jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adriana Popa | Maria Ştefan | Sergiu Macavei | I. Perhaița | Lucian Barbu–Tudoran | Dana Toloman
Source: Polymers (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Facile Preparation of PVDF/CoFe2O4-ZnO Hybrid Membranes for Water Depollution In this investigation, CoFe<sub>2</sub>O<sub>4</sub>-PVDF and CoFe<sub>2</sub>O<sub>4</sub>-ZnO-PVDF hybrid membranes were prepared using a modified phase inversion method in which a magnetic field was applied during the casting process to ensure a uniform distribution of nanomaterials on the membrane surface. Thus, better absorption of light and increased participation of nanoparticles in the photodegradation process is ensured. The influence of nanomaterials on the crystalline structure, surface morphology, and hydrophilicity properties of the PVDF membrane was investigated. The obtained results indicated that the hybrid membrane exhibited significant differences in its intrinsic properties due to the nanomaterials addition. The hydrophilicity properties of the PVDF membrane were improved by the presence of nanoparticles. The photocatalytic decomposition of aqueous Rhodamine B solution in the presence of the prepared membrane and under visible light irradiation was tested. The hybrid membrane containing CoFe<sub>2</sub>O<sub>4</sub>-ZnO on its surface exhibited a high removal rate. Membrane ; Materials science ; Nanomaterials ; Photodegradation ; Rhodamine B ; Photocatalysis ; Chemical engineering ; Nanoparticle ; Phase inversion ; Aqueous solution ; Hybrid material ; Nanotechnology ; Chemistry ; Catalysis ; Organic chemistry ZnO doping and properties ; Membrane Separation Technologies ; Copper-based nanomaterials and applications
Authors: Zhi Zhang | Yi Wang | Zelin Zhao | Weijing Song | Xiaoli Zhou | Zejun Li
Source: Molecules (review, 2023-01-18)
Field: Materials Science — Materials Chemistry

Interlayer Chemical Modulation of Phase Transitions in Two-Dimensional Metal Chalcogenides Two-dimensional metal chalcogenides (2D-MCs) with complex interactions are usually rich in phase transition behavior, such as superconductivity, charge density wave (CDW), and magnetic transitions, which hold great promise for the exploration of exciting physical properties and functional applications. Interlayer chemical modulation, as a renewed surface modification method, presents congenital advantages to regulate the phase transitions of 2D-MCs due to its confined space, strong guest-host interactions, and local and reversible modulation without destructing the host lattice, whereby new phenomena and functionalities can be produced. Herein, recent achievements in the interlayer chemical modulation of 2D-MCs are reviewed from the aspects of superconducting transition, CDW transition, semiconductor-to-metal transition, magnetic phase transition, and lattice transition. We systematically discuss the roles of charge transfer, spin coupling, and lattice strain on the modulation of phase transitions in the guest-host architectures of 2D-MCs established by electrochemical intercalation, solution-processed intercalation, and solid-state intercalation. New physical phenomena, new insight into the mechanism of phase transitions, and derived functional applications are presented. Finally, a prospectus of the challenges and opportunities of interlayer chemical modulation for future research is pointed out. Phase transition ; Intercalation (chemistry) ; Superconductivity ; Condensed matter physics ; Materials science ; Modulation (music) ; Transition metal ; Lattice (music) ; Charge density wave ; Chemical physics ; Spintronics ; Semiconductor ; Nanotechnology ; Chemistry ; Optoelectronics ; Ferromagnetism ; Physics ; Inorganic chemistry 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Isabelle Lisiecki
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

The drastic influence of the NaBH4 reducing agent on the growth of cobalt nanoparticles and their 3D assemblies Dispersity ; Nanoparticle ; Nucleation ; Micelle ; Cobalt ; Nanotechnology ; Materials science ; Reducing agent ; Colloid ; Chemical engineering ; Chemistry ; Organic chemistry ; Polymer chemistry ; Metallurgy Pickering emulsions and particle stabilization ; Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science
Authors: Bojana Ranković | Ryan‐Rhys Griffiths | Henry B. Moss | Philippe Schwaller
Source: ChemRxiv (preprint, 2023-06-15)
Field: Materials Science — Materials Chemistry

Bayesian optimisation for additive screening and yieldimprovements in chemical reactions – beyond one-hot encoding Reaction additives play a significant role in controlling the reactivity and outcomes of chemical reactions. For example, a recent high-throughput additive screening identified a phthalimide ligand additive for Ni-catalysed photoredox decarboxylative arylations. This discovery enabled a 4-fold yield improvement by stabilising oxidative addition complexes and breaking up deactivated catalyst aggregates. Despite the promise of such large-scale screenings, they remain inaccessible to most research groups due to their cost and complexity. In this work, we demonstrate how to uncover similar results under much lower experimental budgets using Bayesian optimisation empowered methodology. We consider a unique reaction screening setting with 720 additives, forcing us to go beyond simple one-hot encoding of the reaction components. By investigating a range of molecular and reaction representations, we demonstrate convincing improvements over random search-inspired baselines. Importantly, our approach is generalisable and not limited to Ni-catalysed reactions, but can be applied to achieve yield improvements in diverse cross-couplings or other reactions, potentially unlocking access to new chemical spaces that are of interest to the chemical and pharmaceutical industries. The code is available at: https://github.com/schwallergroup/chaos. Yield (engineering) ; Bayesian probability ; Encoding (memory) ; Computer science ; Combinatorial chemistry ; Catalysis ; Throughput ; Biochemical engineering ; Chemical reaction ; Chemistry ; Materials science ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Photocatalysis Techniques
Authors: Gil M. Repa | Lisa A. Fredin
Source: The Journal of Physical Chemistry C (review, 2023-11-08)
Field: Materials Science — Materials Chemistry

Lessons Learned from Catalysis to Qubits: General Strategies to Build Accessible and Accurate First-Principles Models of Point Defects Defects and dopants play critical roles in defining the properties of a material. Achieving a mechanistic understanding of how such properties arise is challenging with current experimental methods, and computational approaches suffer from significant modeling limitations that frequently require <i>a posteriori</i> fitting. Consequently, the pace of dopant discovery as a means of tuning material properties for a particular application has been slow. However, recent advances in computation have enabled researchers to move away from semiempirical schemes to reposition density functional theory as a predictive tool and improve the accessibility of highly accurate first-principles methods to all researchers. This Perspective discusses some of these recent achievements that provide more accurate first-principles geometric, thermodynamic, optical, and electronic properties simultaneously. Advancements related to supercells, basis sets, functionals, and optimization protocols, as well as suggestions for evaluating the quality of a computational model through comparison to experimental data, are discussed. Moreover, recent computational results in the fields of energy materials, heterogeneous catalysis, and quantum informatics are reviewed along with an evaluation of current frontiers and opportunities in the field of computational materials chemistry. Computer science ; Field (mathematics) ; Qubit ; Pace ; Materials informatics ; Computation ; Management science ; Data science ; Theoretical computer science ; Quantum ; Nanotechnology ; Health informatics ; Algorithm ; Physics ; Materials science ; Engineering ; Engineering informatics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides
Authors: Sunday L. Lawal | Sunday A. Afolalu | Tien‐Chien Jen | Esther T. Akinlabi
Source: Revue des composites et des matériaux avancés (review, 2023-04-30)
Field: Materials Science — Materials Chemistry

Effects of Varying Microstructural Constituents on Corrosion Resistance: A Review Several detrimental phases usually result in materials due to improper variation and modification of the microstructural constituents.This usually cause serious defect and result in materials with poor mechanical properties as well as corrosion resistance.Majority of this problem could be traced to even the techniques of manufacturing.Thus, this study focused on a forensic review of the microstructural constituents and its how its variation affects the electrochemical performances of metals and alloys.The various types of microstructures were highlighted and their importance in several applications were explored.Furthermore, the study discussed the various methods of characterizing the microstructures of constituents and several of their mechanical properties that could have effects on the corrosion resistance were presented.Additionally, the effect of pores and composition of microstructural constituents were presented in detail.Also, it was established that it is necessary to modify microstructural constituents in a way that will improve the microstructural and corrosion behavior.The study provided potential information on the techniques of modification of the microstructural constituents that can influence the surface morphology and the mechanical properties of engineering material. Corrosion ; Materials science ; Resistance (ecology) ; Metallurgy ; Composite material ; Biology ; Agronomy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors
Authors: Andrés M. Bran | Peter F. Stadler | Jürgen Jost | Guillermo Restrepo
Source: Communications Chemistry (article, 2023-05-02)
Field: Chemistry — Physical and Theoretical Chemistry

The six stages of the convergence of the periodic system to its final structure The periodic system encodes order and similarity among chemical elements arising from known substances at a given time that constitute the chemical space. Although the system has incorporated new elements, the connection with the remaining space is still to be analysed, which leads to the question of how the exponentially growing space has affected the periodic system. Here we show, by analysing the space between 1800 and 2021, that the system has converged towards its current stable structure through six stages, respectively characterised by the finding of elements (1800-1826), the emergence of the core structure of the system (1826-1860), its organic chemistry bias (1860-1900) and its further stabilisation (1900-1948), World War 2 new chemistry (1948-1980) and the system final stabilisation (1980-). Given the self-reinforced low diversity of the space and the limited chemical possibilities of the elements to be synthesised, we hypothesise that the periodic system will remain largely untouched. Periodic system ; Space (punctuation) ; Convergence (economics) ; Connection (principal bundle) ; Similarity (geometry) ; Order (exchange) ; Pure mathematics ; Chemistry ; Mathematics ; Computer science ; Mathematical analysis ; Geometry ; Artificial intelligence History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure ; Radioactive element chemistry and processing
Authors: Songül Şahin
Source: GAZI UNIVERSITY JOURNAL OF SCIENCE (article, 2023-07-30)
Field: Chemistry — Organic Chemistry

2-(((2,4-Dichlorophenyl)imino)methyl)-3,4-difluorophenol: X-ray, DFT, MEP, HOMO-LUMO, NLO, Hirshfeld Surfaces, ADMET Profiling, Target Identification, Antipsychotic Activity Against Dopamine D2 and Serotonin 5-HT2A Receptors Halogenated compounds, especially fluorine and chlorine, play a key role in drug development. They account for a large proportion of all approved drug molecules. The importance of these two halogens stems from their remarkable effects on biological activity and pharmacokinetic properties. The study presented here aims to give the results obtained by the DFT methods and in silico medicinal evaluations of a newly synthesized small molecule. The small molecule belongs to the Schiff base class of organic compounds and is substituted with halogen atoms. The tetrahalogenated compound (THSB) Schiff base, 2-(((2,4-dichlorophenyl)imino)methyl)-3,4-difluorophenol, was first synthesized via the classical condensation method and then characterized by spectroscopic techniques. The THSB optimized by the B3LYP method was evaluated in terms of geometrical parameters, surface area (MEP and Hirshfeld analysis) and secondary interaction analysis, NLO properties, and electronic properties (HOMO-LUMO and UV-Vis). Second, THSB was evaluated regarding medicinal chemistry, physicochemical and pharmacokinetic properties, and toxicity (ADMET). Then, we comprehensively investigated the potential biological targets of THSB. Using the results of the SwissSimilarity analysis, we investigated the antagonistic effects of THSB against serotonin 5-HT2A and dopamine D2 receptors. Docking results were compared with the known antipsychotics, clozapine and risperidone. THSB showed a higher antagonistic effect than clozapine for the D2 receptor. However, risperidone proved to be the most effective antagonist for both targets. The binding energies of THSB, risperidone, and clozapine were -8.30, -11.84, and -8.07 kcal/mol, respectively, for D2; those of THSB, risperidone, and clozapine were -6.94, -11.47, and -10.10 kcal/mol, respectively, for 5-HT2A. Chemistry ; HOMO/LUMO ; Dopamine receptor D2 ; Risperidone ; Docking (animal) ; Stereochemistry ; Molecule ; Combinatorial chemistry ; Computational chemistry ; Receptor ; Organic chemistry ; Biochemistry Synthesis and biological activity ; Synthesis and Biological Evaluation ; Synthesis and Reactions of Organic Compounds
Authors: Raz L. Benson | Sai Sharath Yadavalli | Michail Stamatakis
Source: The Journal of Physical Chemistry A (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The <i>Zacros</i> software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in <i>Zacros</i>. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages. Kinetic Monte Carlo ; Benchmark (surveying) ; Computer science ; Monte Carlo method ; Isomorphism (crystallography) ; Range (aeronautics) ; Graph ; Chemistry ; Statistical physics ; Algorithm ; Computational science ; Theoretical computer science ; Materials science ; Mathematics ; Physics Machine Learning in Materials Science ; Complex Network Analysis Techniques ; Catalysis and Oxidation Reactions
Authors: Jeff J. Guo | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Consequently, these oracles cannot be directly optimized under a practical budget. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, outperforming the previous best performing method on 19/23 tasks. Benchmark (surveying) ; Oracle ; Computer science ; Task (project management) ; Reinforcement learning ; Generative model ; Sample (material) ; Machine learning ; Generative grammar ; Sample complexity ; Artificial intelligence ; Code (set theory) ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chalom Zemmour | Sofya Zakharova | Ofra Benny
Source: Discover Nano (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Generating porous metal surfaces as a mean to incorporate thymol-loaded nanoparticles Porous metals have gained interest in many fields such as biomedicine, electronics, and energy. Despite the many benefits that these structures may offer, one of the major challenges in utilizing porous metals is to incorporate active compounds, either small molecules or macromolecules, on these surfaces. Coatings that contain active molecules have previously been used for biomedical applications to enable the slow release of drugs, e.g., with drug-eluting cardiovascular stents. However, direct deposition of organic materials on metals by coatings is very difficult due to the challenge of obtaining uniform coatings, as well as issues related to layer adherence and mechanical stability. Our study describes an optimization of a production process of different porous metals, aluminum, gold, and titanium, using wet-etching. Pertinent physicochemical measurements were carried out to characterize the porous surfaces. Following the production of porous metal surface, a new methodology for incorporating active materials onto the metals by using mechanical entrapment of polymeric nanoparticles in metal pores was developed. To demonstrate our concept of active material incorporation, we produced an odor-releasing metal object with embedded particles loaded with thymol, an odoriferous molecule. Polymer particles were placed inside nanopores in a 3D-printed titanium ring. Chemical analysis, followed by smell tests, indicated that the smell intensity lasts significantly longer in the porous material containing the nanoparticles, compared with the free thymol. Materials science ; Porosity ; Porous medium ; Nanoparticle ; Polymer ; Titanium ; Nanotechnology ; Chemical engineering ; Metal ; Etching (microfabrication) ; Deposition (geology) ; Molecule ; Layer (electronics) ; Composite material ; Metallurgy ; Organic chemistry ; Chemistry Nanoporous metals and alloys ; Innovative Microfluidic and Catalytic Techniques Innovation ; Pickering emulsions and particle stabilization
Authors: Eugene Adamovich | Eugenia Buryanskaya | A. B. Elfimov | И. А. Маклакова | Олег Градов | Margaret A. Gradova | Theodor K. Orehov
Source: Recent Progress in Materials (article, 2023-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards Femtoscan-Assisted Analysis of Liquid Crystal Self-Organization on Different Polymer and Glass Surfaces for Lab-on-a-Chip and Lab-on-a-Dish Applications, Including Optofluidic and Flexoelectric Ones &lt;span lang="EN-US"&gt;In this paper, starting with an introductory review of the applications of liquid crystals and polymer-dispersed liquid crystal systems in (bio)sensors and microfluidics, the possibilities of visualizing self-organization products of liquid crystalline media or field-induced instabilities of liquid crystalline systems are considered. In particular illustrated cases, it is proposed to use FemtoScan software-containing metrological complexes to visualize instabilities in liquid crystalline systems and products of self-organization in liquid crystalline media. Liquid crystal ; Microfluidics ; Liquid crystalline ; Polymer ; Materials science ; Nanotechnology ; Optoelectronics ; Composite material Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Mechanical and Optical Resonators
Authors: Andrey A. Knizhnik | П. В. Комаров | Б. В. Потапкин | Denis Shirabaykin | Alexander S. Sinitsa | С. В. Трепалин
Source: Nanomanufacturing (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

New Polymers In Silico Generation and Properties Prediction We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data. Polymer ; In silico ; Monte Carlo method ; Dielectric ; Refractive index ; Computer science ; Polythiophene ; Constant (computer programming) ; Materials science ; Mathematics ; Chemistry ; Statistics ; Composite material ; Optoelectronics Machine Learning in Materials Science ; Polymer crystallization and properties ; Computational Drug Discovery Methods