jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksandrs Leitis | Ming Lun Tseng | Aurelian John‐Herpin | Yuri S. Kivshar | Hatice Altug
Source: Advanced Materials (article, 2021-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wafer‐Scale Functional Metasurfaces for Mid‐Infrared Photonics and Biosensing Metasurfaces have emerged as a breakthrough platform for manipulating light at the nanoscale and enabling on-demand optical functionalities for next-generation biosensing, imaging, and light-generating photonic devices. However, translating this technology to practical applications requires low-cost and high-throughput fabrication methods. Due to the limited choice of materials with suitable optical properties, it is particularly challenging to produce metasurfaces for the technologically relevant mid-infrared spectral range. These constraints are overcome by realizing functional metasurfaces on almost completely transparent free-standing metal-oxide membranes. A versatile nanofabrication process is developed and implemented for highly efficient dielectric and plasmonic mid-infrared metasurfaces with wafer-scale and complementary metal-oxide-semiconductor (CMOS)-compatible manufacturing techniques. The advantages of this method are revealed by demonstrating highly uniform and functional metasurfaces, including high-Q structures enabling fine spectral selectivity, large-area metalenses with diffraction-limited focusing capabilities, and birefringent metasurfaces providing polarization control at record-high conversion efficiencies. Aluminum plasmonic devices and their integration into microfluidics for real-time and label-free mid-infrared biosensing of proteins and lipid vesicles are further demonstrated. The versatility of this approach and its compatibility with mass-production processes bring infrared metasurfaces markedly closer to commercial applications, such as thermal imaging, spectroscopy, and biosensing. Materials science ; Wafer ; Photonics ; Nanotechnology ; Infrared ; Biosensor ; Scale (ratio) ; Optoelectronics ; Optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Mohammad Okkeh | Nora Bloise | Elisa Restivo | Lorenzo De Vita | Piersandro Pallavicini | Livia Visai
Source: Nanomaterials (review, 2021-01-26)
Field: Materials Science — Materials Chemistry

Gold Nanoparticles: Can They Be the Next Magic Bullet for Multidrug-Resistant Bacteria? In 2017 the World Health Organization (WHO) announced a list of the 12 multidrug-resistant (MDR) families of bacteria that pose the greatest threat to human health, and recommended that new measures should be taken to promote the development of new therapies against these superbugs. Few antibiotics have been developed in the last two decades. Part of this slow progression can be attributed to the surge in the resistance acquired by bacteria, which is holding back pharma companies from taking the risk to invest in new antibiotic entities. With limited antibiotic options and an escalating bacterial resistance there is an urgent need to explore alternative ways of meeting this global challenge. The field of medical nanotechnology has emerged as an innovative and a powerful tool for treating some of the most complicated health conditions. Different inorganic nanomaterials including gold, silver, and others have showed potential antibacterial efficacies. Interestingly, gold nanoparticles (AuNPs) have gained specific attention, due to their biocompatibility, ease of surface functionalization, and their optical properties. In this review, we will focus on the latest research, done in the field of antibacterial gold nanoparticles; by discussing the mechanisms of action, antibacterial efficacies, and future implementations of these innovative antibacterial systems. Magic bullet ; Nanotechnology ; Antibiotics ; Antibiotic resistance ; Human health ; Multiple drug resistance ; Business ; Medicine ; Materials science ; Microbiology ; Biology ; Bioinformatics Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Bacterial biofilms and quorum sensing
Authors: Monika Pareek | Yernaidu Reddi | Raghavan B. Sunoj
Source: Chemical Science (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Tale of the Breslow intermediate, a central player in N-heterocyclic carbene organocatalysis: then and now Molecular insights on the formation, detection, and even isolation of the Breslow intermediate, which is the most important species in N-heterocyclic carbene (NHC) catalysis, as obtained from experimental and computational studies, are presented. Carbene ; Organocatalysis ; Catalysis ; Chemistry ; Isolation (microbiology) ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Enantioselective synthesis ; Biology ; Bioinformatics N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Synthetic Organic Chemistry Methods
Authors: K. Srinivasu | Arun Gopalan | Maciej Harańczyk | Randall Q. Snurr
Source: Journal of Chemical Theory and Computation (article, 2021-03-19)
Field: Chemistry — Inorganic Chemistry

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO<sub>2</sub>. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors. Partial charge ; Electronegativity ; Density functional theory ; Atom (system on chip) ; Chemistry ; Atomic charge ; Dipole ; Computation ; Charge (physics) ; Chemical physics ; Atomic radius ; Molecule ; Statistical physics ; Computational chemistry ; Computer science ; Physics ; Algorithm ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhenze Yang | Chi-Hua Yu | Kai Guo | Markus J. Buehler
Source: Journal of the Mechanics and Physics of Solids (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures Composite number ; Microstructure ; Materials science ; Infinitesimal strain theory ; Tensor (intrinsic definition) ; Cauchy stress tensor ; Stress–strain curve ; Stress (linguistics) ; Computer science ; Structural engineering ; Composite material ; Mathematics ; Geometry ; Finite element method ; Mathematical analysis ; Engineering ; Deformation (meteorology) Machine Learning in Materials Science ; Cellular and Composite Structures ; Fiber-reinforced polymer composites
Authors: Jie Wang | Victor Malgras | Yoshiyuki Sugahara | Yusuke Yamauchi
Source: Nature Communications (article, 2021-06-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrochemical energy storage performance of 2D nanoarchitectured hybrid materials The fast-growing interest for two-dimensional (2D) nanomaterials is undermined by their natural restacking tendency, which severely limits their practical application. Novel porous heterostructures that coordinate 2D nanosheets with monolayered mesoporous scaffolds offer an opportunity to greatly expand the library of advanced materials suitable for electrochemical energy storage technologies. Electrochemical energy storage ; Mesoporous material ; Energy storage ; Nanotechnology ; Nanomaterials ; Electrochemistry ; Heterojunction ; Materials science ; Supercapacitor ; Porosity ; Electrochemical energy conversion ; Computer science ; Chemistry ; Electrode ; Optoelectronics ; Catalysis ; Composite material ; Physics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Cheol Woo Park | Mordechai Kornbluth | Jonathan Vandermause | Chris Wolverton | Boris Kozinsky | Jonathan P. Mailoa
Source: npj Computational Materials (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems. Molecular dynamics ; Force field (fiction) ; Scalability ; Covariant transformation ; Computer science ; Artificial neural network ; Limiting ; Invariant (physics) ; Graph ; Statistical physics ; Physics ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Computational chemistry ; Engineering ; Mechanical engineering ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Richard J. Mandle | Nerea Sebastián | Josu Martinez-Perdiguero | Alenka Mertelj
Source: Nature Communications (article, 2021-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the molecular origins of the ferroelectric splay nematic phase Nematic liquid crystals have been known for more than a century, but it was not until the 60s-70s that, with the development of room temperature nematics, they became widely used in applications. Polar nematic phases have been long-time predicted, but have only been experimentally realized recently. Synthesis of materials with nematic polar ordering at room temperature is certainly challenging and requires a deep understanding of its formation mechanisms, presently lacking. Here, we compare two materials of similar chemical structure and demonstrate that just a subtle change in the molecular structure enables denser packing of the molecules when they exhibit polar order, which shows that reduction of excluded volume is in the origin of the polar nematic phase. Additionally, we propose that molecular dynamics simulations are potent tools for molecular design in order to predict, identify and design materials showing the polar nematic phase and its precursor nematic phases. Liquid crystal ; Polar ; Biaxial nematic ; Phase (matter) ; Chemical polarity ; Ferroelectricity Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Chemical and Physical Properties of Materials
Authors: Lies De Keer | Karsu I. Kilic | Paul Van Steenberge | Lode Daelemans | Daniel Kodura | Hendrik Frisch | Karen De Clerck | Marie‐Françoise Reyniers | Christopher Barner‐Kowollik | Reinhold H. Dauskardt | Dagmar D’hooge
Source: Nature Materials (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Computational prediction of the molecular configuration of three-dimensional network polymers Intramolecular force ; Materials science ; Kinetic Monte Carlo ; Molecular dynamics ; Biological system ; Computer science ; Statistical physics ; Monte Carlo method ; Computational chemistry ; Chemistry ; Physics ; Mathematics Mesoporous Materials and Catalysis ; Polyoxometalates: Synthesis and Applications ; Polymer composites and self-healing
Authors: Sara Rebeccani | Alessandra Zanut | Claudio Santo | Giovanni Valenti | Francesco Paolucci
Source: Analytical Chemistry (article, 2021-12-15)
Field: Chemistry — Electrochemistry

A Guide Inside Electrochemiluminescent Microscopy Mechanisms for Analytical Performance Improvement ECL is luminescence generated by electrochemical reactions and for this reason it possesses better spatio-temporal control and low background in comparison with photoluminescence or other optical methods that rely on external light illumination.1–3 In the last 20 years ECL has proved to be a versatile and powerful analytical technique in different fields, ranging from fundamental research to commercial clinical and biological applications.4,5 The main reason behind its success is that ECL offers remarkable advantages in comparison to other transduction methods: high sensitivity, an extremely wide dynamic range, very low background signal, good temporal and spatial control, and insensitivity to matrix effects.6,7 Thanks to its simplified optical setup, ECL has been implemented as a powerful imaging technique to visualize electrochemical objects and entities bringing important insight in the ECL mechanism generation. Here we aim to incorporate all the work done in the field of ECL imaging mainly in the last three years with a particular focus on ECL generation mechanisms and their applications. Library science ; Citation ; Chemistry ; Computer science Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Jianfeng Wen | Bingang Xu | Yuanyuan Gao | Meiqi Li | Hong Fu
Source: Energy storage materials (article, 2021-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wearable technologies enable high-performance textile supercapacitors with flexible, breathable and wearable characteristics for future energy storage Supercapacitor ; Wearable computer ; Wearable technology ; Flexibility (engineering) ; Energy storage ; Textile ; Materials science ; Electronics ; Nanotechnology ; Computer science ; Embedded system ; Electrical engineering ; Power (physics) ; Engineering ; Capacitance Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Advanced battery technologies research
Authors: Vahid Nozari | Courtney Calahoo | Joshua M. Tuffnell | David A. Keen | Thomas D. Bennett | Lothar Wondraczek
Source: Nature Communications (article, 2021-09-29)
Field: Chemistry — Inorganic Chemistry

Ionic liquid facilitated melting of the metal-organic framework ZIF-8 Hybrid glasses from melt-quenched metal-organic frameworks (MOFs) have been emerging as a new class of materials, which combine the functional properties of crystalline MOFs with the processability of glasses. However, only a handful of the crystalline MOFs are meltable. Porosity and metal-linker interaction strength have both been identified as crucial parameters in the trade-off between thermal decomposition of the organic linker and, more desirably, melting. For example, the inability of the prototypical zeolitic imidazolate framework (ZIF) ZIF-8 to melt, is ascribed to the instability of the organic linker upon dissociation from the metal center. Here, we demonstrate that the incorporation of an ionic liquid (IL) into the porous interior of ZIF-8 provides a means to reduce its melting temperature to below its thermal decomposition temperature. Our structural studies show that the prevention of decomposition, and successful melting, is due to the IL interactions stabilizing the rapidly dissociating ZIF-8 linkers upon heating. This understanding may act as a general guide for extending the range of meltable MOF materials and, hence, the chemical and structural variety of MOF-derived glasses. Zeolitic imidazolate framework ; Materials science ; Metal-organic framework ; Porosity ; Ionic liquid ; Thermal decomposition ; Imidazolate ; Chemical engineering ; Linker ; Ionic bonding ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Ion ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Zeolite Catalysis and Synthesis
Authors: Brandon C. Bukowski | Frerich J. Keil | Peter I. Ravikovitch | Germán Sastre | Randall Q. Snurr | Marc‐Olivier Coppens
Source: Adsorption (article, 2021-04-22)
Field: Chemistry — Inorganic Chemistry

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids Abstract Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a number of semi-crystalline and amorphous mesoporous solids, as well as hierarchically structured materials, containing both nanopores and wider meso- or macropores to facilitate transport over macroscopic distances. The ranges of pore sizes, shapes, and topologies spanned by these materials represent a considerable challenge for predicting molecular diffusivities, but fundamental understanding also provides an opportunity to guide the design of new nanoporous materials to increase the performance of transport limited processes. Remarkable progress in synthesis increasingly allows these designs to be put into practice. Molecular simulation techniques have been used in conjunction with experimental measurements to examine in detail the fundamental diffusion processes within nanoporous solids, to provide insight into the free energy landscape navigated by adsorbates, and to better understand nano-confinement effects. Pore network models, discrete particle models and synthesis-mimicking atomistic models allow to tackle diffusion in mesoporous and hierarchically structured porous materials, where multiscale approaches benefit from ever cheaper parallel computing and higher resolution imaging. Here, we discuss synergistic combinations of simulation and experiment to showcase theoretical progress and computational techniques that have been successful in predicting guest diffusion and providing insights. We also outline where new fundamental developments and experimental techniques are needed to enable more accurate predictions for complex systems. Nanoporous ; Mesoporous material ; Materials science ; Nanotechnology ; Diffusion ; Microporous material ; Nanopore ; Porous medium ; Amorphous solid ; Porosity ; Macropore ; Catalysis ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis
Authors: E. Keith Keenan | Derek K. Zachman | Matthew D. Hirschey
Source: Molecular Cell (review, 2021-04-01)
Field: Chemistry — Spectroscopy

Discovering the landscape of protein modifications Biology ; Archaea ; Proteomics ; Posttranslational modification ; Function (biology) ; Computational biology ; Protein function ; Data science ; Biochemistry ; Computer science ; Evolutionary biology Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Ubiquitin and proteasome pathways
Authors: Moisés Bustamante-Torres | David Romero-Fierro | Belén Arcentales-Vera | Samantha Pardo | Emilio Bucio
Source: Polymers (article, 2021-09-04)
Field: Materials Science — Polymers and Plastics

Interaction between Filler and Polymeric Matrix in Nanocomposites: Magnetic Approach and Applications In recent years, polymer nanocomposites produced by combining nanofillers and a polymeric matrix are emerging as interesting materials. Polymeric composites have a wide range of applications due to the outstanding and enhanced properties that are obtained thanks to the introduction of nanoparticles. Therefore, understanding the filler-matrix relationship is an important factor in the continued growth of this scientific area and the development of new materials with desired properties and specific applications. Due to their performance in response to a magnetic field magnetic nanocomposites represent an important class of functional nanocomposites. Due to their properties, magnetic nanocomposites have found numerous applications in biomedical applications such as drug delivery, theranostics, etc. This article aims to provide an overview of the filler-polymeric matrix relationship, with a special focus on magnetic nanocomposites and their potential applications in the biomedical field. Nanocomposite ; Materials science ; Filler (materials) ; Magnetic nanoparticles ; Nanoparticle ; Polymer ; Nanotechnology ; Composite material ; Matrix (chemical analysis) Polymer Nanocomposite Synthesis and Irradiation ; Nanoparticle-Based Drug Delivery ; Polymer Nanocomposites and Properties
Authors: Xiaorui Wang | Yuquan Li | Jiezhong Qiu | Guangyong Chen | Huanxiang Liu | Benben Liao | Chang‐Yu Hsieh | Xiaojun Yao
Source: Chemical Engineering Journal (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions Retrosynthetic analysis ; Transformer ; Computer science ; Synthon ; Octet ; Thermal inertia ; Artificial intelligence ; Engineering ; Chemistry ; Electrical engineering ; Voltage ; Geography ; Physics ; Stereochemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods