jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel J. Hutton | Florian Göltl
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Free and internal energies for the adsorption of short alkanes into the zeolite SSZ-13 from <i>ab initio</i> molecular dynamics simulations Electronic structure calculations have become a valuable tool in understanding chemical reactions of hydrocarbons in zeolite pores. However, commonly applied approaches to calculate free energies based on static electronic structure calculations significantly overestimate the entropic penalty for molecular adsorption into zeolite pores. Here, we use <i>ab initio</i> molecular dynamics (AIMD) simulations to model the adsorption of methane, ethane, and propane to purely siliceous and protonated SSZ-13. In our analyses we focus on the internal and Helmholtz free energies of adsorption of each molecule and compare our results to various approaches for the calculation of free energies based on static calculations. We find that only an approach that retains two thirds of the translational entropy of the adsorbate upon adsorption compares favorably with AIMD simulations. However, comparison to experimental measurements of Gibbs free energies of adsorption reported in the literature implies that we might not have captured the full complexity of alkane adsorption in our model. We expect that results in this work will help to develop a better understanding of alkane adsorption in zeolites, and that the provided data will serve as a benchmark for free energy calculations of alkane adsorption in zeolites in the future. Adsorption ; Alkane ; Chemistry ; Zeolite ; Helmholtz free energy ; Ab initio ; Thermodynamics ; Molecular dynamics ; Computational chemistry ; Molecule ; Gibbs free energy ; Physical chemistry ; Chemical physics ; Hydrocarbon ; Organic chemistry ; Physics ; Catalysis Zeolite Catalysis and Synthesis ; Phase Equilibria and Thermodynamics ; Advanced Chemical Physics Studies
Authors: Jing Xie | Kyounghyoun Lee | Hyeonjin Park | Hyun Jung | Jae‐Min Oh
Source: Nanomaterials (article, 2023-01-30)
Field: Chemistry — Organic Chemistry

Enhanced Emulsifying Ability of Deoxycholate through Dynamic Interaction with Layered Double Hydroxide The emulsifying ability of the naturally occurring surfactant deoxycholic acid (DCA) was improved by dynamic interaction with nanometric layered particles, layered double hydroxide (LDH). As DCA molecules are rigid due to the facial configuration of hydrophobic-hydrophilic groups, they tend to form molecular aggregation in an acidic condition or imbalanced water-lipid ratios. In this study, the homogeneous hybrids of DCA and LDH were obtained by the in situ growth of LDH at a DCA molecule. The DCA-LDH hybrid successfully prevented the molecular aggregation of DCA at an acidic pH and imbalanced water-to-oil ratio. The dynamic light scattering showed that the hydrodynamic radius of micelle in the emulsion made with DCA-LDH maintained its small size (<500 nm), while upon pH change and dilution with water, that made with DCA only uncontrollably increased up to ~3000 nm. The polydispersity index value of the DCA-LDH emulsion remained constant (<0.3) after the pH change and dilution with water, indicating the high stability of the formulation. Furthermore, time-dependent turbidity monitoring revealed that the DCA-only formulation suffered from serious coalescence and creaming compared with the DCA-LDH formulation. It is suggested that the dynamic interaction between LDH layers and DCA prevented molecular aggregation under unfavorable conditions for the oil-in-water emulsion. Dispersity ; Chemistry ; Dynamic light scattering ; Hydrodynamic radius ; Hydroxide ; Micelle ; Pulmonary surfactant ; Emulsion ; Chemical engineering ; Dilution ; Chromatography ; Hydrophobic effect ; Aqueous solution ; Nanoparticle ; Inorganic chemistry ; Polymer chemistry ; Organic chemistry ; Biochemistry Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Polymer Surface Interaction Studies
Authors: Lei Song | Dekun Yang | Zhidan Lei | Qimeng Sun | Zhiwen Chen | Yi Song
Source: Materials (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography To enable high-density optical storage, better storage media structures, diversified recording methods, and improved accuracy of readout schemes should be considered. In this study, we propose a novel three-dimensional (3D) sloppy nanostructure as the optical storage device, and this nanostructure can be fabricated using the 3D laser direct writing technology. It is a 900 nm high, 1 × 2 µm wide Si slope on a 200 nm SiO2 layer with 200 nm Si<sub>3</sub>N<sub>4</sub> deposited on top to enhance reflectivity. In this study, we propose a reflected spectrum-based method as the readout recording strategy to stabilize information readout more stable. The corresponding reflected spectrum varied when the side wall angle of the slope and the azimuth angle of the nanostructure were tuned. In addition, an artificial neural network was applied to readout the stored information from the reflected spectrum. To simulate the realistic fabrication error and measurement error, a 20% noise level was added to the study. Our findings showed that the readout accuracy was 99.86% for all 120 data sequences when the slope and azimuth angle were varied. We investigated the possibility of a higher storage density to fully demonstrate the storage superiority of this designed structure. Our findings also showed that the readout accuracy can reach its highest level at 97.25% when the storage step of the encoded structure becomes 7.5 times smaller. The study provides the possibility to further explore different nanostructures to achieve high-density optical storage. Nanostructure ; Materials science ; Azimuth ; Optical storage ; Computer data storage ; 3D optical data storage ; Laser ; Fabrication ; Lithography ; Optoelectronics ; Optics ; Noise (video) ; Nanotechnology ; Computer science ; Physics ; Image (mathematics) ; Computer hardware Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; Photonic Crystals and Applications
Authors: Cristina Plamadeala | Sebastian Lifka | Gerda Buchberger | Werner Baumgärtner | J. Heitz
Source: Frontiers in Materials (article, 2023-11-22)
Field: Materials Science — Surfaces, Coatings and Films

Bio-inspired hierarchical polymer micro- and nanostructures for anti-adhesion applications In this paper we present polymer surfaces inspired by the calamistrum of cribellate spiders. The calamistrum resembles a micro-comb with a finger-print-like nanorippled topography, which allows cribellate spiders to handle and process nanofibers, without sticking to them. Due to its morphology, the calamistrum has a reduced surface for contact with the nanofibers, which therefore reduces the adhesive forces. Three different types of structured surfaces were prepared: nanostructured surfaces (ripples), microstructured surfaces (lines), and a combination of micro- and nanostructured surfaces (lines superimposed with ripples). Polymer lines were created using UV mask lithography. Nanoripples, i.e., laser-induced periodic surface structures, were fabricated by exposure to a KrF* laser beam. Nanofibers were produced and deposited onto each sample by electrospinning. To quantify each samples’ adhesiveness, a peel-off test was used, and the results were plotted and compared against the control samples–a flat polymer film. Our results indicate that lines have a stronger influence on the adhesion reduction than the nanoripples: nanoripples reduce fiber adhesion by 7%, whereas the lines reduce it by 28%. The highest adhesion reduction of 33% is obtained for the polymer surfaces with a combination of both lines and nanoripples. Our results open new insights in the field of artificial nanofiber adhesion on micro- and nanopatterned surfaces, which are essential when designing tools for nanofiber handling. Materials science ; Nanofiber ; Adhesion ; Electrospinning ; Polymer ; Nanotechnology ; Adhesive ; Nanostructure ; Composite material ; Contact angle Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Nanofabrication and Lithography Techniques
Authors: Juan Viguera Diez | Sara Romeo Atance | Ola Engkvist | Simon Olsson
Source: ChemRxiv (preprint, 2023-11-24)
Field: Materials Science — Materials Chemistry

Generation of conformational ensembles of small molecules via Surrogate Model-Assisted Molecular Dynamics The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce Surrogate Model-Assisted Molecular Dynamics (SMA-MD), a new procedure to sample the equilibrium ensemble of molecules. First, SMA-MD leverages Deep Generative Models to enhance the sampling of slow degrees of freedom. Subsequently, the generated ensemble undergoes statistical reweighting, followed by short simulations. Our empirical results show that SMA-MD generates more diverse and lower energy ensembles than conventional Molecular Dynamics simulations. Furthermore, we showcase the application of SMA-MD for the computation of thermodynamical properties by estimating implicit solvation free energies. Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Computer science ; Statistical physics ; Solvation ; Sampling (signal processing) ; SMA* ; Physics ; Algorithm ; Chemistry ; Computational chemistry ; Molecule ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Multi-Objective Optimization Algorithms
Authors: Maisa Vuorte | Aapo Lokka | Alberto Scacchi | Maria Sammalkorpi
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Dioctyl sodium sulfosuccinate surfactant self-assembly dependency of solvent hydrophilicity: a modelling study The self-assembly of dioctyl sodium sulfosuccinate (AOT) model surfactant in solvent environments of differing polarity is examined by means of dissipative particle dynamics (DPD) bead model parametrized against Hildebrand solubility parameters from atomistic molecular dynamics (MD) simulations. The model predicts that in hydrophobic solvents (<i>e.g.</i> dodecane) the surfactant forms small (<i>N</i><sub>agg</sub> ∼ 8) reverse micellar aggregates, while in a solvent corresponding to water lamellar assembly takes place, in good agreement with literature structural parameters. Interestingly, solvents of intermediate polarity lead to formation of large, internally structured aggregates. In these, the surfactant headgroups cluster within the aggregate, surrounded by a continuous phase formed by the hydrocarbon tails. We show that the partitioning of the headgroups between the aggregate surface layer and the inner clustered phase depends primarily on solvent polarity, and can be controlled by the solvent, but also system composition. Finally, we compare the DPD assembly response to simplified effective interaction potentials derived at dilute concentration limit for the interactions. The comparison reveals that the simplified effective potential descriptions provide good level of insight on the assembly morphologies, despite drastic, isotropic interactions simplification involved. Dissipative particle dynamics ; Pulmonary surfactant ; Solvent ; Sodium ; Chemical engineering ; Molecular dynamics ; Chemistry ; Dependency (UML) ; Materials science ; Organic chemistry ; Computational chemistry ; Computer science ; Polymer ; Engineering Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Lipid Membrane Structure and Behavior
Authors: Beytullah Erdoğan | Oğuz Koçar | Halil Ibrahim Topal
Source: Scientia Iranica (article, 2023-12-31)
Field: Chemistry — Analytical Chemistry

Measurement of the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO using the Artificial Neural Network method Nanofluids are strong candidates as heat carriers due to their excellent thermophysical properties. Among these thermophysical properties, viscosity is critical in heat transfer and pressure loss calculations. In this study, three different water-based nanofluids, Al2O3, TiO2, and ZnO, were prepared with volumetric concentrations ranging from 0.1% to 1%. The dynamic viscosities of these nanofluids were experimentally measured within a temperature range of 20 °C to 50 °C. Artificial neural networks (ANN) were employed to predict the results based on the experimental data. Two different approaches were applied in the implementation of the ANN method. The first approach involved creating three separate ANN models, each dedicated to predicting the viscosities of the three different nanofluids. The second approach used a single generalized ANN to predict the viscosities of all nanofluids. The results were evaluated using the criteria of R-squared (R2) and root mean square error (RMSE) values. In all models, R2 values exceeded 99%, while the RMSE values were calculated for the Al2O3, TiO2, and ZnO nanofluid ANN models and the generalized ANN model to be 0.40%, 0.30%, 0.04%, and 0.28% respectively. These results demonstrate that a nanofluid's viscosity can be effectively predicted individually and multiple nanofluids using an ANN model. Nanofluid ; Artificial neural network ; Viscosity ; Materials science ; Biological system ; Computer science ; Composite material ; Nanotechnology ; Artificial intelligence ; Nanoparticle Petroleum Processing and Analysis ; Nanofluid Flow and Heat Transfer ; Enhanced Oil Recovery Techniques
Authors: Jürgen Roth | Douglas J. Taatjes
Source: Histochemistry and Cell Biology (review, 2023-05-17)
Field: Chemistry — Spectroscopy

Histochemistry and Cell Biology—a glance into the past and a look ahead At the occasion of the 65th anniversary of Histochemistry and Cell Biology, we browse through its first ten years of publication and highlight a selection of papers from the early days of enzyme, protein, and carbohydrate histochemistry. In addition, we narrate recent progress to identify, quantify, and precisely determine the tissue localization of proteins and lipids, and small molecules by the combination of spectroscopic techniques and histology. Immunohistochemistry ; Biology ; Developmental biology ; Histology ; Evolutionary biology ; Cell biology ; Genetics ; Immunology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Molecular Biology Techniques and Applications
Authors: Ioannis Michos | Vasilios Raptis
Source: Entropy (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Graph Partitions in Chemistry We study partitions (equitable, externally equitable, or other) of graphs that describe physico-chemical systems at the atomic or molecular level; provide examples that show how these partitions are intimately related with symmetries of the systems; and discuss how such a link can further lead to insightful relations with the systems' physical and chemical properties. We define a particular kind of graph partition, which we call Chemical Equitable Partition (CEP), accounting for chemical composition as well as connectivity and associate it with a quantitative measure of information reduction that accompanies its derivation. These concepts are applied to model molecular and crystalline solid systems, illustrating their potential as a means to classify atoms according to their chemical or crystallographic role. We also cluster materials in meaningful manners that take their microstructure into account and even correlate them with the materials' physical properties. Partition (number theory) ; Homogeneous space ; Graph ; Computer science ; Cluster (spacecraft) ; Graph partition ; Measure (data warehouse) ; Graph theory ; Statistical physics ; Theoretical computer science ; Mathematics ; Data mining ; Combinatorics ; Physics Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Caterina Maria Tone | Alessandra Zizzari | Lorenza Spina | Monica Bianco | Maria Penelope De Santo | Valentina Arima | R. Barberi | Federica Ciuchi
Source: Langmuir (article, 2023-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sunset Yellow Confined in Curved Geometry: A Microfluidic Approach The behavior of lyotropic chromonic liquid crystals (LCLCs) in confined environments is an interesting research field that still awaits exploration, with multiple key variables to be uncovered and understood. Microfluidics is a highly versatile technique that allows us to confine LCLCs in micrometric spheres. As microscale networks offer distinct interplays between the surface effects, geometric confinement, and viscosity parameters, rich and unique interactions emerging at the LCLC-microfluidic channel interfaces are expected. Here, we report on the behavior of pure and chiral doped nematic Sunset Yellow (SSY) chromonic microdroplets produced through a microfluidic flow-focusing device. The continuous production of SSY microdroplets with controllable size gives the possibility to systematically study their topological textures as the function of their diameters. Indeed, doped SSY microdroplets produced via microfluidics, show topologies that are typical of common chiral thermotropic liquid crystals. Furthermore, few droplets exhibit a peculiar texture never observed for chiral chromonic liquid crystals. Finally, the achieved precise control of the produced LCLC microdroplets is a crucial step for technological applications in biosensing and anticounterfeiting. Microfluidics ; Microscale chemistry ; Liquid crystal ; Thermotropic crystal ; Lyotropic ; Nanotechnology ; Materials science ; Chemical physics ; Optoelectronics ; Chemistry Liquid Crystal Research Advancements ; Photonic Crystals and Applications ; Electrowetting and Microfluidic Technologies
Authors: Xuan Wei | Shiyang Liu | Chuangyuan Xie | Wei Fang | C.J. Deng | Zhiqiang Wen | Dapeng Ye | Dengfei Jie
Source: Frontiers in Plant Science (article, 2023-12-06)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of <i>Pleurotus geesteranus</i> strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of <i>Pleurotus geesteranus</i> strains, which could further inspire new methods for the phenotypic identification of edible fungi. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Pleurotus ; Computer science ; Feature extraction ; Feature (linguistics) ; Biological system ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Yibo Guo | Xue Wu | Jie Fu
Source: Journal of Physics D Applied Physics (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Revisiting the stable structures of gold clusters: Au<i> <sub>n</sub> </i> (<i>n</i> = 16–25) by artificial neural network potential Abstract Identifying the stable structures of gold (Au) clusters is a huge challenge in cluster science. In this work, we have searched the ground-state structures of neutral Au n ( n = 16–25) clusters using the potential of an artificial neural network (ANN) trained with density functional theory (DFT) data. Compared with the DFT data, the root mean square error of binding energy predicted by the ANN potential is about 8.66 meV/atom. Applying the ANN potential to search the ground-state structures by comprehensive genetic algorithm, we have found several new candidates of Au 18 , Au 22 , and Au 23 , which have not been previously reported. Au 18 has a hollow cage structure, whereas Au 22 and Au 23 are flat cage structures. From the electronic analysis, we elucidate the stability mechanism of the newly found structures that are associated with the electronic shell closure of superatomic orbitals. Additonally, we also clarified how to clean a database to train an efficient ANN potential in detail. Overall, this work proves that applying machine learning to the description of atomic interactions can accelerate the search of ground-state structures of clusters and help to find new candidates for stable cluster structures. Cluster (spacecraft) ; Artificial neural network ; Ground state ; Density functional theory ; Atom (system on chip) ; Stability (learning theory) ; Atomic orbital ; Electronic structure ; Closure (psychology) ; Computer science ; Atomic physics ; Materials science ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Nuoyan Yang | Spencer Hill | Sergei Manzhos | Tucker Carrington
Source: Journal of Molecular Spectroscopy (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

A local Gaussian Processes method for fitting potential surfaces that obviates the need to invert large matrices Interpolation (computer graphics) ; Potential energy ; Potential energy surface ; Gaussian ; Gaussian process ; Kriging ; Superposition principle ; Matrix (chemical analysis) ; Statistical physics ; Mathematics ; Computer science ; Ab initio ; Physics ; Mathematical analysis ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics