jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adewumi O. Dada | Adejumoke A. Inyinbor | Olugbenga Solomon Bello | Blessing Enyojo Tokula
Source: Biomass Conversion and Biorefinery (article, 2021-08-21)
Field: Materials Science — Biomaterials

Novel plantain peel activated carbon–supported zinc oxide nanocomposites (PPAC-ZnO-NC) for adsorption of chloroquine synthetic pharmaceutical used for COVID-19 treatment Adsorption ; Nanocomposite ; Sorption ; Langmuir adsorption model ; Activated carbon ; Crystallinity ; Nuclear chemistry ; Langmuir ; Fourier transform infrared spectroscopy ; Chemical engineering ; Chemistry ; Point of zero charge ; Materials science ; Organic chemistry ; Nanotechnology ; Composite material Nanoparticle-Based Drug Delivery ; Adsorption and biosorption for pollutant removal ; Graphene and Nanomaterials Applications
Authors: Xuejian Mu | Hao Zhang | Chen‐Yan Zhang | Shuya Yang | Jie Xu | Yicong Huang | Yongcheng Zhang | Qiang Li | Xia Wang | Derang Cao | Shandong Li
Source: Journal of Materials Science (article, 2021-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Poly(vinylidene fluoride-trifluoroethylene)/cobalt ferrite composite films with a self-biased magnetoelectric effect for flexible AC magnetic sensors Materials science ; Composite number ; Composite material ; Ferrite (magnet) ; Ferroelectricity ; Nanoparticle ; Superparamagnetism ; Ferromagnetism ; Magnetization ; Magnetic field ; Optoelectronics ; Nanotechnology ; Condensed matter physics ; Dielectric Multiferroics and related materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Piezoelectric Materials
Authors: Martin Nichterwitz | S. Honnali | Maksim Kutuzau | Shanshan Guo | Jonas Zehner | Kornelius Nielsch | Karin Leistner
Source: APL Materials (article, 2021-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances in magneto-ionic materials and perspectives for their application The possibility of tuning magnetic material properties by ionic means is exciting both for basic science and, especially in view of the excellent energy efficiency and room temperature operation, for potential applications. In this perspective, we shortly introduce the functionality of magneto-ionic materials and focus on important recent advances in this field. We present a comparative overview of state-of-the-art magneto-ionic materials considering the achieved magnetoelectric voltage coefficients for magnetization and coercivity and the demonstrated time scales for magneto-ionic switching. Furthermore, the application perspectives of magneto-ionic materials in data storage and computing, magnetic actuation, and sensing are evaluated. Finally, we propose potential research directions to push this field forward and tackle the challenges related to future applications. Ionic bonding ; Materials science ; Magneto ; Engineering physics ; Coercivity ; Nanotechnology ; Field (mathematics) ; Voltage ; Focus (optics) ; Magnetization ; Magnetic field ; Ion ; Condensed matter physics ; Electrical engineering ; Physics ; Engineering Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; ZnO doping and properties
Authors: Amritha Vijayan | C. K. Nanditha | G. S. Vinod Kumar
Source: Nanoscale Advances (article, 2021-01-01)
Field: Materials Science — Biomaterials

ECM-mimicking nanofibrous scaffold enriched with dual growth factor carrying nanoparticles for diabetic wound healing Polymeric nanofibrous scaffolds provide fine-tuned structures with inter-connecting pores resembling the natural extracellular matrix (ECM) in tissues, and show good potential in assisting the creation of artificial functional tissue. Additional application of growth factors helps to regulate the cellular behaviors and tissue assembly in the scaffolds, which eases the healing process. In this study, we synthesized an electrospun polymer scaffold system enriched with nanoparticles containing growth factors for accelerated healing of diabetic wounds. BSA nanoparticles were synthesized by cross-linking with PEG aldehyde. To free the amino group of BSA, heparin was conjugated by EDC/NHS chemistry. The angiogenic growth factors bFGF and VEGF were bound to heparin by electrostatic interaction. These nanoparticles were adsorbed on to electrospun collagen/PLGA/chitosan nanofibers. The synthesized nanofiber system was evaluated <i>in vitro</i> for its cell viability and proliferation. <i>In vivo</i> experiments conducted in a streptozotocin-induced diabetic mice model showed accelerated wound healing. The excellent healing efficiency of this ECM-mimicking nanofiber scaffold makes it a great candidate for therapeutic application in diabetic wounds. Scaffold ; Wound healing ; Growth factor ; Dual (grammatical number) ; Nanofiber ; Nanoparticle ; Materials science ; Biomedical engineering ; Chemistry ; Nanotechnology ; Medicine ; Surgery ; Biochemistry ; Art ; Receptor Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Serena Coiai | Maria Laura Di Lorenzo | Patrizia Cinelli | Maria Cristina Righetti | Elisa Passaglia
Source: Polymers (review, 2021-07-28)
Field: Materials Science — Biomaterials

Binary Green Blends of Poly(lactic acid) with Poly(butylene adipate-co-butylene terephthalate) and Poly(butylene succinate-co-butylene adipate) and Their Nanocomposites Poly(lactic acid) (PLA) is the most widely produced biobased, biodegradable and biocompatible polyester. Despite many of its properties are similar to those of common petroleum-based polymers, some drawbacks limit its utilization, especially high brittleness and low toughness. To overcome these problems and improve the ductility and the impact resistance, PLA is often blended with other biobased and biodegradable polymers. For this purpose, poly(butylene adipate-<i>co</i>-butylene terephthalate) (PBAT) and poly(butylene succinate-<i>co</i>-butylene adipate) (PBSA) are very advantageous copolymers, because their toughness and elongation at break are complementary to those of PLA. Similar to PLA, both these copolymers are biodegradable and can be produced from annual renewable resources. This literature review aims to collect results on the mechanical, thermal and morphological properties of PLA/PBAT and PLA/PBSA blends, as binary blends with and without addition of coupling agents. The effect of different compatibilizers on the PLA/PBAT and PLA/PBSA blends properties is here elucidated, to highlight how the PLA toughness and ductility can be improved and tuned by using appropriate additives. In addition, the incorporation of solid nanoparticles to the PLA/PBAT and PLA/PBSA blends is discussed in detail, to demonstrate how the nanofillers can act as morphology stabilizers, and so improve the properties of these PLA-based formulations, especially mechanical performance, thermal stability and gas/vapor barrier properties. Key points about the biodegradation of the blends and the nanocomposites are presented, together with current applications of these novel green materials. Adipate ; Materials science ; Polybutylene succinate ; Toughness ; Polyester ; Nanocomposite ; Biodegradable polymer ; Polylactic acid ; Heat deflection temperature ; Thermal stability ; Ductility (Earth science) ; Compatibilization ; Biodegradation ; Lactic acid ; Copolymer ; Polymer ; Polymer blend ; Chemical engineering ; Composite material ; Polymer chemistry ; Izod impact strength test ; Organic chemistry ; Chemistry ; Ultimate tensile strength biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Bone Tissue Engineering Materials
Authors: Johan Ø. Ipsen | Magnus Hallas-Møller | Søren Brander | Leila Lo Leggio | Katja S. Johansen
Source: Biochemical Society Transactions (review, 2021-01-15)
Field: Chemistry — Inorganic Chemistry

Lytic polysaccharide monooxygenases and other histidine-brace copper proteins: structure, oxygen activation and biotechnological applications Lytic polysaccharide monooxygenases (LPMOs) are mononuclear copper enzymes that catalyse the oxidative cleavage of glycosidic bonds. They are characterised by two histidine residues that coordinate copper in a configuration termed the Cu-histidine brace. Although first identified in bacteria and fungi, LPMOs have since been found in all biological kingdoms. LPMOs are now included in commercial enzyme cocktails used in industrial biorefineries. This has led to increased process yield due to the synergistic action of LPMOs with glycoside hydrolases. However, the introduction of LPMOs makes control of the enzymatic step in industrial stirred-tank reactors more challenging, and the operational stability of the enzymes is reduced. It is clear that much is still to be learned about the interaction between LPMOs and their complex natural and industrial environments, and fundamental scientific studies are required towards this end. Several atomic-resolution structures have been solved providing detailed information on the Cu-coordination sphere and the interaction with the polysaccharide substrate. However, the molecular mechanisms of LPMOs are still the subject of intense investigation; the key question being how the proteinaceous environment controls the copper cofactor towards the activation of the O-O bond in O2 and cleavage of the glycosidic bonds in polysaccharides. The need for biochemical characterisation of each putative LPMO is discussed based on recent reports showing that not all proteins with a Cu-histidine brace are enzymes. Glycosidic bond ; Histidine ; Monooxygenase ; Chemistry ; Lytic cycle ; Polysaccharide ; Biochemistry ; Enzyme ; Cofactor ; Biology Metal-Catalyzed Oxygenation Mechanisms ; Enzyme Production and Characterization ; Enzyme-mediated dye degradation
Authors: Aviv Karnieli | Yongyao Li | Ady Arie
Source: Frontiers of Physics (article, 2021-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The geometric phase in nonlinear frequency conversion Abstract The geometric phase of light has been demonstrated in various platforms of the linear optical regime, raising interest both for fundamental science as well as applications, such as flat optical elements. Recently, the concept of geometric phases has been extended to nonlinear optics, following advances in engineering both bulk nonlinear photonic crystals and nonlinear metasurfaces. These new technologies offer a great promise of applications for nonlinear manipulation of light. In this review, we cover the recent theoretical and experimental advances in the field of geometric phases accompanying nonlinear frequency conversion. We first consider the case of bulk nonlinear photonic crystals, in which the interaction between propagating waves is quasi-phase-matched, with an engineerable geometric phase accumulated by the light. Nonlinear photonic crystals can offer efficient and robust frequency conversion in both the linearized and fully-nonlinear regimes of interaction, and allow for several applications including adiabatic mode conversion, electromagnetic nonreciprocity and novel topological effects for light. We then cover the rapidly-growing field of nonlinear Pancharatnam-Berry metasurfaces, which allow the simultaneous nonlinear generation and shaping of light by using ultrathin optical elements with subwavelength phase and amplitude resolution. We discuss the macroscopic selection rules that depend on the rotational symmetry of the constituent meta-atoms, the order of the harmonic generations, and the change in circular polarization. Continuous geometric phase gradients allow the steering of light beams and shaping of their spatial modes. More complex designs perform nonlinear imaging and multiplex nonlinear holograms, where the functionality is varied according to the generated harmonic order and polarization. Recent advancements in the fabrication of three dimensional nonlinear photonic crystals, as well as the pursuit of quantum light sources based on nonlinear metasurfaces, offer exciting new possibilities for novel nonlinear optical applications based on geometric phases. Nonlinear system ; Geometric phase ; Nonlinear optics ; Physics ; Photonics ; Second-harmonic generation ; Polarization (electrochemistry) ; Phase (matter) ; Adiabatic process ; Optics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Fiber Laser Technologies ; Photonic and Optical Devices
Authors: Hang Yin | Zhehao Sun | Zhuo Wang | Dawei Tang | Cheng Heng Pang | Xue‐Feng Yu | Amanda S. Barnard | Haitao Zhao | Zongyou Yin
Source: Cell Reports Physical Science (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning Machine learning (ML) has experienced rapid development in recent years and been widely applied to assist studies in various research areas. Two-dimensional (2D) materials, due to their unique chemical and physical properties, have been receiving increasing attention since the isolation of graphene. The combination of ML and 2D materials science has significantly accelerated the development of new functional 2D materials, and a timely review may inspire further ML-assisted 2D materials development. In this review, we provide a horizontal and vertical summary of the recent advances at the intersection of the fields of ML and 2D materials, discussing ML-assisted 2D materials preparation (design, discovery, and synthesis of 2D materials), atomistic structure analysis (structure identification and formation mechanism), and properties prediction (electronic properties, thermodynamic properties, mechanical properties, and other properties) and revealing their connections. Finally, we highlight current research challenges and provide insight into future research opportunities. Intersection (aeronautics) ; Isolation (microbiology) ; Identification (biology) ; Computer science ; Data science ; Graphene ; Nanotechnology ; Research development ; Mechanism (biology) ; Biochemical engineering ; Materials science ; Engineering ; Physics ; Geology ; Aerospace engineering ; Bioinformatics Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Xin Li | Bo Li | Ze Yang | Zhiwen Chen | Wang Gao | Qing Jiang
Source: Journal of Materials Chemistry A (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies We propose a transferable machine-learning model based on the intrinsic descriptors, which can predict the adsorption energies of single-atom alloys, AB intermetallics and high-entropy alloys , simply by training the properties of transition metals. Adsorption ; Intermetallic ; Materials science ; Atom (system on chip) ; Entropy (arrow of time) ; Thermodynamics ; Statistical physics ; Computer science ; Metallurgy ; Chemistry ; Physical chemistry ; Physics ; Alloy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; nanoparticles nucleation surface interactions
Authors: Krishnendu Mukherjee | Yamil J. Colón
Source: Molecular Simulation (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Machine learning and descriptor selection for the computational discovery of metal-organic frameworks Metal-organic frameworks (MOFs), crystalline materials with high internal surface area and pore volume, have demonstrated great potential for many applications. In the past decade, as large number of MOFs have come into existence, there has been an effort to model them using computers. High-throughput screening techniques in tandem with molecular simulations or ab-initio calculations are being used to calculate their properties. However, the number of MOFs that can be hypothetically created are in the millions, and though computer simulations have shown remarkable accuracy, we cannot deploy them for all structures due to their high-computational cost. In this regard, machine learning (ML)-based algorithms have proven to be effective in predicting material properties and reducing the need for expensive calculations. Adopting this methodology can save time and allow researchers to explore materials in unchartered chemical space, thus ushering an era of high-throughput in-silico material design using ML. In this work, we present what is ML, its associated workflow, selecting descriptors, and how it can help build reliable models for discovering MOFs. We present some popular and novel ones. Thereafter, we review some of the recent studies with respect to ML-based implementation for MOF discovery emphasizing descriptors selected and the workflow adopted. Workflow ; Computer science ; Chemical space ; Throughput ; Selection (genetic algorithm) ; Metal-organic framework ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Computational science ; Nanotechnology ; Drug discovery ; Materials science ; Chemistry ; Database ; Engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Shufeng Kong | Dan Guevarra | Carla P. Gomes | John M. Gregoire
Source: Applied Physics Reviews (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Materials representation and transfer learning for multi-property prediction The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences. Property (philosophy) ; Computer science ; Machine learning ; Transfer of learning ; Generative grammar ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jonas Elm
Source: ACS Omega (article, 2021-03-12)
Field: Chemistry — Inorganic Chemistry

Clusteromics I: Principles, Protocols, and Applications to Sulfuric Acid–Base Cluster Formation We recently coined the term clusteromics as a holistic approach for obtaining insight into the chemical complexity of atmospheric molecular cluster formation and at the same time providing the foundation for thermochemical databases that can be utilized for developing machine learning models. Here, we present the first paper in the series that applies state-of-the-art computational methods to study multicomponent (SA)<sub>0-2</sub>(base)<sub>0-2</sub> clusters, with SA = sulfuric acid and base = [ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA)] with all combinations of the five bases. The initial cluster configurations are obtained using the ABCluster program and the number of relevant configurations are reduced based on PM7 and ωB97X-D/6-31++G(d,p) calculations. Thermochemical parameters are calculated based on the ωB97X-D/6-31++G(d,p) cluster structures and vibrational frequencies using the quasi-harmonic approximation. The single-point energies are refined with a high-level DLPNO-CCSD(T<sub>0</sub>)/aug-cc-pVTZ calculation. Using the calculated thermochemical data, we perform kinetics simulations to evaluate the potential of these small (SA)<sub>0-2</sub>(base)<sub>0-2</sub> clusters to grow into larger cluster sizes. In all cases we find that having more than one type of base molecule present in the cluster will increase the potential for forming larger clusters primarily due to the increased available vapor concentration. Sulfuric acid ; Cluster (spacecraft) ; Base (topology) ; Computer science ; Chemistry ; Inorganic chemistry ; Computer network ; Mathematics Radioactive element chemistry and processing ; nanoparticles nucleation surface interactions ; Quantum Dots Synthesis And Properties
Authors: Diego E. Navarro-López | Rebeca García‐Varela | O. Ceballos-Sánchez | A. Sánchez-Martínez | Gildardo Sanchez‐Ante | Kaled Corona-Romero | D.A. Buentello–Montoya | Alex Elı́as-Zúñiga | Edgar R. López-Mena
Source: Materials Science and Engineering C (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Effective antimicrobial activity of ZnO and Yb-doped ZnO nanoparticles against Staphylococcus aureus and Escherichia coli Wurtzite crystal structure ; Ytterbium ; X-ray photoelectron spectroscopy ; Crystallite ; Absorbance ; Materials science ; Polyvinyl alcohol ; Doping ; Nanoparticle ; Antimicrobial ; Nuclear chemistry ; Escherichia coli ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Chemical engineering ; Chromatography ; Zinc ; Organic chemistry ; Optoelectronics ; Metallurgy ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Conducting polymers and applications
Authors: Jahaun Azadmanesh | William E. Lutz | Leighton Coates | Kevin L. Weiss | Gloria E. O. Borgstahl
Source: Nature Communications (article, 2021-04-06)
Field: Chemistry — Inorganic Chemistry

Direct detection of coupled proton and electron transfers in human manganese superoxide dismutase Human manganese superoxide dismutase is a critical oxidoreductase found in the mitochondrial matrix. Concerted proton and electron transfers are used by the enzyme to rid the mitochondria of O<sub>2</sub><sup>•-</sup>. The mechanisms of concerted transfer enzymes are typically unknown due to the difficulties in detecting the protonation states of specific residues and solvent molecules at particular redox states. Here, neutron diffraction of two redox-controlled manganese superoxide dismutase crystals reveal the all-atom structures of Mn<sup>3+</sup> and Mn<sup>2+</sup> enzyme forms. The structures deliver direct data on protonation changes between oxidation states of the metal. Observations include glutamine deprotonation, the involvement of tyrosine and histidine with altered pK<sub>a</sub>s, and four unusual strong-short hydrogen bonds, including a low barrier hydrogen bond. We report a concerted proton and electron transfer mechanism for human manganese superoxide dismutase from the direct visualization of active site protons in Mn<sup>3+</sup> and Mn<sup>2+</sup> redox states. Chemistry ; Protonation ; Oxidoreductase ; Electron transfer ; Superoxide dismutase ; Redox ; Manganese ; Deprotonation ; Photochemistry ; Enzyme ; Biochemistry ; Inorganic chemistry ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Mitochondrial Function and Pathology ; Electron Spin Resonance Studies