jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bo Li | Jing Zhang | Ziyu Luo | Xinpei Duan | Wei‐Qing Huang | Wangyu Hu | Anlian Pan | Lei Liao | Lang Jiang | Gui‐Fang Huang
Source: Science China Materials (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Amorphous B-doped graphitic carbon nitride quantum dots with high photoluminescence quantum yield of near 90% and their sensitive detection of Fe2+/Cd2+ Materials science ; Photoluminescence ; Quantum dot ; Amorphous solid ; Fluorescence ; Amorphous carbon ; Quantum yield ; Quenching (fluorescence) ; Boron nitride ; Nanotechnology ; Doping ; Boron ; Dopant ; Carbon fibers ; Optoelectronics ; Crystallography ; Chemistry ; Composite material ; Composite number ; Organic chemistry ; Physics ; Optics Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Advanced Photocatalysis Techniques
Authors: Morteza Hasanzadeh Kafshgari | Leonidas Agiotis | Isabelle Largillière | Sergiy Patskovsky | Michel Meunier
Source: Small (article, 2021-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Antibody‐Functionalized Gold Nanostar‐Mediated On‐Resonance Picosecond Laser Optoporation for Targeted Delivery of RNA Therapeutics The rapid advances of genetic and genomic technology indicate promising therapeutic potential of genetic materials for regulating abnormal gene expressions causing diseases and disorders. However, targeted intracellular delivery of RNA therapeutics still remains a major challenge hindering the clinical translation. In this study, an elaborated plasmonic optoporation approach is proposed to efficiently and selectively transfect specific cells. The site-specific optoporation is obtained by tuning the spectral range of a supercontinuum pulsed picosecond laser in order for each individual cell binding gold nanostar with their unique resonance peak to magnify the local field strength in the near-infrared region and facilitate a selective delivery of small interfering RNA, messenger RNA, and Cas9-ribonucleoprotein into human retinal pigment epithelial cells. Numerical simulations indicate that optoporation is not due to a plasma-mediated process but rather due to a highly localized temperature rise both in time (few nanoseconds) and space (few nanometers). Taking advantage of the numerical simulation and fine-tuning of the optical strategy, the perforated lipid bilayer of targeted cells undergoes a membrane recovery process, important to retain their viability. The results signify the prospects of antibody functionalized nanostar-mediated optoporation as a simple and realistic gene delivery approach for future clinical practices. Supercontinuum ; Transfection ; RNA ; Materials science ; Nanotechnology ; Gene delivery ; Biophysics ; Small interfering RNA ; Chemistry ; Cell culture ; Optoelectronics ; Biology ; Gene ; Genetics ; Biochemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nonlinear Optical Materials Studies ; Plasmonic and Surface Plasmon Research
Authors: Timothy C. Wang | Ashley M. Wright | William J. Hoover | Kevin J. Stoffel | Rachelle K. Richardson | Stephanie Rodriguez | Roberto C. Flores | John P. Siegfried | Nicolaas A. Vermeulen | Patrick E. Fuller | Mitchell H. Weston | Omar K. Farha | William Morris
Source: ACS Applied Materials & Interfaces (review, 2021-08-12)
Field: Chemistry — Inorganic Chemistry

Surviving Under Pressure: The Role of Solvent, Crystal Size, and Morphology During Pelletization of Metal–Organic Frameworks As metal-organic frameworks (MOFs) gain traction for applications, such as hydrogen storage, it is essential to form the as-synthesized powder materials into shaped bodies with high packing densities to maximize their volumetric performance. Mechanical compaction, which involves compressing the materials at high pressure, has been reported to yield high monolith density but often results in a significant loss in accessible porosity. Herein, we sought to systematically control (1) crystal size, (2) solvation, and (3) compacting pressure in the pelletization process to achieve high packing density without compromising the porosity that makes MOFs functional. It was determined that solvation is the most critical factor among the three factors examined. Solvation that exceeds the pore volume prevents the framework from collapsing, allowing for porosity to be maintained through pelletization. Higher pelletization pressure results in higher packing density, with extensive loss of porosity being observed at a higher pressure if the solvation is below the pore volume. Lastly, we observed that the morphology and size of the MOF particles result in variation in the highest achievable packing efficiency, but these numbers (75%) are still greater than many existing techniques used to form MOFs. We concluded that the application of pressure through pelletization is a suitable and widely applicable technique for forming high-density MOF-monoliths. Materials science ; Solvent ; Morphology (biology) ; Metal-organic framework ; Pelletizing ; Metal ; Chemical engineering ; Organic solvent ; Crystal (programming language) ; Nanotechnology ; Composite material ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Reut Mashiach | Haim Weissman | Liat Avram | Lothar Houben | Olga Brontvein | Anna Lavie | Vaishali Arunachalam | Michal Leskes | Boris Rybtchinski | Amnon Bar‐Shir
Source: Nature Communications (article, 2021-01-11)
Field: Materials Science — Materials Chemistry

In situ NMR reveals real-time nanocrystal growth evolution via monomer-attachment or particle-coalescence Understanding inorganic nanocrystal (NC) growth dynamic pathways under their native fabrication environment remains a central goal of science, as it is crucial for rationalizing novel nanoformulations with desired architectures and functionalities. We here present an in-situ method for quantifying, in real time, NCs' size evolution at sub-nm resolution, their concentration, and reactants consumption rate for studying NC growth mechanisms. Analyzing sequential high-resolution liquid-state <sup>19</sup>F-NMR spectra obtained in-situ and validating by ex-situ cryoTEM, we explore the growth evolution of fluoride-based NCs (CaF<sub>2</sub> and SrF<sub>2</sub>) in water, without disturbing the synthesis conditions. We find that the same nanomaterial (CaF<sub>2</sub>) can grow by either a particle-coalescence or classical-growth mechanism, as regulated by the capping ligand, resulting in different crystallographic properties and functional features of the fabricated NC. The ability to reveal, in real time, mechanistic pathways at which NCs grow open unique opportunities for tunning the properties of functional materials. Coalescence (physics) ; Nanocrystal ; In situ ; Nanomaterials ; Materials science ; Nanotechnology ; Nanoparticle ; Particle (ecology) ; Monomer ; Chemical physics ; Density functional theory ; Chemical engineering ; Chemistry ; Computational chemistry ; Polymer ; Organic chemistry ; Physics Quantum Dots Synthesis And Properties ; ZnO doping and properties ; Luminescence Properties of Advanced Materials
Authors: José Carlos Rodríguez‐Cabello | Israel González de Torre | Miguel González‐Pérez | Fernando González‐Pérez | I. Montequi
Source: Frontiers in Bioengineering and Biotechnology (review, 2021-07-16)
Field: Materials Science — Biomaterials

Fibrous Scaffolds From Elastin-Based Materials Current cutting-edge strategies in biomaterials science are focused on mimicking the design of natural systems which, over millions of years, have evolved to exhibit extraordinary properties. Based on this premise, one of the most challenging tasks is to imitate the natural extracellular matrix (ECM), due to its ubiquitous character and its crucial role in tissue integrity. The anisotropic fibrillar architecture of the ECM has been reported to have a significant influence on cell behaviour and function. A new paradigm that pivots around the idea of incorporating biomechanical and biomolecular cues into the design of biomaterials and systems for biomedical applications has emerged in recent years. Indeed, current trends in materials science address the development of innovative biomaterials that include the dynamics, biochemistry and structural features of the native ECM. In this context, one of the most actively studied biomaterials for tissue engineering and regenerative medicine applications are nanofiber-based scaffolds. Herein we provide a broad overview of the current status, challenges, manufacturing methods and applications of nanofibers based on elastin-based materials. Starting from an introduction to elastin as an inspiring fibrous protein, as well as to the natural and synthetic elastin-based biomaterials employed to meet the challenge of developing ECM-mimicking nanofibrous-based scaffolds, this review will follow with a description of the leading strategies currently employed in nanofibrous systems production, which in the case of elastin-based materials are mainly focused on supramolecular self-assembly mechanisms and the use of advanced manufacturing technologies. Thus, we will explore the tendency of elastin-based materials to form intrinsic fibers, and the self-assembly mechanisms involved. We will describe the function and self-assembly mechanisms of silk-like motifs, antimicrobial peptides and leucine zippers when incorporated into the backbone of the elastin-based biomaterial. Advanced polymer-processing technologies, such as electrospinning and additive manufacturing, as well as their specific features, will be presented and reviewed for the specific case of elastin-based nanofiber manufacture. Finally, we will present our perspectives and outlook on the current challenges facing the development of nanofibrous ECM-mimicking scaffolds based on elastin and elastin-like biomaterials, as well as future trends in nanofabrication and applications. Elastin ; Tissue engineering ; Nanotechnology ; Context (archaeology) ; Extracellular matrix ; Scaffold ; Nanofiber ; Regenerative medicine ; Computer science ; Materials science ; Biomedical engineering ; Chemistry ; Engineering ; Biology ; Cell Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Connective tissue disorders research
Authors: Laura Maggini | Rúben R. Ferreira
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

2D material hybrid heterostructures: achievements and challenges towards high throughput fabrication 2D materials display unique optical/electronic/mechanical properties and a manipulable bidimensional morphology. High throughput assembly processes can be applied for their large scale manufacturing leading to integration into mass produced products. Materials science ; Throughput ; Fabrication ; Heterojunction ; Nanotechnology ; Scale (ratio) ; Systems engineering ; Optoelectronics ; Computer science ; Engineering ; Telecommunications MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Rémi Lavernhe | Rubén O. Torres‐Ochoa | Qian Wang | Jieping Zhu
Source: Angewandte Chemie International Edition (article, 2021-09-01)
Field: Chemistry — Organic Chemistry

Copper‐Catalyzed Aza‐Sonogashira Cross‐Coupling To Form Ynimines: Development and Application to the Synthesis of Heterocycles Nitrogen-substituted alkynes, such as ynamines and ynamides, are versatile synthetic building blocks. Ynimines bearing additional nucleophilic and electrophilic centers relative to ynamines and ynamides are expected to have high synthetic potential. However, their chemical reactivity remains unexplored owing mainly to the lack of synthetic accessibility. We report herein a versatile copper-catalyzed synthesis of ynimines from readily available O-acetyl ketoximes and terminal alkynes. A wide range of O-acetyl ketoximes derived from diaryl ketones, aryl alkyl ketones and dialkyl ketones underwent cross-coupling with a diverse set of terminal alkynes to afford the ynimines in good to excellent yields. An unprecedented [5+1] heteroannulation reaction exploiting the reactivity of the ynimine generated in situ was subsequently developed for the synthesis of medicinally important heterocycles, including isoquinolines, azaindoles, azabenzofurans, azabenzothiophenes and carbolines. Sonogashira coupling ; Chemistry ; Aryl ; Electrophile ; Combinatorial chemistry ; Reactivity (psychology) ; Nucleophile ; Alkyl ; Catalysis ; Alkyne ; Coupling reaction ; Organic chemistry ; Palladium Catalytic Alkyne Reactions ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Adam L. Borne | Jeffrey W. Brulet | Kun Yuan | Ku‐Lung Hsu
Source: RSC Chemical Biology (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Development and biological applications of sulfur–triazole exchange (SuTEx) chemistry Sulfur electrophiles constitute an important class of covalent small molecules that have found widespread applications in synthetic chemistry and chemical biology. Various electrophilic scaffolds, including sulfonyl fluorides and arylfluorosulfates as recent examples, have been applied for protein bioconjugation to probe ligand sites amenable for chemical proteomics and drug discovery. In this review, we describe the development of sulfonyl-triazoles as a new class of electrophiles for sulfur-triazole exchange (SuTEx) chemistry. SuTEx achieves covalent reaction with protein sites through irreversible modification of a residue with an adduct group (AG) upon departure of a leaving group (LG). A principal differentiator of SuTEx from other chemotypes is the selection of a triazole heterocycle as the LG, which introduces additional capabilities for tuning the sulfur electrophile. We describe the opportunities afforded by modifications to the LG and AG alone or in tandem to facilitate nucleophilic substitution reactions at the SO<sub>2</sub> center in cell lysates and live cells. As a result of these features, SuTEx serves as an efficient platform for developing chemical probes with tunable bioactivity to study novel nucleophilic sites on established and poorly annotated protein targets. Here, we highlight a suite of biological applications for the SuTEx electrophile and discuss future goals for this enabling covalent chemistry. Electrophile ; Chemistry ; Nucleophile ; Bioconjugation ; Combinatorial chemistry ; Chemical biology ; Covalent bond ; Adduct ; Sulfonyl ; Moiety ; Organic chemistry ; Biochemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Protein Degradation and Inhibitors
Authors: Mathieu Mirjolet | Mikko Kataja | Tommi K. Hakala | Philipp Komissinskiy | Lambert Alff | G. Herranz | Josep Fontcuberta
Source: Advanced Optical Materials (article, 2021-06-14)
Field: Materials Science — Materials Chemistry

Optical Plasmon Excitation in Transparent Conducting SrNbO<sub>3</sub> and SrVO<sub>3</sub> Thin Films Abstract Transparent and metallic oxides based on 3d and 4d metals are promising materials for plasmonics. Here, the growth window to obtain epitaxial SrNbO 3 (4d 1 ) thin films is determined and the role of substrates to achieve optimal electrical conductivity and the largest residual resistivity ratio is disclosed. Optical measurements on optimized films indicate a large transparency at the visible with a sharp edge at about 1.9 eV, which coincides with the zero‐crossing of the permittivity, allowing to identify the plasma frequency ω p . Similar features are observed in SrVO 3 (3d 1 ) films. Optical losses display well pronounced maxima at the corresponding ω p . Polarization‐dependent optical transmittance measurements and ellipsometric data show a dip at ω p , occurring for p ‐ but not s ‐polarized light, which is a fingerprint of optical bulk plasmon excitation. Remarkably, plasmon resonance is achieved here by using oblique incidence of light rather than phase‐matching arrangements, and by exploiting charge density gradients at the film surface. This observation points to new opportunities for engineering plasmons in heterostructures. Materials science ; Plasmon ; Optical conductivity ; Thin film ; Optoelectronics ; Optics ; Transmittance ; Surface plasmon resonance ; Surface plasmon ; Permittivity ; Excitation ; Electrical resistivity and conductivity ; Dielectric ; Condensed matter physics ; Nanotechnology Electronic and Structural Properties of Oxides ; ZnO doping and properties ; Semiconductor materials and devices
Authors: Hu Liu | Mohd Hasrizam Che Man | Kin Huat Low
Source: Aerospace Science and Technology (article, 2021-04-06)
Field: Materials Science — Materials Chemistry

UAV airborne collision to manned aircraft engine: Damage of fan blades and resultant thrust loss Collision ; Climb ; Aerospace engineering ; Thrust ; Aeronautics ; Collision avoidance ; Marine engineering ; Engineering ; Computer science ; Environmental science ; Automotive engineering ; Computer security High-Velocity Impact and Material Behavior ; Computational Fluid Dynamics and Aerodynamics ; Combustion and Detonation Processes
Authors: Sarah Callaghan
Source: Patterns (article, 2021-04-01)
Field: Chemistry — Inorganic Chemistry

Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery Metal-organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (the "QMOF database") has been created and made available to the community along with code for machine learning and other related resources. Metal-organic framework ; Supercapacitor ; Materials science ; Hydrogen ; Hydrogen storage ; Carbon fibers ; Nanotechnology ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xingyong Zhu | Chuan‐Kun Ran | Ming Wen | Gui‐Ling Guo | Yuan Liu | Li‐Li Liao | Yizhou Li | Meng‐Long Li | Da‐Gang Yu
Source: Chinese Journal of Chemistry (article, 2021-08-04)
Field: Materials Science — Materials Chemistry

Prediction of Multicomponent Reaction Yields Using Machine Learning Main observation and conclusion Prediction of reaction yields using machine learning (ML) can help chemists select high‐yielding reactions and provide prior experience before wet‐lab experimenting to improve efficiency. However, the exploration of a multicomponent organic reaction features many complex variables and limited number of experimental data, which are challenging for the application of ML. Herein, we perform yield prediction for the synthesis of 2‐oxazolidones via Cu‐catalyzed radical‐type oxy‐alkylation of allylamines and herteroarylmethylamines with CO 2 , which is a three‐component reaction. Using physicochemical descriptors as features to launch ML modelling, we find that XGBoost shows significantly improved performance over linear models and these features are effective for the yield prediction. Moreover, out‐of‐sample prediction indicates the application potential of the model. This study demonstrates great potential of regression‐modelling‐based ML in organic synthesis even with complex factors and a general small size of reaction data, which are generated from the classical research pattern of method for the inquiry of multicomponent reactions. Chemistry ; Yield (engineering) ; Alkylation ; Linear regression ; Regression ; Artificial intelligence ; Biological system ; Machine learning ; Organic chemistry ; Computer science ; Catalysis ; Statistics ; Thermodynamics Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Yuquan Zhu | Tao Xu | Qinghua Wei | Jiawei Mai | Hongxin Yang | Huiran Zhang | Takahiro Shimada | Takayuki Kitamura | Tong‐Yi Zhang
Source: npj Computational Materials (article, 2021-12-15)
Field: Materials Science — Materials Chemistry

Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning Abstract The optimal design of shape memory alloys (SMAs) with specific properties is crucial for the innovative application in advanced technologies. Herein, inspired by the recently proposed design concept of concentration modulation, we explore martensitic transformation (MT) in and design the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Systematic phase-field simulations generate data of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom. An ML-assisted strategy is adopted to perform multiobjective optimization of the mechanical properties, through which promising nanocomposite configurations are prescreened for the next set of phase-field simulations. The ML-guided simulations discover an optimized nanocomposite, composed of Nb-rich matrix and Nb-lean nanofillers, that exhibits a combination of mechanical properties, including ultralow modulus, linear super-elasticity, and near-hysteresis-free in a loading-unloading cycle. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work demonstrates the great potential of ML-guided phase-field simulations in the design of advanced materials with extraordinary properties. Nanocomposite ; Materials science ; Smart material ; Shape-memory alloy ; Phase (matter) ; Mechanical engineering ; Diffusionless transformation ; Modulus ; Composite material ; Material Design ; Field (mathematics) ; Nanotechnology ; Martensite ; Microstructure ; Engineering ; Physics ; Mathematics Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Banjade, Huta R | Hauri, Sandro | Zhang, Shanshan | Ricci, Francesco | Gong, Weiyi | Hautier, Geoffroy | Vucetic, Slobodan | Yan, Qimin
Source:  (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Structure motif-centric learning framework for inorganic crystalline systems. Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling's rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles. Motif (music) ; Computer science ; Computational biology ; Nanotechnology ; Materials science ; Biology ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography