioconductor ; Data mining ; Database ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Xiaolu Liu | Yang Li | Zhongshan Chen | Hui Yang | Suhua Wang | Zhenwu Tang | Xiangke Wang
Source: Eco-Environment & Health (review, 2023-07-12)
Field: Materials Science — Materials Chemistry

Recent progress of covalent organic frameworks membranes: Design, synthesis, and application in water treatment To date, significant efforts have been devoted to eliminating hazardous components to purify wastewater through the development of various nanomaterials. Covalent organic frameworks (COFs), an important branch of the porous crystalline family, possess the peculiarity of ultrahigh surface area, adjustable pore size, and facile functionality. Exciting studies from design fabrication to potential applications in water treatment by COF-based membranes (COMs) have emerged. This review summarizes various preparation strategies and synthesis mechanisms for COMs, including layer-by-layer stacking, <i>in situ</i> growth, interfacial polymerization, and electrochemical synthesis, and briefly describes the advanced characterization techniques for COMs. Moreover, the application of COMs in heavy metal removal, dye separation, purification of radionuclides, pollutant detection, sea water desalination, and so on, is described and discussed. Finally, the perspectives on future opportunities for designing COMs in water purification have been proposed. Desalination ; Membrane ; Nanotechnology ; Nanomaterials ; Materials science ; Portable water purification ; Water treatment ; Stacking ; Wastewater ; Hazardous waste ; Covalent organic framework ; Process engineering ; Chemical engineering ; Porosity ; Environmental science ; Waste management ; Environmental engineering ; Chemistry ; Engineering ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: N. Ian Rinehart | Rakesh K. Saunthwal | Joël Wellauer | Andrew F. Zahrt | Lukas Schlemper | Alexander S. Shved | Raphael Bigler | Serena Fantasia | Scott E. Denmark
Source: Science (article, 2023-08-31)
Field: Chemistry — Organic Chemistry

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows. Workflow ; Artificial neural network ; Substrate (aquarium) ; Scope (computer science) ; Computer science ; Yield (engineering) ; Set (abstract data type) ; Process (computing) ; Artificial intelligence ; Catalysis ; Range (aeronautics) ; Palladium ; Machine learning ; Biological system ; Chemistry ; Materials science ; Database Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Tsz Wai Ko | Shyue Ping Ong
Source: Nature Computational Science (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

Recent advances and outstanding challenges for machine learning interatomic potentials Interatomic potential ; Computer science ; Ab initio ; Artificial intelligence ; Machine learning ; Physics ; Molecular dynamics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Farid Hajareh Haghighi | Martina Mercurio | Sara Cerra | Tommaso A. Salamone | Roya Bianymotlagh | Cleofe Palocci | Vincenzo Romano Spica | Ilaria Fratoddi
Source: Journal of Materials Chemistry B (review, 2023-01-01)
Field: Materials Science — Biomaterials

Surface modification of TiO<sub>2</sub>nanoparticles with organic molecules and their biological applications In recent years, titanium(IV) dioxide nanoparticles (TiO<sub>2</sub>NPs) have shown promising potential in various biological applications such as antimicrobials, drug delivery, photodynamic therapy, biosensors, and tissue engineering. For employing TiO<sub>2</sub>NPs in these fields, their nanosurface must be coated or conjugated with organic and/or inorganic agents. This modification can improve their stability, photochemical properties, biocompatibility, and even surface area for further conjugation with other molecules such as drugs, targeting molecules, polymers, <i>etc.</i> This review describes the organic-based modification of TiO<sub>2</sub>NPs and their potential applications in the mentioned biological fields. In the first part of this review, around 75 recent publications (2017-2022) are mentioned on the common TiO<sub>2</sub>NP modifiers including organosilanes, polymers, small molecules, and hydrogels, which improve the photochemical features of TiO<sub>2</sub>NPs. In the second part of this review, we presented 149 recent papers (2020-2022) about the use of modified TiO<sub>2</sub>NPs in biological applications, in which specific bioactive modifiers are introduced in this part with their advantages. In this review, the following information is presented: (1) the common organic modifiers for TiO<sub>2</sub>NPs, (2) biologically important modifiers and their benefits, and (3) recent publications on biological studies on the modified TiO<sub>2</sub>NPs with their achievements. This review shows the paramount significance of the organic-based modification of TiO<sub>2</sub>NPs to enhance their biological effectiveness, paving the way toward the development of advanced TiO<sub>2</sub>-based nanomaterials in nanomedicine. Materials science ; Surface modification ; Nanoparticle ; Organic molecules ; Nanotechnology ; Molecule ; Chemical engineering ; Organic chemistry ; Chemistry Nanoparticle-Based Drug Delivery ; Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications
Authors: Li‐Hui Mou | TianTian Han | Pieter E. S. Smith | Edward Sharman | Jun Jiang
Source: Advanced Science (review, 2023-05-16)
Field: Materials Science — Materials Chemistry

Machine Learning Descriptors for Data‐Driven Catalysis Study Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented. Catalysis ; Computer science ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Diego P. dos Santos | Marcelo M. Sena | Mariana Ramos Almeida | Ítalo Odone Mazali | Alejandro C. Olivieri | Javier E.L. Villa
Source: Analytical and Bioanalytical Chemistry (review, 2023-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends Chemometrics ; Surface-enhanced Raman spectroscopy ; Computer science ; Benchmarking ; Nanotechnology ; Standardization ; Biochemical engineering ; Data science ; Artificial intelligence ; Machine learning ; Raman spectroscopy ; Engineering ; Materials science ; Raman scattering ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Abdullahi Haruna Birniwa | Shehu Sa’ad Abdullahi | Mujahid Ali | Rania Edrees Adam Mohammad | Ahmad Hussaini Jagaba | Mugahed Amran | Siva Avudaiappan | Nelson Maureira-Carsalade | Erick I. Saavedra Flores
Source: Journal of Composites Science (review, 2023-03-15)
Field: Materials Science — Polymers and Plastics

Recent Trends in Treatment and Fabrication of Plant-Based Fiber-Reinforced Epoxy Composite: A Review Natural fiber (NF) is one of the many resources that nature has provided. NFs decompose quickly and are biodegradable, renewable, and cost-effective. It may be scavenged from a variety of plant and animal sources. They are employed as reinforcing materials in polymers for NF composite development. Because of its environmental friendliness and long-term survivability, NF is growing in appeal among academics and researchers for usage in polymer composites. This study aims to offer a thorough evaluation of the most suitable and widely utilized natural fiber-reinforced polymer composites (NFPCs), along with their manufacture, processing, and applications. It also defines several external treatments of NF and their influence on the characteristics of NFPCs. The characteristics of NFPCs are affected by fiber supply, fiber type, and fiber structure. Numerous physical and chemical treatments were tested to see how they affected the thermal and strength properties of natural fiber-reinforced thermoplastic and thermosetting composites. Several polymer composite fabrication techniques were also studied. NFPCs have several disadvantages, notably low fire protection, poor strength properties, and greater moisture absorption, which have prevented their application. It is shown how NFPCs are employed in a variety of industries, particularly automotive and research industries. The review discovered that intentionally changing the regular fiber enhanced the thermochemical and physico-mechanical properties of the NFPCs by means of improving the grip between the fiber surface and the polymer framework. This study aims to provide important and fundamental facts on NF and their composites, which will aid in new investigations, the creation of a creative framework for polymer composite types, and the achievement of Sustainable Development Goals. Thermosetting polymer ; Natural fiber ; Materials science ; Composite material ; Fiber ; Composite number ; Epoxy ; Kenaf ; Fibre-reinforced plastic ; Fiber-reinforced composite ; Polymer ; Synthetic fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research
Authors: Qing Zhu | Yan Huang | Donglai Zhou | Luyuan Zhao | Lulu Guo | Ruyu Yang | Z. T. Sun | Man Luo | Fei Zhang | Hengyu Xiao | Xinsheng Tang | Xuchun Zhang | Tao Song | Xiang Li | Baochen Chong | Junyi Zhou | Yihan Zhang | Baicheng Zhang | Jiaqi Cao | Guozhen Zhang | Song Wang | Guilin Ye | Wanjun Zhang | Haitao Zhao | Shuang Cong | Huirong Li | Li-Li Ling | Zhe Zhang | Weiwei Shang | Jun Jiang | Yi Luo
Source: Nature Synthesis (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist Mars Exploration Program ; Martian ; Meteorite ; Artificial intelligence ; Astrobiology ; Catalysis ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Patricia Izquierdo‐García | Jesús M. Fernández‐García | Samara Medina Rivero | Michal Šámal | Jiří Rybáček | Lucie Bednářová | Sergio Ramírez‐Barroso | F. RAMIREZ | Rafael Rodríguez | Josefina Perles | David García‐Fresnadillo | Jeanne Crassous | Juan Casado | Irena G. Stará̈ | Nazario Martı́n
Source: Journal of the American Chemical Society (article, 2023-05-02)
Field: Chemistry — Organic Chemistry

Helical Bilayer Nanographenes: Impact of the Helicene Length on the Structural, Electrochemical, Photophysical, and Chiroptical Properties Helical bilayer nanographenes (HBNGs) are chiral π-extended aromatic compounds consisting of two π-π stacked hexabenzocoronenes (HBCs) joined by a helicene, thus resembling van der Waals layered 2D materials. Herein, we compare <b>[9]HBNG</b>, <b>[10]HBNG</b>, and <b>[11]HBNG</b> helical bilayers endowed with [9], [10], and [11]helicenes embedded in their structure, respectively. Interestingly, the helicene length defines the overlapping degree between the two HBCs (number of benzene rings involved in π-π interactions between the two layers), being 26, 14, and 10 benzene rings, respectively, according to the X-ray analysis. Unexpectedly, the electrochemical study shows that the lesser π-extended system <b>[9]HBNG</b> shows the strongest electron donor character, in part by interlayer exchange resonance, and more red-shifted values of emission. Furthermore, <b>[9]HBNG</b> also shows exceptional chiroptical properties with the biggest values of <i>g</i><sub>abs</sub> and <i>g</i><sub>lum</sub> (3.6 × 10<sup>-2</sup>) when compared to <b>[10]HBNG</b> and <b>[11]HBNG</b> owing to the fine alignment in the configuration of <b>[9]HBNG</b> between its electric and magnetic dipole transition moments. Furthermore, spectroelectrochemical studies as well as the fluorescence spectroscopy support the aforementioned experimental findings, thus confirming the strong impact of the helicene length on the properties of this new family of bilayer nanographenes. Helicene ; Chemistry ; Bilayer ; van der Waals force ; Crystallography ; Molecule ; Membrane ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Graphene research and applications ; Supramolecular Self-Assembly in Materials