jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paola S. Pauletto | Sabrina F. Lütke | Guilherme Luiz Dotto | Nina Paula Gonçalves Salau
Source: Chemical Engineering Journal (article, 2020-10-27)
Field: Chemistry — Analytical Chemistry

Forecasting the multicomponent adsorption of nimesulide and paracetamol through artificial neural network Nimesulide ; Adsorption ; Chemistry ; Particle size ; Chromatography ; Chemical engineering ; Materials science ; Organic chemistry ; Physical chemistry ; Engineering Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Analytical Methods in Pharmaceuticals
Authors: Yang Huang | Xuehua Li | Shujuan Xu | Huizhen Zheng | Lili Zhang | Jingwen Chen | Huixiao Hong | Rebecca Kusko | Ruibin Li
Source: Environmental Health Perspectives (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Quantitative Structure–Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>IL-</mml:mtext><mml:mn>1</mml:mn><mml:mi>β</mml:mi></mml:mrow></mml:math> released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>IL-</mml:mtext><mml:mn>1</mml:mn><mml:mi>β</mml:mi></mml:mrow></mml:math> were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508. Quantitative structure–activity relationship ; Proinflammatory cytokine ; Nanotoxicology ; Chemistry ; Inflammation ; Toxicity ; Biology ; Immunology ; Stereochemistry Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Andrographolide Research and Applications
Authors: Amirhossein Mahtabani | Ilkka Rytöluoto | Rafał Anyszka | Xiaozhen He | Eetta Saarimäki | Kari Lahti | Mika Paajanen | Wilma K. Dierkes | Anke Blume
Source: ACS Applied Polymer Materials (article, 2020-07-22)
Field: Materials Science — Materials Chemistry

On the Silica Surface Modification and Its Effect on Charge Trapping and Transport in PP-Based Dielectric Nanocomposites The effect of filler surface functionalization with 3-aminopropyltriethoxysilane (APTES) on the charge trapping and transport was studied in polypropylene (PP)/(ethylene-octene) copolymer (EOC)/silica nanodielectrics. Different reaction conditions were utilized for silica functionalization to alter the deposited layer morphology. This approach made it possible to engineer the filler–polymer interface to achieve optimized dielectric properties for the nanocomposites. The successful chemical modification of the silica surface was confirmed via thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Subsequently, the effect of the engineered filler–polymer interface on the nanocomposites’ crystallinity was analyzed with differential scanning calorimetry (DSC). Scanning electron microscopy (SEM) was utilized to observe the morphology of the nanocomposite as well as the silica dispersion. Finally, the effect of the silica functionalization on the dielectric properties of PP/EOC/silica nanocomposites was tested via thermally stimulated depolarization current (TSDC) and broadband dielectric spectroscopy (BDS). The results suggested that the presence of the amine functionality on the silica reduces interfacial losses in nanocomposites, and hinders further injection of space charge by introducing deep trap states at the filler–polymer interface. Under certain conditions, APTES can form an “island-like” morphology on the silica surface. These islands can facilitate nucleation, inducing transcrystallization at the filler–polymer interface. The island-like structures present on the silica would further contribute to the induction of deep traps at the filler–polymer interface resulting in the reduction of space charge injection. Materials science ; Surface modification ; Nanocomposite ; Polypropylene ; Chemical engineering ; Thermogravimetric analysis ; Differential scanning calorimetry ; Polymer ; X-ray photoelectron spectroscopy ; Fourier transform infrared spectroscopy ; Polymer nanocomposite ; Dielectric ; Composite material ; Polymer chemistry High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Yousra Nahas | Sergei Prokhorenko | Qi Zhang | Vivasha Govinden | V. Nagarajan | L. Bellaïche
Source: Nature Communications (article, 2020-11-13)
Field: Materials Science — Materials Chemistry

Topology and control of self-assembled domain patterns in low-dimensional ferroelectrics Abstract Whilst often discussed as non-trivial phases of low-dimensional ferroelectrics, modulated polar phases such as the dipolar maze and the nano-bubble state have been appraised as essentially distinct. Here we emphasize their topological nature and show that these self-patterned polar states, but also additional mesophases such as the disconnected labyrinthine phase and the mixed bimeron-skyrmion phase, can be fathomed in their plurality through the unifying canvas of phase separation kinetics. Under compressive strain, varying the control parameter, i.e., the external electric field, conditions the nonequilibrium self-assembly of domains, and bridges nucleation and spinodal decomposition via the sequential onset of topological transitions. The evolutive topology of these polar textures is driven by the (re)combination of the elementary topological defects, merons and antimerons, into a plethora of composite topological defects such as the fourfold junctions, the bimeron and the target skyrmion. Moreover, we demonstrate that these manipulable defects are stable at room temperature and feature enhanced functionalities, appealing for devising future topological-based nanoelectronics. Topology (electrical circuits) ; Topological defect ; Polar ; Nucleation ; Phase (matter) ; Physics ; Materials science ; Condensed matter physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Alain J. van Gool | Fernando J. Corrales | Mirjana B. Čolović | Danijela Krstić | Begoña Oliver‐Martos | Eva Martínez‐Cáceres | Ivone Jakaša | Goran Gajski | Virginie Brun | Kyriacos Kyriacou | Izabela Burzyńska-Pędziwiatr | Lucyna A. Woźniak | Stephan Nierkens | César Pascual García | Jaroslav Katrlı́k | Žanka Bojić‐Trbojević | Jan Vacek | Alicia Llorente | Felicia Antohe | Viorel I. Suicã | Guillaume Suárez | Ruben t’Kindt | Petra Martin | Deborah Penque | Ines Lanca Martins | Ede Bodoki | Bogdan‐Cezar Iacob | Eda Celikbas | Suna Tımur | John Allinson | Chris W. Sutton | Theo M. Luider | Saara Wittfooth | Marei Sammar
Source: Expert Review of Proteomics (review, 2020-04-02)
Field: Chemistry — Spectroscopy

Analytical techniques for multiplex analysis of protein biomarkers The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine. Multiplex ; Biomarker discovery ; Biomarker ; Computational biology ; Drug development ; Personalized medicine ; Drug discovery ; Precision medicine ; Medicine ; Proteomics ; Bioinformatics ; Computer science ; Drug ; Biology ; Pharmacology ; Pathology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Osman El‐Atwani | Andrew Alvarado | Kaan Unal | Saryu Fensin | J.A. Hinks | Graeme Greaves | Jon K. Baldwin | S.A. Maloy | Enrique Martínez
Source: Materials Today Energy (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Helium implantation damage resistance in nanocrystalline W-Ta-V-Cr high entropy alloys Materials science ; Radiation resistance ; Tungsten ; Nanocrystalline material ; Alloy ; Grain boundary ; Radiation damage ; Fluence ; Helium ; Ion implantation ; Hardening (computing) ; Bubble ; Irradiation ; Thermal diffusivity ; High entropy alloys ; Thermodynamics ; Microstructure ; Atomic physics ; Metallurgy ; Ion ; Composite material ; Nanotechnology ; Nuclear physics Nuclear Materials and Properties ; Fusion materials and technologies ; High Entropy Alloys Studies
Authors: Raphaël Pestourie | Youssef Mroueh | Thanh V. Nguyen | Payel Das | Steven G. Johnson
Source: npj Computational Materials (article, 2020-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Active learning of deep surrogates for PDEs: application to metasurface design Abstract Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization. Surrogate model ; Computer science ; Artificial neural network ; Scale (ratio) ; Photonics ; Artificial intelligence ; Differential evolution ; Mathematical optimization ; Machine learning ; Mathematics ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Microwave Engineering and Waveguides
Authors: S. Hémery | Patrick Villechaise | D. Banerjee
Source: Metallurgical and Materials Transactions A (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Microplasticity at Room Temperature in α/β Titanium Alloys Microscale chemistry ; Materials science ; Microstructure ; Deformation (meteorology) ; Titanium alloy ; Plasticity ; Deformation mechanism ; Grain boundary ; Metallurgy ; Composite material Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Daniel Schneider | Annika Grundmann | Andreas Bablich | Vikram Passi | Satender Kataria | H. Kalisch | M. Heuken | Andrei Vescan | Daniel Neumaier | Max C. Lemme
Source: ACS Photonics (article, 2020-05-21)
Field: Materials Science — Materials Chemistry

Highly Responsive Flexible Photodetectors Based on MOVPE Grown Uniform Few-Layer MoS<sub>2</sub> Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are seen as promising candidates for flexible electronic and optoelectronic devices due to their high tensile strength and favorable optical properties. Molybdenum disulfide (MoS2) is a benchmark material for TMDCs, which has already been studied extensively. Here, we report on highly responsive flexible few-layer MoS2 photodetectors based on MoS2 synthesized uniformly for full coverage of 2 in. sapphire wafers using metalorganic vapor-phase epitaxy (MOVPE). Device performance is studied by electro-optical characterization. Electrostatic gating allows tuning both the responsivity between 150 and 920 A/W and the specific detectivity between almost 1012 and 1010 Jones. The measured spectrally resolved responsivities of the detectors suggest applications in the blue-light range, with opportunities for fine-tuning the most sensitive wavelength through gating, as shown through optical simulations. Finally, the flexible devices were bent to demonstrate their suitability for flexible electronics in fields of future Internet of Things and medical devices. Responsivity ; Materials science ; Optoelectronics ; Photodetector ; Metalorganic vapour phase epitaxy ; Molybdenum disulfide ; Wafer ; Sapphire ; Flexible electronics ; Layer (electronics) ; Electronics ; Epitaxy ; Laser ; Nanotechnology ; Optics 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications
Authors: Lifeng Huang | Naresh Eedugurala | Anthony R. Benasco | Song Zhang | Kevin S. Mayer | D.J. Adams | Benjamin Fowler | Molly M. Lockart | Mohammad Saghayezhian | Hamas Tahir | Eric R. King | Sarah E. Morgan | Michael K. Bowman | Xiaodan Gu | Jason D. Azoulay
Source: Advanced Functional Materials (article, 2020-05-04)
Field: Materials Science — Polymers and Plastics

Open‐Shell Donor–Acceptor Conjugated Polymers with High Electrical Conductivity Abstract Conductive polymers largely derive their electronic functionality from chemical doping, processes by which redox and charge‐transfer reactions form mobile carriers. While decades of research have demonstrated fundamentally new technologies that merge the unique functionality of these materials with the chemical versatility of macromolecules, doping and the resultant material properties are not ideal for many applications. Here, it is demonstrated that open‐shell conjugated polymers comprised of alternating cyclopentadithiophene and thiadiazoloquinoxaline units can achieve high electrical conductivities in their native “undoped” form. Spectroscopic, electrochemical, electron paramagnetic resonance, and magnetic susceptibility measurements demonstrate that this donor–acceptor architecture promotes very narrow bandgaps, strong electronic correlations, high‐spin ground states, and long‐range π‐delocalization. A comparative study of structural variants and processing methodologies demonstrates that the conductivity can be tuned up to 8.18 S cm −1 . This exceeds other neutral narrow bandgap conjugated polymers, many doped polymers, radical conductors, and is comparable to commercial grades of poly(styrene‐sulfonate)‐doped poly(3,4‐ethylenedioxythiophene). X‐ray and morphological studies trace the high conductivity to rigid backbone conformations emanating from strong π‐interactions and long‐range ordered structures formed through self‐organization that lead to a network of delocalized open‐shell sites in electronic communication. The results offer a new platform for the transport of charge in molecular systems. Delocalized electron ; Materials science ; Conjugated system ; Polymer ; Chemical physics ; Doping ; Conductive polymer ; Acceptor ; Conductivity ; Nanotechnology ; Charge carrier ; Open shell ; Organic electronics ; Optoelectronics ; Organic chemistry ; Condensed matter physics ; Physical chemistry ; Chemistry ; Transistor Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Junkai Ren | Luca Malfatti | Plinio Innocenzi
Source: C – Journal of Carbon Research (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Citric Acid Derived Carbon Dots, the Challenge of Understanding the Synthesis-Structure Relationship Carbon dots (CDs) are highly-emissive nanoparticles obtained through fast and cheap syntheses. The understanding of CDs’ luminescence, however, is still far from being comprehensive. The intense photoluminescence can have different origins: molecular mechanisms, oxidation of polyaromatic graphene-like layers, and core-shell interactions of carbonaceous nanoparticles. The citric acid (CA) is one of the most common precursors for CD preparation because of its high biocompatibility, and this review is mainly focused on CA-based CDs. The different parameters that control the synthesis, such as the temperature, the reaction time, and the choice of solvents, were critically described. Particular attention was devoted to the CDs’ optical properties, such as tunable emission and quantum yields, in light of functional applications. The survey of the literature allowed correlating the preparation methods with the structures and the properties of CA-based CDs. Some basic rules to fabricate highly luminescent nanoparticles were selected by the metanalysis of the current literature in the field. In some cases, these findings can be generalized to other types of CDs prepared via liquid phase. Nanoparticle ; Luminescence ; Photoluminescence ; Citric acid ; Biocompatibility ; Materials science ; Nanotechnology ; Graphene ; Quantum dot ; Carbon fibers ; Chemistry ; Organic chemistry ; Optoelectronics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Jacek K. Wychowaniec | Jagoda Litowczenko | Krzysztof Tadyszak | Varun Natu | Claudia Aparicio | Barbara Peplińska | Michel W. Barsoum | Michal Otyepka | Błażej Scheibe
Source: Acta Biomaterialia (article, 2020-08-11)
Field: Materials Science — Materials Chemistry

Unique cellular network formation guided by heterostructures based on reduced graphene oxide - Ti3C2Tx MXene hydrogels Two-dimensional (2D) materials remain highly interesting for assembling three-dimensional (3D) structures, amongst others, in the form of macroscopic hydrogels. Herein, we present a novel approach for inducing chemical inter-sheet crosslinks via an ethylenediamine mediated reaction between Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> and graphene oxide in order to obtain a reduced graphene oxide-MXene (rGO-MXene) hydrogel. The composite hydrogels are hydrophilic with a stiffness of ~20 kPa. They also possess a unique inter-connected porous architecture, which led to a hitherto unprecedented ability of human cells across three different types, epithelial adenocarcinoma, neuroblastoma and fibroblasts, to form inter-connected three-dimensional networks. The attachments of the cells to the rGO-MXene hydrogels were superior to those of the sole rGO-control gels. This phenomenon stems from the strong affinity of cellular protrusions (neurites, lamellipodia and filopodia) to grow and connect along architectural network paths within the rGO-MXene hydrogel, which could lead to advanced control over macroscopic formations of cellular networks for technologically relevant bioengineering applications, including tissue engineering and personalized diagnostic networks-on-chip. STATEMENT OF SIGNIFICANCE: Conventional hydrogels are made of interconnected polymeric fibres. Unlike conventional case, we used hydrothermal and chemical approach to form interconnected porous hydrogels made of two-dimensional flakes from graphene oxide and metal carbide from a new family of MXenes (Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>). This way, we formed three-dimensional porous hydrogels with unique porous architecture of well-suited chemical surfaces and stiffness. Cells from three different types cultured on these scaffolds formed extended three-dimensional networks - a feature of extended cellular proliferation and pre-requisite for formation of organoids. Considering the studied 2D materials typically constitute materials exhibiting enhanced supercapacitor performances, our study points towards better understanding of design of tissue engineering materials for the future bioengineering fields including personalized diagnostic networks-on-chip, such as artificial heart actuators. Self-healing hydrogels ; Materials science ; Graphene ; Nanotechnology ; Oxide ; MXenes ; Porosity ; Heterojunction ; Composite material ; Optoelectronics MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Joseph H. Montoya | Kirsten T. Winther | Raul A. Flores | Thomas Bligaard | Jens S. Hummelshøj | Muratahan Aykol
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Autonomous intelligent agents for accelerated materials discovery We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers. Computer science ; Drug discovery ; Data science ; Artificial intelligence ; Bioinformatics ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Jian Peng | Yukinori Yamamoto | Jeffrey A. Hawk | Edgar Lara-Curzio | Dongwon Shin
Source: npj Computational Materials (article, 2020-09-18)
Field: Materials Science — Materials Chemistry

Coupling physics in machine learning to predict properties of high-temperatures alloys Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials. Consistency (knowledge bases) ; Coupling (piping) ; Yield (engineering) ; Workflow ; Alloy ; Titanium alloy ; Microstructure Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and Mechanical Properties of Steels
Authors: Tiberiu Stan | Zachary Thompson | Peter W. Voorhees
Source: Materials Characterization (article, 2020-01-08)
Field: Materials Science — Materials Chemistry

Optimizing convolutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning Segmentation ; Convolutional neural network ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Materials science ; Tomography ; Optics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Mineral Processing and Grinding