jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander G. Martynov | Yoji Horii | Keiichi Katoh | Yongzhong Bian | Jianzhuang Jiang | Masahiro Yamashita | Yulia G. Gorbunova
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rare-earth based tetrapyrrolic sandwiches: chemistry, materials and applications The unique properties of natural tetrapyrrolic compounds have inspired the rapid growth of research interest in the design and synthesis of artificial porphyrinoids and their metal complexes as a basis of modern functional materials. A special role in the design of such materials is played by sandwich complexes formed by tetrapyrrolic macrocycles with rare earth elements, especially lanthanides. The development of synthetic approaches to the functionalization of tetrapyrrolic compounds and their rare earth complexes has facilitated the intensive development of new applications over the last decade. As a way of expanding the functionalities of rare earth complexes, sophisticated examples have been obtained, including mixed-ligand complexes, π-extended analogues, covalently linked and fused sandwiches, complexes with less-common tetrapyrrols, sandwiches with non-tetrapyrrolic macrocycles and even complexes containing up to six stacked ligands. This review intends to offer a general overview of the preparation of such sophisticated REE tetrapyrrolic sandwiches over the last decade as well as emphasizes the current challenges and perspectives of their application in areas such as single-molecule magnetism (SMM), organic field-effect transistors (OFET), conductive materials and nonlinear optics (NLO). Tetrapyrrole ; Rare earth ; Nanotechnology ; Magnetism ; Chemistry ; Nonlinear optical ; Materials science ; Physics ; Mineralogy ; Nonlinear system ; Condensed matter physics ; Organic chemistry Magnetism in coordination complexes ; Porphyrin and Phthalocyanine Chemistry ; Organic and Molecular Conductors Research
Authors: Honglin Lv | Xueye Chen
Source: Nanoscale (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Intelligent control of nanoparticle synthesis through machine learning The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed. Nanoparticle ; Nanotechnology ; Control (management) ; Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Hamed Jafarzadeh | Ceren Karaman | Afşin Güngör | Onur Karaman | Pau-Loke Show | Parisa Sami | Abbasali Abouei Mehrizi
Source: Process Safety and Environmental Protection (article, 2022-05-31)
Field: Materials Science — Materials Chemistry

Hydrogen production via sodium borohydride hydrolysis catalyzed by cobalt ferrite anchored nitrogen-and sulfur co-doped graphene hybrid nanocatalyst: Artificial neural network modeling approach Sodium borohydride ; Catalysis ; Hydrogen production ; Hydrolysis ; Graphene ; Hydrogen ; Materials science ; Cobalt ; Ferrite (magnet) ; Chemical engineering ; Inorganic chemistry ; Sulfur ; Chemistry ; Nanotechnology ; Composite material ; Organic chemistry ; Metallurgy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nanomaterials for catalytic reactions
Authors: Zhao Zhizheng | Mohamed E. El‐Khouly | Qiang Che | Fangcheng Sun | Bin Zhang | Haidong He | Yu Chen
Source: Angewandte Chemie International Edition (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Redox‐Active Azulene‐based 2D Conjugated Covalent Organic Framework for Organic Memristors As a conjugated and unsymmetric building block composed of an electron-poor seven-membered sp<sup>2</sup> carbon ring and an electron-rich five-membered carbon ring, azulene and its derivatives have been recognized as one of the most promising building blocks for novel electronic devices due to its intrinsic redox activity. By using 1,3,5-tris(4-aminophenyl)-benzene and azulene-1,3-dicarbaldehyde as the starting materials, an azulene(Azu)-based 2D conjugated covalent organic framework, COF-Azu, is prepared through liquid-liquid interface polymerization strategy for the first time. The as-fabricated Al/COF-Azu/indium tin oxide (ITO) memristor shows typical non-volatile resistive switching performance due to the electric filed induced intramolecular charge transfer effect. Associated with the unique memristive performance, a simple convolutional neural network is built for image recognition. After 8 epochs of training, image recognition accuracy of 80 % for a neutral network trained on a larger data set is achieved. Azulene ; Conjugated system ; Materials science ; Covalent organic framework ; Memristor ; Indium tin oxide ; Covalent bond ; Photochemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Layer (electronics) ; Electronic engineering Covalent Organic Framework Applications ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: M. Khalid Hossain | S M Kamrul Hasan | M. Imran Hossain | Ranjit Chandra Das | H. Bencherif | Mirza H. K. Rubel | Md. Ferdous Rahman | Md Tanvir Emrose | Kenichi Hashizume
Source: Nanomaterials (review, 2022-10-13)
Field: Materials Science — Materials Chemistry

A Review of Applications, Prospects, and Challenges of Proton-Conducting Zirconates in Electrochemical Hydrogen Devices In the future, when fossil fuels are exhausted, alternative energy sources will be essential for everyday needs. Hydrogen-based energy can play a vital role in this aspect. This energy is green, clean, and renewable. Electrochemical hydrogen devices have been used extensively in nuclear power plants to manage hydrogen-based renewable fuel. Doped zirconate materials are commonly used as an electrolyte in these electrochemical devices. These materials have excellent physical stability and high proton transport numbers, which make them suitable for multiple applications. Doping enhances the physical and electronic properties of zirconate materials and makes them ideal for practical applications. This review highlights the applications of zirconate-based proton-conducting materials in electrochemical cells, particularly in tritium monitors, tritium recovery, hydrogen sensors, and hydrogen pump systems. The central section of this review summarizes recent investigations and provides a comprehensive insight into the various doping schemes, experimental setup, instrumentation, optimum operating conditions, morphology, composition, and performance of zirconate electrolyte materials. In addition, different challenges that are hindering zirconate materials from achieving their full potential in electrochemical hydrogen devices are discussed. Finally, this paper lays out a few pathways for aspirants who wish to undertake research in this field. Materials science ; Renewable energy ; Electrochemical energy conversion ; Electrolyte ; Zirconate ; Hydrogen fuel ; Nanotechnology ; Hydrogen ; Electrochemistry ; Process engineering ; Electrical engineering ; Chemistry ; Engineering ; Electrode ; Composite material Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Fabian Bertoldo | Sajid Ali | Simone Manti | Kristian S. Thygesen
Source: npj Computational Materials (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Quantum point defects in 2D materials - the QPOD database Abstract Atomically thin two-dimensional (2D) materials are ideal host systems for quantum defects as they offer easier characterisation, manipulation and read-out of defect states as compared to bulk defects. Here we introduce the Quantum Point Defect (QPOD) database with more than 1900 defect systems comprising various charge states of 503 intrinsic point defects (vacancies and antisites) in 82 different 2D semiconductors and insulators. The Atomic Simulation Recipes (ASR) workflow framework was used to perform density functional theory (DFT) calculations of defect formation energies, charge transition levels, Fermi level positions, equilibrium defect and carrier concentrations, transition dipole moments, hyperfine coupling, and zero-field splitting. Excited states and photoluminescence spectra were calculated for selected high-spin defects. In this paper we describe the calculations and workflow behind the QPOD database, present an overview of its content, and discuss some general trends and correlations in the data. We analyse the degree of defect tolerance as well as intrinsic dopability of the host materials and identify promising defects for quantum technological applications. The database is freely available and can be browsed via a web-app interlinked with the Computational 2D Materials Database (C2DB). Point (geometry) ; Crystallographic defect ; Quantum ; Database ; Computer science ; Materials science ; Condensed matter physics ; Physics ; Quantum mechanics ; Geometry ; Mathematics Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Qinlin Xiao | Wentan Tang | Chu Zhang | Lei Zhou | Lei Feng | Jianxun Shen | Tianying Yan | Pan Gao | Yong He | Na Wu
Source: Plant Phenomics (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field. Partial least squares regression ; Preprocessor ; Hyperspectral imaging ; Artificial intelligence ; Computer science ; Transfer of learning ; Transformation (genetics) ; Data pre-processing ; Support vector machine ; Pattern recognition (psychology) ; Deep learning ; Convolutional neural network ; Biological system ; Mathematics ; Machine learning ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Aleksandra Serafin | Mario Culebras Rubio | Marta Carsí | P. Ortiz‐Serna | M. J. Sanchı́s | Atul K. Garg | Joaquím M. Oliveira | Jacob Koffler | Maurice N. Collins
Source: Biomaterials Research (article, 2022-09-30)
Field: Materials Science — Biomaterials

Electroconductive PEDOT nanoparticle integrated scaffolds for spinal cord tissue repair Based on these results, the incorporation of PEDOT NPs into Gel:HA biomaterial scaffolds enhances not only the conductive capabilities of the material, but also the provision of a healing environment around lesions in SCI. Hence, gel:HA:PEDOT-NPs scaffolds are a promising TE option for stimulating regeneration for SCI. PEDOT:PSS ; Materials science ; Biocompatibility ; Interpenetrating polymer network ; Tissue engineering ; Biomedical engineering ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Nerve injury and regeneration ; Graphene and Nanomaterials Applications
Authors: Ziyan Fu | Jorge Corker | Theodosios K. Papathanasiou | Yuxuan Wang | Yonghui Zhou | Omar Abo Madyan | Feiyu Liao | Mizi Fan
Source: Journal of Building Engineering (article, 2022-06-14)
Field: Chemistry — Spectroscopy

Critical review on the thermal conductivity modelling of silica aerogel composites As a new generation of thermal insulation materials, the effective thermal conductivity of aerogel and its composites is extremely low. The nanoporous structure of aerogels demobilises the movement of gas molecules, and the nano-skeleton system restricts solid heat transfer because of the size effect. Numerous research and modelling works have been carried out to understand and predict heat transfers. This review thoroughly discusses the existing theories and models of silica aerogel composites in gas, solid and radiative heat transfers. It investigates the correlation of the pore size distribution and solid skeleton network of the composites with the thermal conductivity. The review then assesses the advances of the development and questions remaining for further development, including 1) some unexplainable performance of existing models and 2) improvements required for gas and solid thermal conductivity models. Bridging the identified research gaps shall lead researchers to understand existing models better, develop a more accurate model based on more realistic microstructure simulation and further innovate the models for other emerging composites. Aerogel ; Nanoporous ; Thermal conductivity ; Materials science ; Thermal insulation ; Composite material ; Heat transfer ; Bridging (networking) ; Thermal ; Thermal transfer ; Microstructure ; Radiative transfer ; Computer science ; Nanotechnology ; Mechanics ; Thermodynamics Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity ; Glass properties and applications
Authors: Ruoxi Yang | Caitlin A. McCandler | Oxana Andriuc | Martin Siron | Rachel Woods‐Robinson | Matthew K. Horton | Kristin A. Persson
Source: ACS Nano (review, 2022-11-15)
Field: Materials Science — Materials Chemistry

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm. Computer science ; Workflow ; Nanotechnology ; Curse of dimensionality ; Computational model ; Data-driven ; Big data ; Chemical space ; Nanomaterials ; Data science ; Materials science ; Artificial intelligence ; Data mining ; Drug discovery ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nanocluster Synthesis and Applications
Authors: Faith E. Chen | Tristan A. Pitt | Diane J. Okong’o | Luc G. Wetherbee | José J. Fuentes-Rivera | Phillip J. Milner
Source: Chemistry of Materials (article, 2022-03-28)
Field: Chemistry — Inorganic Chemistry

A Structure–Activity Study of Aromatic Acid Modulators for the Synthesis of Zirconium-Based Metal–Organic Frameworks Acid modulation is among the most widely employed methods for preparing metal-organic frameworks (MOFs) that are both stable and highly crystalline, yet there exist few guiding principles for selecting the optimal modulator for a given system. Using the Zr-based MOFs UiO-66 and UiO-68-Me<sub>2</sub> (UiO = Universitetet i Oslo) as representative materials, here we present for the first time an in-depth structure-activity study of acid modulators and identify key principles of modulation for the synthesis of highly crystalline Zr-MOFs. By applying whole pattern fitting of powder X-ray diffraction (PXRD) patterns as a technique for evaluating modulator efficacy, complemented by scanning electron microscopy (SEM), <sup>1</sup>H NMR, and thermogravimetric analysis (TGA), we demonstrate that the key to effective modulation is competition between the linker and modulator for coordination to the Zr secondary building units (SBUs). Specifically, we illustrate that a close match in p<i>K</i> <sub>a</sub> and structure between the linker and modulator favors larger and more well-defined crystallites, particularly with sterically unhindered aromatic acid modulators. Based on our findings, we demonstrate that 5-membered heteroaromatic carboxylic acids are among the most efficient acid modulators identified to date for the synthesis of several representative Zr-MOFs with <b>fcu</b> net topologies. In addition, we find that coordination modulation is superior to exogenous acid modulation at higher modulator concentrations. Finally, we compare <sup>1</sup>H NMR and TGA as data-driven methods for quantifying linker deficiencies in modulated MOF syntheses. The guiding principles established herein have critical implications for the scalable and controllable synthesis of highly crystalline and stable MOFs relevant to chemical separations, gas storage, and catalysis. Linker ; Metal-organic framework ; Zirconium ; Thermogravimetric analysis ; Materials science ; SBus ; Steric effects ; Modulation (music) ; Crystallite ; Nanotechnology ; Chemistry ; Stereochemistry ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Zeolite Catalysis and Synthesis
Authors: Li Shao | Jinrong Ma | Jesse L. Prelesnik | Yicheng Zhou | Mary Nguyen | Mingfei Zhao | Samson A. Jenekhe | Sergei V. Kalinin | Andrew L. Ferguson | Jim Pfaendtner | Christopher J. Mundy | James J. De Yoreo | François Baneyx | Chun‐Long Chen
Source: Chemical Reviews (review, 2022-10-19)
Field: Materials Science — Biomaterials

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks. Hierarchy ; Hierarchical organization ; Flexibility (engineering) ; Chemistry ; Nanotechnology ; Control reconfiguration ; Function (biology) ; Computer science ; Biochemical engineering ; Engineering ; Materials science Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Surface Chemistry and Catalysis
Authors: Federico Bosia | Vinícius F. Dal Poggetto | Antonio S. Gliozzi | Gabriele Greco | Martin Lott | Marco Miniaci | Federica Ongaro | Miguel Onorato | S. F. Seyyedizadeh | Mauro Tortello | Nicola M. Pugno
Source: Matter (review, 2022-10-01)
Field: Materials Science — Biomaterials

Optimized structures for vibration attenuation and sound control in nature: A review Metamaterial ; Computer science ; Field (mathematics) ; Vibration ; Materials science ; Attenuation ; Nanotechnology ; Mechanical engineering ; Acoustics ; Engineering ; Physics Silk-based biomaterials and applications ; Plant and Biological Electrophysiology Studies ; Cephalopods and Marine Biology