jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carlos Alberto Guerrero Fajardo | Liliana Giraldo | Juan Carlos Moreno‐Piraján
Source: Nanomaterials (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Preparation and Characterization of Graphene Oxide for Pb(II) and Zn(II) Ions Adsorption from Aqueous Solution: Experimental, Thermodynamic and Kinetic Study A thermodynamic and kinetic study of the adsorption process of Zn (II) and Pb (II) ions from aqueous solution on the surface of graphene oxide (GO) to establish the mechanisms of adsorbate-adsorbent interaction on this surface. The effect of pH on the retention capacity was studied and adsorption isotherms were determined from aqueous solution of the ions; once the experimental data was obtained, the kinetic and thermodynamic study of the sorption process was carried out. The data were fitted to the Langmuir, Freundlich, Dubinin-Raduskevich and Temkin isotherm models. The results showed that Zn(II) and Pb(II) on the GO adsorbing surface fitted the Langmuir model with correlation coefficients (R<sup>2</sup>) of 0.996. Kinetic models studied showed that a pseudo-second-order model was followed and thermodynamically, the process was spontaneous according to the values of Gibbs free energy (ΔG°). N<sub>2</sub> adsorption isotherms were determined and modeled with the NLDFT (nonlocal density functional theory) and QSDFT (quenched solid density functional theory) kernels. Adsorption ; Aqueous solution ; Freundlich equation ; Gibbs free energy ; Langmuir ; Sorption ; Oxide ; Chemistry ; Thermodynamics ; Kinetic energy ; Langmuir adsorption model ; Graphene ; Ion ; Density functional theory ; Inorganic chemistry ; Physical chemistry ; Materials science ; Computational chemistry ; Organic chemistry ; Nanotechnology Graphene research and applications ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal
Authors: David López‐Díaz | María Dolores Merchán Moreno | M. Mercedes Velázquez
Source: Advances in Colloid and Interface Science (review, 2020-11-02)
Field: Materials Science — Materials Chemistry

The behavior of graphene oxide trapped at the air water interface Graphene ; Oxide ; Graphene oxide paper ; Materials science ; Monolayer ; Chemical engineering ; Adsorption ; Aqueous solution ; Graphite ; Carbon fibers ; Graphite oxide ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Composite number Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon and Quantum Dots Applications
Authors: Fan Yang | Wenhui Hu | Chongqing Yang | Margaret Patrick | Andrew L. Cooksy | Jian Zhang | Jeffery A. Aguiar | Chengcheng Fang | Ying‐Hua Zhou | Ying Shirley Meng | Jier Huang | Jing Gu
Source: Angewandte Chemie International Edition (article, 2020-01-08)
Field: Chemistry — Inorganic Chemistry

Tuning Internal Strain in Metal–Organic Frameworks via Vapor Phase Infiltration for CO<sub>2</sub> Reduction A gas-phase approach to form Zn coordination sites on metal-organic frameworks (MOFs) by vapor-phase infiltration (VPI) was developed. Compared to Zn sites synthesized by the solution-phase method, VPI samples revealed approximately 2.8 % internal strain. Faradaic efficiency towards conversion of CO<sub>2</sub> to CO was enhanced by up to a factor of four, and the initial potential was positively shifted by 200-300 mV. Using element-specific X-ray absorption spectroscopy, the local coordination environment of the Zn center was determined to have square-pyramidal geometry with four Zn-N bonds in the equatorial plane and one Zn-OH<sub>2</sub> bond in the axial plane. The fine-tuned internal strain was further supported by monitoring changes in XRD and UV/Visible absorption spectra across a range of infiltration cycles. The ability to use internal strain to increase catalytic activity of MOFs suggests that applying this strategy will enhance intrinsic catalytic capabilities of a variety of porous materials. Materials science ; Porosity ; Absorption spectroscopy ; Catalysis ; Metal-organic framework ; Phase (matter) ; Metal ; Infiltration (HVAC) ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Composite material ; Physical chemistry ; Environmental chemistry ; Metallurgy ; Organic chemistry ; Optics ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; CO2 Reduction Techniques and Catalysts
Authors: Adriano Martinez Basso | Bruna Pes Nicola | Kátia Bernardo-Gusmão | Sibele B. C. Pergher
Source: Applied Sciences (article, 2020-02-02)
Field: Materials Science — Materials Chemistry

Tunable Effect of the Calcination of the Silanol Groups of KIT-6 and SBA-15 Mesoporous Materials The calcination process is a crucial step during SBA-15 and KIT-6 synthesis. It is used to completely remove the organic template and condense silanol groups, and it allows the determination of the textural and physical properties of these materials, depending on the adopted conditions. Moreover, calcination influences the number of silanols available on the surface of the material. The concentration of silanols is important if these materials were synthesized for use in adsorption or functionalization. To understand and optimize the silanol groups of SBA-15 and KIT-6, in this study, the temperature and time calcination parameters were varied. The experiments were performed at 300, 400, and 500 °C for 300, 400, and 500 min. The results show that the ideal temperature to preserve the silanol groups is 300 °C, but to optimize the textural properties, it is better to calcine these molecular sieves at 400 °C. A calcination for 10 h did not give better results than a calcination for 5 h, demonstrating that the former duration is excessive for use. Silanol ; Calcination ; Mesoporous material ; Materials science ; Molecular sieve ; Chemical engineering ; Adsorption ; Surface modification ; Chemistry ; Organic chemistry ; Catalysis Mesoporous Materials and Catalysis ; Polyoxometalates: Synthesis and Applications ; Zeolite Catalysis and Synthesis
Authors: Theona Şucu | Michael P. Shaver
Source: Polymer Chemistry (article, 2020-01-01)
Field: Materials Science — Biomaterials

Inherently degradable cross-linked polyesters and polycarbonates: resins to be cheerful We summarise the most recent advances in the synthesis and characterisation of degradable thermosetting polyester and polycarbonates, including partially degradable systems derived from itaconic acid and isosorbide. Polyester ; Thermosetting polymer ; Isosorbide ; Itaconic acid ; Polymer science ; Polymer chemistry ; Materials science ; Chemistry ; Organic chemistry ; Polymer ; Copolymer biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Polymer composites and self-healing
Authors: Jean‐François Lemineur | Jean‐Marc Noël | Alexa Courty | Dominique Ausserré | Catherine Combellas | Frédéric Kanoufi
Source: Journal of the American Chemical Society (article, 2020-03-30)
Field: Chemistry — Electrochemistry

In Situ Optical Monitoring of the Electrochemical Conversion of Dielectric Nanoparticles: From Multistep Charge Injection to Nanoparticle Motion By shortening solid-state diffusion times, the nanoscale size reduction of dielectric materials-such as ionic crystals-has fueled synthetic efforts toward their use as nanoparticles, NPs, in electrochemical storage and conversion cells. Meanwhile, there is a lack of strategies able to image the dynamics of such conversion, operando and at the single NP level. It is achieved here by optical microscopy for a model dielectric ionic nanocrystal, a silver halide NP. Rather than the classical core-shrinking mechanism often used to rationalize the complete electrochemical conversion and charge storage in NPs, an alternative mechanism is proposed here. Owing to its poor conductivity, the NP conversion proceeds to completion through the formation of multiple inclusions. The superlocalization of NP during such heterogeneous multiple-step conversion suggests the local release of ions, which propels the NP toward reacting sites enabling its full conversion. Chemistry ; Nanoparticle ; Dielectric ; Ionic bonding ; Electrochemistry ; Nanotechnology ; Nanocrystal ; Nanoscopic scale ; Ionic conductivity ; Ion ; Chemical engineering ; Electrode ; Chemical physics ; Optoelectronics ; Physical chemistry ; Materials science ; Organic chemistry Electrochemical Analysis and Applications ; Ionic liquids properties and applications ; Electrochemical sensors and biosensors
Authors: Yujing Wang | Qi Gong | Qian Miao
Source: Materials Chemistry Frontiers (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Structured and functionalized organic semiconductors for chemical and biological sensors based on organic field effect transistors This review gives an overview of strategies to tailor organic semiconductors with designed physical structures and chemical functionalities for development of chemical and biological sensors based on organic field effect transistors. Organic semiconductor ; Field-effect transistor ; Materials science ; Transistor ; Nanotechnology ; Semiconductor ; Optoelectronics ; Electrical engineering ; Engineering Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Pedro Mena‐Giraldo | Sandra Pérez-Buitrago | Maritza Londoño-Berrío | Isabel Cristina Ortiz-Trujillo | Lina Marcela Hoyos Palacio | Jahir Orozco
Source: Scientific Reports (article, 2020-02-07)
Field: Materials Science — Surfaces, Coatings and Films

Photosensitive nanocarriers for specific delivery of cargo into cells Nanoencapsulation is a rapidly expanding technology to enclose cargo into inert material at the nanoscale size, which protects cargo from degradation, improves bioavailability and allows for controlled release. Encapsulation of drugs into functional nanocarriers enhances their specificity, targeting ability, efficiency, and effectiveness. Functionality may come from cell targeting biomolecules that direct nanocarriers to a specific cell or tissue. Delivery is usually mediated by diffusion and erosion mechanisms, but in some cases, this is not sufficient to reach the expected therapeutic effects. This work reports on the development of a new photoresponsive polymeric nanocarrier (PNc)-based nanobioconjugate (NBc) for specific photo-delivery of cargo into target cells. We readily synthesized the PNcs by modification of chitosan with ultraviolet (UV)-photosensitive azobenzene molecules, with Nile red and dofetilide as cargo models to prove the encapsulation/release concept. The PNcs were further functionalized with the cardiac targeting transmembrane peptide and efficiently internalized into cardiomyocytes, as a cell line model. Intracellular cargo-release was dramatically accelerated upon a very short UV-light irradiation time. Delivering cargo in a time-space controlled fashion by means of NBcs is a promising strategy to increase the intracellular cargo concentration, to decrease dose and cargo side effects, thereby improving the effectiveness of a therapeutic regime. Nanocarriers ; Intracellular ; Biophysics ; Nanotechnology ; Drug delivery ; Chemistry ; Materials science ; Biochemistry ; Biology Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Nanoparticle-Based Drug Delivery
Authors: Hyun-Chae Loh | Thibaut Divoux | Bernd Gludovatz | Pupa U. P. A. Gilbert | Robert O. Ritchie | Franz‐Josef Ulm | Admir Mašić
Source: Communications Materials (article, 2020-10-28)
Field: Materials Science — Biomaterials

Nacre toughening due to cooperative plastic deformation of stacks of co-oriented aragonite platelets Abstract Nacre’s structure-property relationships have been a source of inspiration for designing advanced functional materials with both high strength and toughness. These outstanding mechanical properties have been mostly attributed to the interplay between aragonite platelets and organic matrices in the typical brick-and-mortar structure. Here, we show that crystallographically co-oriented stacks of aragonite platelets, in both columnar and sheet nacre, define another hierarchical level that contributes to the toughening of nacre. By correlating piezo-Raman and micro-indentation results, we quantify the residual strain energy associated with strain hardening capacity. Our findings suggest that the aragonite stacks, with characteristic dimensions of around 20 µm, effectively store energy through cooperative plastic deformation. The existence of a larger length scale beyond the brick-and-mortar structure offers an opportunity for a more efficient implementation of biomimetic design. Materials science ; Indentation ; Aragonite ; Toughness ; Toughening ; Composite material ; Strain hardening exponent ; Hardening (computing) ; Amorphous solid ; Deformation (meteorology) ; Crystallography ; Calcium carbonate Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Paleontology and Stratigraphy of Fossils
Authors: Jin‐Jiang Zhang | Man‐Chung Tang | Yubin Fu | Kam‐Hung Low | Ji Ma | Lin Yang | Jan J. Weigand | Junzhi Liu | Vivian Wing‐Wah Yam | Xinliang Feng
Source: Angewandte Chemie International Edition (article, 2020-10-28)
Field: Chemistry — Organic Chemistry

One‐Pot Synthesis of Boron‐Doped Polycyclic Aromatic Hydrocarbons via 1,4‐Boron Migration Herein, we demonstrate a novel one-pot synthetic method towards a series of boron-doped polycyclic aromatic hydrocarbons (B-PAHs, 1 a-1 o), including hitherto unknown B-doped zethrene derivatives, from ortho-aryl substituted diarylalkynes with high atom efficiency and broad substrate scopes. A reaction mechanism is proposed based on the experimental investigation together with the theoretical calculations, which involves a unique 1,4-boron migration process. The resultant benchtop-stable B-PAHs are thoroughly investigated by X-ray crystallography, cyclic voltammetry, UV/Vis absorption, and fluorescence spectroscopies. The blue and green organic light-emitting diode (OLED) devices based on 1 f and 1 k are further fabricated, demonstrating the promising application potential of B-PAHs in organic optoelectronics. Boron ; Doping ; Chemistry ; Materials science ; Environmental chemistry ; Nanotechnology ; Organic chemistry ; Optoelectronics Organoboron and organosilicon chemistry ; Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials
Authors: Yujie Tu | Yeqing Yu | Diwen Xiao | Junkai Liu | Zheng Zhao | Zhiyang Liu | Jacky W. Y. Lam | Ben Zhong Tang
Source: Advanced Science (article, 2020-09-06)
Field: Materials Science — Materials Chemistry

An Intelligent AIEgen with Nonmonotonic Multiresponses to Multistimuli Intelligent stimulus-response (S/R) systems are the basis of natural process and machine control, which are intensively explored in biomimetic design and analytical/biological applications. However, nonmonotonic multi-S/R systems are still rarely studied so far. In this work, a rational design strategy is proposed to achieve such a unique S/R system by integrating opposite luminescence behaviors in one molecule. When solvent polarity increases, many heterocyclic or carbonyl-containing compounds often become more emissive due to the suppression of the proximity effect, whereas molecules with donor-acceptor (D-A) structures tend to be less emissive because of the twisted intramolecular charge transfer. Meanwhile, protonation on D/A moieties will weaken/strengthen the D-A interaction to result in blue/redshifted emissions. By combining a protonatable heterocyclic acceptor and a protonatable donor together in one molecule, nonmonotonic brightness responses to polarity stimuli and nonmonotonic color responses to pH stimuli can be achieved. The design strategy is successfully verified by a simple molecule named 4-(dimethylamino)styryl)quinoxalin-2(1<i>H</i>)-one (ASQ). ASQ exhibits nonmonotonic responses to polarity and pH stimuli, and aggregation-induced emission (AIE) with a nonmonotonic AIE curve. Meanwhile, ASQ can be adjusted to emit white light in an acidic environment, and it shows multivalent functionalities including albumin protein sensing, ratiometric pH sensing, and amine gas sensing. Intramolecular force ; Chemistry ; Molecule ; Protonation ; Acceptor ; Polarity (international relations) ; Amine gas treating ; Luminescence ; Chemical physics ; Photochemistry ; Stereochemistry ; Materials science ; Organic chemistry ; Optoelectronics Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research
Authors: Qingmeng Xu | Giovanna Poggi | Claudio Resta | Michele Baglioni | Piero Baglioni
Source: Journal of Colloid and Interface Science (article, 2020-05-06)
Field: Materials Science — Biomaterials

Grafted nanocellulose and alkaline nanoparticles for the strengthening and deacidification of cellulosic artworks Nanocellulose ; Cellulosic ethanol ; Cellulose ; Nanoparticle ; Polymer science ; Chemical engineering ; Chemistry ; Materials science ; Pulp and paper industry ; Nanotechnology ; Organic chemistry ; Engineering Advanced Cellulose Research Studies ; Cultural Heritage Materials Analysis ; Dyeing and Modifying Textile Fibers
Authors: Marek Florkowski
Source: Energies (article, 2020-10-20)
Field: Materials Science — Materials Chemistry

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems. Partial discharge ; Convolutional neural network ; Reliability (semiconductor) ; High voltage ; Insulation system ; Voltage ; Computer science ; Artificial neural network ; Process (computing) ; Artificial intelligence ; Electronic engineering ; Electrical engineering ; Power (physics) ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Philippe Schwaller | Alain C. Vaucher | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-08-05)
Field: Materials Science — Materials Chemistry

Prediction of Chemical Reaction Yields using Deep Learning Artificial intelligence is driving one of the most important revolutions in organic chemistry. Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic chemists' daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, reaction yields models have been less investigated, despite the enormous potential of accurately predicting them. Reaction yields models, describing the percentage of the reactants that is converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the dataset applicability in reaction yields predictions. Computer science ; Categorical variable ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Omar Allam | Robert Kuramshin | Zlatomir Stoichev | B.W. Cho | S.W. Lee | Seung Soon Jang
Source: Materials Today Energy (article, 2020-07-15)
Field: Materials Science — Polymers and Plastics

Molecular structure–redox potential relationship for organic electrode materials: density functional theory–Machine learning approach Density functional theory ; Pipeline (software) ; Artificial intelligence ; HOMO/LUMO ; Computer science ; Machine learning ; Materials science ; Molecular descriptor ; Ensemble learning ; Feature selection ; Artificial neural network ; Biological system ; Pattern recognition (psychology) ; Quantitative structure–activity relationship ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Molecule Conducting polymers and applications ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Hao Jin | Huijun Zhang | Jianwei Li | Tao Wang | Langhui Wan | Hong Guo | Yadong Wei
Source: The Journal of Physical Chemistry Letters (article, 2020-04-02)
Field: Materials Science — Materials Chemistry

Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning Searching for novel, high-performance, two-dimensional photovoltaic (2DPV) materials is an important pursuit for solar cell applications. In this work, an efficient method based on the machine learning algorithm combined with high-throughput screening is developed. Twenty-six 2DPV candidates are successfully ruled out from 187093 experimentally identified inorganic crystal structures, whose conversion efficiencies are predicted by density functional theory calculations. Our results indicate that Sb<sub>2</sub>Se<sub>2</sub>Te, Sb<sub>2</sub>Te<sub>3</sub>, and Bi<sub>2</sub>Se<sub>3</sub> exhibit conversion efficiencies that are much higher than those of others, which make them promising 2DPV candidates for further applications. The superior photovoltaic performance is then analyzed, and the hidden structure-related relationships with photovoltaic properties are established, thus providing important information for the further examination of 2DPV materials. Given the rapid development of the database of materials, this approach not only provides an efficient way of searching for novel 2DPV materials but also can be applied to exploration of a broad range of functional materials. Photovoltaic system ; Computer science ; Throughput ; Range (aeronautics) ; Density functional theory ; Materials science ; Energy conversion efficiency ; Artificial intelligence ; Nanotechnology ; Machine learning ; Optoelectronics ; Electrical engineering ; Engineering ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films