jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pushpendra Singh | Rajiv K. Singh | Ranveer Kumar
Source: RSC Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Journey of ZnO quantum dots from undoped to rare-earth and transition metal-doped and their applications Currently, developments in the field of quantum dots (QDs) have attracted researchers worldwide. A large variety of QDs have been discovered in the few years, which have excellent optoelectronic, antibacterial, magnetic, and other properties. However, ZnO is the single known material that can exist in the quantum state and can hold all the above properties. There is a lot of work going on in this field and we will be shorthanded if we do not accommodate this treasure at one place. This manuscript will prove to be a milestone in this noble cause. Having a tremendous potential, there is a developing enthusiasm toward the application of ZnO QDs in diverse areas. Sol-gel method being the simplest is the widely-favored synthetic method. Synthesis <i>via</i> this method is largely affected by a number of factors such as the reaction temperature, duration of the reaction, type of solvent, pH of the solution, and the precipitating agent. Doping enhances the optical, magnetic, anti-bacterial, anti-microbial, and other properties of ZnO QDs. However, doping elements reside mostly on the surface of the QDs. The presence of doping elements inside the core is still a major challenge for doping techniques. In this review article, we have focused on pure, rare-earth, and transition metal-doped ZnO QD properties, and the various synthetic processes and applications. Quantum confinement effect is present in nearly every aspect of the QDs. The effect of quantum confinement has also been summarized in this manuscript. Furthermore, the doping of rare earth elements and transition metal, synthetic methods for different organic molecule-capped ZnO QDs, mechanisms for reactive oxygen species (ROS) generation, drug delivery system for cancer treatment, and many more application are discussed in this paper. Quantum dot ; Doping ; Nanotechnology ; Materials science ; Rare earth ; Transition metal ; Chemistry ; Optoelectronics ; Metallurgy ; Catalysis ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; ZnO doping and properties
Authors: Patrick T. Sekoai | Anish Ghimire | Obinna T. Ezeokoli | Subramanya Rao | Wing Yui Ngan | Olivier Habimana | Yuan Yao | Yang Pu | Aster Hei Yiu Fung | Kelvin O. Yoro | Michael O. Daramola | Chun‐Hsiung Hung
Source: Renewable and Sustainable Energy Reviews (article, 2021-03-24)
Field: Materials Science — Biomaterials

Valorization of volatile fatty acids from the dark fermentation waste Streams-A promising pathway for a biorefinery concept Biorefinery ; Dark fermentation ; Acidogenesis ; Biogas ; Biochemical engineering ; Waste management ; Fermentation ; Biofuel ; Anaerobic digestion ; Pulp and paper industry ; Chemistry ; Biohydrogen ; Engineering ; Hydrogen production ; Food science ; Organic chemistry ; Hydrogen biodegradable polymer synthesis and properties ; Anaerobic Digestion and Biogas Production ; Carbon dioxide utilization in catalysis
Authors: Dong Zhao | Zhelin Lin | Wenqi Zhu | Henri J. Lezec | Ting Xu | Amit Agrawal | Cheng Zhang | Kun Huang
Source: Nanophotonics (article, 2021-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in ultraviolet nanophotonics: from plasmonics and metamaterials to metasurfaces Abstract Nanophotonic devices, composed of metals, dielectrics, or semiconductors, enable precise and high‐spatial‐resolution manipulation of electromagnetic waves by leveraging diverse light–matter interaction mechanisms at subwavelength length scales. Their compact size, light weight, versatile functionality and unprecedented performance are rapidly revolutionizing how optical devices and systems are constructed across the infrared, visible, and ultraviolet spectra. Here, we review recent advances and future opportunities of nanophotonic elements operating in the ultraviolet spectral region, which include plasmonic devices, optical metamaterials, and optical metasurfaces. We discuss their working principles, material platforms, fabrication, and characterization techniques, followed by representative device applications across various interdisciplinary areas such as imaging, sensing and spectroscopy. We conclude this review by elaborating on future opportunities and challenges for ultraviolet nanophotonic devices. Nanophotonics ; Metamaterial ; Plasmon ; Ultraviolet ; Materials science ; Nanotechnology ; Optoelectronics ; Multispectral image ; Optics ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Wireless Communication Technologies
Authors: Nimisha Singh | Célia Marets | Julien Boudon | Nadine Millot | Lucien Saviot | Lionel Maurizi
Source: Nanoscale Advances (review, 2021-01-01)
Field: Materials Science — Biomaterials

<i>In vivo</i> protein corona on nanoparticles: does the control of all material parameters orient the biological behavior? Nanomaterials have a huge potential in research fields from nanomedicine to medical devices. However, surface modifications of nanoparticles (NPs) and thus of their physicochemical properties failed to predict their biological behavior. This requires investigating the "missing link" at the nano-bio interface. The protein corona (PC), the set of proteins binding to the NPs surface, plays a critical role in particle recognition by the innate immune system. Still, <i>in vitro</i> incubation offers a limited understanding of biological interactions and fails to explain the <i>in vivo</i> fate. To date, several reports explained the impact of PC <i>in vitro</i> but its applications in the clinical field have been very limited. Furthermore, PC is often considered as a biological barrier reducing the targeting efficiency of nano vehicles. But the protein binding can actually be controlled by altering PC both <i>in vitro</i> and <i>in vivo</i>. Analyzing PC <i>in vivo</i> could accordingly provide a deep understanding of its biological effect and speed up the transfer to clinical applications. This review demonstrates the need for clarifications on the effect of PC <i>in vivo</i> and the control of its behavior by changing its physicochemical properties. It unfolds the recent <i>in vivo</i> developments to understand mechanisms and challenges at the nano-bio interface. Finally, it reports recent advances in the <i>in vivo</i> PC to overcome and control the limitations of the <i>in vitro</i> PC by employing PC as a boosting resource to prolong the NPs half-life, to improve their formulations and thereby to increase its use for biomedical applications. In vivo ; Nanoparticle ; Corona (planetary geology) ; Nanotechnology ; Materials science ; Biology ; Biotechnology Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Minerals Flotation and Separation Techniques
Authors: Emilio Martínez‐Núñez | George L. Barnes | David R. Glowacki | Sabine Kopec | Daniel Peláez | Aurelio Rodrı́guez | Roberto Rodríguez‐Fernández | Robin J. Shannon | James J.P. Stewart | Pablo G. Tahoces | Saulo A. Vázquez
Source: Journal of Computational Chemistry (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

<scp>AutoMeKin2021</scp>: An open‐source program for automated reaction discovery Abstract AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms ( J. Comput. Chem . 2018 , 39 , 1922). This release features a number of new capabilities: rare‐event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond‐order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin Computer science ; Extension (predicate logic) ; Open source ; Source code ; Event (particle physics) ; Code (set theory) ; Data mining ; Programming language ; Software ; Physics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: F. Benhiba | Rachid Hsissou | Zakaria Benzekri | S. Echihi | J. El-Blilak | Saı̈d Boukhris | A. Bellaouchou | A. Guenbour | H. Oudda | Ismail Warad | Nada Kheira Sebbar | A. Zarrouk
Source: Journal of Molecular Liquids (article, 2021-05-21)
Field: Chemistry — Organic Chemistry

DFT/electronic scale, MD simulation and evaluation of 6-methyl-2-(p-tolyl)-1,4-dihydroquinoxaline as a potential corrosion inhibition Corrosion ; Dielectric spectroscopy ; Adsorption ; Langmuir adsorption model ; Chemistry ; Metal ; Scanning electron microscope ; Electrochemistry ; Analytical Chemistry (journal) ; Surface charge ; Molecular dynamics ; Computational chemistry ; Physical chemistry ; Materials science ; Chromatography ; Organic chemistry ; Composite material Synthesis and Biological Evaluation ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Stanislav Obruča | Petr Sedláček | Martin Koller
Source: Bioresource Technology (review, 2021-01-27)
Field: Materials Science — Biomaterials

The underexplored role of diverse stress factors in microbial biopolymer synthesis Polyhydroxyalkanoates ; Biopolymer ; Biochemical engineering ; Stressor ; Biotechnology ; Biology ; Bacteria ; Chemistry ; Polymer ; Engineering biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Electrospun Nanofibers in Biomedical Applications
Authors: Ahmed M. El‐Zohry | Esam A. Orabi | Martin Karlsson | Burkhard Zietz
Source: The Journal of Physical Chemistry A (article, 2021-04-05)
Field: Chemistry — Physical and Theoretical Chemistry

Twisted Intramolecular Charge Transfer (TICT) Controlled by Dimerization: An Overlooked Piece of the TICT Puzzle Organic dyes have shown high efficiencies in solar cells, which is mainly attributed to the push-pull strategy present in such dyes upon attaching to the semiconductor surfaces. We deeply studied the fundamental photophysical properties of cyanoacrylic dyes, mostly the L1 dye, and found unique emission properties that depend on many factors such as the solvent polarity and the concentration of the dye and could present a complete emission picture about this family of dyes. The L1 dye shows an intramolecular charge transfer (ICT) emission state at low concentrations (approximately nanomolar scale) and shows a twisted intramolecular charge transfer (TICT) emission state in specific solvents upon increasing the concentration to the micromolar scale. Moreover, the associated emission lifetimes of the ICT and TICT states of the L1 dye depend on solvent basicity, highlighting the role of hydrogen bond formation on controlling such states. Density functional theory calculations are performed to gain insight into the photophysical properties of the dye and revealed that H-bonding between the carboxylic groups triggers the dimerization at low concentrations. Using femtosecond transient absorption, we assigned the rate of TICT formation to be in the range (160-650 fs)<sup>-1</sup>, depending on the size of the studied cyanoacrylic dye. Therefore, we add herein a new dimension for controlling the formation of the TICT state, in addition to the solvent polarity and acceptor strength parameters. These findings are not limited to the studied dyes, and we expect that numerous organic carboxylic acids dyes show similar properties. Intramolecular force ; Photochemistry ; Chemistry ; Acceptor ; Density functional theory ; Hydrogen bond ; Solvent ; Ultrafast laser spectroscopy ; Chemical physics ; Molecule ; Computational chemistry ; Organic chemistry ; Spectroscopy Photochemistry and Electron Transfer Studies ; Quantum Dots Synthesis And Properties ; Spectroscopy and Quantum Chemical Studies
Authors: Jiucheng Cheng | Chunkai Zhang | Lifeng Dong
Source: Communications Materials (article, 2021-09-06)
Field: Materials Science — Materials Chemistry

A geometric-information-enhanced crystal graph network for predicting properties of materials Abstract Graph neural networks (GNNs) have been used previously for identifying new crystalline materials. However, geometric structure is not usually taken into consideration, or only partially. Here, we develop a geometric-information-enhanced crystal graph neural network (GeoCGNN) to predict the properties of crystalline materials. By considering the distance vector between each node and its neighbors, our model can learn full topological and spatial geometric structure information. Furthermore, we incorporate an effective method based on the mixed basis functions to encode the geometric information into our model, which outperforms other GNN methods in a variety of databases. For example, for predicting formation energy our model is 25.6%, 14.3% and 35.7% more accurate than CGCNN, MEGNet and iCGCNN models, respectively. For band gap, our model outperforms CGCNN by 27.6% and MEGNet by 12.4%. ENCODE ; Spatial network ; Geometric networks ; Computer science ; Graph ; Artificial neural network ; Geometric modeling ; Data mining ; Theoretical computer science ; Topology (electrical circuits) ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Combinatorics ; Complex network ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: Ninh M. La‐Beck | Md. Rakibul Islam | Maciej M. Markiewski
Source: Frontiers in Immunology (review, 2021-01-08)
Field: Materials Science — Biomaterials

Nanoparticle-Induced Complement Activation: Implications for Cancer Nanomedicine Nanoparticle-based anticancer medications were first approved for cancer treatment almost 2 decades ago. Patients benefit from these approaches because of the targeted-drug delivery and reduced toxicity, however, like other therapies, adverse reactions often limit their use. These reactions are linked to the interactions of nanoparticles with the immune system, including the activation of complement. This activation can cause well-characterized acute inflammatory reactions mediated by complement effectors. However, the long-term implications of chronic complement activation on the efficacy of drugs carried by nanoparticles remain obscured. The recent discovery of protumor roles of complement raises the possibility that nanoparticle-induced complement activation may actually reduce antitumor efficacy of drugs carried by nanoparticles. We discuss here the initial evidence supporting this notion. Better understanding of the complex interactions between nanoparticles, complement, and the tumor microenvironment appears to be critical for development of nanoparticle-based anticancer therapies that are safer and more efficacious. Nanomedicine ; Complement system ; Complement (music) ; Medicine ; Drug delivery ; Cancer ; Immune system ; Nanoparticle ; Chemistry ; Pharmacology ; Nanotechnology ; Immunology ; Materials science ; Internal medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Complement system in diseases ; Immunotherapy and Immune Responses
Authors: Miaoling Que | Chong Lin | Jiawei Sun | Lixiang Chen | Xiaohong Sun | Yunfei Sun
Source: Sensors (review, 2021-08-16)
Field: Materials Science — Materials Chemistry

Progress in ZnO Nanosensors Developing various nanosensors with superior performance for accurate and sensitive detection of some physical signals is essential for advances in electronic systems. Zinc oxide (ZnO) is a unique semiconductor material with wide bandgap (3.37 eV) and high exciton binding energy (60 meV) at room temperature. ZnO nanostructures have been investigated extensively for possible use as high-performance sensors, due to their excellent optical, piezoelectric and electrochemical properties, as well as the large surface area. In this review, we primarily introduce the morphology and major synthetic methods of ZnO nanomaterials, with a brief discussion of the advantages and weaknesses of each method. Then, we mainly focus on the recent progress in ZnO nanosensors according to the functional classification, including pressure sensor, gas sensor, photoelectric sensor, biosensor and temperature sensor. We provide a comprehensive analysis of the research status and constraints for the development of ZnO nanosensor in each category. Finally, the challenges and future research directions of nanosensors based on ZnO are prospected and summarized. It is of profound significance to research ZnO nanosensors in depth, which will promote the development of artificial intelligence, medical and health, as well as industrial, production. Nanosensor ; Nanotechnology ; Nanomaterials ; Materials science ; Semiconductor ; Biosensor ; Computer science ; Optoelectronics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Yun Zhang | Xiaojie Xu
Source: Solid State Sciences (article, 2021-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting lattice parameters for orthorhombic distorted-perovskite oxides via machine learning Orthorhombic crystal system ; Electronegativity ; Ionic radius ; Materials science ; Valence (chemistry) ; Lattice constant ; Perovskite (structure) ; Lattice (music) ; Crystal structure ; Chemistry ; Ion ; Crystallography ; Physics Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Francesca Perin | Antonella Motta | Devid Maniglio
Source: Materials Science and Engineering C (review, 2021-02-09)
Field: Chemistry — Organic Chemistry

Amphiphilic copolymers in biomedical applications: Synthesis routes and property control Amphiphile ; Materials science ; Copolymer ; Biocompatibility ; Nanotechnology ; Drug delivery ; Polymer ; Self-healing hydrogels ; Polymer chemistry Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications
Authors: Marianna Mamusa | Claudio Resta | Constantina Sofroniou | Piero Baglioni
Source: Advances in Colloid and Interface Science (review, 2021-10-12)
Field: Chemistry — Organic Chemistry

Encapsulation of volatile compounds in liquid media: Fragrances, flavors, and essential oils in commercial formulations Cosmetics ; Biochemical engineering ; Sustainability ; Encapsulation (networking) ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Engineering Antimicrobial agents and applications ; Environmental Chemistry and Analysis ; Microencapsulation and Drying Processes
Authors: Maksim Kulichenko | Justin S. Smith | Benjamin Nebgen | Ying Wai Li | Nikita Fedik | Alexander I. Boldyrev | Nicholas Lubbers | Kipton Barros | Sergei Tretiak
Source: The Journal of Physical Chemistry Letters (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

The Rise of Neural Networks for Materials and Chemical Dynamics Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials. Computer science ; Fidelity ; Artificial neural network ; Set (abstract data type) ; Data set ; Retraining ; Quality (philosophy) ; Experimental data ; Artificial intelligence ; Machine learning ; Perspective (graphical) ; Density functional theory ; Chemistry ; Mathematics ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zongrui Pei | Kyle A. Rozman | Ömer Doğan | Youhai Wen | Nan Gao | Elizabeth A. Holm | Jeffrey A. Hawk | David E. Alman | Michael C. Gao
Source: Advanced Science (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Machine‐Learning Microstructure for Inverse Material Design Metallurgy and material design have thousands of years' history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here, a successful machine-learning (ML) method has been developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features. Microstructure ; Inverse ; Materials science ; Computer science ; Composite material ; Mathematics ; Geometry Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Jingjin He | Junjie Li | Chuanbao Liu | Changxin Wang | Yan Zhang | Cheng Wen | Dezhen Xue | Jiangli Cao | Yanjing Su | Lijie Qiao | Yang Bai
Source: Acta Materialia (article, 2021-03-20)
Field: Materials Science — Materials Chemistry

Machine learning identified materials descriptors for ferroelectricity Ferroelectricity ; Electronegativity ; Materials science ; Valence (chemistry) ; Machine learning ; Bootstrapping (finance) ; Ferroelectric ceramics ; Perovskite (structure) ; Artificial intelligence ; Ceramic ; Piezoelectricity ; Computer science ; Mathematics ; Crystallography ; Physics ; Chemistry ; Dielectric ; Composite material ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides