jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louis Godeffroy | Paolo Ciocci | Jean‐François Lemineur | Frédéric Kanoufi
Source: Current Opinion in Electrochemistry (article, 2022-10-18)
Field: Chemistry — Electrochemistry

Watching operando nanoscale electrochemical deposition by optical microscopy Electrodeposition is a popular electrochemical technique which has recently shifted from the deposition of conformal thin films to the deposition of nanoparticles and the fabrication of nanostructured electrodes. It is straightforward to implement for catalysis and sensing applications and it allows producing surfactant-free and perfectly connected nanoparticles whose size, shape, and composition can be modulated by the applied potential or current. Meanwhile, the underlying mechanisms of nanoparticle nucleation and growth are still under intensive investigation thanks to the considerable technological improvements made in many high resolution (operando or identical location) imaging techniques over the last two decades. This review focuses on the recent advances made by optical microscopy in this field. It is indeed now possible to use high resolution optical microscopes to probe objects noninvasively and operando at the nanoscale with a very high temporal resolution and high throughput, which is of particular interest for electrodeposition studies. Nanotechnology ; Nanoscopic scale ; Materials science ; Deposition (geology) ; Nanoparticle ; Nucleation ; Microscopy ; Optical microscope ; Fabrication ; Resolution (logic) ; Microscope ; Electrode ; Chemistry ; Optics ; Computer science ; Physics ; Scanning electron microscope Electrochemical Analysis and Applications ; Electrodeposition and Electroless Coatings ; Force Microscopy Techniques and Applications
Authors: Scott A. Southern | Da‐Jiang Liu | Puranjan Chatterjee | Yuting Li | Frédéric A. Perras
Source: Physical Chemistry Chemical Physics (article, 2022-11-11)
Field: Chemistry — Spectroscopy

<sup>1</sup>H chemical shift anisotropy: a high sensitivity solid-state NMR dynamics probe for surface studies? Dynamics play significant roles in chemistry and biochemistry-molecular motions impact both large- and small-scale chemical reactions in addition to biochemical processes. In many systems, including heterogeneous catalysts, the characterization of dynamics remains a challenge. The most common approaches involve the solid-state NMR measurement of anisotropic interactions, in particular <sup>2</sup>H quadrupolar coupling and <sup>1</sup>H-X dipolar coupling, which generally require isotope enrichment. Due to the high sensitivity of <sup>1</sup>H NMR, <sup>1</sup>H chemical shift anisotropy (CSA) is a particularly enticing, and underexplored, dynamics probe. We carried out <sup>1</sup>H CSA and <sup>1</sup>H-<sup>13</sup>C dipolar coupling measurements in a series of model supported complexes to understand how <sup>1</sup>H CSA can be leveraged to gain dynamic information for heterogeneous catalysts. Mathematical descriptions are given for the dynamic averaging of the CSA tensor, and its dependence on orientation and asymmetry. The variability of the orientation of the tensor in the molecular frame, in addition to its magnitude and asymmetry, negatively impacts attempts to extract quantitative dynamic information. Nevertheless, <sup>1</sup>H CSA measurements can reveal useful qualitative insights into the motions of a particularly dilute site, such as from a surface species. Anisotropy ; Asymmetry ; Solid-state nuclear magnetic resonance ; Chemical physics ; Chemistry ; Magnetic dipole–dipole interaction ; Molecular dynamics ; Coupling (piping) ; J-coupling ; Characterization (materials science) ; Nuclear magnetic resonance spectroscopy ; Tensor (intrinsic definition) ; Orientation (vector space) ; Residual dipolar coupling ; Chemical shift ; Dipole ; Dynamics (music) ; Nuclear magnetic resonance ; Computational chemistry ; Materials science ; Physical chemistry ; Nanotechnology ; Physics ; Stereochemistry ; Organic chemistry ; Optics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Muon and positron interactions and applications
Authors: Hugues Lambert | Alvaro Castillo Bonillo | Qiang Zhu | Yong‐Wei Zhang | Tung‐Chun Lee
Source: npj Computational Materials (article, 2022-01-27)
Field: Chemistry — Organic Chemistry

Supramolecular gating of guest release from cucurbit[7]uril using de novo design Abstract Herein we computationally explore the modulation of the release kinetics of an encapsulated guest molecule from the cucurbit[7]uril ( CB7 ) cavity by ligands binding to the host portal. We uncovered a correlation between the ligand-binding affinity with CB7 and the guest residence time, allowing us to rapidly predict the release kinetics through straightforward energy minimization calculations. These high-throughput predictions in turn enable a Monte-Carlo Tree Search (MCTS) to de novo design a series of cap-shaped ligand molecules with large binding affinities and boosting guest residence times by up to 7 orders of magnitude. Notably, halogenated aromatic compounds emerge as top-ranking ligands. Detailed modeling suggests the presence of halogen-bonding between the ligands and the CB7 portal. Meanwhile, the binding of top-ranked ligands is supported by 1 H NMR and 2D DOSY-NMR. Our findings open up possibilities in gating of molecular transport through a nanoscale cavity with potential applications in nanopore technology and controlled drug release. Chemistry ; Supramolecular chemistry ; Gating ; Trimer ; Receptor–ligand kinetics ; Ligand (biochemistry) ; Cucurbituril ; Affinities ; Molecular binding ; Molecule ; Kinetics ; Stereochemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Biophysics ; Dimer ; Organic chemistry ; Receptor ; Physics Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions ; Advanced NMR Techniques and Applications
Authors: Albert Queraltó | Adrià Pacheco | Nerea Jiménez | S. Ricart | X. Obradors | Teresa Puig
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Defining inkjet printing conditions of superconducting cuprate films through machine learning The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%. Model interpretation using Shapley Additive Explanations (SHAP) revealed the most important variables for each study. We could determine that to ensure homogeneous CSD films of 1 micron thickness without cracks after the pyrolysis, we need average drop volumes of 190-210 pl, and no. of drops between 5000 and 6000, delivering a total volume deposited close to 1 μl. Materials science ; Cuprate ; Superconductivity ; Inkjet printing ; Condensed matter physics ; Nanotechnology ; Engineering physics ; Inkwell ; Composite material ; Engineering ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Fluid Dynamics and Thin Films
Authors: Hongyu Cao | Meina Gong | Mengyan Wang | Qian Tang | Lihao Wang | Xuefang Zheng
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Steady/transient state spectral researches on the solvent-triggered and photo-induced novel properties of metal-coordinated phthalocyanines The large conjugated system and the d orbitals of metallic phthalocyanines (MPcs) are easily triggered by solvent molecules and light to cause variations in photo-physical and photo-chemical properties. To clarify the novel properties of photo-excited MPcs in solvents, the steady/transient state spectral data were collected to investigate four MPc templates, including FePc, ZnPc, CoPc and AlClPc. The Q bands of FePc and CoPc were prone to redshift, while the peaks of ZnPc and AlClPc tended to blueshift in various solvents compared with that in DMSO. With xenon lamp irradiation, the characteristic absorption peak intensity of FePc and ZnPc in DMSO decreased gradually and then increased after being subjected to dark condition. The transient absorption spectra and kinetic data illustrated that photo-excited FePc and CoPc produced relatively short lifetime transient intermediates. The positive absorption at 650 nm in the Q bands of FePc and CoPc could be assigned to the d-π* electron transition in transient intermediates with unpaired d-orbital electron in the open shell configuration of Fe<sup>2+</sup> and Co<sup>2+</sup>. The above studies were of great significance for the application of solvent-triggered and photo-induced novel properties of MPcs. Transient (computer programming) ; Solvent ; Metal ; Photochemistry ; Spectral properties ; Chemistry ; Materials science ; Computational chemistry ; Computer science ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies
Authors: Sviatoslav Baranets | Gregory M. Darone | Svilen Bobev
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2022-02-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural diversity among multinary pnictide oxides: a minireview focused on semiconducting and superconducting heteroanionic materials Abstract Incorporating different anions with varied ionic sizes and charges is a rapidly growing approach to bring out unusual physical properties among various classes of solid-state materials, pnictides and chalcogenides in particular. This minireview is focused on hetero-anionic materials based on the pnictogens, which have been demonstrated to offer an impressive diversity of crystal chemistry and electronic structures. In addition, many pnictide oxides or oxypnictides, over the course of the last decade, have been shown to exhibit a broad spectrum of superconducting, magnetic, and semiconducting properties. However, the structural diversity of the mixed-anion materials is far greater than the several known structure types, or their variants, of the well-known layered superconductive materials. Therefore, with this treatise, we aim to provide a comprehensive overview of the crystal chemistry of pnictide oxides by recounting almost 40 different structures of such ternary and multinary compounds. In addition to the structural aspects, we also highlight some of the challenges associated with the synthesis, and briefly summarize reported, to date, physical properties of this remarkable class of solids. Pnictogen ; Superconductivity ; Ternary operation ; Materials science ; Ionic bonding ; Solid-state chemistry ; Nanotechnology ; Chemistry ; Ion ; Condensed matter physics ; Crystallography ; Physics ; Computer science ; Organic chemistry Iron-based superconductors research ; Pneumocystis jirovecii pneumonia detection and treatment ; Inorganic Chemistry and Materials
Authors: Nesrine Amor | Muhammad Tayyab Noman | Adla Ismail | Michal Petrů | Neethu Sebastian
Source: Polymers (article, 2022-02-26)
Field: Materials Science — Polymers and Plastics

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values. Ultimate tensile strength ; Titanium dioxide ; Artificial neural network ; Materials science ; Linear regression ; Nano- ; Backpropagation ; Composite material ; Regression analysis ; Biological system ; Machine learning ; Computer science Textile materials and evaluations ; Surface Roughness and Optical Measurements
Authors: Jie Zhang | Lirong Tan | Peter‐Leon Hagedoorn | Ruiqi Wang | Li Wen | Siwei Wu | Xuemei Tan | Hui Xu | Xing Zhou
Source: Journal of Water Process Engineering (article, 2022-05-23)
Field: Chemistry — Analytical Chemistry

Micro-nano bubbles assisted laccase for biocatalytic degradation of bisphenols Bisphenol A ; Degradation (telecommunications) ; Laccase ; Chemistry ; Bisphenol S ; Pollutant ; Environmental chemistry ; Chemical engineering ; Pulp and paper industry ; Organic chemistry Analytical chemistry methods development ; Advanced oxidation water treatment ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Abeer M. Adel | Fatma N. El-Shall | Mohamed A. Diab | Mona T. Al–Shemy
Source: Biomass Conversion and Biorefinery (article, 2022-12-17)
Field: Chemistry — Inorganic Chemistry

Biogenic silver-doped mesoporous silica nanoparticles for multifunctional eco-designed textile printing Abstract The current study concerns the valorization of waste rice hulls into value-added materials. To achieve this goal, first, the extraction route of mesoporous silica nanoparticles (SiO 2 NPs) from rice hulls using citric acid as a carbon template was compared with that prepared by conventional methods of incineration only. Both routes were performed at different temperatures of 600 and 700°C, and the fabricated SiO 2 NPs were studied by N 2 physisorption isotherm, X-ray diffraction (XRD), Fourier transform infrared (FTIR), dynamic light scattering (DLS), and thermogravimetric (TG) analyses. The results show the influence of both routes and temperature on the porous structure, surface area, and charge of as-fabricated SiO 2 NPs. Furthermore, the physico-chemical features of as-prepared mesoporous SiO 2 NPs were utilized, in a facile and green way, to prepare silver NPs with high anti-microbial activity. The results were validated by the tools of UV-visible, FTIR, XRD, and XPS spectroscopy. Finally, the fabricated silver-doped mesoporous SiO 2 NPs were used as auxiliary additives through one-pot printing of different types of fabric (i.e., polyester (synthetic fabric), cotton (natural fabric), and cotton/polyester (blended fabric)). The EDX mapping of the pigment-printed fabrics with Ag-doped SiO 2 NPs exhibited the homogeneous distribution of Ag and Si atoms together with C and O throughout the fabric matrix. Interestingly, the pigment-printed fabrics with Ag-doped SiO 2 NPs demonstrated superior antibacterial activity, ultraviolet protection factor (UPF), and color characteristics in comparison to the conventional dyeing component. Graphical Abstract Materials science ; Mesoporous material ; Fourier transform infrared spectroscopy ; Chemical engineering ; Polyester ; X-ray photoelectron spectroscopy ; Mesoporous silica ; Nanoparticle ; Textile ; Nanotechnology ; Composite material ; Organic chemistry ; Catalysis ; Chemistry Pigment Synthesis and Properties ; Dyeing and Modifying Textile Fibers ; Nanoparticles: synthesis and applications
Authors: Yongtao Lü | Yi Huo | Zhuoyue Yang | Yibiao Niu | Ming Zhao | Sergei Bosiakov | Lei Li
Source: Frontiers in Bioengineering and Biotechnology (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure In recent years, the convolutional neural network (CNN) technique has emerged as an efficient new method for designing porous structure, but a CNN model generally contains a large number of parameters, each of which could influence the predictive ability of the CNN model. Furthermore, there is no consensus on the setting of each parameter in the CNN model. Therefore, the present study aimed to investigate the sensitivity of the parameters in the CNN model for the prediction of the mechanical property of porous structures. 10,500 samples of porous structure were randomly generated, and their effective compressive moduli obtained from finite element analysis were used as the ground truths to construct and train a CNN model. 8,000 of the samples were used to train the CNN model, 2000 samples were used for the cross-validation of the CNN model and the remaining 500 new structures, which did not participate in the CNN training process, were used to test the predictive power of the CNN model. The sensitivity of the number of convolutional layers, the number of convolution kernels, the number of pooling layers, the number of fully connected layers and the optimizer in the CNN model were then investigated. The results showed that the optimizer has the largest influence on the training speed, while the fully connected layer has the least impact on the training speed. Additionally, the pooling layer has the largest impact on the predictive ability while the optimizer has the least impact on the predictive ability. In conclusion, the parameters of the CNN model play an important role in the performance of the CNN model and the parameter sensitivity analysis can help optimize the CNN model to increase the computational efficiency. Convolutional neural network ; Pooling ; Computer science ; Convolution (computer science) ; Sensitivity (control systems) ; Artificial intelligence ; Artificial neural network ; Algorithm ; Finite element method ; Pattern recognition (psychology) ; Structural engineering ; Engineering Machine Learning in Materials Science ; Advanced Neural Network Applications ; Infrastructure Maintenance and Monitoring
Authors: Antal Jákli | Yu. A. Nastishin | Oleg D. Lavrentovich
Source: Liquid Crystals Reviews (article, 2022-06-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Defects in bent-core liquid crystals Topological defects and defect phases of rigid and flexibly bent-shaped liquid crystals are reviewed with emphasis on how they are affected by the departure of molecular shapes from a simple rod. The review discusses defects in bent-core uniaxial and hypothetical biaxial nematics, twist-bend nematic, and various frustrated layered bent-core liquid crystals, such as twist-grain boundary phase, nanoscale helical nanofilament phase, and the so-called B7 textures with helical ribbons. Bent molecular geometry ; Twist ; Liquid crystal ; Core (optical fiber) ; Materials science ; Phase (matter) ; Nanoscopic scale ; Grain boundary ; Condensed matter physics ; Crystallography ; Geometry ; Microstructure ; Nanotechnology ; Physics ; Composite material ; Chemistry ; Mathematics ; Optoelectronics Liquid Crystal Research Advancements ; Plant Reproductive Biology ; Advanced Materials and Mechanics
Authors: Gabriel M. Nascimento | Elton Ogoshi | A. Fazzio | Carlos Mera Acosta | Gustavo M. Dalpian
Source: Scientific Data (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property. Spintronics ; Workflow ; Bayesian optimization ; Computer science ; Density functional theory ; Inference ; Inverse ; Ab initio ; Materials science ; Algorithm ; Physics ; Artificial intelligence ; Mathematics ; Database ; Computational chemistry ; Chemistry ; Ferromagnetism ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Xiaoxu Xuan | Mengjie Wang | Sivakumar Manickam | Grzegorz Boczkaj | Joon Yong Yoon | Xun Sun
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-05-26)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks-Based Sensors for the Detection of Toxins in Food: A Critical Mini-Review on the Applications and Mechanisms Using scientific technologies to detect toxins in food is significant to prevent food safety problems and protect people's health. Recently, the rise of sensors has made rapid, efficient, and safe detection of food toxins possible. One of the key factors impacting the sensor's performance is the nanomaterials employed. Metal-organic frameworks (MOFs), with high specific surface area, tunable composition, porous structure, and flexible properties, have aroused the interest of researchers. The applications of MOFs in detecting food toxins have seen remarkable success in the past few years. In this critical mini-review, the impact of various synthesis methods on MOFs' properties is first presented. Then, the applications and mechanisms of MOFs-based sensors in detecting various toxins are summarized and analyzed. Finally, future perspectives, potential opportunities, and challenges in this field are discussed. Nanotechnology ; Metal-organic framework ; Biochemical engineering ; Computer science ; Food safety ; Risk analysis (engineering) ; Materials science ; Business ; Engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Chemical Sensor Technologies ; Melamine detection and toxicity